From bde7ccd980a7592ca3759a65b9f978010ce86a2f Mon Sep 17 00:00:00 2001
From: letai2001 <nguyenletai16122001@gmail.com>
Date: Sun, 18 Jan 2026 02:07:32 +0700
Subject: [PATCH] -

---
 COMPLETE_25_FEATURES.md                       |    304 -
 ...ASET_WITH_TEXT_BACKUP_20260105_213507.xlsx |    Bin 304952 -> 0 bytes
 ML_PIPELINE_PLAN.md                           |    647 -
 ML_Phase2_Text_Features.ipynb                 |   2384 -
 ML_Training_Pipeline.ipynb                    |   2034 -
 ML_Training_Pipeline_Complete.ipynb           |   2224 -
 README.md                                     |    491 +
 __pycache__/predict.cpython-313.pyc           |    Bin 0 -> 8652 bytes
 all_predict.py                                |    228 +
 artifacts/X_proc_columns.joblib               |    Bin 0 -> 853 bytes
 artifacts/extratrees_log1p.joblib             |    Bin 0 -> 13229329 bytes
 artifacts/extratrees_staff_model.joblib       |    Bin 0 -> 13229329 bytes
 artifacts/model_meta.joblib                   |    Bin 0 -> 192 bytes
 create_aggregate_buildings.py                 |    295 -
 data_raw/Link LLV 1_5_2025.json               |  26050 +
 data_raw/Link LLV 2025_clean.json             | 424176 +++++++++++++++
 example_text_features.py                      |    367 -
 extract_25_features.py                        |    439 -
 extract_25_features_new.py                    |    250 +
 extract_shift_features.py                     |    220 -
 extract_text_features.py                      |    500 -
 final.ipynb                                   |   3693 +-
 input.json                                    |     31 +
 input_explained.jsonc                         |     72 +
 merge_all_features.py                         |    270 -
 outputs/val_predictions_extratrees.csv        |     69 +
 predict.py                                    |    207 +
 test3.ipynb                                   |      0
 train.ipynb                                   |   2157 +
 29 files changed, 457400 insertions(+), 9708 deletions(-)
 delete mode 100644 COMPLETE_25_FEATURES.md
 delete mode 100644 FINAL_DATASET_WITH_TEXT_BACKUP_20260105_213507.xlsx
 delete mode 100644 ML_PIPELINE_PLAN.md
 delete mode 100644 ML_Phase2_Text_Features.ipynb
 delete mode 100644 ML_Training_Pipeline.ipynb
 delete mode 100644 ML_Training_Pipeline_Complete.ipynb
 create mode 100644 README.md
 create mode 100644 __pycache__/predict.cpython-313.pyc
 create mode 100644 all_predict.py
 create mode 100644 artifacts/X_proc_columns.joblib
 create mode 100644 artifacts/extratrees_log1p.joblib
 create mode 100644 artifacts/extratrees_staff_model.joblib
 create mode 100644 artifacts/model_meta.joblib
 delete mode 100644 create_aggregate_buildings.py
 create mode 100644 data_raw/Link LLV 1_5_2025.json
 create mode 100644 data_raw/Link LLV 2025_clean.json
 delete mode 100644 example_text_features.py
 delete mode 100644 extract_25_features.py
 create mode 100644 extract_25_features_new.py
 delete mode 100644 extract_shift_features.py
 delete mode 100644 extract_text_features.py
 create mode 100644 input.json
 create mode 100644 input_explained.jsonc
 delete mode 100644 merge_all_features.py
 create mode 100644 outputs/val_predictions_extratrees.csv
 create mode 100644 predict.py
 delete mode 100644 test3.ipynb
 create mode 100644 train.ipynb

diff --git a/COMPLETE_25_FEATURES.md b/COMPLETE_25_FEATURES.md
deleted file mode 100644
index 576e6b5..0000000
--- a/COMPLETE_25_FEATURES.md
+++ /dev/null
@@ -1,304 +0,0 @@
-# ✅ HOÀN THÀNH: TRÍCH XUẤT 25 KEYWORD FEATURES
-
-**Ngày hoàn thành:** January 5, 2026  
-**Trạng thái:** ✅ COMPLETED
-
----
-
-## 📊 KẾT QUẢ
-
-### **Files đã tạo:**
-
-1. ✅ **`extract_25_features.py`** (411 dòng)
-   - Function extraction hoàn chỉnh với 25 features
-   - Script chạy batch cho 302 tòa nhà
-   - Helper functions đầy đủ
-   - Main script với thống kê chi tiết
-
-2. ✅ **`features_25_keywords.csv`** (302 rows × 26 columns)
-   - 302 tòa nhà với 25 features + mã địa điểm
-   - Ready để join với building features
-   - Encoding: UTF-8-sig (hỗ trợ tiếng Việt)
-
-3. ✅ **`analyze_task_keywords.py`** (208 dòng)
-   - Phân tích 30,917 công việc từ 302 tòa nhà
-   - Top động từ và khu vực phổ biến
-   - Thống kê chi tiết cho mỗi loại công việc
-
-4. ✅ **`RA_SOAT_PHAN_LOAI_FEATURES.md`** (báo cáo 267 dòng)
-   - Phân tích chi tiết dữ liệu thực tế
-   - So sánh phân loại cũ vs mới
-   - Lý do thay đổi từng feature
-   - Kế hoạch implementation
-
-5. ✅ **`TASK_FEATURES.ipynb`** (cập nhật)
-   - Documentation đầy đủ về 25 features
-   - Code cells để chạy extraction
-   - Visualization và analysis
-   - Tích hợp với workflow
-
----
-
-## 🎯 25 FEATURES ĐÃ IMPLEMENT
-
-### **🔹 NHÓM 1: TASK COUNTS (9 features)**
-
-| # | Feature | Trung bình | Mô tả |
-|:-:|:--------|:----------:|:------|
-| 1 | `num_tasks` | 102.87 | Tổng số công việc |
-| 2 | `num_cleaning_tasks` | 59.75 | Vệ sinh thường ngày (55.9%) |
-| 3 | `num_trash_collection_tasks` ⭐ | 20.01 | Thu gom rác (7.9%) |
-| 4 | `num_monitoring_tasks` ⭐ | 18.73 | Trực/kiểm tra (16.1%) |
-| 5 | `num_room_cleaning_tasks` ⭐ | 0.82 | Dọn phòng Y TẾ (0.4%) |
-| 6 | `num_deep_cleaning_tasks` ⭐ | 7.54 | Vệ sinh chuyên sâu (4.5%) |
-| 7 | `num_maintenance_tasks` | 0.98 | Bảo trì/sửa chữa (0.6%) |
-| 8 | `num_support_tasks` ⭐ | 6.50 | Hỗ trợ (5.8%) |
-| 9 | `num_other_tasks` | 22.15 | Công việc khác |
-
-### **🔹 NHÓM 2: AREA COVERAGE (10 features)**
-
-| # | Feature | Trung bình | Mô tả |
-|:-:|:--------|:----------:|:------|
-| 10 | `num_wc_tasks` | 20.90 | WC (20.4%) |
-| 11 | `num_hallway_tasks` | 14.69 | Hành lang (13.7%) |
-| 12 | `num_lobby_tasks` | 7.74 | Sảnh (7.6%) |
-| 13 | `num_patient_room_tasks` ⭐ | 1.55 | Phòng bệnh (1.5%) |
-| 14 | `num_clinic_room_tasks` ⭐ | 0.32 | Phòng khám (0.3%) |
-| 15 | `num_surgery_room_tasks` ⭐ | 0.36 | Phòng mổ (0.4%) |
-| 16 | `num_outdoor_tasks` | 4.37 | Ngoại cảnh (4.3%) |
-| 17 | `num_elevator_tasks` | 13.58 | Thang máy (10.6%) |
-| 18 | `num_office_tasks` ⭐ | 6.13 | Văn phòng (4.4%) |
-| 19 | `num_technical_room_tasks` ⭐ | 0.75 | Phòng kỹ thuật (0.2%) |
-
-### **🔹 NHÓM 3: RATIOS & COMPLEXITY (6 features)**
-
-| # | Feature | Trung bình | Mô tả |
-|:-:|:--------|:----------:|:------|
-| 20 | `cleaning_ratio` | 0.6148 | Tỷ lệ vệ sinh (61.48%) |
-| 21 | `trash_collection_ratio` ⭐ | 0.1951 | Tỷ lệ thu gom rác (19.51%) |
-| 22 | `monitoring_ratio` ⭐ | 0.1824 | Tỷ lệ trực/kiểm tra (18.24%) |
-| 23 | `room_cleaning_ratio` ⭐ | 0.0079 | Tỷ lệ dọn phòng (0.79%) |
-| 24 | `area_diversity` | 4.14 | Độ đa dạng (4.14/10) |
-| 25 | `task_complexity_score` ⭐ | 4.47 | Điểm phức tạp (4.47/10) |
-
-**⭐ = 10 features mới so với phân loại ban đầu**
-
----
-
-## 🏆 TOP PERFORMERS
-
-### **Top 5 tòa có nhiều công việc nhất:**
-
-| Tòa | Số công việc | Cleaning ratio | Complexity score |
-|:---:|:------------:|:--------------:|:----------------:|
-| 79-1 | 1,009 | 70.7% | 9.0/10 |
-| 288-1 | 971 | 47.6% | 10.0/10 |
-| 105-1 | 775 | 57.4% | 9.2/10 |
-| 105-2 | 775 | 57.4% | 9.2/10 |
-| 55-1 | 772 | 55.3% | 9.6/10 |
-
-### **Top 5 tòa phức tạp nhất (complexity score = 10):**
-
-| Tòa | Số công việc | Area diversity | Cleaning ratio |
-|:---:|:------------:|:--------------:|:--------------:|
-| 101-1 | 441 | 10/10 | 58.5% |
-| 114-1 | 593 | 10/10 | 56.3% |
-| 288-1 | 971 | 10/10 | 47.6% |
-| 81-1 | 159 | 10/10 | 51.6% |
-
----
-
-## 📈 THỐNG KÊ CHI TIẾT
-
-### **Phân bố công việc:**
-```
-Vệ sinh thường ngày:    55.9% (dominant)
-Trực/kiểm tra:          16.1% (significant)
-Thu gom rác:             7.9% (important)
-Vệ sinh chuyên sâu:      4.5%
-Hỗ trợ:                  5.8%
-Khác:                   ~10%
-```
-
-### **Khu vực quan trọng nhất:**
-```
-1. WC/Nhà vệ sinh:      20.4% (highest)
-2. Hành lang:           13.7%
-3. Thang máy:           10.6%
-4. Sảnh:                 7.6%
-5. Văn phòng:            4.4%
-```
-
-### **Độ phức tạp:**
-- **Trung bình:** 4.47/10 (medium complexity)
-- **Max:** 10.0/10 (4 tòa)
-- **Min:** 0.0/10 (4 tòa thiếu data)
-- **75th percentile:** 7.0/10
-
----
-
-## 💡 INSIGHTS QUAN TRỌNG
-
-### **1. Vệ sinh thường ngày là dominant (55.9%)**
-→ Mô hình dự đoán cần weight cao cho feature này
-
-### **2. "Trực phát sinh" rất phổ biến (16.1%)**
-→ Đặc trưng của bệnh viện, cần feature riêng
-→ Khác với "kiểm tra" thông thường
-
-### **3. Thu gom rác là công việc quan trọng (7.9%)**
-→ Không thể gộp chung với vệ sinh
-→ Có thể ảnh hưởng khác nhau đến số nhân sự
-
-### **4. WC chiếm 20% công việc**
-→ Feature quan trọng nhất trong nhóm area coverage
-→ Correlation cao với số nhân sự
-
-### **5. Tòa Y TẾ có đặc thù riêng**
-→ Dọn mổ, dọn đẻ, phòng bệnh
-→ Cần features riêng dù tỷ lệ thấp
-
-### **6. Complexity score đa dạng (0-10)**
-→ Giúp phân biệt tòa đơn giản vs phức tạp
-→ Kết hợp nhiều yếu tố: text length, tasks, areas, technical keywords
-
----
-
-## 🔍 VALIDATION & QUALITY CHECK
-
-### **✅ Data Quality:**
-- [x] 302/302 tòa được xử lý thành công (100%)
-- [x] Không có missing values trong output
-- [x] Tất cả features có giá trị hợp lệ
-- [x] Ratios trong khoảng [0, 1]
-- [x] Area diversity trong khoảng [0, 10]
-- [x] Complexity score trong khoảng [0, 10]
-
-### **✅ Logical Consistency:**
-- [x] num_tasks = sum của các loại công việc (có overlap cho phép)
-- [x] Ratios tính đúng công thức
-- [x] Area diversity = số khu vực có tasks > 0
-- [x] Complexity score tăng theo: length, tasks, technical, diversity
-
-### **✅ Sample Validation:**
-Tòa 101-1 (test case):
-- ✅ num_tasks = 441 (hợp lý cho bệnh viện lớn)
-- ✅ cleaning_ratio = 58.5% (phù hợp với phân bố chung)
-- ✅ area_diversity = 10/10 (bệnh viện có đầy đủ khu vực)
-- ✅ complexity_score = 10.0 (tòa phức tạp nhất)
-
----
-
-## 📂 FILE STRUCTURE
-
-```
-Predict_calamviecHM/
-├── ket_qua_cong_viec_full.xlsx        [INPUT] 302 tòa × 3 cột
-├── extract_25_features.py             [CODE] Main extraction script
-├── analyze_task_keywords.py           [CODE] Analysis script
-├── features_25_keywords.csv           [OUTPUT] 302 tòa × 26 cột ✅
-├── RA_SOAT_PHAN_LOAI_FEATURES.md     [DOC] Phân tích chi tiết
-├── TASK_FEATURES.ipynb                [NOTEBOOK] Interactive analysis ✅
-└── COMPLETE_25_FEATURES.md            [DOC] Báo cáo này
-```
-
----
-
-## 🎯 NEXT STEPS
-
-### **✅ COMPLETED (Phase 1):**
-- [x] Phân tích 30,917 công việc từ 302 tòa
-- [x] Thiết kế 25 features dựa trên dữ liệu thực tế
-- [x] Implement extraction function
-- [x] Test và validate
-- [x] Chạy batch cho 302 tòa
-- [x] Generate output CSV
-- [x] Documentation đầy đủ
-
-### **⏳ TODO (Phase 2): TF-IDF Features**
-- [ ] Implement TfidfVectorizer (500 features)
-- [ ] Apply SVD giảm chiều (500 → 10)
-- [ ] Validate 10 TF-IDF features
-- [ ] Save to `features_10_tfidf.csv`
-
-### **⏳ TODO (Phase 3): Integration**
-- [ ] Join keyword + TF-IDF features (35 features)
-- [ ] Join với `Du_Lieu_Toa_Nha_Aggregate.xlsx` (18 features)
-- [ ] Add shift features (3-4 features)
-- [ ] Final dataset: ~56 features
-
-### **⏳ TODO (Phase 4): Modeling**
-- [ ] Obtain target variable (`so_nhan_su`)
-- [ ] Expand to per-shift samples (×3)
-- [ ] Train/validation/test split
-- [ ] Train models (RF, GB, XGB, LGBM)
-- [ ] Hyperparameter tuning
-- [ ] Model evaluation & comparison
-
----
-
-## 📊 EXPECTED FINAL DATASET
-
-```
-FEATURES BREAKDOWN:
-├── 25 Keyword features (from tasks text)      ✅ DONE
-├── 10 TF-IDF features (from tasks text)       ⏳ TODO
-├── 18 Building features (from aggregate)      ⏳ TODO
-└── 3-4 Shift features (time-based)            ⏳ TODO
-    ─────────────────────────────────────────
-    TOTAL: ~56 features
-
-SAMPLES:
-├── 302 tòa nhà
-├── × 3 ca/ngày (sáng, chiều, tối)
-    ─────────────────────────────────────────
-    TOTAL: ~900 samples
-
-TARGET:
-└── so_nhan_su (số nhân sự mỗi ca)            ⚠️ CẦN THU THẬP
-```
-
----
-
-## 🎉 ACHIEVEMENTS
-
-✅ **Phân tích dữ liệu thực tế** - Không dựa vào giả định  
-✅ **Thiết kế features phù hợp** - Dựa trên tần suất xuất hiện  
-✅ **Tách rõ đặc trưng Y TẾ** - Phòng mổ, dọn đẻ, phòng bệnh  
-✅ **Thêm complexity score** - Đo độ phức tạp đa chiều  
-✅ **Code quality cao** - Type hints, docstrings, error handling  
-✅ **Documentation đầy đủ** - Markdown reports, notebook cells  
-✅ **Reproducible** - Script có thể chạy lại bất cứ lúc nào  
-
----
-
-## 📞 SUMMARY FOR USER
-
-**🎯 Đã hoàn thành:**
-- ✅ Phân loại lại features dựa trên phân tích 30,917 công việc thực tế
-- ✅ Tăng từ 15 → 25 features (thêm 10 features quan trọng)
-- ✅ Loại bỏ features ít xuất hiện, thêm features phổ biến (thu gom rác, trực phát sinh)
-- ✅ Tách rõ khu vực Y TẾ (phòng bệnh, phòng mổ, phòng khám)
-- ✅ Thêm task_complexity_score để đo độ phức tạp
-- ✅ Implement và test thành công trên 302 tòa
-- ✅ Tạo file output `features_25_keywords.csv` (302 × 26)
-
-**🎯 File quan trọng:**
-1. `extract_25_features.py` - Script extraction
-2. `features_25_keywords.csv` - Output data
-3. `RA_SOAT_PHAN_LOAI_FEATURES.md` - Báo cáo phân tích
-4. `TASK_FEATURES.ipynb` - Notebook interactive
-
-**🎯 Bước tiếp theo:**
-Bạn có thể:
-- **A)** Tiếp tục với TF-IDF features (10 features)
-- **B)** Join với building features ngay
-- **C)** Phân tích correlation giữa 25 features
-- **D)** Visualize distribution của features
-
-Bạn muốn làm gì tiếp theo? 😊
-
----
-
-_Hoàn thành: January 5, 2026_  
-_Status: ✅ PHASE 1 COMPLETED - READY FOR PHASE 2_
diff --git a/FINAL_DATASET_WITH_TEXT_BACKUP_20260105_213507.xlsx b/FINAL_DATASET_WITH_TEXT_BACKUP_20260105_213507.xlsx
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diff --git a/ML_PIPELINE_PLAN.md b/ML_PIPELINE_PLAN.md
deleted file mode 100644
index 0cf0668..0000000
--- a/ML_PIPELINE_PLAN.md
+++ /dev/null
@@ -1,647 +0,0 @@
-# 📋 KẾ HOẠCH TIỀN XỬ LÝ DỮ LIỆU & HUẤN LUYỆN MODEL
-
-**Ngày:** January 5, 2026  
-**Target Variable:** `so_luong` (Số lượng nhân sự)  
-**Dataset:** FINAL_DATASET_WITH_TEXT.xlsx (454 rows × 51 columns)
-
----
-
-## 🎯 MỤC TIÊU
-
-Dự đoán số lượng nhân sự cần thiết cho mỗi ca làm việc dựa trên:
-- ✅ Task features (25 features từ text)
-- ✅ Shift features (5 features về ca làm việc)
-- ✅ Building features (17 features về tòa nhà)
-- ⚪ Text columns (2 cột) - **GIỮ LẠI CHO SAU**
-
-**Tổng features sử dụng:** 47 features (không tính 2 cột text và ma_dia_diem)
-
----
-
-## 📊 PHÂN TÍCH DATASET HIỆN TẠI
-
-### Thông tin cơ bản:
-- **Tổng samples:** 454 shifts
-- **Target:** `so_luong` (0-64, mean=4.64, median=4.0)
-- **Features:** 47 numeric + categorical
-- **Missing values:** Cần kiểm tra
-
-### Phân bố features:
-
-#### 1. Shift Features (5):
-- `loai_ca` - Categorical (Part time, Ca sáng, Ca chiều, Hành chính, etc.)
-- `bat_dau` - Time (cần parse)
-- `ket_thuc` - Time (cần parse)
-- `tong_gio_lam` - Numeric/Time (cần parse)
-- `so_ca_cua_toa` - Numeric (1-41)
-
-#### 2. Task Features (25):
-- Counts (9): `num_tasks`, `num_cleaning_tasks`, etc.
-- Areas (10): `num_wc_tasks`, `num_hallway_tasks`, etc.
-- Ratios (4): `cleaning_ratio`, `trash_collection_ratio`, etc.
-- Complexity (2): `area_diversity`, `task_complexity_score`
-
-#### 3. Building Features (17):
-- Categorical (3): `loai_hinh`, `ten_toa_thap`, `muc_do_luu_luong`
-- Numeric (14): `so_tang`, `dien_tich_*`, binary features
-
----
-
-## 🔧 PHASE 1: TIỀN XỬ LÝ DỮ LIỆU
-
-### Step 1.1: Exploratory Data Analysis (EDA)
-
-**Script:** `01_eda_analysis.py`
-
-```python
-Nhiệm vụ:
-1. Load dataset và kiểm tra shape, dtypes
-2. Phân tích missing values
-3. Thống kê mô tả (describe) cho tất cả features
-4. Phân tích target variable (so_luong):
-   - Distribution (histogram, boxplot)
-   - Outliers detection
-   - Skewness và kurtosis
-5. Correlation matrix với target
-6. Identify zero-variance features
-7. Save EDA report
-```
-
-**Output:**
-- `EDA_REPORT.md` - Báo cáo chi tiết
-- `eda_plots/` - Các biểu đồ phân tích
-
-**Thời gian:** ~30 phút
-
----
-
-### Step 1.2: Data Cleaning
-
-**Script:** `02_data_cleaning.py`
-
-```python
-Nhiệm vụ:
-1. Xử lý missing values:
-   - Task features: Fill 0 (không có task đó)
-   - Building features: Fill median hoặc mode
-   - Shift features: Xác định cách xử lý
-   
-2. Xử lý outliers trong target:
-   - Kiểm tra so_luong = 0 (có 16 ca)
-   - Quyết định keep/remove/cap
-   
-3. Loại bỏ duplicate rows (nếu có)
-
-4. Loại bỏ features không cần thiết:
-   - ma_dia_diem (identifier, không dùng cho training)
-   - all_task_normal (giữ lại cho sau)
-   - all_task_dinhky (giữ lại cho sau)
-   - ten_toa_thap (có thể redundant với ma_dia_diem)
-   
-5. Validate data quality sau cleaning
-```
-
-**Output:**
-- `CLEANED_DATA.csv` - Dataset sau cleaning
-- `CLEANING_REPORT.md` - Báo cáo chi tiết
-
-**Thời gian:** ~20 phút
-
----
-
-### Step 1.3: Feature Engineering
-
-**Script:** `03_feature_engineering.py`
-
-```python
-Nhiệm vụ:
-
-A. TIME FEATURES (từ bat_dau, ket_thuc, tong_gio_lam):
-   - hour_start (giờ bắt đầu: 0-23)
-   - hour_end (giờ kết thúc: 0-23)
-   - work_hours_numeric (số giờ làm: float)
-   - is_morning_shift (6-12h: 1/0)
-   - is_afternoon_shift (12-18h: 1/0)
-   - is_evening_shift (18-24h: 1/0)
-   - is_night_shift (0-6h: 1/0)
-   - is_cross_day (ca qua đêm: 1/0)
-
-B. INTERACTION FEATURES:
-   - tasks_per_hour = num_tasks / work_hours_numeric
-   - tasks_per_floor = num_tasks / so_tang
-   - wc_per_floor = num_wc_tasks / so_tang
-   - cleaning_workload = num_cleaning_tasks * area_diversity
-   
-C. AGGREGATION FEATURES:
-   - total_area = sum(dien_tich_*)
-   - area_per_floor = total_area / so_tang
-   - has_special_areas = (num_patient_room_tasks + 
-                           num_surgery_room_tasks + 
-                           num_clinic_room_tasks) > 0
-
-D. CATEGORICAL ENCODING:
-   - loai_ca: One-hot encoding
-   - loai_hinh: Label encoding (có thứ tự)
-   - muc_do_luu_luong: Label encoding (có thứ tự)
-   
-E. FEATURE SELECTION (optional):
-   - Remove zero-variance features
-   - Remove highly correlated features (>0.95)
-```
-
-**Output:**
-- `ENGINEERED_DATA.csv` - Dataset với features mới
-- `FEATURE_ENGINEERING_REPORT.md` - Báo cáo
-
-**Thời gian:** ~40 phút
-
----
-
-### Step 1.4: Feature Scaling
-
-**Script:** `04_feature_scaling.py`
-
-```python
-Nhiệm vụ:
-1. Tách features theo loại:
-   - Numeric features cần scaling
-   - Binary features (0/1) - không cần scaling
-   - One-hot encoded features - không cần scaling
-
-2. Apply scaling methods:
-   Option A: StandardScaler (mean=0, std=1) - Recommended
-   Option B: MinMaxScaler (0-1)
-   Option C: RobustScaler (robust to outliers)
-
-3. Save scaler object để dùng cho inference
-
-4. Validate scaling: check mean, std sau khi scale
-```
-
-**Output:**
-- `SCALED_DATA.csv` - Dataset đã scale
-- `scaler.pkl` - Scaler object
-- `SCALING_REPORT.md` - Báo cáo
-
-**Thời gian:** ~15 phút
-
----
-
-### Step 1.5: Train/Validation/Test Split
-
-**Script:** `05_train_test_split.py`
-
-```python
-Nhiệm vụ:
-1. Split strategy:
-   - Train: 70% (318 samples)
-   - Validation: 15% (68 samples)
-   - Test: 15% (68 samples)
-
-2. Stratified split (nếu cần):
-   - Stratify by loai_ca hoặc binned so_luong
-   
-3. Random state = 42 (reproducibility)
-
-4. Validate split:
-   - Check distribution của so_luong trong mỗi set
-   - Check no data leakage
-
-5. Save splits
-```
-
-**Output:**
-- `train.csv` (318 rows)
-- `val.csv` (68 rows)
-- `test.csv` (68 rows)
-- `SPLIT_REPORT.md`
-
-**Thời gian:** ~10 phút
-
----
-
-## 🤖 PHASE 2: MODEL TRAINING
-
-### Step 2.1: Baseline Model
-
-**Script:** `06_baseline_model.py`
-
-```python
-Mục đích: Tạo baseline đơn giản để so sánh
-
-Models:
-1. Mean Baseline: Dự đoán = mean(so_luong) cho tất cả
-2. Linear Regression: Simple linear model
-3. Decision Tree (max_depth=5): Simple tree
-
-Metrics:
-- MAE (Mean Absolute Error)
-- RMSE (Root Mean Squared Error)
-- R² Score
-- MAPE (Mean Absolute Percentage Error)
-
-Output:
-- Baseline scores
-- Simple visualizations
-```
-
-**Output:**
-- `BASELINE_RESULTS.md`
-- `baseline_models/` - Saved models
-
-**Thời gian:** ~15 phút
-
----
-
-### Step 2.2: Advanced Models Training
-
-**Script:** `07_train_models.py`
-
-```python
-Models to train:
-
-1. Random Forest Regressor
-   - n_estimators: [100, 200, 300]
-   - max_depth: [10, 20, 30, None]
-   - min_samples_split: [2, 5, 10]
-   
-2. Gradient Boosting Regressor
-   - n_estimators: [100, 200]
-   - learning_rate: [0.01, 0.05, 0.1]
-   - max_depth: [3, 5, 7]
-   
-3. XGBoost Regressor
-   - n_estimators: [100, 200]
-   - learning_rate: [0.01, 0.05, 0.1]
-   - max_depth: [3, 5, 7]
-   - subsample: [0.8, 1.0]
-   
-4. LightGBM Regressor
-   - n_estimators: [100, 200]
-   - learning_rate: [0.01, 0.05, 0.1]
-   - num_leaves: [31, 50, 70]
-
-Training approach:
-- Cross-validation (5-fold) trên train set
-- Hyperparameter tuning với GridSearchCV/RandomizedSearchCV
-- Evaluate trên validation set
-- Save best models
-```
-
-**Output:**
-- `trained_models/` - Saved models
-- `MODEL_TRAINING_REPORT.md`
-- `training_logs/` - Training logs
-
-**Thời gian:** ~1-2 giờ (tùy hyperparameter tuning)
-
----
-
-### Step 2.3: Model Evaluation
-
-**Script:** `08_model_evaluation.py`
-
-```python
-Nhiệm vụ:
-
-1. Load all trained models
-
-2. Evaluate trên validation set:
-   - MAE, RMSE, R², MAPE
-   - Residual plots
-   - Actual vs Predicted plots
-   
-3. Compare models:
-   - Performance comparison table
-   - Visual comparison (bar charts)
-   
-4. Analyze errors:
-   - Where models fail (high errors)
-   - Error distribution by loai_ca, so_tang, etc.
-   
-5. Feature importance:
-   - Top 20 important features
-   - SHAP values (if possible)
-```
-
-**Output:**
-- `EVALUATION_REPORT.md`
-- `evaluation_plots/`
-- `feature_importance.csv`
-
-**Thời gian:** ~30 phút
-
----
-
-### Step 2.4: Final Model Selection & Test
-
-**Script:** `09_final_evaluation.py`
-
-```python
-Nhiệm vụ:
-
-1. Select best model based on validation performance
-
-2. Re-train best model on train+val combined (optional)
-
-3. Final evaluation on test set:
-   - MAE, RMSE, R², MAPE
-   - Confidence intervals
-   - Error analysis
-   
-4. Create production-ready pipeline:
-   - Preprocessing + Model
-   - Save as single pickle file
-   
-5. Generate final report
-```
-
-**Output:**
-- `FINAL_MODEL.pkl` - Production model
-- `FINAL_EVALUATION_REPORT.md`
-- `final_plots/`
-
-**Thời gian:** ~20 phút
-
----
-
-## 📊 PHASE 3: ANALYSIS & INSIGHTS
-
-### Step 3.1: Feature Importance Analysis
-
-**Script:** `10_feature_analysis.py`
-
-```python
-Nhiệm vụ:
-1. Feature importance from best model
-2. SHAP values analysis (detailed)
-3. Partial dependence plots
-4. Feature interactions
-5. Recommendations for feature engineering v2
-```
-
-**Output:**
-- `FEATURE_IMPORTANCE_REPORT.md`
-- `shap_plots/`
-
-**Thời gian:** ~30 phút
-
----
-
-### Step 3.2: Business Insights
-
-**Script:** `11_business_insights.py`
-
-```python
-Nhiệm vụ:
-1. Phân tích theo loại ca:
-   - Ca nào cần nhiều nhân sự nhất?
-   - Features quan trọng cho từng loại ca
-   
-2. Phân tích theo loại tòa:
-   - Loại tòa nào phức tạp nhất?
-   - Correlation với số nhân sự
-   
-3. Task features impact:
-   - Task nào ảnh hưởng nhiều nhất?
-   - Recommendations cho task planning
-   
-4. Optimization opportunities:
-   - Cách giảm số nhân sự mà vẫn hiệu quả
-   - Resource allocation recommendations
-```
-
-**Output:**
-- `BUSINESS_INSIGHTS_REPORT.md`
-- `insights_plots/`
-
-**Thời gian:** ~30 phút
-
----
-
-## 📁 FOLDER STRUCTURE
-
-```
-Predict_calamviecHM/
-├── data/
-│   ├── raw/
-│   │   └── FINAL_DATASET_WITH_TEXT.xlsx
-│   ├── cleaned/
-│   │   ├── CLEANED_DATA.csv
-│   │   ├── ENGINEERED_DATA.csv
-│   │   └── SCALED_DATA.csv
-│   └── splits/
-│       ├── train.csv
-│       ├── val.csv
-│       └── test.csv
-│
-├── models/
-│   ├── baseline_models/
-│   ├── trained_models/
-│   │   ├── random_forest.pkl
-│   │   ├── xgboost.pkl
-│   │   ├── lightgbm.pkl
-│   │   └── gradient_boosting.pkl
-│   ├── scaler.pkl
-│   └── FINAL_MODEL.pkl
-│
-├── scripts/
-│   ├── 01_eda_analysis.py
-│   ├── 02_data_cleaning.py
-│   ├── 03_feature_engineering.py
-│   ├── 04_feature_scaling.py
-│   ├── 05_train_test_split.py
-│   ├── 06_baseline_model.py
-│   ├── 07_train_models.py
-│   ├── 08_model_evaluation.py
-│   ├── 09_final_evaluation.py
-│   ├── 10_feature_analysis.py
-│   └── 11_business_insights.py
-│
-├── reports/
-│   ├── EDA_REPORT.md
-│   ├── CLEANING_REPORT.md
-│   ├── FEATURE_ENGINEERING_REPORT.md
-│   ├── MODEL_TRAINING_REPORT.md
-│   ├── EVALUATION_REPORT.md
-│   ├── FINAL_EVALUATION_REPORT.md
-│   ├── FEATURE_IMPORTANCE_REPORT.md
-│   └── BUSINESS_INSIGHTS_REPORT.md
-│
-├── plots/
-│   ├── eda_plots/
-│   ├── evaluation_plots/
-│   ├── final_plots/
-│   ├── shap_plots/
-│   └── insights_plots/
-│
-└── notebooks/
-    ├── EDA_Notebook.ipynb
-    └── Model_Comparison.ipynb
-```
-
----
-
-## 📊 EXPECTED PERFORMANCE
-
-### Target Metrics (Realistic):
-
-| Metric | Baseline | Target | Stretch |
-|--------|----------|--------|---------|
-| MAE | ~2.5 | <2.0 | <1.5 |
-| RMSE | ~3.5 | <3.0 | <2.5 |
-| R² | 0.30 | >0.60 | >0.75 |
-| MAPE | ~50% | <30% | <20% |
-
-**Giải thích:**
-- Baseline: Simple mean/linear model
-- Target: Mục tiêu hợp lý với dataset này
-- Stretch: Mục tiêu lý tưởng (có thể khó đạt được)
-
----
-
-## ⏱️ TIMELINE TỔNG QUAN
-
-| Phase | Time | Status |
-|-------|------|--------|
-| **Phase 1: Tiền xử lý** | ~2 giờ | 📋 Planned |
-| - EDA | 30 min | |
-| - Cleaning | 20 min | |
-| - Feature Engineering | 40 min | |
-| - Scaling | 15 min | |
-| - Split | 10 min | |
-| **Phase 2: Training** | ~2-3 giờ | 📋 Planned |
-| - Baseline | 15 min | |
-| - Advanced Models | 1-2h | |
-| - Evaluation | 30 min | |
-| - Final Selection | 20 min | |
-| **Phase 3: Analysis** | ~1 giờ | 📋 Planned |
-| - Feature Analysis | 30 min | |
-| - Business Insights | 30 min | |
-| **TOTAL** | **~5-6 giờ** | 📋 Planned |
-
----
-
-## 🎯 FEATURES SỬ DỤNG (47 features)
-
-### ✅ Sẽ dùng ngay:
-
-**Shift Features (5):**
-1. loai_ca (encoded)
-2. hour_start (engineered)
-3. hour_end (engineered)
-4. work_hours_numeric (engineered)
-5. so_ca_cua_toa
-
-**Task Features (25):**
-6-14. Task counts (9)
-15-24. Area coverage (10)
-25-28. Ratios (4)
-29-30. Diversity & Complexity (2)
-
-**Building Features (17):**
-31-33. Categorical (3 - encoded)
-34-47. Numeric (14)
-
-### ⚪ Giữ lại cho Phase 2 (sau này):
-- `all_task_normal` - Text column (TF-IDF/BERT)
-- `all_task_dinhky` - Text column (TF-IDF/BERT)
-
-### ❌ Không dùng:
-- `ma_dia_diem` - Identifier
-- `bat_dau` - Raw time (đã engineer)
-- `ket_thuc` - Raw time (đã engineer)
-- `tong_gio_lam` - Raw time (đã engineer)
-
----
-
-## 🚀 BẮT ĐẦU TỪ BƯỚC NÀO?
-
-### Option 1: Toàn bộ pipeline tự động
-```python
-# Script: run_full_pipeline.py
-# Chạy tất cả steps từ 1-11 tự động
-```
-
-### Option 2: Từng bước (Recommended)
-```python
-# Bắt đầu với EDA
-python scripts/01_eda_analysis.py
-# Sau đó tiếp tục 02, 03, ...
-```
-
-### Option 3: Notebook interactive
-```python
-# Sử dụng Jupyter Notebook
-# EDA_Notebook.ipynb để khám phá và thử nghiệm
-```
-
----
-
-## 📝 NOTES QUAN TRỌNG
-
-### 1. Data Quality:
-- ⚠️ 27 rows không có task text (5.9%)
-- ⚠️ 16 shifts có so_luong = 0
-- ✅ 429/454 shifts có đầy đủ data (94.5%)
-
-### 2. Feature Engineering:
-- Time features rất quan trọng (giờ bắt đầu ảnh hưởng lớn)
-- Interaction features có thể boost performance
-- Task complexity score đã tính sẵn, có thể dùng trực tiếp
-
-### 3. Model Selection:
-- Tree-based models (RF, XGB, LGBM) thường tốt nhất cho tabular data
-- Ensemble methods có thể kết hợp nhiều models
-- Consider model interpretability cho business
-
-### 4. Text Features (Phase 2):
-- Sau khi có baseline với 47 features
-- Thêm TF-IDF vectors từ text
-- Compare performance improvement
-- Có thể tăng R² thêm 5-10%
-
----
-
-## ✅ CHECKLIST TRƯỚC KHI BẮT ĐẦU
-
-- [ ] Dataset sẵn sàng: `FINAL_DATASET_WITH_TEXT.xlsx`
-- [ ] Python environment setup (pandas, sklearn, xgboost, lightgbm)
-- [ ] Tạo folder structure
-- [ ] Backup data gốc
-- [ ] Set random seed = 42 (reproducibility)
-- [ ] Chuẩn bị notebook/IDE để code
-
----
-
-## 🎯 SUCCESS CRITERIA
-
-### Minimum Viable Model:
-- ✅ MAE < 2.0
-- ✅ R² > 0.60
-- ✅ Model có thể explain được (feature importance)
-- ✅ Reproducible (có scripts + saved models)
-
-### Stretch Goals:
-- 🎯 MAE < 1.5
-- 🎯 R² > 0.75
-- 🎯 SHAP analysis hoàn chỉnh
-- 🎯 Business insights actionable
-
----
-
-**Kế hoạch tạo bởi:** GitHub Copilot  
-**Ngày:** January 5, 2026  
-**Status:** 📋 READY TO START  
-**Next Step:** Bắt đầu với `01_eda_analysis.py`
-
----
-
-## ❓ BẠN MUỐN BẮT ĐẦU VỚI BƯỚC NÀO?
-
-1. **Tạo toàn bộ scripts** (11 files) - Recommended
-2. **Chỉ tạo EDA script** để khám phá data trước
-3. **Tạo full pipeline script** chạy một lần
-4. **Tạo Jupyter Notebook** để interactive
-
-Hãy cho tôi biết! 🚀
diff --git a/ML_Phase2_Text_Features.ipynb b/ML_Phase2_Text_Features.ipynb
deleted file mode 100644
index 42a272d..0000000
--- a/ML_Phase2_Text_Features.ipynb
+++ /dev/null
@@ -1,2384 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "3d9c3502",
-   "metadata": {},
-   "source": [
-    "# 🚀 ML Phase 2: Text Features với TF-IDF + SVD\n",
-    "\n",
-    "**Mục tiêu:** Thêm text features từ 2 cột task để cải thiện R² từ ~0.4 lên 0.5-0.7\n",
-    "\n",
-    "**Dataset:** FINAL_DATASET_WITH_TEXT_BACKUP_20260105_213507.xlsx  \n",
-    "⚠️ **IMPORTANT:** Dùng CÙNG DATASET với Phase 1 để so sánh công bằng!\n",
-    "\n",
-    "**Phase 1 Results (Baseline - Numeric Features Only):**\n",
-    "- Best Model: **Decision Tree**\n",
-    "- Val R²: **0.4136**\n",
-    "- Val MAE: **3.3841**\n",
-    "- Test R²: TBD\n",
-    "\n",
-    "**Phase 2 Strategy:**\n",
-    "- TF-IDF vectorization cho 2 cột text (all_task_normal + all_task_dinhky)\n",
-    "- SVD (Singular Value Decomposition) để giảm chiều (200 → 50 dimensions)\n",
-    "- Kết hợp với numeric features (bỏ các cột categorical: loai_ca, bat_dau, ket_thuc, muc_do_luu_luong)\n",
-    "- Re-train và so sánh với Phase 1\n",
-    "\n",
-    "---\n",
-    "\n",
-    "## 📋 Nội dung:\n",
-    "\n",
-    "1. ✅ Load Data & Setup\n",
-    "2. ✅ Text Preprocessing\n",
-    "3. ✅ TF-IDF Vectorization\n",
-    "4. ✅ SVD Dimensionality Reduction\n",
-    "5. ✅ Feature Combination\n",
-    "6. ✅ Train/Val/Test Split\n",
-    "7. ✅ Model Training\n",
-    "8. ✅ Results Comparison (Phase 1 vs Phase 2)\n",
-    "9. ✅ Feature Importance Analysis\n",
-    "10. ✅ Final Recommendations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5e6f79fc",
-   "metadata": {},
-   "source": [
-    "# 1️⃣ Import Libraries & Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "94c942bd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "✅ Libraries imported successfully!\n",
-      "📅 Date: 2026-01-06 01:12:18\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Import essential libraries\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "import warnings\n",
-    "import os\n",
-    "import re\n",
-    "from datetime import datetime\n",
-    "\n",
-    "# Text processing\n",
-    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
-    "from sklearn.decomposition import TruncatedSVD\n",
-    "\n",
-    "# Machine Learning\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.tree import DecisionTreeRegressor\n",
-    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
-    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
-    "import xgboost as xgb\n",
-    "import lightgbm as lgb\n",
-    "\n",
-    "# Configuration\n",
-    "warnings.filterwarnings('ignore')\n",
-    "plt.style.use('seaborn-v0_8-darkgrid')\n",
-    "sns.set_palette(\"husl\")\n",
-    "pd.set_option('display.max_columns', None)\n",
-    "pd.set_option('display.max_rows', 100)\n",
-    "\n",
-    "# Random seed\n",
-    "RANDOM_STATE = 42\n",
-    "np.random.seed(RANDOM_STATE)\n",
-    "\n",
-    "# Create folders\n",
-    "os.makedirs('phase2_output/models', exist_ok=True)\n",
-    "os.makedirs('phase2_output/plots', exist_ok=True)\n",
-    "os.makedirs('phase2_output/data', exist_ok=True)\n",
-    "\n",
-    "print(\"✅ Libraries imported successfully!\")\n",
-    "print(f\"📅 Date: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7becc15d",
-   "metadata": {},
-   "source": [
-    "# 2️⃣ Load Data & Verify"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "bc631294",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "📂 Loading dataset...\n",
-      "✅ Dataset loaded!\n",
-      "📊 Shape: (454, 51)\n",
-      "\n",
-      "📋 Columns (51):['ma_dia_diem', 'all_task_normal', 'all_task_dinhky', 'loai_ca', 'bat_dau', 'ket_thuc', 'tong_gio_lam', 'so_ca_cua_toa', 'so_luong', 'num_tasks']... (showing first 10)\n",
-      "\n",
-      "🔍 Checking text columns:\n",
-      "   all_task_normal: ✅ Found\n",
-      "   all_task_dinhky: ✅ Found\n",
-      "\n",
-      "   all_task_normal - Missing: 27 (5.9%)\n",
-      "   all_task_dinhky - Missing: 168 (37.0%)\n",
-      "\n",
-      "📄 Sample text data:\n",
-      "\n",
-      "all_task_normal (first 200 chars):\n",
-      "Làm sạch toàn bộ phòng giao dịch tầng 1 (kể cả wc); Làm sạch bậc tam cấp + ngoại cảnh + tầng hầm; Làm sạch toàn bộ văn phòng tầng 2 + phòng lãnh đạo (kể cả wc); Làm sạch toàn bộ thang bộ từ tầng 1 lên...\n",
-      "\n",
-      "all_task_dinhky (first 200 chars):\n",
-      "nan...\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ma_dia_diem</th>\n",
-       "      <th>all_task_normal</th>\n",
-       "      <th>all_task_dinhky</th>\n",
-       "      <th>loai_ca</th>\n",
-       "      <th>bat_dau</th>\n",
-       "      <th>ket_thuc</th>\n",
-       "      <th>tong_gio_lam</th>\n",
-       "      <th>so_ca_cua_toa</th>\n",
-       "      <th>so_luong</th>\n",
-       "      <th>num_tasks</th>\n",
-       "      <th>num_cleaning_tasks</th>\n",
-       "      <th>num_trash_collection_tasks</th>\n",
-       "      <th>num_monitoring_tasks</th>\n",
-       "      <th>num_room_cleaning_tasks</th>\n",
-       "      <th>num_deep_cleaning_tasks</th>\n",
-       "      <th>num_maintenance_tasks</th>\n",
-       "      <th>num_support_tasks</th>\n",
-       "      <th>num_other_tasks</th>\n",
-       "      <th>num_wc_tasks</th>\n",
-       "      <th>num_hallway_tasks</th>\n",
-       "      <th>num_lobby_tasks</th>\n",
-       "      <th>num_patient_room_tasks</th>\n",
-       "      <th>num_clinic_room_tasks</th>\n",
-       "      <th>num_surgery_room_tasks</th>\n",
-       "      <th>num_outdoor_tasks</th>\n",
-       "      <th>num_elevator_tasks</th>\n",
-       "      <th>num_office_tasks</th>\n",
-       "      <th>num_technical_room_tasks</th>\n",
-       "      <th>cleaning_ratio</th>\n",
-       "      <th>trash_collection_ratio</th>\n",
-       "      <th>monitoring_ratio</th>\n",
-       "      <th>room_cleaning_ratio</th>\n",
-       "      <th>area_diversity</th>\n",
-       "      <th>task_complexity_score</th>\n",
-       "      <th>loai_hinh</th>\n",
-       "      <th>ten_toa_thap</th>\n",
-       "      <th>muc_do_luu_luong</th>\n",
-       "      <th>so_tang</th>\n",
-       "      <th>so_cua_thang_may</th>\n",
-       "      <th>dien_tich_ngoai_canh</th>\n",
-       "      <th>dien_tich_sanh</th>\n",
-       "      <th>dien_tich_hanh_lang</th>\n",
-       "      <th>dien_tich_wc</th>\n",
-       "      <th>dien_tich_phong</th>\n",
-       "      <th>dien_tich_tham</th>\n",
-       "      <th>doc_ham</th>\n",
-       "      <th>vien_phan_quang</th>\n",
-       "      <th>op_tuong</th>\n",
-       "      <th>op_chan_tuong</th>\n",
-       "      <th>ranh_thoat_nuoc</th>\n",
-       "      <th>dien_tich_kinh</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>115-2</td>\n",
-       "      <td>Làm sạch toàn bộ phòng giao dịch tầng 1 (kể cả...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Part time</td>\n",
-       "      <td>06:30:00</td>\n",
-       "      <td>10:30:00</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0.1429</td>\n",
-       "      <td>0.2857</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1.38</td>\n",
-       "      <td>0</td>\n",
-       "      <td>AGRIBANK CHI NHÁNH MỸ ĐÌNH</td>\n",
-       "      <td>Trung bình (11–20 người)</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>290.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>20.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>115-5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Hành chính</td>\n",
-       "      <td>06:30:00</td>\n",
-       "      <td>16:00:00</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>AGRIBANK PGD SỐ 05</td>\n",
-       "      <td>Trung bình (11–20 người)</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>300.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>30.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>101-1</td>\n",
-       "      <td>Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...</td>\n",
-       "      <td>Lau bảng biển, bình cứu hỏa , cây nước hành la...</td>\n",
-       "      <td>Hành chính</td>\n",
-       "      <td>06:30:00</td>\n",
-       "      <td>16:00:00</td>\n",
-       "      <td>7.5</td>\n",
-       "      <td>6</td>\n",
-       "      <td>24</td>\n",
-       "      <td>441.0</td>\n",
-       "      <td>258.0</td>\n",
-       "      <td>145.0</td>\n",
-       "      <td>134.0</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>39.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>0.585</td>\n",
-       "      <td>0.3288</td>\n",
-       "      <td>0.3039</td>\n",
-       "      <td>0.1474</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>10.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Tòa 5 tầng</td>\n",
-       "      <td>Rất cao (Trên 40 người)</td>\n",
-       "      <td>10</td>\n",
-       "      <td>18</td>\n",
-       "      <td>1700.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2600.0</td>\n",
-       "      <td>348.0</td>\n",
-       "      <td>6825.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9176.0</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>25</td>\n",
-       "      <td>894.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>101-1</td>\n",
-       "      <td>Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...</td>\n",
-       "      <td>Lau bảng biển, bình cứu hỏa , cây nước hành la...</td>\n",
-       "      <td>Ca sáng</td>\n",
-       "      <td>06:00:00</td>\n",
-       "      <td>14:00:00</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>3</td>\n",
-       "      <td>441.0</td>\n",
-       "      <td>258.0</td>\n",
-       "      <td>145.0</td>\n",
-       "      <td>134.0</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>39.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>0.585</td>\n",
-       "      <td>0.3288</td>\n",
-       "      <td>0.3039</td>\n",
-       "      <td>0.1474</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>10.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Tòa 5 tầng</td>\n",
-       "      <td>Rất cao (Trên 40 người)</td>\n",
-       "      <td>10</td>\n",
-       "      <td>18</td>\n",
-       "      <td>1700.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2600.0</td>\n",
-       "      <td>348.0</td>\n",
-       "      <td>6825.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9176.0</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>25</td>\n",
-       "      <td>894.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>101-1</td>\n",
-       "      <td>Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...</td>\n",
-       "      <td>Lau bảng biển, bình cứu hỏa , cây nước hành la...</td>\n",
-       "      <td>Ca chiều</td>\n",
-       "      <td>14:00:00</td>\n",
-       "      <td>22:00:00</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>5</td>\n",
-       "      <td>441.0</td>\n",
-       "      <td>258.0</td>\n",
-       "      <td>145.0</td>\n",
-       "      <td>134.0</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>39.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>0.585</td>\n",
-       "      <td>0.3288</td>\n",
-       "      <td>0.3039</td>\n",
-       "      <td>0.1474</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>10.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Tòa 5 tầng</td>\n",
-       "      <td>Rất cao (Trên 40 người)</td>\n",
-       "      <td>10</td>\n",
-       "      <td>18</td>\n",
-       "      <td>1700.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2600.0</td>\n",
-       "      <td>348.0</td>\n",
-       "      <td>6825.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9176.0</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>25</td>\n",
-       "      <td>894.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  ma_dia_diem                                    all_task_normal  \\\n",
-       "0       115-2  Làm sạch toàn bộ phòng giao dịch tầng 1 (kể cả...   \n",
-       "1       115-5                                                NaN   \n",
-       "2       101-1  Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...   \n",
-       "3       101-1  Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...   \n",
-       "4       101-1  Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...   \n",
-       "\n",
-       "                                     all_task_dinhky     loai_ca   bat_dau  \\\n",
-       "0                                                NaN   Part time  06:30:00   \n",
-       "1                                                NaN  Hành chính  06:30:00   \n",
-       "2  Lau bảng biển, bình cứu hỏa , cây nước hành la...  Hành chính  06:30:00   \n",
-       "3  Lau bảng biển, bình cứu hỏa , cây nước hành la...     Ca sáng  06:00:00   \n",
-       "4  Lau bảng biển, bình cứu hỏa , cây nước hành la...    Ca chiều  14:00:00   \n",
-       "\n",
-       "   ket_thuc  tong_gio_lam  so_ca_cua_toa  so_luong  num_tasks  \\\n",
-       "0  10:30:00           4.0              1         1        7.0   \n",
-       "1  16:00:00           8.0              1         1        NaN   \n",
-       "2  16:00:00           7.5              6        24      441.0   \n",
-       "3  14:00:00           8.0              6         3      441.0   \n",
-       "4  22:00:00           8.0              6         5      441.0   \n",
-       "\n",
-       "   num_cleaning_tasks  num_trash_collection_tasks  num_monitoring_tasks  \\\n",
-       "0                 7.0                         1.0                   2.0   \n",
-       "1                 NaN                         NaN                   NaN   \n",
-       "2               258.0                       145.0                 134.0   \n",
-       "3               258.0                       145.0                 134.0   \n",
-       "4               258.0                       145.0                 134.0   \n",
-       "\n",
-       "   num_room_cleaning_tasks  num_deep_cleaning_tasks  num_maintenance_tasks  \\\n",
-       "0                      0.0                      1.0                    1.0   \n",
-       "1                      NaN                      NaN                    NaN   \n",
-       "2                     65.0                     75.0                   62.0   \n",
-       "3                     65.0                     75.0                   62.0   \n",
-       "4                     65.0                     75.0                   62.0   \n",
-       "\n",
-       "   num_support_tasks  num_other_tasks  num_wc_tasks  num_hallway_tasks  \\\n",
-       "0                0.0              0.0           4.0                0.0   \n",
-       "1                NaN              NaN           NaN                NaN   \n",
-       "2               57.0             45.0          89.0               90.0   \n",
-       "3               57.0             45.0          89.0               90.0   \n",
-       "4               57.0             45.0          89.0               90.0   \n",
-       "\n",
-       "   num_lobby_tasks  num_patient_room_tasks  num_clinic_room_tasks  \\\n",
-       "0              0.0                     0.0                    0.0   \n",
-       "1              NaN                     NaN                    NaN   \n",
-       "2              5.0                    41.0                   25.0   \n",
-       "3              5.0                    41.0                   25.0   \n",
-       "4              5.0                    41.0                   25.0   \n",
-       "\n",
-       "   num_surgery_room_tasks  num_outdoor_tasks  num_elevator_tasks  \\\n",
-       "0                     0.0                2.0                 3.0   \n",
-       "1                     NaN                NaN                 NaN   \n",
-       "2                    30.0                4.0                12.0   \n",
-       "3                    30.0                4.0                12.0   \n",
-       "4                    30.0                4.0                12.0   \n",
-       "\n",
-       "   num_office_tasks  num_technical_room_tasks  cleaning_ratio  \\\n",
-       "0               1.0                       0.0           1.000   \n",
-       "1               NaN                       NaN             NaN   \n",
-       "2              39.0                      16.0           0.585   \n",
-       "3              39.0                      16.0           0.585   \n",
-       "4              39.0                      16.0           0.585   \n",
-       "\n",
-       "   trash_collection_ratio  monitoring_ratio  room_cleaning_ratio  \\\n",
-       "0                  0.1429            0.2857               0.0000   \n",
-       "1                     NaN               NaN                  NaN   \n",
-       "2                  0.3288            0.3039               0.1474   \n",
-       "3                  0.3288            0.3039               0.1474   \n",
-       "4                  0.3288            0.3039               0.1474   \n",
-       "\n",
-       "   area_diversity  task_complexity_score  loai_hinh  \\\n",
-       "0             4.0                   1.38          0   \n",
-       "1             NaN                    NaN          0   \n",
-       "2            10.0                  10.00          0   \n",
-       "3            10.0                  10.00          0   \n",
-       "4            10.0                  10.00          0   \n",
-       "\n",
-       "                 ten_toa_thap          muc_do_luu_luong  so_tang  \\\n",
-       "0  AGRIBANK CHI NHÁNH MỸ ĐÌNH  Trung bình (11–20 người)        4   \n",
-       "1          AGRIBANK PGD SỐ 05  Trung bình (11–20 người)        1   \n",
-       "2                  Tòa 5 tầng   Rất cao (Trên 40 người)       10   \n",
-       "3                  Tòa 5 tầng   Rất cao (Trên 40 người)       10   \n",
-       "4                  Tòa 5 tầng   Rất cao (Trên 40 người)       10   \n",
-       "\n",
-       "   so_cua_thang_may  dien_tich_ngoai_canh  dien_tich_sanh  \\\n",
-       "0                 0                   0.0             0.0   \n",
-       "1                 0                   0.0             0.0   \n",
-       "2                18                1700.0             0.0   \n",
-       "3                18                1700.0             0.0   \n",
-       "4                18                1700.0             0.0   \n",
-       "\n",
-       "   dien_tich_hanh_lang  dien_tich_wc  dien_tich_phong  dien_tich_tham  \\\n",
-       "0                  0.0          15.0            290.0             0.0   \n",
-       "1                  0.0          15.0            300.0             0.0   \n",
-       "2               2600.0         348.0           6825.0             0.0   \n",
-       "3               2600.0         348.0           6825.0             0.0   \n",
-       "4               2600.0         348.0           6825.0             0.0   \n",
-       "\n",
-       "   doc_ham  vien_phan_quang  op_tuong  op_chan_tuong  ranh_thoat_nuoc  \\\n",
-       "0        0                0       0.0            0.0                0   \n",
-       "1        0                0       0.0            0.0                0   \n",
-       "2       70                0    9176.0           89.0               25   \n",
-       "3       70                0    9176.0           89.0               25   \n",
-       "4       70                0    9176.0           89.0               25   \n",
-       "\n",
-       "   dien_tich_kinh  \n",
-       "0            20.0  \n",
-       "1            30.0  \n",
-       "2           894.0  \n",
-       "3           894.0  \n",
-       "4           894.0  "
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(\"📂 Loading dataset...\")\n",
-    "# ⚠️ IMPORTANT: Using the SAME dataset as Phase 1 for fair comparison!\n",
-    "df = pd.read_excel('FINAL_DATASET_WITH_TEXT_BACKUP_20260105_213507.xlsx')\n",
-    "\n",
-    "print(f\"✅ Dataset loaded!\")\n",
-    "print(f\"📊 Shape: {df.shape}\")\n",
-    "print(f\"\\n📋 Columns ({len(df.columns)}):{df.columns.tolist()[:10]}... (showing first 10)\")\n",
-    "\n",
-    "# Verify text columns\n",
-    "print(\"\\n🔍 Checking text columns:\")\n",
-    "print(f\"   all_task_normal: {'✅ Found' if 'all_task_normal' in df.columns else '❌ Missing'}\")\n",
-    "print(f\"   all_task_dinhky: {'✅ Found' if 'all_task_dinhky' in df.columns else '❌ Missing'}\")\n",
-    "\n",
-    "# Check missing values in text columns\n",
-    "if 'all_task_normal' in df.columns:\n",
-    "    print(f\"\\n   all_task_normal - Missing: {df['all_task_normal'].isnull().sum()} ({df['all_task_normal'].isnull().sum()/len(df)*100:.1f}%)\")\n",
-    "if 'all_task_dinhky' in df.columns:\n",
-    "    print(f\"   all_task_dinhky - Missing: {df['all_task_dinhky'].isnull().sum()} ({df['all_task_dinhky'].isnull().sum()/len(df)*100:.1f}%)\")\n",
-    "\n",
-    "# Sample text data\n",
-    "print(\"\\n📄 Sample text data:\")\n",
-    "print(\"\\nall_task_normal (first 200 chars):\")\n",
-    "print(str(df['all_task_normal'].iloc[0])[:200] + \"...\")\n",
-    "print(\"\\nall_task_dinhky (first 200 chars):\")\n",
-    "print(str(df['all_task_dinhky'].iloc[0])[:200] + \"...\")\n",
-    "\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e54a1e43",
-   "metadata": {},
-   "source": [
-    "# 3️⃣ Text Preprocessing\n",
-    "\n",
-    "Chuẩn bị text data cho TF-IDF:\n",
-    "- Lowercase\n",
-    "- Remove special characters\n",
-    "- Handle missing values\n",
-    "- Combine both task columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "8cab7fd2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "🧹 TEXT PREPROCESSING\n",
-      "================================================================================\n",
-      "\n",
-      "1. Preprocessing all_task_normal...\n",
-      "   ✓ Done. Non-empty: 427 / 454\n",
-      "\n",
-      "2. Preprocessing all_task_dinhky...\n",
-      "   ✓ Done. Non-empty: 286 / 454\n",
-      "\n",
-      "3. Combining both task columns...\n",
-      "\n",
-      "4. Text statistics:\n",
-      "   Average length: 9394 characters\n",
-      "   Average words: 2243 words\n",
-      "   Min words: 0\n",
-      "   Max words: 10342\n",
-      "   Empty texts: 27\n",
-      "\n",
-      "5. Sample cleaned text:\n",
-      "làm sạch toàn bộ phòng giao dịch tầng 1 kể cả wc làm sạch bậc tam cấp ngoại cảnh tầng hầm làm sạch toàn bộ văn phòng tầng 2 phòng lãnh đạo kể cả wc làm sạch toàn bộ thang bộ từ tầng 1 lên tầng 4 trực lại toàn bộ wc từ tầng 1 tầng 2 lưu ý đặt giấy lau vết bẩn phát sinh gạt kính cửa ra vào thay rác tr...\n",
-      "\n",
-      "✅ Text preprocessing complete!\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"=\" * 80)\n",
-    "print(\"🧹 TEXT PREPROCESSING\")\n",
-    "print(\"=\" * 80)\n",
-    "\n",
-    "def preprocess_text(text):\n",
-    "    \"\"\"\n",
-    "    Preprocess Vietnamese text\n",
-    "    \"\"\"\n",
-    "    if pd.isna(text) or str(text).strip() == '':\n",
-    "        return ''\n",
-    "    \n",
-    "    text = str(text).lower()\n",
-    "    \n",
-    "    # Keep Vietnamese characters, numbers, spaces\n",
-    "    # Remove special chars except spaces\n",
-    "    text = re.sub(r'[^a-zàáạảãâầấậẩẫăằắặẳẵèéẹẻẽêềếệểễìíịỉĩòóọỏõôồốộổỗơờớợởỡùúụủũưừứựửữỳýỵỷỹđ0-9\\s]', ' ', text)\n",
-    "    \n",
-    "    # Remove multiple spaces\n",
-    "    text = re.sub(r'\\s+', ' ', text).strip()\n",
-    "    \n",
-    "    return text\n",
-    "\n",
-    "# Preprocess both columns\n",
-    "print(\"\\n1. Preprocessing all_task_normal...\")\n",
-    "df['task_normal_clean'] = df['all_task_normal'].apply(preprocess_text)\n",
-    "print(f\"   ✓ Done. Non-empty: {(df['task_normal_clean'] != '').sum()} / {len(df)}\")\n",
-    "\n",
-    "print(\"\\n2. Preprocessing all_task_dinhky...\")\n",
-    "df['task_dinhky_clean'] = df['all_task_dinhky'].apply(preprocess_text)\n",
-    "print(f\"   ✓ Done. Non-empty: {(df['task_dinhky_clean'] != '').sum()} / {len(df)}\")\n",
-    "\n",
-    "# Combine both task columns\n",
-    "print(\"\\n3. Combining both task columns...\")\n",
-    "df['all_tasks_combined'] = df['task_normal_clean'] + ' ' + df['task_dinhky_clean']\n",
-    "df['all_tasks_combined'] = df['all_tasks_combined'].str.strip()\n",
-    "\n",
-    "# Statistics\n",
-    "print(\"\\n4. Text statistics:\")\n",
-    "df['text_length'] = df['all_tasks_combined'].str.len()\n",
-    "df['text_word_count'] = df['all_tasks_combined'].str.split().str.len()\n",
-    "\n",
-    "print(f\"   Average length: {df['text_length'].mean():.0f} characters\")\n",
-    "print(f\"   Average words: {df['text_word_count'].mean():.0f} words\")\n",
-    "print(f\"   Min words: {df['text_word_count'].min():.0f}\")\n",
-    "print(f\"   Max words: {df['text_word_count'].max():.0f}\")\n",
-    "print(f\"   Empty texts: {(df['all_tasks_combined'] == '').sum()}\")\n",
-    "\n",
-    "# Sample cleaned text\n",
-    "print(\"\\n5. Sample cleaned text:\")\n",
-    "print(df['all_tasks_combined'].iloc[0][:300] + \"...\")\n",
-    "\n",
-    "print(\"\\n✅ Text preprocessing complete!\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "11cb1a6d",
-   "metadata": {},
-   "source": [
-    "# 4️⃣ TF-IDF Vectorization\n",
-    "\n",
-    "Chuyển text thành numeric features với TF-IDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "59aa536f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "📊 TF-IDF VECTORIZATION\n",
-      "================================================================================\n",
-      "\n",
-      "TF-IDF Configuration:\n",
-      "   max_features: 200\n",
-      "   ngram_range: (1, 2)\n",
-      "   min_df: 2\n",
-      "   max_df: 0.95\n",
-      "\n",
-      "Creating TF-IDF vectorizer...\n",
-      "\n",
-      "Fitting TF-IDF on all documents...\n",
-      "\n",
-      "✅ TF-IDF complete!\n",
-      "   Output shape: (454, 200)\n",
-      "   Actual features extracted: 200\n",
-      "   Sparsity: 47.20%\n",
-      "\n",
-      "📋 Top 20 TF-IDF features (by document frequency):\n",
-      " feature  doc_frequency\n",
-      "     rác            412\n",
-      "     lau            408\n",
-      "    sinh            408\n",
-      "    kính            404\n",
-      "     làm            404\n",
-      "      vệ            399\n",
-      "    định            397\n",
-      "   phòng            395\n",
-      "     thu            393\n",
-      " vệ sinh            392\n",
-      "    sạch            386\n",
-      "    quét            385\n",
-      "    nghỉ            381\n",
-      "    trực            380\n",
-      "     cửa            380\n",
-      "làm sạch            378\n",
-      " định kỳ            370\n",
-      "      kỳ            370\n",
-      "      cụ            368\n",
-      "    dụng            368\n",
-      "\n",
-      "✅ TF-IDF DataFrame created: (454, 200)\n",
-      "\n",
-      "✅ TF-IDF complete!\n",
-      "   Output shape: (454, 200)\n",
-      "   Actual features extracted: 200\n",
-      "   Sparsity: 47.20%\n",
-      "\n",
-      "📋 Top 20 TF-IDF features (by document frequency):\n",
-      " feature  doc_frequency\n",
-      "     rác            412\n",
-      "     lau            408\n",
-      "    sinh            408\n",
-      "    kính            404\n",
-      "     làm            404\n",
-      "      vệ            399\n",
-      "    định            397\n",
-      "   phòng            395\n",
-      "     thu            393\n",
-      " vệ sinh            392\n",
-      "    sạch            386\n",
-      "    quét            385\n",
-      "    nghỉ            381\n",
-      "    trực            380\n",
-      "     cửa            380\n",
-      "làm sạch            378\n",
-      " định kỳ            370\n",
-      "      kỳ            370\n",
-      "      cụ            368\n",
-      "    dụng            368\n",
-      "\n",
-      "✅ TF-IDF DataFrame created: (454, 200)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"=\" * 80)\n",
-    "print(\"📊 TF-IDF VECTORIZATION\")\n",
-    "print(\"=\" * 80)\n",
-    "\n",
-    "# TF-IDF parameters\n",
-    "max_features = 200  # Giới hạn số features từ TF-IDF\n",
-    "ngram_range = (1, 2)  # Unigrams và bigrams\n",
-    "min_df = 2  # Xuất hiện ít nhất 2 documents\n",
-    "max_df = 0.95  # Xuất hiện tối đa 95% documents\n",
-    "\n",
-    "print(f\"\\nTF-IDF Configuration:\")\n",
-    "print(f\"   max_features: {max_features}\")\n",
-    "print(f\"   ngram_range: {ngram_range}\")\n",
-    "print(f\"   min_df: {min_df}\")\n",
-    "print(f\"   max_df: {max_df}\")\n",
-    "\n",
-    "# Create TF-IDF vectorizer\n",
-    "print(\"\\nCreating TF-IDF vectorizer...\")\n",
-    "tfidf = TfidfVectorizer(\n",
-    "    max_features=max_features,\n",
-    "    ngram_range=ngram_range,\n",
-    "    min_df=min_df,\n",
-    "    max_df=max_df,\n",
-    "    sublinear_tf=True,  # Use log scaling\n",
-    "    strip_accents=None  # Keep Vietnamese accents\n",
-    ")\n",
-    "\n",
-    "# Fit and transform\n",
-    "print(\"\\nFitting TF-IDF on all documents...\")\n",
-    "tfidf_matrix = tfidf.fit_transform(df['all_tasks_combined'])\n",
-    "\n",
-    "print(f\"\\n✅ TF-IDF complete!\")\n",
-    "print(f\"   Output shape: {tfidf_matrix.shape}\")\n",
-    "print(f\"   Actual features extracted: {len(tfidf.get_feature_names_out())}\")\n",
-    "print(f\"   Sparsity: {(1.0 - tfidf_matrix.nnz / (tfidf_matrix.shape[0] * tfidf_matrix.shape[1])) * 100:.2f}%\")\n",
-    "\n",
-    "# Top features by importance\n",
-    "print(\"\\n📋 Top 20 TF-IDF features (by document frequency):\")\n",
-    "feature_names = tfidf.get_feature_names_out()\n",
-    "doc_freq = (tfidf_matrix > 0).sum(axis=0).A1\n",
-    "top_features = pd.DataFrame({\n",
-    "    'feature': feature_names,\n",
-    "    'doc_frequency': doc_freq\n",
-    "}).sort_values('doc_frequency', ascending=False)\n",
-    "\n",
-    "print(top_features.head(20).to_string(index=False))\n",
-    "\n",
-    "# Save TF-IDF matrix as DataFrame (optional, for inspection)\n",
-    "tfidf_df = pd.DataFrame(\n",
-    "    tfidf_matrix.toarray(),\n",
-    "    columns=[f'tfidf_{i}' for i in range(tfidf_matrix.shape[1])]\n",
-    ")\n",
-    "\n",
-    "print(f\"\\n✅ TF-IDF DataFrame created: {tfidf_df.shape}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aa687eb6",
-   "metadata": {},
-   "source": [
-    "# 5️⃣ SVD Dimensionality Reduction\n",
-    "\n",
-    "Giảm chiều TF-IDF features với SVD để tránh overfitting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "77ae5dbb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "🔬 SVD DIMENSIONALITY REDUCTION\n",
-      "================================================================================\n",
-      "\n",
-      "SVD Configuration:\n",
-      "   Input dimensions: 200\n",
-      "   Output dimensions: 50\n",
-      "   Reduction: 75.0%\n",
-      "\n",
-      "Applying SVD...\n",
-      "\n",
-      "✅ SVD complete!\n",
-      "   Output shape: (454, 50)\n",
-      "   Explained variance: 89.66%\n",
-      "\n",
-      "📊 Variance explained by top 10 components:\n",
-      "   Component 1: 11.08%\n",
-      "   Component 2: 8.74%\n",
-      "   Component 3: 7.92%\n",
-      "   Component 4: 5.04%\n",
-      "   Component 5: 3.60%\n",
-      "   Component 6: 3.36%\n",
-      "   Component 7: 3.29%\n",
-      "   Component 8: 3.13%\n",
-      "   Component 9: 3.06%\n",
-      "   Component 10: 2.75%\n",
-      "\n",
-      "   Cumulative variance (first 10): 51.95%\n",
-      "   Cumulative variance (all 50): 89.66%\n",
-      "\n",
-      "✅ SVD features DataFrame created: (454, 50)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "✅ Saved: phase2_output/plots/01_svd_variance.png\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"=\" * 80)\n",
-    "print(\"🔬 SVD DIMENSIONALITY REDUCTION\")\n",
-    "print(\"=\" * 80)\n",
-    "\n",
-    "# SVD parameters\n",
-    "n_components = 50  # Giảm xuống 50 components\n",
-    "\n",
-    "print(f\"\\nSVD Configuration:\")\n",
-    "print(f\"   Input dimensions: {tfidf_matrix.shape[1]}\")\n",
-    "print(f\"   Output dimensions: {n_components}\")\n",
-    "print(f\"   Reduction: {(1 - n_components/tfidf_matrix.shape[1]) * 100:.1f}%\")\n",
-    "\n",
-    "# Apply SVD\n",
-    "print(\"\\nApplying SVD...\")\n",
-    "svd = TruncatedSVD(n_components=n_components, random_state=RANDOM_STATE)\n",
-    "text_features_svd = svd.fit_transform(tfidf_matrix)\n",
-    "\n",
-    "print(f\"\\n✅ SVD complete!\")\n",
-    "print(f\"   Output shape: {text_features_svd.shape}\")\n",
-    "print(f\"   Explained variance: {svd.explained_variance_ratio_.sum()*100:.2f}%\")\n",
-    "\n",
-    "# Variance explained by each component\n",
-    "print(\"\\n📊 Variance explained by top 10 components:\")\n",
-    "for i in range(min(10, n_components)):\n",
-    "    print(f\"   Component {i+1}: {svd.explained_variance_ratio_[i]*100:.2f}%\")\n",
-    "\n",
-    "# Cumulative variance\n",
-    "cumsum_var = np.cumsum(svd.explained_variance_ratio_)\n",
-    "print(f\"\\n   Cumulative variance (first 10): {cumsum_var[9]*100:.2f}%\")\n",
-    "print(f\"   Cumulative variance (all {n_components}): {cumsum_var[-1]*100:.2f}%\")\n",
-    "\n",
-    "# Create DataFrame with SVD features\n",
-    "text_features_df = pd.DataFrame(\n",
-    "    text_features_svd,\n",
-    "    columns=[f'text_svd_{i+1}' for i in range(n_components)]\n",
-    ")\n",
-    "\n",
-    "print(f\"\\n✅ SVD features DataFrame created: {text_features_df.shape}\")\n",
-    "\n",
-    "# Visualize variance explained\n",
-    "plt.figure(figsize=(12, 5))\n",
-    "\n",
-    "plt.subplot(1, 2, 1)\n",
-    "plt.plot(range(1, n_components+1), svd.explained_variance_ratio_, 'b-', linewidth=2)\n",
-    "plt.xlabel('Component Number', fontsize=12)\n",
-    "plt.ylabel('Explained Variance Ratio', fontsize=12)\n",
-    "plt.title('Variance Explained by Each SVD Component', fontsize=14, fontweight='bold')\n",
-    "plt.grid(alpha=0.3)\n",
-    "\n",
-    "plt.subplot(1, 2, 2)\n",
-    "plt.plot(range(1, n_components+1), cumsum_var, 'r-', linewidth=2)\n",
-    "plt.axhline(y=0.9, color='green', linestyle='--', label='90% variance')\n",
-    "plt.xlabel('Number of Components', fontsize=12)\n",
-    "plt.ylabel('Cumulative Variance Explained', fontsize=12)\n",
-    "plt.title('Cumulative Variance Explained', fontsize=14, fontweight='bold')\n",
-    "plt.legend()\n",
-    "plt.grid(alpha=0.3)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.savefig('phase2_output/plots/01_svd_variance.png', dpi=300, bbox_inches='tight')\n",
-    "plt.show()\n",
-    "\n",
-    "print(\"\\n✅ Saved: phase2_output/plots/01_svd_variance.png\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c0c967c7",
-   "metadata": {},
-   "source": [
-    "# 6️⃣ Combine All Features\n",
-    "\n",
-    "Kết hợp 47 numeric features + 50 text SVD features = 97 features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "dc702312",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "🔗 COMBINING ALL FEATURES\n",
-      "================================================================================\n",
-      "\n",
-      "1. Numeric features from original dataset:\n",
-      "   Total columns in df: 56\n",
-      "   Columns to drop: 10\n",
-      "   Numeric features: 46\n",
-      "\n",
-      "2. Feature dimensions:\n",
-      "   Numeric features: (454, 46)\n",
-      "   Text SVD features: (454, 50)\n",
-      "\n",
-      "3. Combining features...\n",
-      "\n",
-      "✅ Features combined!\n",
-      "   Final feature matrix: (454, 96)\n",
-      "   Target vector: (454,)\n",
-      "   Feature breakdown:\n",
-      "     - Numeric features: 46\n",
-      "     - Text SVD features: 50\n",
-      "     - Total: 96\n",
-      "\n",
-      "   Missing values: 550\n",
-      "\n",
-      "⚠️ Handling missing values...\n",
-      "   ✓ Filled with 0\n",
-      "\n",
-      "✅ Saved: phase2_output/data/combined_features_with_text.csv\n",
-      "\n",
-      "📊 Sample combined features (first 5 rows, first 10 cols):\n",
-      "      loai_ca   bat_dau  ket_thuc  tong_gio_lam  so_ca_cua_toa  num_tasks  \\\n",
-      "0   Part time  06:30:00  10:30:00           4.0              1        7.0   \n",
-      "1  Hành chính  06:30:00  16:00:00           8.0              1        0.0   \n",
-      "2  Hành chính  06:30:00  16:00:00           7.5              6      441.0   \n",
-      "3     Ca sáng  06:00:00  14:00:00           8.0              6      441.0   \n",
-      "4    Ca chiều  14:00:00  22:00:00           8.0              6      441.0   \n",
-      "\n",
-      "   num_cleaning_tasks  num_trash_collection_tasks  num_monitoring_tasks  \\\n",
-      "0                 7.0                         1.0                   2.0   \n",
-      "1                 0.0                         0.0                   0.0   \n",
-      "2               258.0                       145.0                 134.0   \n",
-      "3               258.0                       145.0                 134.0   \n",
-      "4               258.0                       145.0                 134.0   \n",
-      "\n",
-      "   num_room_cleaning_tasks  \n",
-      "0                      0.0  \n",
-      "1                      0.0  \n",
-      "2                     65.0  \n",
-      "3                     65.0  \n",
-      "4                     65.0  \n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"=\" * 80)\n",
-    "print(\"🔗 COMBINING ALL FEATURES\")\n",
-    "print(\"=\" * 80)\n",
-    "\n",
-    "# Columns to drop (non-feature columns)\n",
-    "cols_to_drop = [\n",
-    "    'ma_dia_diem',\n",
-    "    'so_luong',  # Target\n",
-    "    'all_task_normal',  # Original text\n",
-    "    'all_task_dinhky',  # Original text\n",
-    "    'task_normal_clean',  # Cleaned text\n",
-    "    'task_dinhky_clean',  # Cleaned text\n",
-    "    'all_tasks_combined',  # Combined text\n",
-    "    'text_length',  # Temporary column\n",
-    "    'text_word_count',  # Temporary column\n",
-    "    'ten_toa_thap'  # Non-predictive\n",
-    "]\n",
-    "\n",
-    "# Get numeric features\n",
-    "numeric_cols = [col for col in df.columns if col not in cols_to_drop]\n",
-    "\n",
-    "print(f\"\\n1. Numeric features from original dataset:\")\n",
-    "print(f\"   Total columns in df: {len(df.columns)}\")\n",
-    "print(f\"   Columns to drop: {len(cols_to_drop)}\")\n",
-    "print(f\"   Numeric features: {len(numeric_cols)}\")\n",
-    "\n",
-    "# Create feature matrix\n",
-    "X_numeric = df[numeric_cols].copy()\n",
-    "\n",
-    "print(f\"\\n2. Feature dimensions:\")\n",
-    "print(f\"   Numeric features: {X_numeric.shape}\")\n",
-    "print(f\"   Text SVD features: {text_features_df.shape}\")\n",
-    "\n",
-    "# Combine\n",
-    "print(\"\\n3. Combining features...\")\n",
-    "X_combined = pd.concat([X_numeric.reset_index(drop=True), text_features_df.reset_index(drop=True)], axis=1)\n",
-    "y = df['so_luong'].copy()\n",
-    "\n",
-    "print(f\"\\n✅ Features combined!\")\n",
-    "print(f\"   Final feature matrix: {X_combined.shape}\")\n",
-    "print(f\"   Target vector: {y.shape}\")\n",
-    "print(f\"   Feature breakdown:\")\n",
-    "print(f\"     - Numeric features: {len(numeric_cols)}\")\n",
-    "print(f\"     - Text SVD features: {n_components}\")\n",
-    "print(f\"     - Total: {X_combined.shape[1]}\")\n",
-    "\n",
-    "# Check for missing values\n",
-    "missing_count = X_combined.isnull().sum().sum()\n",
-    "print(f\"\\n   Missing values: {missing_count}\")\n",
-    "\n",
-    "if missing_count > 0:\n",
-    "    print(\"\\n⚠️ Handling missing values...\")\n",
-    "    X_combined = X_combined.fillna(0)\n",
-    "    print(\"   ✓ Filled with 0\")\n",
-    "\n",
-    "# Save combined features\n",
-    "combined_with_target = pd.concat([X_combined, y.rename('so_luong')], axis=1)\n",
-    "combined_with_target.to_csv('phase2_output/data/combined_features_with_text.csv', index=False)\n",
-    "print(\"\\n✅ Saved: phase2_output/data/combined_features_with_text.csv\")\n",
-    "\n",
-    "# Sample\n",
-    "print(\"\\n📊 Sample combined features (first 5 rows, first 10 cols):\")\n",
-    "print(X_combined.iloc[:5, :10])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "57f4e5d0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "🔍 DEBUGGING - CHECKING DATA TYPES\n",
-      "================================================================================\n",
-      "\n",
-      "📊 X_combined shape: (454, 96)\n",
-      "\n",
-      "🔢 Data types in X_combined:\n",
-      "float64    85\n",
-      "int64       7\n",
-      "object      4\n",
-      "Name: count, dtype: int64\n",
-      "\n",
-      "⚠️ Found 4 non-numeric columns:\n",
-      "   - loai_ca: object (sample values: ['Part time' 'Hành chính' 'Ca sáng' 'Ca chiều' 'Ca đêm'])\n",
-      "   - bat_dau: object (sample values: ['06:30:00' '06:00:00' '14:00:00' '2025-01-01 22:00:00' '12:00:00'])\n",
-      "   - ket_thuc: object (sample values: ['10:30:00' '16:00:00' '14:00:00' '22:00:00' '2025-01-02 06:00:00'])\n",
-      "   - muc_do_luu_luong: object (sample values: ['Trung bình (11–20 người)' 'Rất cao (Trên 40 người)' 'Thấp (6–10 người)'\n",
-      " 'Cao (21–40 người)' 0])\n",
-      "\n",
-      "🔧 These columns need to be removed or encoded before scaling!\n",
-      "\n",
-      "📋 First 20 columns in X_combined:\n",
-      "['loai_ca', 'bat_dau', 'ket_thuc', 'tong_gio_lam', 'so_ca_cua_toa', 'num_tasks', 'num_cleaning_tasks', 'num_trash_collection_tasks', 'num_monitoring_tasks', 'num_room_cleaning_tasks', 'num_deep_cleaning_tasks', 'num_maintenance_tasks', 'num_support_tasks', 'num_other_tasks', 'num_wc_tasks', 'num_hallway_tasks', 'num_lobby_tasks', 'num_patient_room_tasks', 'num_clinic_room_tasks', 'num_surgery_room_tasks']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 🔍 DEBUG: Check for non-numeric columns\n",
-    "print(\"=\" * 80)\n",
-    "print(\"🔍 DEBUGGING - CHECKING DATA TYPES\")\n",
-    "print(\"=\" * 80)\n",
-    "\n",
-    "print(f\"\\n📊 X_combined shape: {X_combined.shape}\")\n",
-    "print(f\"\\n🔢 Data types in X_combined:\")\n",
-    "print(X_combined.dtypes.value_counts())\n",
-    "\n",
-    "# Find non-numeric columns\n",
-    "non_numeric_cols = X_combined.select_dtypes(include=['object', 'category']).columns.tolist()\n",
-    "\n",
-    "if len(non_numeric_cols) > 0:\n",
-    "    print(f\"\\n⚠️ Found {len(non_numeric_cols)} non-numeric columns:\")\n",
-    "    for col in non_numeric_cols:\n",
-    "        unique_vals = X_combined[col].unique()[:5]\n",
-    "        print(f\"   - {col}: {X_combined[col].dtype} (sample values: {unique_vals})\")\n",
-    "    \n",
-    "    print(f\"\\n🔧 These columns need to be removed or encoded before scaling!\")\n",
-    "else:\n",
-    "    print(f\"\\n✅ All columns are numeric!\")\n",
-    "\n",
-    "# Show first few column names\n",
-    "print(f\"\\n📋 First 20 columns in X_combined:\")\n",
-    "print(X_combined.columns[:20].tolist())\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "e9ab7e56",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "🔧 REMOVING NON-NUMERIC COLUMNS\n",
-      "================================================================================\n",
-      "\n",
-      "⚠️ Removing 4 non-numeric columns:\n",
-      "   - loai_ca\n",
-      "   - bat_dau\n",
-      "   - ket_thuc\n",
-      "   - muc_do_luu_luong\n",
-      "\n",
-      "✅ Removed non-numeric columns\n",
-      "📊 New X_combined shape: (454, 92)\n",
-      "\n",
-      "✅ All columns are now numeric!\n",
-      "📊 Final X_combined shape: (454, 92)\n",
-      "🔢 Data types: {dtype('float64'): 85, dtype('int64'): 7}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 🔧 FIX: Remove non-numeric columns\n",
-    "print(\"=\" * 80)\n",
-    "print(\"🔧 REMOVING NON-NUMERIC COLUMNS\")\n",
-    "print(\"=\" * 80)\n",
-    "\n",
-    "# Get non-numeric columns\n",
-    "non_numeric_cols = X_combined.select_dtypes(include=['object', 'category']).columns.tolist()\n",
-    "\n",
-    "if len(non_numeric_cols) > 0:\n",
-    "    print(f\"\\n⚠️ Removing {len(non_numeric_cols)} non-numeric columns:\")\n",
-    "    for col in non_numeric_cols:\n",
-    "        print(f\"   - {col}\")\n",
-    "    \n",
-    "    # Remove non-numeric columns\n",
-    "    X_combined = X_combined.drop(columns=non_numeric_cols)\n",
-    "    \n",
-    "    print(f\"\\n✅ Removed non-numeric columns\")\n",
-    "    print(f\"📊 New X_combined shape: {X_combined.shape}\")\n",
-    "else:\n",
-    "    print(f\"\\n✅ No non-numeric columns to remove\")\n",
-    "\n",
-    "# Verify all columns are now numeric\n",
-    "remaining_non_numeric = X_combined.select_dtypes(include=['object', 'category']).columns.tolist()\n",
-    "if len(remaining_non_numeric) > 0:\n",
-    "    print(f\"\\n❌ ERROR: Still have non-numeric columns: {remaining_non_numeric}\")\n",
-    "else:\n",
-    "    print(f\"\\n✅ All columns are now numeric!\")\n",
-    "    print(f\"📊 Final X_combined shape: {X_combined.shape}\")\n",
-    "    print(f\"🔢 Data types: {X_combined.dtypes.value_counts().to_dict()}\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "96d75ad4",
-   "metadata": {},
-   "source": [
-    "# 7️⃣ Train/Validation/Test Split\n",
-    "\n",
-    "Chia dữ liệu với tỷ lệ 70/15/15, stratified sampling để tránh data leakage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "bcb7d3c5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "✂️ TRAIN/VALIDATION/TEST SPLIT\n",
-      "================================================================================\n",
-      "\n",
-      "1. Checking duplicates...\n",
-      "   Duplicates found: 120\n",
-      "   ✓ Removed 120 duplicates\n",
-      "   ✓ New dataset size: 413\n",
-      "\n",
-      "2. Creating stratified bins...\n",
-      "   ✓ Created 4 bins\n",
-      "   ✓ Samples per bin: {0: 171, 1: 95, 2: 80, 3: 67}\n",
-      "\n",
-      "3. Splitting data...\n",
-      "\n",
-      "✅ Split complete!\n",
-      "   Train: 289 samples (70.0%)\n",
-      "   Val:   62 samples (15.0%)\n",
-      "   Test:  62 samples (15.0%)\n",
-      "\n",
-      "📊 Target distribution:\n",
-      "   Train - Mean: 4.87, Std: 7.21, Var: 52.05\n",
-      "   Val   - Mean: 4.10, Std: 5.26, Var: 27.66\n",
-      "   Test  - Mean: 4.65, Std: 6.80, Var: 46.30\n",
-      "\n",
-      "4. Checking for data leakage...\n",
-      "   Train & Test overlap: 23 rows ✓\n",
-      "   ⚠️ WARNING: Data leakage detected!\n",
-      "\n",
-      "5. Scaling features...\n",
-      "   ✓ Scaled all features using StandardScaler\n",
-      "\n",
-      "================================================================================\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"=\" * 80)\n",
-    "print(\"✂️ TRAIN/VALIDATION/TEST SPLIT\")\n",
-    "print(\"=\" * 80)\n",
-    "\n",
-    "# Remove duplicates first\n",
-    "print(\"\\n1. Checking duplicates...\")\n",
-    "dups_before = X_combined.duplicated().sum()\n",
-    "print(f\"   Duplicates found: {dups_before}\")\n",
-    "\n",
-    "if dups_before > 0:\n",
-    "    # Combine X and y before removing duplicates\n",
-    "    combined_df = pd.concat([X_combined, y], axis=1)\n",
-    "    combined_df = combined_df.drop_duplicates()\n",
-    "    X_combined = combined_df.iloc[:, :-1]\n",
-    "    y = combined_df.iloc[:, -1]\n",
-    "    print(f\"   ✓ Removed {dups_before} duplicates\")\n",
-    "    print(f\"   ✓ New dataset size: {len(X_combined)}\")\n",
-    "\n",
-    "# Stratified split using quantile bins\n",
-    "print(\"\\n2. Creating stratified bins...\")\n",
-    "n_bins = 5\n",
-    "try:\n",
-    "    y_binned = pd.qcut(y, q=n_bins, labels=False, duplicates='drop')\n",
-    "    bin_counts = y_binned.value_counts().sort_index()\n",
-    "    print(f\"   ✓ Created {len(y_binned.unique())} bins\")\n",
-    "    print(f\"   ✓ Samples per bin: {bin_counts.to_dict()}\")\n",
-    "    use_stratify = bin_counts.min() >= 10\n",
-    "except:\n",
-    "    print(\"   ⚠️ Cannot create bins, using random split\")\n",
-    "    use_stratify = False\n",
-    "\n",
-    "# Split: 70% train, 15% val, 15% test\n",
-    "print(\"\\n3. Splitting data...\")\n",
-    "\n",
-    "if use_stratify:\n",
-    "    X_train, X_temp, y_train, y_temp = train_test_split(\n",
-    "        X_combined, y, test_size=0.30, random_state=RANDOM_STATE, stratify=y_binned\n",
-    "    )\n",
-    "    \n",
-    "    # Split temp into val and test\n",
-    "    try:\n",
-    "        y_temp_binned = pd.qcut(y_temp, q=3, labels=False, duplicates='drop')\n",
-    "        X_val, X_test, y_val, y_test = train_test_split(\n",
-    "            X_temp, y_temp, test_size=0.50, random_state=RANDOM_STATE, stratify=y_temp_binned\n",
-    "        )\n",
-    "    except:\n",
-    "        X_val, X_test, y_val, y_test = train_test_split(\n",
-    "            X_temp, y_temp, test_size=0.50, random_state=RANDOM_STATE\n",
-    "        )\n",
-    "else:\n",
-    "    X_train, X_temp, y_train, y_temp = train_test_split(\n",
-    "        X_combined, y, test_size=0.30, random_state=RANDOM_STATE\n",
-    "    )\n",
-    "    X_val, X_test, y_val, y_test = train_test_split(\n",
-    "        X_temp, y_temp, test_size=0.50, random_state=RANDOM_STATE\n",
-    "    )\n",
-    "\n",
-    "print(f\"\\n✅ Split complete!\")\n",
-    "print(f\"   Train: {len(X_train)} samples ({len(X_train)/len(X_combined)*100:.1f}%)\")\n",
-    "print(f\"   Val:   {len(X_val)} samples ({len(X_val)/len(X_combined)*100:.1f}%)\")\n",
-    "print(f\"   Test:  {len(X_test)} samples ({len(X_test)/len(X_combined)*100:.1f}%)\")\n",
-    "\n",
-    "print(f\"\\n📊 Target distribution:\")\n",
-    "print(f\"   Train - Mean: {y_train.mean():.2f}, Std: {y_train.std():.2f}, Var: {y_train.var():.2f}\")\n",
-    "print(f\"   Val   - Mean: {y_val.mean():.2f}, Std: {y_val.std():.2f}, Var: {y_val.var():.2f}\")\n",
-    "print(f\"   Test  - Mean: {y_test.mean():.2f}, Std: {y_test.std():.2f}, Var: {y_test.var():.2f}\")\n",
-    "\n",
-    "# Check for data leakage\n",
-    "print(\"\\n4. Checking for data leakage...\")\n",
-    "train_test_overlap = pd.merge(X_train.reset_index(drop=True), X_test.reset_index(drop=True), how='inner')\n",
-    "print(f\"   Train & Test overlap: {len(train_test_overlap)} rows ✓\")\n",
-    "\n",
-    "if len(train_test_overlap) > 0:\n",
-    "    print(\"   ⚠️ WARNING: Data leakage detected!\")\n",
-    "else:\n",
-    "    print(\"   ✅ No data leakage!\")\n",
-    "\n",
-    "# Feature Scaling\n",
-    "print(\"\\n5. Scaling features...\")\n",
-    "scaler = StandardScaler()\n",
-    "\n",
-    "X_train_scaled = X_train.copy()\n",
-    "X_val_scaled = X_val.copy()\n",
-    "X_test_scaled = X_test.copy()\n",
-    "\n",
-    "X_train_scaled[X_train.columns] = scaler.fit_transform(X_train)\n",
-    "X_val_scaled[X_val.columns] = scaler.transform(X_val)\n",
-    "X_test_scaled[X_test.columns] = scaler.transform(X_test)\n",
-    "\n",
-    "print(f\"   ✓ Scaled all features using StandardScaler\")\n",
-    "\n",
-    "print(\"\\n\" + \"=\" * 80)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e0084db5",
-   "metadata": {},
-   "source": [
-    "# 8️⃣ Model Training với Text Features\n",
-    "\n",
-    "Train tất cả models với 97 features (47 numeric + 50 text SVD)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "2df5c047",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "🤖 MODEL TRAINING - PHASE 2 (WITH TEXT FEATURES)\n",
-      "================================================================================\n",
-      "\n",
-      "1️⃣ Baseline (Mean Prediction)...\n",
-      "   Val MAE: 3.7132, R²: 0.0000\n",
-      "\n",
-      "2️⃣ Linear Regression...\n",
-      "   Val MAE: 4.3488, R²: -0.4161\n",
-      "\n",
-      "3️⃣ Decision Tree...\n",
-      "   Val MAE: 2.5525, R²: 0.3339\n",
-      "\n",
-      "4️⃣ Random Forest...\n",
-      "   Val MAE: 2.8388, R²: 0.2521\n",
-      "\n",
-      "5️⃣ Gradient Boosting...\n",
-      "   Val MAE: 2.8388, R²: 0.2521\n",
-      "\n",
-      "5️⃣ Gradient Boosting...\n",
-      "   Val MAE: 2.7644, R²: 0.1917\n",
-      "\n",
-      "6️⃣ XGBoost...\n",
-      "   Val MAE: 2.7488, R²: 0.1542\n",
-      "\n",
-      "7️⃣ LightGBM...\n",
-      "   Val MAE: 3.2101, R²: 0.0009\n",
-      "\n",
-      "✅ All models trained!\n",
-      "\n",
-      "================================================================================\n",
-      "📊 PHASE 2 RESULTS (WITH TEXT FEATURES)\n",
-      "================================================================================\n",
-      "            Model  Train_R2  Train_MAE  Train_RMSE    Val_R2  Val_MAE  Val_RMSE\n",
-      "    Decision Tree  0.740968   2.012207    3.665671  0.333863 2.552472  4.257920\n",
-      "    Random Forest  0.733891   2.083618    3.715410  0.252113 2.838840  4.511633\n",
-      "Gradient Boosting  0.873662   1.078451    2.560020  0.191742 2.764354  4.690192\n",
-      "          XGBoost  0.865798   1.272851    2.638496  0.154215 2.748771  4.797840\n",
-      "         LightGBM  0.696963   2.180261    3.964830  0.000899 3.210078  5.214593\n",
-      "         Baseline  0.000000   4.483567    7.202397  0.000000 3.713249  5.273205\n",
-      "Linear Regression  0.610783   2.897918    4.493377 -0.416064 4.348784  6.208078\n",
-      "\n",
-      "🏆 Best Model: Decision Tree\n",
-      "   Val R²:   0.3339\n",
-      "   Val MAE:  2.5525\n",
-      "   Val RMSE: 4.2579\n",
-      "\n",
-      "✅ Saved: phase2_output/data/phase2_model_results.csv\n",
-      "   Val MAE: 2.7644, R²: 0.1917\n",
-      "\n",
-      "6️⃣ XGBoost...\n",
-      "   Val MAE: 2.7488, R²: 0.1542\n",
-      "\n",
-      "7️⃣ LightGBM...\n",
-      "   Val MAE: 3.2101, R²: 0.0009\n",
-      "\n",
-      "✅ All models trained!\n",
-      "\n",
-      "================================================================================\n",
-      "📊 PHASE 2 RESULTS (WITH TEXT FEATURES)\n",
-      "================================================================================\n",
-      "            Model  Train_R2  Train_MAE  Train_RMSE    Val_R2  Val_MAE  Val_RMSE\n",
-      "    Decision Tree  0.740968   2.012207    3.665671  0.333863 2.552472  4.257920\n",
-      "    Random Forest  0.733891   2.083618    3.715410  0.252113 2.838840  4.511633\n",
-      "Gradient Boosting  0.873662   1.078451    2.560020  0.191742 2.764354  4.690192\n",
-      "          XGBoost  0.865798   1.272851    2.638496  0.154215 2.748771  4.797840\n",
-      "         LightGBM  0.696963   2.180261    3.964830  0.000899 3.210078  5.214593\n",
-      "         Baseline  0.000000   4.483567    7.202397  0.000000 3.713249  5.273205\n",
-      "Linear Regression  0.610783   2.897918    4.493377 -0.416064 4.348784  6.208078\n",
-      "\n",
-      "🏆 Best Model: Decision Tree\n",
-      "   Val R²:   0.3339\n",
-      "   Val MAE:  2.5525\n",
-      "   Val RMSE: 4.2579\n",
-      "\n",
-      "✅ Saved: phase2_output/data/phase2_model_results.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"=\" * 80)\n",
-    "print(\"🤖 MODEL TRAINING - PHASE 2 (WITH TEXT FEATURES)\")\n",
-    "print(\"=\" * 80)\n",
-    "\n",
-    "def evaluate_model_phase2(model, X_train, y_train, X_val, y_val, model_name):\n",
-    "    \"\"\"Train and evaluate model\"\"\"\n",
-    "    model.fit(X_train, y_train)\n",
-    "    \n",
-    "    y_train_pred = model.predict(X_train)\n",
-    "    y_val_pred = model.predict(X_val)\n",
-    "    \n",
-    "    return {\n",
-    "        'Model': model_name,\n",
-    "        'Train_R2': r2_score(y_train, y_train_pred),\n",
-    "        'Train_MAE': mean_absolute_error(y_train, y_train_pred),\n",
-    "        'Train_RMSE': np.sqrt(mean_squared_error(y_train, y_train_pred)),\n",
-    "        'Val_R2': r2_score(y_val, y_val_pred),\n",
-    "        'Val_MAE': mean_absolute_error(y_val, y_val_pred),\n",
-    "        'Val_RMSE': np.sqrt(mean_squared_error(y_val, y_val_pred))\n",
-    "    }, model\n",
-    "\n",
-    "results_phase2 = []\n",
-    "models_phase2 = {}\n",
-    "\n",
-    "# 1. Baseline\n",
-    "print(\"\\n1️⃣ Baseline (Mean Prediction)...\")\n",
-    "mean_pred = np.full(len(y_val), y_train.mean())\n",
-    "baseline_metrics = {\n",
-    "    'Model': 'Baseline',\n",
-    "    'Train_R2': 0.0,\n",
-    "    'Train_MAE': mean_absolute_error(y_train, [y_train.mean()]*len(y_train)),\n",
-    "    'Train_RMSE': np.sqrt(mean_squared_error(y_train, [y_train.mean()]*len(y_train))),\n",
-    "    'Val_R2': 0.0,\n",
-    "    'Val_MAE': mean_absolute_error(y_val, mean_pred),\n",
-    "    'Val_RMSE': np.sqrt(mean_squared_error(y_val, mean_pred))\n",
-    "}\n",
-    "results_phase2.append(baseline_metrics)\n",
-    "print(f\"   Val MAE: {baseline_metrics['Val_MAE']:.4f}, R²: {baseline_metrics['Val_R2']:.4f}\")\n",
-    "\n",
-    "# 2. Linear Regression\n",
-    "print(\"\\n2️⃣ Linear Regression...\")\n",
-    "lr = LinearRegression()\n",
-    "lr_metrics, lr_model = evaluate_model_phase2(lr, X_train_scaled, y_train, X_val_scaled, y_val, \"Linear Regression\")\n",
-    "results_phase2.append(lr_metrics)\n",
-    "models_phase2['Linear Regression'] = lr_model\n",
-    "print(f\"   Val MAE: {lr_metrics['Val_MAE']:.4f}, R²: {lr_metrics['Val_R2']:.4f}\")\n",
-    "\n",
-    "# 3. Decision Tree\n",
-    "print(\"\\n3️⃣ Decision Tree...\")\n",
-    "dt = DecisionTreeRegressor(max_depth=10, min_samples_split=10, random_state=RANDOM_STATE)\n",
-    "dt_metrics, dt_model = evaluate_model_phase2(dt, X_train_scaled, y_train, X_val_scaled, y_val, \"Decision Tree\")\n",
-    "results_phase2.append(dt_metrics)\n",
-    "models_phase2['Decision Tree'] = dt_model\n",
-    "print(f\"   Val MAE: {dt_metrics['Val_MAE']:.4f}, R²: {dt_metrics['Val_R2']:.4f}\")\n",
-    "\n",
-    "# 4. Random Forest\n",
-    "print(\"\\n4️⃣ Random Forest...\")\n",
-    "rf = RandomForestRegressor(n_estimators=100, max_depth=15, min_samples_split=5,\n",
-    "                          random_state=RANDOM_STATE, n_jobs=-1)\n",
-    "rf_metrics, rf_model = evaluate_model_phase2(rf, X_train_scaled, y_train, X_val_scaled, y_val, \"Random Forest\")\n",
-    "results_phase2.append(rf_metrics)\n",
-    "models_phase2['Random Forest'] = rf_model\n",
-    "print(f\"   Val MAE: {rf_metrics['Val_MAE']:.4f}, R²: {rf_metrics['Val_R2']:.4f}\")\n",
-    "\n",
-    "# 5. Gradient Boosting\n",
-    "print(\"\\n5️⃣ Gradient Boosting...\")\n",
-    "gb = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, max_depth=5,\n",
-    "                               random_state=RANDOM_STATE)\n",
-    "gb_metrics, gb_model = evaluate_model_phase2(gb, X_train_scaled, y_train, X_val_scaled, y_val, \"Gradient Boosting\")\n",
-    "results_phase2.append(gb_metrics)\n",
-    "models_phase2['Gradient Boosting'] = gb_model\n",
-    "print(f\"   Val MAE: {gb_metrics['Val_MAE']:.4f}, R²: {gb_metrics['Val_R2']:.4f}\")\n",
-    "\n",
-    "# 6. XGBoost\n",
-    "print(\"\\n6️⃣ XGBoost...\")\n",
-    "xgb_model = xgb.XGBRegressor(n_estimators=100, learning_rate=0.1, max_depth=5,\n",
-    "                             random_state=RANDOM_STATE, n_jobs=-1)\n",
-    "xgb_metrics, xgb_trained = evaluate_model_phase2(xgb_model, X_train_scaled, y_train, X_val_scaled, y_val, \"XGBoost\")\n",
-    "results_phase2.append(xgb_metrics)\n",
-    "models_phase2['XGBoost'] = xgb_trained\n",
-    "print(f\"   Val MAE: {xgb_metrics['Val_MAE']:.4f}, R²: {xgb_metrics['Val_R2']:.4f}\")\n",
-    "\n",
-    "# 7. LightGBM\n",
-    "print(\"\\n7️⃣ LightGBM...\")\n",
-    "lgb_model = lgb.LGBMRegressor(n_estimators=100, learning_rate=0.1, max_depth=5,\n",
-    "                             num_leaves=31, random_state=RANDOM_STATE, n_jobs=-1, verbose=-1)\n",
-    "lgb_metrics, lgb_trained = evaluate_model_phase2(lgb_model, X_train_scaled, y_train, X_val_scaled, y_val, \"LightGBM\")\n",
-    "results_phase2.append(lgb_metrics)\n",
-    "models_phase2['LightGBM'] = lgb_trained\n",
-    "print(f\"   Val MAE: {lgb_metrics['Val_MAE']:.4f}, R²: {lgb_metrics['Val_R2']:.4f}\")\n",
-    "\n",
-    "print(\"\\n✅ All models trained!\")\n",
-    "\n",
-    "# Results DataFrame\n",
-    "results_df_phase2 = pd.DataFrame(results_phase2).sort_values('Val_R2', ascending=False)\n",
-    "\n",
-    "print(\"\\n\" + \"=\" * 80)\n",
-    "print(\"📊 PHASE 2 RESULTS (WITH TEXT FEATURES)\")\n",
-    "print(\"=\" * 80)\n",
-    "print(results_df_phase2.to_string(index=False))\n",
-    "\n",
-    "# Best model\n",
-    "best_model_phase2 = results_df_phase2.iloc[0]['Model']\n",
-    "print(f\"\\n🏆 Best Model: {best_model_phase2}\")\n",
-    "print(f\"   Val R²:   {results_df_phase2.iloc[0]['Val_R2']:.4f}\")\n",
-    "print(f\"   Val MAE:  {results_df_phase2.iloc[0]['Val_MAE']:.4f}\")\n",
-    "print(f\"   Val RMSE: {results_df_phase2.iloc[0]['Val_RMSE']:.4f}\")\n",
-    "\n",
-    "# Save results\n",
-    "results_df_phase2.to_csv('phase2_output/data/phase2_model_results.csv', index=False)\n",
-    "print(\"\\n✅ Saved: phase2_output/data/phase2_model_results.csv\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6b39614a",
-   "metadata": {},
-   "source": [
-    "# 9️⃣ So Sánh Phase 1 vs Phase 2\n",
-    "\n",
-    "**Phase 1:** 47 numeric features only (R² ~ 0.30)  \n",
-    "**Phase 2:** 47 numeric + 50 text SVD features (R² = ?)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "440873ac",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "📊 PHASE 1 vs PHASE 2 COMPARISON\n",
-      "================================================================================\n",
-      "\n",
-      "📊 DETAILED COMPARISON:\n",
-      "================================================================================\n",
-      "            Model  Phase1_R2  Phase2_R2  R2_Improvement  R2_Improvement_%  Phase1_MAE  Phase2_MAE  MAE_Improvement  MAE_Improvement_%\n",
-      "    Decision Tree     0.4136   0.333863       -0.079737        -19.278797      3.3841    2.552472         0.831628          24.574560\n",
-      "    Random Forest     0.3093   0.252113       -0.057187        -18.489238      3.5571    2.838840         0.718260          20.192294\n",
-      "Gradient Boosting     0.3981   0.191742       -0.206358        -51.835697      3.0356    2.764354         0.271246           8.935501\n",
-      "          XGBoost     0.3225   0.154215       -0.168285        -52.181476      3.2599    2.748771         0.511129          15.679271\n",
-      "         LightGBM     0.2948   0.000899       -0.293901        -99.695122      3.6027    3.210078         0.392622          10.898002\n",
-      "         Baseline     0.0000   0.000000        0.000000          0.000000      5.1082    3.713249         1.394951          27.308068\n",
-      "Linear Regression    -0.0820  -0.416064       -0.334064       -407.395704      4.9313    4.348784         0.582516          11.812616\n",
-      "\n",
-      "================================================================================\n",
-      "📈 IMPROVEMENT SUMMARY\n",
-      "================================================================================\n",
-      "\n",
-      "Average R² Score:\n",
-      "   Phase 1 (Numeric only):  0.2366\n",
-      "   Phase 2 (With text):     0.0738\n",
-      "   Improvement:             -0.1628 (-68.8%)\n",
-      "\n",
-      "Average MAE:\n",
-      "   Phase 1 (Numeric only):  3.8398\n",
-      "   Phase 2 (With text):     3.1681\n",
-      "   Improvement:             +0.6718 (+17.5%)\n",
-      "\n",
-      "🏆 Best Models:\n",
-      "   Phase 1: Decision Tree (R²=0.4136, MAE=3.3841)\n",
-      "   Phase 2: Decision Tree (R²=0.3339, MAE=2.5525)\n"
-     ]
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 1600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "✅ Saved: phase2_output/plots/02_phase1_vs_phase2_comparison.png\n",
-      "✅ Saved: phase2_output/data/phase1_vs_phase2_comparison.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"=\" * 80)\n",
-    "print(\"📊 PHASE 1 vs PHASE 2 COMPARISON\")\n",
-    "print(\"=\" * 80)\n",
-    "\n",
-    "# Phase 1 results (from ML_Training_Pipeline.ipynb with FINAL_DATASET_WITH_TEXT_BACKUP_20260105_213507.xlsx)\n",
-    "# ⚠️ UPDATED with correct results from the same dataset!\n",
-    "phase1_results = {\n",
-    "    'Baseline': {'Val_R2': 0.0000, 'Val_MAE': 5.1082, 'Val_RMSE': 9.7433},\n",
-    "    'Linear Regression': {'Val_R2': -0.0820, 'Val_MAE': 4.9313, 'Val_RMSE': 10.1314},\n",
-    "    'Decision Tree': {'Val_R2': 0.4136, 'Val_MAE': 3.3841, 'Val_RMSE': 7.4583},\n",
-    "    'Random Forest': {'Val_R2': 0.3093, 'Val_MAE': 3.5571, 'Val_RMSE': 8.0948},\n",
-    "    'Gradient Boosting': {'Val_R2': 0.3981, 'Val_MAE': 3.0356, 'Val_RMSE': 7.5565},\n",
-    "    'XGBoost': {'Val_R2': 0.3225, 'Val_MAE': 3.2599, 'Val_RMSE': 8.0168},\n",
-    "    'LightGBM': {'Val_R2': 0.2948, 'Val_MAE': 3.6027, 'Val_RMSE': 8.1789}\n",
-    "}\n",
-    "\n",
-    "# Create comparison table\n",
-    "comparison_data = []\n",
-    "for model_name in results_df_phase2['Model'].values:\n",
-    "    phase2_metrics = results_df_phase2[results_df_phase2['Model'] == model_name].iloc[0]\n",
-    "    \n",
-    "    if model_name in phase1_results:\n",
-    "        phase1_metrics = phase1_results[model_name]\n",
-    "        r2_improvement = phase2_metrics['Val_R2'] - phase1_metrics['Val_R2']\n",
-    "        mae_improvement = phase1_metrics['Val_MAE'] - phase2_metrics['Val_MAE']\n",
-    "        \n",
-    "        comparison_data.append({\n",
-    "            'Model': model_name,\n",
-    "            'Phase1_R2': phase1_metrics['Val_R2'],\n",
-    "            'Phase2_R2': phase2_metrics['Val_R2'],\n",
-    "            'R2_Improvement': r2_improvement,\n",
-    "            'R2_Improvement_%': (r2_improvement / max(abs(phase1_metrics['Val_R2']), 0.01)) * 100,\n",
-    "            'Phase1_MAE': phase1_metrics['Val_MAE'],\n",
-    "            'Phase2_MAE': phase2_metrics['Val_MAE'],\n",
-    "            'MAE_Improvement': mae_improvement,\n",
-    "            'MAE_Improvement_%': (mae_improvement / phase1_metrics['Val_MAE']) * 100\n",
-    "        })\n",
-    "\n",
-    "comparison_df = pd.DataFrame(comparison_data)\n",
-    "\n",
-    "print(\"\\n📊 DETAILED COMPARISON:\")\n",
-    "print(\"=\" * 80)\n",
-    "print(comparison_df.to_string(index=False))\n",
-    "\n",
-    "# Summary statistics\n",
-    "print(\"\\n\" + \"=\" * 80)\n",
-    "print(\"📈 IMPROVEMENT SUMMARY\")\n",
-    "print(\"=\" * 80)\n",
-    "\n",
-    "avg_r2_phase1 = comparison_df['Phase1_R2'].mean()\n",
-    "avg_r2_phase2 = comparison_df['Phase2_R2'].mean()\n",
-    "avg_mae_phase1 = comparison_df['Phase1_MAE'].mean()\n",
-    "avg_mae_phase2 = comparison_df['Phase2_MAE'].mean()\n",
-    "\n",
-    "print(f\"\\nAverage R² Score:\")\n",
-    "print(f\"   Phase 1 (Numeric only):  {avg_r2_phase1:.4f}\")\n",
-    "print(f\"   Phase 2 (With text):     {avg_r2_phase2:.4f}\")\n",
-    "print(f\"   Improvement:             {avg_r2_phase2 - avg_r2_phase1:+.4f} ({(avg_r2_phase2 - avg_r2_phase1) / max(abs(avg_r2_phase1), 0.01) * 100:+.1f}%)\")\n",
-    "\n",
-    "print(f\"\\nAverage MAE:\")\n",
-    "print(f\"   Phase 1 (Numeric only):  {avg_mae_phase1:.4f}\")\n",
-    "print(f\"   Phase 2 (With text):     {avg_mae_phase2:.4f}\")\n",
-    "print(f\"   Improvement:             {avg_mae_phase1 - avg_mae_phase2:+.4f} ({(avg_mae_phase1 - avg_mae_phase2) / avg_mae_phase1 * 100:+.1f}%)\")\n",
-    "\n",
-    "# Best models comparison\n",
-    "best_phase1 = max(phase1_results.items(), key=lambda x: x[1]['Val_R2'])\n",
-    "best_phase2_row = results_df_phase2.iloc[0]\n",
-    "\n",
-    "print(f\"\\n🏆 Best Models:\")\n",
-    "print(f\"   Phase 1: {best_phase1[0]} (R²={best_phase1[1]['Val_R2']:.4f}, MAE={best_phase1[1]['Val_MAE']:.4f})\")\n",
-    "print(f\"   Phase 2: {best_phase2_row['Model']} (R²={best_phase2_row['Val_R2']:.4f}, MAE={best_phase2_row['Val_MAE']:.4f})\")\n",
-    "\n",
-    "# Visualization\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
-    "\n",
-    "# R² Comparison\n",
-    "models = comparison_df['Model'].values\n",
-    "x_pos = np.arange(len(models))\n",
-    "width = 0.35\n",
-    "\n",
-    "axes[0].bar(x_pos - width/2, comparison_df['Phase1_R2'], width, label='Phase 1 (Numeric only)', alpha=0.8, color='coral')\n",
-    "axes[0].bar(x_pos + width/2, comparison_df['Phase2_R2'], width, label='Phase 2 (With text)', alpha=0.8, color='skyblue')\n",
-    "axes[0].set_xlabel('Models', fontsize=12)\n",
-    "axes[0].set_ylabel('Validation R²', fontsize=12)\n",
-    "axes[0].set_title('R² Comparison: Phase 1 vs Phase 2', fontsize=14, fontweight='bold')\n",
-    "axes[0].set_xticks(x_pos)\n",
-    "axes[0].set_xticklabels(models, rotation=45, ha='right')\n",
-    "axes[0].axhline(y=0, color='gray', linestyle='--', linewidth=1)\n",
-    "axes[0].legend()\n",
-    "axes[0].grid(alpha=0.3, axis='y')\n",
-    "\n",
-    "# MAE Comparison\n",
-    "axes[1].bar(x_pos - width/2, comparison_df['Phase1_MAE'], width, label='Phase 1 (Numeric only)', alpha=0.8, color='coral')\n",
-    "axes[1].bar(x_pos + width/2, comparison_df['Phase2_MAE'], width, label='Phase 2 (With text)', alpha=0.8, color='skyblue')\n",
-    "axes[1].set_xlabel('Models', fontsize=12)\n",
-    "axes[1].set_ylabel('Validation MAE', fontsize=12)\n",
-    "axes[1].set_title('MAE Comparison: Phase 1 vs Phase 2', fontsize=14, fontweight='bold')\n",
-    "axes[1].set_xticks(x_pos)\n",
-    "axes[1].set_xticklabels(models, rotation=45, ha='right')\n",
-    "axes[1].legend()\n",
-    "axes[1].grid(alpha=0.3, axis='y')\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.savefig('phase2_output/plots/02_phase1_vs_phase2_comparison.png', dpi=300, bbox_inches='tight')\n",
-    "plt.show()\n",
-    "\n",
-    "print(\"\\n✅ Saved: phase2_output/plots/02_phase1_vs_phase2_comparison.png\")\n",
-    "\n",
-    "# Save comparison\n",
-    "comparison_df.to_csv('phase2_output/data/phase1_vs_phase2_comparison.csv', index=False)\n",
-    "print(\"✅ Saved: phase2_output/data/phase1_vs_phase2_comparison.csv\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9e1fe71d",
-   "metadata": {},
-   "source": [
-    "# 🔟 Test Set Evaluation\n",
-    "\n",
-    "Đánh giá best model trên test set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "7d452daf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "TEST SET EVALUATION - Decision Tree\n",
-      "================================================================================\n",
-      "\n",
-      "📊 Results:\n",
-      "Metric          Validation      Test            Difference     \n",
-      "------------------------------------------------------------\n",
-      "R²              0.3339          0.2893          0.0446         \n",
-      "MAE             2.5525          3.5730          1.0205         \n",
-      "RMSE            4.2579          5.6899          1.4320         \n",
-      "\n",
-      "🎯 Consistency Check:\n",
-      "   ✓ GOOD! Val and Test R² are reasonably consistent (13.4% difference)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1800x500 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "✅ Saved: phase2_output/plots/03_test_set_evaluation.png\n",
-      "✅ Saved: phase2_output/data/test_predictions.csv\n",
-      "\n",
-      "================================================================================\n",
-      "🎉 PHASE 2 COMPLETE - FINAL SUMMARY\n",
-      "================================================================================\n",
-      "\n",
-      "📊 Dataset:\n",
-      "   Total samples: 454\n",
-      "   Features: 92 (47 numeric + 50 text SVD)\n",
-      "   Train/Val/Test: 289/62/62\n",
-      "\n",
-      "🏆 Best Model: Decision Tree\n",
-      "   Validation R²: 0.3339\n",
-      "   Test R²:       0.2893\n",
-      "   Validation MAE: 2.5525\n",
-      "   Test MAE:       3.5730\n",
-      "\n",
-      "📈 Improvement over Phase 1:\n",
-      "   R² gain:  -0.0797 (-19.3%)\n",
-      "   MAE gain: +0.8316 (+24.6%)\n",
-      "\n",
-      "💾 Outputs saved to:\n",
-      "   phase2_output/data/\n",
-      "   phase2_output/plots/\n",
-      "   phase2_output/models/\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Evaluate best model on test set\n",
-    "best_model = models_phase2[best_model_phase2]\n",
-    "\n",
-    "print(\"=\" * 80)\n",
-    "print(f\"TEST SET EVALUATION - {best_model_phase2}\")\n",
-    "print(\"=\" * 80)\n",
-    "\n",
-    "# Predictions\n",
-    "y_test_pred = best_model.predict(X_test_scaled)\n",
-    "\n",
-    "# Metrics\n",
-    "test_r2 = r2_score(y_test, y_test_pred)\n",
-    "test_mae = mean_absolute_error(y_test, y_test_pred)\n",
-    "test_rmse = np.sqrt(mean_squared_error(y_test, y_test_pred))\n",
-    "\n",
-    "val_r2 = results_df_phase2[results_df_phase2['Model'] == best_model_phase2]['Val_R2'].values[0]\n",
-    "val_mae = results_df_phase2[results_df_phase2['Model'] == best_model_phase2]['Val_MAE'].values[0]\n",
-    "val_rmse = results_df_phase2[results_df_phase2['Model'] == best_model_phase2]['Val_RMSE'].values[0]\n",
-    "\n",
-    "print(f\"\\n📊 Results:\")\n",
-    "print(f\"{'Metric':<15} {'Validation':<15} {'Test':<15} {'Difference':<15}\")\n",
-    "print(\"-\" * 60)\n",
-    "print(f\"{'R²':<15} {val_r2:<15.4f} {test_r2:<15.4f} {abs(test_r2 - val_r2):<15.4f}\")\n",
-    "print(f\"{'MAE':<15} {val_mae:<15.4f} {test_mae:<15.4f} {abs(test_mae - val_mae):<15.4f}\")\n",
-    "print(f\"{'RMSE':<15} {val_rmse:<15.4f} {test_rmse:<15.4f} {abs(test_rmse - val_rmse):<15.4f}\")\n",
-    "\n",
-    "# Consistency check\n",
-    "r2_diff_pct = abs(test_r2 - val_r2) / max(abs(val_r2), 0.01) * 100\n",
-    "\n",
-    "print(f\"\\n🎯 Consistency Check:\")\n",
-    "if r2_diff_pct < 10:\n",
-    "    print(f\"   ✅ EXCELLENT! Val and Test R² are very consistent ({r2_diff_pct:.1f}% difference)\")\n",
-    "elif r2_diff_pct < 20:\n",
-    "    print(f\"   ✓ GOOD! Val and Test R² are reasonably consistent ({r2_diff_pct:.1f}% difference)\")\n",
-    "else:\n",
-    "    print(f\"   ⚠️ Val and Test R² show significant difference ({r2_diff_pct:.1f}%)\")\n",
-    "\n",
-    "# Visualization\n",
-    "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
-    "\n",
-    "# Scatter plot\n",
-    "axes[0].scatter(y_test, y_test_pred, alpha=0.6, edgecolors='k')\n",
-    "axes[0].plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=2)\n",
-    "axes[0].set_xlabel('Actual', fontsize=12)\n",
-    "axes[0].set_ylabel('Predicted', fontsize=12)\n",
-    "axes[0].set_title(f'{best_model_phase2} - Test Set\\\\nPredicted vs Actual', fontsize=14)\n",
-    "axes[0].grid(True, alpha=0.3)\n",
-    "\n",
-    "# Residuals\n",
-    "residuals = y_test - y_test_pred\n",
-    "axes[1].scatter(y_test_pred, residuals, alpha=0.6, edgecolors='k')\n",
-    "axes[1].axhline(y=0, color='r', linestyle='--', lw=2)\n",
-    "axes[1].set_xlabel('Predicted', fontsize=12)\n",
-    "axes[1].set_ylabel('Residuals', fontsize=12)\n",
-    "axes[1].set_title('Residual Plot', fontsize=14)\n",
-    "axes[1].grid(True, alpha=0.3)\n",
-    "\n",
-    "# Residuals distribution\n",
-    "axes[2].hist(residuals, bins=30, edgecolor='black', alpha=0.7)\n",
-    "axes[2].axvline(x=0, color='r', linestyle='--', lw=2)\n",
-    "axes[2].set_xlabel('Residuals', fontsize=12)\n",
-    "axes[2].set_ylabel('Frequency', fontsize=12)\n",
-    "axes[2].set_title('Distribution of Residuals', fontsize=14)\n",
-    "axes[2].grid(True, alpha=0.3)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.savefig('phase2_output/plots/03_test_set_evaluation.png', dpi=300, bbox_inches='tight')\n",
-    "plt.show()\n",
-    "\n",
-    "print(\"\\n✅ Saved: phase2_output/plots/03_test_set_evaluation.png\")\n",
-    "\n",
-    "# Save test predictions\n",
-    "test_results = pd.DataFrame({\n",
-    "    'actual': y_test,\n",
-    "    'predicted': y_test_pred,\n",
-    "    'error': residuals,\n",
-    "    'abs_error': np.abs(residuals)\n",
-    "}).sort_values('abs_error', ascending=False)\n",
-    "\n",
-    "test_results.to_csv('phase2_output/data/test_predictions.csv', index=False)\n",
-    "print(\"✅ Saved: phase2_output/data/test_predictions.csv\")\n",
-    "\n",
-    "# Final Summary\n",
-    "print(\"\\n\" + \"=\" * 80)\n",
-    "print(\"🎉 PHASE 2 COMPLETE - FINAL SUMMARY\")\n",
-    "print(\"=\" * 80)\n",
-    "\n",
-    "print(f\"\\n📊 Dataset:\")\n",
-    "print(f\"   Total samples: {len(df)}\")\n",
-    "print(f\"   Features: {X_combined.shape[1]} (47 numeric + 50 text SVD)\")\n",
-    "print(f\"   Train/Val/Test: {len(X_train)}/{len(X_val)}/{len(X_test)}\")\n",
-    "\n",
-    "print(f\"\\n🏆 Best Model: {best_model_phase2}\")\n",
-    "print(f\"   Validation R²: {val_r2:.4f}\")\n",
-    "print(f\"   Test R²:       {test_r2:.4f}\")\n",
-    "print(f\"   Validation MAE: {val_mae:.4f}\")\n",
-    "print(f\"   Test MAE:       {test_mae:.4f}\")\n",
-    "\n",
-    "print(f\"\\n📈 Improvement over Phase 1:\")\n",
-    "if best_model_phase2 in phase1_results:\n",
-    "    phase1_best = phase1_results[best_model_phase2]\n",
-    "    r2_gain = val_r2 - phase1_best['Val_R2']\n",
-    "    mae_gain = phase1_best['Val_MAE'] - val_mae\n",
-    "    print(f\"   R² gain:  {r2_gain:+.4f} ({r2_gain / max(abs(phase1_best['Val_R2']), 0.01) * 100:+.1f}%)\")\n",
-    "    print(f\"   MAE gain: {mae_gain:+.4f} ({mae_gain / phase1_best['Val_MAE'] * 100:+.1f}%)\")\n",
-    "\n",
-    "print(f\"\\n💾 Outputs saved to:\")\n",
-    "print(f\"   phase2_output/data/\")\n",
-    "print(f\"   phase2_output/plots/\")\n",
-    "print(f\"   phase2_output/models/\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bc294335",
-   "metadata": {},
-   "source": [
-    "# 1️⃣1️⃣ Save Best Model\n",
-    "\n",
-    "Lưu model tốt nhất cùng với scaler và các artifacts cần thiết để deploy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "b98dcbbb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "💾 SAVING BEST MODEL & ARTIFACTS\n",
-      "================================================================================\n",
-      "\n",
-      "✅ Saved best model: phase2_output/models/best_model_Decision_Tree_20260106_011633.pkl\n",
-      "   Model type: Decision Tree\n",
-      "   Val R²: 0.3339\n",
-      "   Test R²: 0.2893\n",
-      "\n",
-      "✅ Saved scaler: phase2_output/models/scaler_20260106_011633.pkl\n",
-      "\n",
-      "✅ Saved TF-IDF vectorizer: phase2_output/models/tfidf_vectorizer_20260106_011633.pkl\n",
-      "\n",
-      "✅ Saved SVD model: phase2_output/models/svd_model_20260106_011633.pkl\n",
-      "\n",
-      "✅ Saved feature names: phase2_output/models/feature_names_20260106_011633.pkl\n",
-      "\n",
-      "✅ Saved metadata: phase2_output/models/model_metadata_20260106_011633.pkl\n",
-      "\n",
-      "✅ Saved complete package: phase2_output/models/phase2_complete_package_20260106_011633.joblib\n",
-      "   Package size: 102.67 KB\n",
-      "\n",
-      "✅ Saved README: phase2_output/models/README_20260106_011633.md\n",
-      "\n",
-      "================================================================================\n",
-      "✅ ALL ARTIFACTS SAVED SUCCESSFULLY!\n",
-      "================================================================================\n",
-      "\n",
-      "📁 Saved files:\n",
-      "   1. Best model: phase2_output/models/best_model_Decision_Tree_20260106_011633.pkl\n",
-      "   2. Scaler: phase2_output/models/scaler_20260106_011633.pkl\n",
-      "   3. TF-IDF: phase2_output/models/tfidf_vectorizer_20260106_011633.pkl\n",
-      "   4. SVD: phase2_output/models/svd_model_20260106_011633.pkl\n",
-      "   5. Features: phase2_output/models/feature_names_20260106_011633.pkl\n",
-      "   6. Metadata: phase2_output/models/model_metadata_20260106_011633.pkl\n",
-      "   7. Complete package: phase2_output/models/phase2_complete_package_20260106_011633.joblib\n",
-      "   8. README: phase2_output/models/README_20260106_011633.md\n",
-      "\n",
-      "💡 For deployment, use: phase2_output/models/phase2_complete_package_20260106_011633.joblib\n",
-      "================================================================================\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pickle\n",
-    "import joblib\n",
-    "from datetime import datetime\n",
-    "\n",
-    "print(\"=\" * 80)\n",
-    "print(\"💾 SAVING BEST MODEL & ARTIFACTS\")\n",
-    "print(\"=\" * 80)\n",
-    "\n",
-    "# Create timestamp\n",
-    "timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')\n",
-    "\n",
-    "# 1. Save the best model\n",
-    "model_filename = f'phase2_output/models/best_model_{best_model_phase2.replace(\" \", \"_\")}_{timestamp}.pkl'\n",
-    "with open(model_filename, 'wb') as f:\n",
-    "    pickle.dump(best_model, f)\n",
-    "print(f\"\\n✅ Saved best model: {model_filename}\")\n",
-    "print(f\"   Model type: {best_model_phase2}\")\n",
-    "print(f\"   Val R²: {val_r2:.4f}\")\n",
-    "print(f\"   Test R²: {test_r2:.4f}\")\n",
-    "\n",
-    "# 2. Save the scaler\n",
-    "scaler_filename = f'phase2_output/models/scaler_{timestamp}.pkl'\n",
-    "with open(scaler_filename, 'wb') as f:\n",
-    "    pickle.dump(scaler, f)\n",
-    "print(f\"\\n✅ Saved scaler: {scaler_filename}\")\n",
-    "\n",
-    "# 3. Save TF-IDF vectorizer\n",
-    "tfidf_filename = f'phase2_output/models/tfidf_vectorizer_{timestamp}.pkl'\n",
-    "with open(tfidf_filename, 'wb') as f:\n",
-    "    pickle.dump(tfidf, f)\n",
-    "print(f\"\\n✅ Saved TF-IDF vectorizer: {tfidf_filename}\")\n",
-    "\n",
-    "# 4. Save SVD model\n",
-    "svd_filename = f'phase2_output/models/svd_model_{timestamp}.pkl'\n",
-    "with open(svd_filename, 'wb') as f:\n",
-    "    pickle.dump(svd, f)\n",
-    "print(f\"\\n✅ Saved SVD model: {svd_filename}\")\n",
-    "\n",
-    "# 5. Save feature names\n",
-    "feature_names_file = f'phase2_output/models/feature_names_{timestamp}.pkl'\n",
-    "with open(feature_names_file, 'wb') as f:\n",
-    "    pickle.dump(X_combined.columns.tolist(), f)\n",
-    "print(f\"\\n✅ Saved feature names: {feature_names_file}\")\n",
-    "\n",
-    "# 6. Save model metadata\n",
-    "metadata = {\n",
-    "    'model_name': best_model_phase2,\n",
-    "    'timestamp': timestamp,\n",
-    "    'dataset': 'FINAL_DATASET_WITH_TEXT_BACKUP_20260105_213507.xlsx',\n",
-    "    'n_samples': len(df),\n",
-    "    'n_features': X_combined.shape[1],\n",
-    "    'n_numeric_features': len(numeric_cols),\n",
-    "    'n_text_features': n_components,\n",
-    "    'train_size': len(X_train),\n",
-    "    'val_size': len(X_val),\n",
-    "    'test_size': len(X_test),\n",
-    "    'val_r2': float(val_r2),\n",
-    "    'val_mae': float(val_mae),\n",
-    "    'val_rmse': float(val_rmse),\n",
-    "    'test_r2': float(test_r2),\n",
-    "    'test_mae': float(test_mae),\n",
-    "    'test_rmse': float(test_rmse),\n",
-    "    'tfidf_params': {\n",
-    "        'max_features': max_features,\n",
-    "        'ngram_range': ngram_range,\n",
-    "        'min_df': min_df,\n",
-    "        'max_df': max_df\n",
-    "    },\n",
-    "    'svd_params': {\n",
-    "        'n_components': n_components,\n",
-    "        'explained_variance': float(svd.explained_variance_ratio_.sum())\n",
-    "    },\n",
-    "    'random_state': RANDOM_STATE\n",
-    "}\n",
-    "\n",
-    "metadata_filename = f'phase2_output/models/model_metadata_{timestamp}.pkl'\n",
-    "with open(metadata_filename, 'wb') as f:\n",
-    "    pickle.dump(metadata, f)\n",
-    "print(f\"\\n✅ Saved metadata: {metadata_filename}\")\n",
-    "\n",
-    "# 7. Save as single package (using joblib for better compression)\n",
-    "package_filename = f'phase2_output/models/phase2_complete_package_{timestamp}.joblib'\n",
-    "model_package = {\n",
-    "    'model': best_model,\n",
-    "    'scaler': scaler,\n",
-    "    'tfidf': tfidf,\n",
-    "    'svd': svd,\n",
-    "    'feature_names': X_combined.columns.tolist(),\n",
-    "    'metadata': metadata\n",
-    "}\n",
-    "joblib.dump(model_package, package_filename)\n",
-    "print(f\"\\n✅ Saved complete package: {package_filename}\")\n",
-    "print(f\"   Package size: {os.path.getsize(package_filename) / 1024:.2f} KB\")\n",
-    "\n",
-    "# 8. Create a README for the saved models\n",
-    "readme_content = f\"\"\"# Phase 2 Model Package - {timestamp}\n",
-    "\n",
-    "## Model Information\n",
-    "- **Model Type**: {best_model_phase2}\n",
-    "- **Dataset**: FINAL_DATASET_WITH_TEXT_BACKUP_20260105_213507.xlsx\n",
-    "- **Total Samples**: {len(df)}\n",
-    "- **Total Features**: {X_combined.shape[1]} (Numeric: {len(numeric_cols)}, Text SVD: {n_components})\n",
-    "\n",
-    "## Performance Metrics\n",
-    "\n",
-    "### Validation Set\n",
-    "- **R² Score**: {val_r2:.4f}\n",
-    "- **MAE**: {val_mae:.4f}\n",
-    "- **RMSE**: {val_rmse:.4f}\n",
-    "\n",
-    "### Test Set\n",
-    "- **R² Score**: {test_r2:.4f}\n",
-    "- **MAE**: {test_mae:.4f}\n",
-    "- **RMSE**: {test_rmse:.4f}\n",
-    "\n",
-    "## Files Included\n",
-    "\n",
-    "1. **best_model_{best_model_phase2.replace(\" \", \"_\")}_{timestamp}.pkl** - Trained model\n",
-    "2. **scaler_{timestamp}.pkl** - StandardScaler for numeric features\n",
-    "3. **tfidf_vectorizer_{timestamp}.pkl** - TF-IDF vectorizer for text\n",
-    "4. **svd_model_{timestamp}.pkl** - SVD dimensionality reduction\n",
-    "5. **feature_names_{timestamp}.pkl** - List of all feature names\n",
-    "6. **model_metadata_{timestamp}.pkl** - Complete metadata dictionary\n",
-    "7. **phase2_complete_package_{timestamp}.joblib** - All-in-one package (recommended for deployment)\n",
-    "\n",
-    "## How to Load and Use\n",
-    "\n",
-    "### Option 1: Load Complete Package (Recommended)\n",
-    "```python\n",
-    "import joblib\n",
-    "import pandas as pd\n",
-    "\n",
-    "# Load package\n",
-    "package = joblib.load('phase2_complete_package_{timestamp}.joblib')\n",
-    "model = package['model']\n",
-    "scaler = package['scaler']\n",
-    "tfidf = package['tfidf']\n",
-    "svd = package['svd']\n",
-    "\n",
-    "# Make prediction\n",
-    "# 1. Process text\n",
-    "text_combined = \"your text here\"  # Combined task text\n",
-    "tfidf_features = tfidf.transform([text_combined])\n",
-    "text_svd = svd.transform(tfidf_features)\n",
-    "\n",
-    "# 2. Prepare numeric features\n",
-    "numeric_features = [...] # Your numeric features array\n",
-    "\n",
-    "# 3. Combine and scale\n",
-    "all_features = pd.concat([\n",
-    "    pd.DataFrame(numeric_features, columns=package['feature_names'][:len(numeric_features)]),\n",
-    "    pd.DataFrame(text_svd, columns=package['feature_names'][len(numeric_features):])\n",
-    "], axis=1)\n",
-    "all_features_scaled = scaler.transform(all_features)\n",
-    "\n",
-    "# 4. Predict\n",
-    "prediction = model.predict(all_features_scaled)\n",
-    "print(f\"Predicted staff count: {{prediction[0]:.0f}}\")\n",
-    "```\n",
-    "\n",
-    "### Option 2: Load Individual Files\n",
-    "```python\n",
-    "import pickle\n",
-    "\n",
-    "with open('best_model_{best_model_phase2.replace(\" \", \"_\")}_{timestamp}.pkl', 'rb') as f:\n",
-    "    model = pickle.load(f)\n",
-    "\n",
-    "with open('scaler_{timestamp}.pkl', 'rb') as f:\n",
-    "    scaler = pickle.load(f)\n",
-    "\n",
-    "# ... (same prediction process as above)\n",
-    "```\n",
-    "\n",
-    "## Model Configuration\n",
-    "\n",
-    "### TF-IDF Parameters\n",
-    "- max_features: {max_features}\n",
-    "- ngram_range: {ngram_range}\n",
-    "- min_df: {min_df}\n",
-    "- max_df: {max_df}\n",
-    "\n",
-    "### SVD Parameters\n",
-    "- n_components: {n_components}\n",
-    "- explained_variance: {svd.explained_variance_ratio_.sum()*100:.2f}%\n",
-    "\n",
-    "### Training Parameters\n",
-    "- random_state: {RANDOM_STATE}\n",
-    "- train_size: {len(X_train)} ({len(X_train)/len(df)*100:.1f}%)\n",
-    "- val_size: {len(X_val)} ({len(X_val)/len(df)*100:.1f}%)\n",
-    "- test_size: {len(X_test)} ({len(X_test)/len(df)*100:.1f}%)\n",
-    "\n",
-    "## Phase 1 vs Phase 2 Comparison\n",
-    "\n",
-    "Phase 1 (Numeric only): R² = {phase1_results[best_model_phase2]['Val_R2']:.4f} if best_model_phase2 in phase1_results else 'N/A'\n",
-    "Phase 2 (With text): R² = {val_r2:.4f}\n",
-    "Improvement: {(val_r2 - phase1_results[best_model_phase2]['Val_R2']) if best_model_phase2 in phase1_results else 'N/A'}\n",
-    "\n",
-    "## Notes\n",
-    "- This model includes text features extracted from task descriptions\n",
-    "- Text preprocessing: lowercase, remove special chars, combine task columns\n",
-    "- Feature engineering: TF-IDF → SVD → StandardScaler\n",
-    "- Use the same preprocessing pipeline for new predictions\n",
-    "\n",
-    "Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n",
-    "\"\"\"\n",
-    "\n",
-    "readme_filename = f'phase2_output/models/README_{timestamp}.md'\n",
-    "with open(readme_filename, 'w', encoding='utf-8') as f:\n",
-    "    f.write(readme_content)\n",
-    "print(f\"\\n✅ Saved README: {readme_filename}\")\n",
-    "\n",
-    "print(\"\\n\" + \"=\" * 80)\n",
-    "print(\"✅ ALL ARTIFACTS SAVED SUCCESSFULLY!\")\n",
-    "print(\"=\" * 80)\n",
-    "print(f\"\\n📁 Saved files:\")\n",
-    "print(f\"   1. Best model: {model_filename}\")\n",
-    "print(f\"   2. Scaler: {scaler_filename}\")\n",
-    "print(f\"   3. TF-IDF: {tfidf_filename}\")\n",
-    "print(f\"   4. SVD: {svd_filename}\")\n",
-    "print(f\"   5. Features: {feature_names_file}\")\n",
-    "print(f\"   6. Metadata: {metadata_filename}\")\n",
-    "print(f\"   7. Complete package: {package_filename}\")\n",
-    "print(f\"   8. README: {readme_filename}\")\n",
-    "print(f\"\\n💡 For deployment, use: {package_filename}\")\n",
-    "print(\"=\" * 80)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5441ee0c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b6aeeb0b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "base",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/ML_Training_Pipeline.ipynb b/ML_Training_Pipeline.ipynb
deleted file mode 100644
index 28ce3b2..0000000
--- a/ML_Training_Pipeline.ipynb
+++ /dev/null
@@ -1,2034 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "291bb40a",
-   "metadata": {},
-   "source": [
-    "# 🤖 ML Pipeline: Dự Đoán Số Lượng Nhân Sự\n",
-    "\n",
-    "**Mục tiêu:** Dự đoán số lượng nhân sự (`so_luong`) cần thiết cho mỗi ca làm việc\n",
-    "\n",
-    "**Dataset:** FINAL_DATASET_WITH_TEXT.xlsx (454 rows × 51 columns)\n",
-    "\n",
-    "**Features sử dụng:** 47 features (không bao gồm 2 cột text - để lại cho phase sau)\n",
-    "\n",
-    "**Target:** `so_luong` (0-64 nhân sự)\n",
-    "\n",
-    "---\n",
-    "\n",
-    "## 📋 Nội dung Notebook:\n",
-    "\n",
-    "1. ✅ Import Libraries & Load Data\n",
-    "2. ✅ Exploratory Data Analysis (EDA)\n",
-    "3. ✅ Data Cleaning & Preprocessing  \n",
-    "4. ✅ Feature Engineering\n",
-    "5. ✅ Visualizations & Insights\n",
-    "6. ✅ Train/Val/Test Split\n",
-    "7. ✅ Baseline Models\n",
-    "8. ✅ Advanced Models (RF, XGBoost, LightGBM)\n",
-    "9. ✅ Model Evaluation & Comparison\n",
-    "10. ✅ Feature Importance Analysis\n",
-    "11. ✅ Final Results & Recommendations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d2dd2408",
-   "metadata": {},
-   "source": [
-    "# 1️⃣ Import Libraries & Load Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "9ada57ec",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "✅ Libraries imported successfully!\n",
-      "📅 Date: 2026-01-05 23:29:39\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Import essential libraries\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "import warnings\n",
-    "import os\n",
-    "from datetime import datetime\n",
-    "\n",
-    "# Machine Learning libraries\n",
-    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
-    "from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.tree import DecisionTreeRegressor\n",
-    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
-    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
-    "import xgboost as xgb\n",
-    "import lightgbm as lgb\n",
-    "\n",
-    "# Configuration\n",
-    "warnings.filterwarnings('ignore')\n",
-    "plt.style.use('seaborn-v0_8-darkgrid')\n",
-    "sns.set_palette(\"husl\")\n",
-    "pd.set_option('display.max_columns', None)\n",
-    "pd.set_option('display.max_rows', 100)\n",
-    "\n",
-    "# Random seed for reproducibility\n",
-    "RANDOM_STATE = 42\n",
-    "np.random.seed(RANDOM_STATE)\n",
-    "\n",
-    "# Create folders for outputs\n",
-    "os.makedirs('data/cleaned', exist_ok=True)\n",
-    "os.makedirs('data/splits', exist_ok=True)\n",
-    "os.makedirs('models', exist_ok=True)\n",
-    "os.makedirs('plots', exist_ok=True)\n",
-    "\n",
-    "print(\"✅ Libraries imported successfully!\")\n",
-    "print(f\"📅 Date: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "0350590f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "📂 Loading dataset...\n",
-      "✅ Dataset loaded successfully!\n",
-      "📊 Shape: (454, 51)\n",
-      "📋 Columns: ['ma_dia_diem', 'all_task_normal', 'all_task_dinhky', 'loai_ca', 'bat_dau', 'ket_thuc', 'tong_gio_lam', 'so_ca_cua_toa', 'so_luong', 'num_tasks']... (showing first 10)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ma_dia_diem</th>\n",
-       "      <th>all_task_normal</th>\n",
-       "      <th>all_task_dinhky</th>\n",
-       "      <th>loai_ca</th>\n",
-       "      <th>bat_dau</th>\n",
-       "      <th>ket_thuc</th>\n",
-       "      <th>tong_gio_lam</th>\n",
-       "      <th>so_ca_cua_toa</th>\n",
-       "      <th>so_luong</th>\n",
-       "      <th>num_tasks</th>\n",
-       "      <th>num_cleaning_tasks</th>\n",
-       "      <th>num_trash_collection_tasks</th>\n",
-       "      <th>num_monitoring_tasks</th>\n",
-       "      <th>num_room_cleaning_tasks</th>\n",
-       "      <th>num_deep_cleaning_tasks</th>\n",
-       "      <th>num_maintenance_tasks</th>\n",
-       "      <th>num_support_tasks</th>\n",
-       "      <th>num_other_tasks</th>\n",
-       "      <th>num_wc_tasks</th>\n",
-       "      <th>num_hallway_tasks</th>\n",
-       "      <th>num_lobby_tasks</th>\n",
-       "      <th>num_patient_room_tasks</th>\n",
-       "      <th>num_clinic_room_tasks</th>\n",
-       "      <th>num_surgery_room_tasks</th>\n",
-       "      <th>num_outdoor_tasks</th>\n",
-       "      <th>num_elevator_tasks</th>\n",
-       "      <th>num_office_tasks</th>\n",
-       "      <th>num_technical_room_tasks</th>\n",
-       "      <th>cleaning_ratio</th>\n",
-       "      <th>trash_collection_ratio</th>\n",
-       "      <th>monitoring_ratio</th>\n",
-       "      <th>room_cleaning_ratio</th>\n",
-       "      <th>area_diversity</th>\n",
-       "      <th>task_complexity_score</th>\n",
-       "      <th>loai_hinh</th>\n",
-       "      <th>ten_toa_thap</th>\n",
-       "      <th>muc_do_luu_luong</th>\n",
-       "      <th>so_tang</th>\n",
-       "      <th>so_cua_thang_may</th>\n",
-       "      <th>dien_tich_ngoai_canh</th>\n",
-       "      <th>dien_tich_sanh</th>\n",
-       "      <th>dien_tich_hanh_lang</th>\n",
-       "      <th>dien_tich_wc</th>\n",
-       "      <th>dien_tich_phong</th>\n",
-       "      <th>dien_tich_tham</th>\n",
-       "      <th>doc_ham</th>\n",
-       "      <th>vien_phan_quang</th>\n",
-       "      <th>op_tuong</th>\n",
-       "      <th>op_chan_tuong</th>\n",
-       "      <th>ranh_thoat_nuoc</th>\n",
-       "      <th>dien_tich_kinh</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>115-2</td>\n",
-       "      <td>Làm sạch toàn bộ phòng giao dịch tầng 1 (kể cả...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Part time</td>\n",
-       "      <td>06:30:00</td>\n",
-       "      <td>10:30:00</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0.1429</td>\n",
-       "      <td>0.2857</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1.38</td>\n",
-       "      <td>0</td>\n",
-       "      <td>AGRIBANK CHI NHÁNH MỸ ĐÌNH</td>\n",
-       "      <td>Trung bình (11–20 người)</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>290.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>20.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>115-5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Hành chính</td>\n",
-       "      <td>06:30:00</td>\n",
-       "      <td>16:00:00</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>AGRIBANK PGD SỐ 05</td>\n",
-       "      <td>Trung bình (11–20 người)</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>300.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>30.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>101-1</td>\n",
-       "      <td>Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...</td>\n",
-       "      <td>Lau bảng biển, bình cứu hỏa , cây nước hành la...</td>\n",
-       "      <td>Hành chính</td>\n",
-       "      <td>06:30:00</td>\n",
-       "      <td>16:00:00</td>\n",
-       "      <td>7.5</td>\n",
-       "      <td>6</td>\n",
-       "      <td>24</td>\n",
-       "      <td>441.0</td>\n",
-       "      <td>258.0</td>\n",
-       "      <td>145.0</td>\n",
-       "      <td>134.0</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>39.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>0.585</td>\n",
-       "      <td>0.3288</td>\n",
-       "      <td>0.3039</td>\n",
-       "      <td>0.1474</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>10.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Tòa 5 tầng</td>\n",
-       "      <td>Rất cao (Trên 40 người)</td>\n",
-       "      <td>10</td>\n",
-       "      <td>18</td>\n",
-       "      <td>1700.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2600.0</td>\n",
-       "      <td>348.0</td>\n",
-       "      <td>6825.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9176.0</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>25</td>\n",
-       "      <td>894.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>101-1</td>\n",
-       "      <td>Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...</td>\n",
-       "      <td>Lau bảng biển, bình cứu hỏa , cây nước hành la...</td>\n",
-       "      <td>Ca sáng</td>\n",
-       "      <td>06:00:00</td>\n",
-       "      <td>14:00:00</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>3</td>\n",
-       "      <td>441.0</td>\n",
-       "      <td>258.0</td>\n",
-       "      <td>145.0</td>\n",
-       "      <td>134.0</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>39.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>0.585</td>\n",
-       "      <td>0.3288</td>\n",
-       "      <td>0.3039</td>\n",
-       "      <td>0.1474</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>10.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Tòa 5 tầng</td>\n",
-       "      <td>Rất cao (Trên 40 người)</td>\n",
-       "      <td>10</td>\n",
-       "      <td>18</td>\n",
-       "      <td>1700.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2600.0</td>\n",
-       "      <td>348.0</td>\n",
-       "      <td>6825.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9176.0</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>25</td>\n",
-       "      <td>894.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>101-1</td>\n",
-       "      <td>Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...</td>\n",
-       "      <td>Lau bảng biển, bình cứu hỏa , cây nước hành la...</td>\n",
-       "      <td>Ca chiều</td>\n",
-       "      <td>14:00:00</td>\n",
-       "      <td>22:00:00</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>5</td>\n",
-       "      <td>441.0</td>\n",
-       "      <td>258.0</td>\n",
-       "      <td>145.0</td>\n",
-       "      <td>134.0</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>39.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>0.585</td>\n",
-       "      <td>0.3288</td>\n",
-       "      <td>0.3039</td>\n",
-       "      <td>0.1474</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>10.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Tòa 5 tầng</td>\n",
-       "      <td>Rất cao (Trên 40 người)</td>\n",
-       "      <td>10</td>\n",
-       "      <td>18</td>\n",
-       "      <td>1700.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2600.0</td>\n",
-       "      <td>348.0</td>\n",
-       "      <td>6825.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9176.0</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>25</td>\n",
-       "      <td>894.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  ma_dia_diem                                    all_task_normal  \\\n",
-       "0       115-2  Làm sạch toàn bộ phòng giao dịch tầng 1 (kể cả...   \n",
-       "1       115-5                                                NaN   \n",
-       "2       101-1  Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...   \n",
-       "3       101-1  Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...   \n",
-       "4       101-1  Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...   \n",
-       "\n",
-       "                                     all_task_dinhky     loai_ca   bat_dau  \\\n",
-       "0                                                NaN   Part time  06:30:00   \n",
-       "1                                                NaN  Hành chính  06:30:00   \n",
-       "2  Lau bảng biển, bình cứu hỏa , cây nước hành la...  Hành chính  06:30:00   \n",
-       "3  Lau bảng biển, bình cứu hỏa , cây nước hành la...     Ca sáng  06:00:00   \n",
-       "4  Lau bảng biển, bình cứu hỏa , cây nước hành la...    Ca chiều  14:00:00   \n",
-       "\n",
-       "   ket_thuc  tong_gio_lam  so_ca_cua_toa  so_luong  num_tasks  \\\n",
-       "0  10:30:00           4.0              1         1        7.0   \n",
-       "1  16:00:00           8.0              1         1        NaN   \n",
-       "2  16:00:00           7.5              6        24      441.0   \n",
-       "3  14:00:00           8.0              6         3      441.0   \n",
-       "4  22:00:00           8.0              6         5      441.0   \n",
-       "\n",
-       "   num_cleaning_tasks  num_trash_collection_tasks  num_monitoring_tasks  \\\n",
-       "0                 7.0                         1.0                   2.0   \n",
-       "1                 NaN                         NaN                   NaN   \n",
-       "2               258.0                       145.0                 134.0   \n",
-       "3               258.0                       145.0                 134.0   \n",
-       "4               258.0                       145.0                 134.0   \n",
-       "\n",
-       "   num_room_cleaning_tasks  num_deep_cleaning_tasks  num_maintenance_tasks  \\\n",
-       "0                      0.0                      1.0                    1.0   \n",
-       "1                      NaN                      NaN                    NaN   \n",
-       "2                     65.0                     75.0                   62.0   \n",
-       "3                     65.0                     75.0                   62.0   \n",
-       "4                     65.0                     75.0                   62.0   \n",
-       "\n",
-       "   num_support_tasks  num_other_tasks  num_wc_tasks  num_hallway_tasks  \\\n",
-       "0                0.0              0.0           4.0                0.0   \n",
-       "1                NaN              NaN           NaN                NaN   \n",
-       "2               57.0             45.0          89.0               90.0   \n",
-       "3               57.0             45.0          89.0               90.0   \n",
-       "4               57.0             45.0          89.0               90.0   \n",
-       "\n",
-       "   num_lobby_tasks  num_patient_room_tasks  num_clinic_room_tasks  \\\n",
-       "0              0.0                     0.0                    0.0   \n",
-       "1              NaN                     NaN                    NaN   \n",
-       "2              5.0                    41.0                   25.0   \n",
-       "3              5.0                    41.0                   25.0   \n",
-       "4              5.0                    41.0                   25.0   \n",
-       "\n",
-       "   num_surgery_room_tasks  num_outdoor_tasks  num_elevator_tasks  \\\n",
-       "0                     0.0                2.0                 3.0   \n",
-       "1                     NaN                NaN                 NaN   \n",
-       "2                    30.0                4.0                12.0   \n",
-       "3                    30.0                4.0                12.0   \n",
-       "4                    30.0                4.0                12.0   \n",
-       "\n",
-       "   num_office_tasks  num_technical_room_tasks  cleaning_ratio  \\\n",
-       "0               1.0                       0.0           1.000   \n",
-       "1               NaN                       NaN             NaN   \n",
-       "2              39.0                      16.0           0.585   \n",
-       "3              39.0                      16.0           0.585   \n",
-       "4              39.0                      16.0           0.585   \n",
-       "\n",
-       "   trash_collection_ratio  monitoring_ratio  room_cleaning_ratio  \\\n",
-       "0                  0.1429            0.2857               0.0000   \n",
-       "1                     NaN               NaN                  NaN   \n",
-       "2                  0.3288            0.3039               0.1474   \n",
-       "3                  0.3288            0.3039               0.1474   \n",
-       "4                  0.3288            0.3039               0.1474   \n",
-       "\n",
-       "   area_diversity  task_complexity_score  loai_hinh  \\\n",
-       "0             4.0                   1.38          0   \n",
-       "1             NaN                    NaN          0   \n",
-       "2            10.0                  10.00          0   \n",
-       "3            10.0                  10.00          0   \n",
-       "4            10.0                  10.00          0   \n",
-       "\n",
-       "                 ten_toa_thap          muc_do_luu_luong  so_tang  \\\n",
-       "0  AGRIBANK CHI NHÁNH MỸ ĐÌNH  Trung bình (11–20 người)        4   \n",
-       "1          AGRIBANK PGD SỐ 05  Trung bình (11–20 người)        1   \n",
-       "2                  Tòa 5 tầng   Rất cao (Trên 40 người)       10   \n",
-       "3                  Tòa 5 tầng   Rất cao (Trên 40 người)       10   \n",
-       "4                  Tòa 5 tầng   Rất cao (Trên 40 người)       10   \n",
-       "\n",
-       "   so_cua_thang_may  dien_tich_ngoai_canh  dien_tich_sanh  \\\n",
-       "0                 0                   0.0             0.0   \n",
-       "1                 0                   0.0             0.0   \n",
-       "2                18                1700.0             0.0   \n",
-       "3                18                1700.0             0.0   \n",
-       "4                18                1700.0             0.0   \n",
-       "\n",
-       "   dien_tich_hanh_lang  dien_tich_wc  dien_tich_phong  dien_tich_tham  \\\n",
-       "0                  0.0          15.0            290.0             0.0   \n",
-       "1                  0.0          15.0            300.0             0.0   \n",
-       "2               2600.0         348.0           6825.0             0.0   \n",
-       "3               2600.0         348.0           6825.0             0.0   \n",
-       "4               2600.0         348.0           6825.0             0.0   \n",
-       "\n",
-       "   doc_ham  vien_phan_quang  op_tuong  op_chan_tuong  ranh_thoat_nuoc  \\\n",
-       "0        0                0       0.0            0.0                0   \n",
-       "1        0                0       0.0            0.0                0   \n",
-       "2       70                0    9176.0           89.0               25   \n",
-       "3       70                0    9176.0           89.0               25   \n",
-       "4       70                0    9176.0           89.0               25   \n",
-       "\n",
-       "   dien_tich_kinh  \n",
-       "0            20.0  \n",
-       "1            30.0  \n",
-       "2           894.0  \n",
-       "3           894.0  \n",
-       "4           894.0  "
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Load dataset\n",
-    "print(\"📂 Loading dataset...\")\n",
-    "df = pd.read_excel('FINAL_DATASET_WITH_TEXT_BACKUP_20260105_213507.xlsx')\n",
-    "\n",
-    "print(f\"✅ Dataset loaded successfully!\")\n",
-    "print(f\"📊 Shape: {df.shape}\")\n",
-    "print(f\"📋 Columns: {df.columns.tolist()[:10]}... (showing first 10)\")\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2e99e108",
-   "metadata": {},
-   "source": [
-    "# 2️⃣ Exploratory Data Analysis (EDA)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "8e92132d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "📊 EXPLORATORY DATA ANALYSIS\n",
-      "================================================================================\n",
-      "\n",
-      "1. DATA INFO:\n",
-      "   Shape: (454, 51)\n",
-      "   Memory: 9.10 MB\n",
-      "\n",
-      "2. DATA TYPES:\n",
-      "float64    35\n",
-      "object      8\n",
-      "int64       8\n",
-      "Name: count, dtype: int64\n",
-      "\n",
-      "3. MISSING VALUES:\n",
-      "                            Missing  Percentage\n",
-      "all_task_dinhky                 168       37.00\n",
-      "all_task_normal                  27        5.95\n",
-      "num_tasks                        22        4.85\n",
-      "num_cleaning_tasks               22        4.85\n",
-      "num_trash_collection_tasks       22        4.85\n",
-      "num_monitoring_tasks             22        4.85\n",
-      "num_room_cleaning_tasks          22        4.85\n",
-      "num_deep_cleaning_tasks          22        4.85\n",
-      "num_maintenance_tasks            22        4.85\n",
-      "num_support_tasks                22        4.85\n",
-      "num_other_tasks                  22        4.85\n",
-      "num_wc_tasks                     22        4.85\n",
-      "num_hallway_tasks                22        4.85\n",
-      "num_lobby_tasks                  22        4.85\n",
-      "num_patient_room_tasks           22        4.85\n",
-      "num_clinic_room_tasks            22        4.85\n",
-      "num_surgery_room_tasks           22        4.85\n",
-      "num_outdoor_tasks                22        4.85\n",
-      "num_elevator_tasks               22        4.85\n",
-      "num_office_tasks                 22        4.85\n",
-      "num_technical_room_tasks         22        4.85\n",
-      "cleaning_ratio                   22        4.85\n",
-      "trash_collection_ratio           22        4.85\n",
-      "monitoring_ratio                 22        4.85\n",
-      "room_cleaning_ratio              22        4.85\n",
-      "area_diversity                   22        4.85\n",
-      "task_complexity_score            22        4.85\n",
-      "\n",
-      "4. TARGET VARIABLE (so_luong):\n",
-      "count    454.000000\n",
-      "mean       4.484581\n",
-      "std        6.626822\n",
-      "min        0.000000\n",
-      "25%        1.000000\n",
-      "50%        2.000000\n",
-      "75%        5.000000\n",
-      "max       64.000000\n",
-      "Name: so_luong, dtype: float64\n",
-      "   Zero values: 16\n",
-      "   Skewness: 3.69\n",
-      "   Kurtosis: 19.63\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"=\"*80)\n",
-    "print(\"📊 EXPLORATORY DATA ANALYSIS\")\n",
-    "print(\"=\"*80)\n",
-    "\n",
-    "print(\"\\n1. DATA INFO:\")\n",
-    "print(f\"   Shape: {df.shape}\")\n",
-    "print(f\"   Memory: {df.memory_usage(deep=True).sum() / 1024**2:.2f} MB\")\n",
-    "\n",
-    "print(\"\\n2. DATA TYPES:\")\n",
-    "print(df.dtypes.value_counts())\n",
-    "\n",
-    "print(\"\\n3. MISSING VALUES:\")\n",
-    "missing = df.isnull().sum()\n",
-    "missing_pct = (missing / len(df) * 100).round(2)\n",
-    "missing_df = pd.DataFrame({\n",
-    "    'Missing': missing[missing > 0],\n",
-    "    'Percentage': missing_pct[missing > 0]\n",
-    "}).sort_values('Missing', ascending=False)\n",
-    "print(missing_df)\n",
-    "\n",
-    "print(\"\\n4. TARGET VARIABLE (so_luong):\")\n",
-    "print(df['so_luong'].describe())\n",
-    "print(f\"   Zero values: {(df['so_luong'] == 0).sum()}\")\n",
-    "print(f\"   Skewness: {df['so_luong'].skew():.2f}\")\n",
-    "print(f\"   Kurtosis: {df['so_luong'].kurt():.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2dbbf735",
-   "metadata": {},
-   "source": [
-    "# 3️⃣ Visualizations - Target Analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "75af8fc3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 1500x1000 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "✅ Saved: plots/01_target_analysis.png\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Target Analysis Visualizations\n",
-    "fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n",
-    "\n",
-    "# 1. Histogram\n",
-    "axes[0, 0].hist(df['so_luong'], bins=30, edgecolor='black', alpha=0.7)\n",
-    "axes[0, 0].axvline(df['so_luong'].mean(), color='red', linestyle='--', label=f'Mean={df[\"so_luong\"].mean():.2f}')\n",
-    "axes[0, 0].axvline(df['so_luong'].median(), color='green', linestyle='--', label=f'Median={df[\"so_luong\"].median():.2f}')\n",
-    "axes[0, 0].set_xlabel('Số lượng nhân sự')\n",
-    "axes[0, 0].set_ylabel('Frequency')\n",
-    "axes[0, 0].set_title('Phân bố số lượng nhân sự')\n",
-    "axes[0, 0].legend()\n",
-    "axes[0, 0].grid(alpha=0.3)\n",
-    "\n",
-    "# 2. Boxplot\n",
-    "axes[0, 1].boxplot(df['so_luong'], vert=True)\n",
-    "axes[0, 1].set_ylabel('Số lượng nhân sự')\n",
-    "axes[0, 1].set_title('Boxplot - Phát hiện outliers')\n",
-    "axes[0, 1].grid(alpha=0.3)\n",
-    "\n",
-    "# 3. Distribution by loai_ca\n",
-    "df.boxplot(column='so_luong', by='loai_ca', ax=axes[1, 0])\n",
-    "axes[1, 0].set_xlabel('Loại ca')\n",
-    "axes[1, 0].set_ylabel('Số lượng nhân sự')\n",
-    "axes[1, 0].set_title('Phân bố theo loại ca')\n",
-    "plt.sca(axes[1, 0])\n",
-    "plt.xticks(rotation=45, ha='right')\n",
-    "\n",
-    "# 4. Top features correlation\n",
-    "numeric_cols = df.select_dtypes(include=[np.number]).columns\n",
-    "corr_with_target = df[numeric_cols].corr()['so_luong'].sort_values(ascending=False)\n",
-    "top_10 = corr_with_target[1:11]\n",
-    "axes[1, 1].barh(range(len(top_10)), top_10.values)\n",
-    "axes[1, 1].set_yticks(range(len(top_10)))\n",
-    "axes[1, 1].set_yticklabels(top_10.index)\n",
-    "axes[1, 1].set_xlabel('Correlation with so_luong')\n",
-    "axes[1, 1].set_title('Top 10 Features có correlation cao nhất')\n",
-    "axes[1, 1].grid(alpha=0.3)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.savefig('plots/01_target_analysis.png', dpi=300, bbox_inches='tight')\n",
-    "plt.show()\n",
-    "\n",
-    "print(\"✅ Saved: plots/01_target_analysis.png\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "546ce253",
-   "metadata": {},
-   "source": [
-    "# 4️⃣ Data Cleaning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "4f4fe933",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "🧹 DATA CLEANING\n",
-      "================================================================================\n",
-      "\n",
-      "1. Handling missing values...\n",
-      "   Filled num_tasks with 0\n",
-      "   Filled num_cleaning_tasks with 0\n",
-      "   Filled num_trash_collection_tasks with 0\n",
-      "   Filled num_monitoring_tasks with 0\n",
-      "   Filled num_room_cleaning_tasks with 0\n",
-      "   Filled num_deep_cleaning_tasks with 0\n",
-      "   Filled num_maintenance_tasks with 0\n",
-      "   Filled num_support_tasks with 0\n",
-      "   Filled num_other_tasks with 0\n",
-      "   Filled num_wc_tasks with 0\n",
-      "   Filled num_hallway_tasks with 0\n",
-      "   Filled num_lobby_tasks with 0\n",
-      "   Filled num_patient_room_tasks with 0\n",
-      "   Filled num_clinic_room_tasks with 0\n",
-      "   Filled num_surgery_room_tasks with 0\n",
-      "   Filled num_outdoor_tasks with 0\n",
-      "   Filled num_elevator_tasks with 0\n",
-      "   Filled num_office_tasks with 0\n",
-      "   Filled num_technical_room_tasks with 0\n",
-      "   Filled cleaning_ratio with 0\n",
-      "   Filled trash_collection_ratio with 0\n",
-      "   Filled monitoring_ratio with 0\n",
-      "   Filled room_cleaning_ratio with 0\n",
-      "   Filled area_diversity with 0\n",
-      "   Filled task_complexity_score with 0\n",
-      "\n",
-      "2. Analyzing target outliers...\n",
-      "   Zero values: 16\n",
-      "   Decision: KEEP all values (including zeros)\n",
-      "\n",
-      "3. Checking duplicates...\n",
-      "   Duplicates found: 9\n",
-      "\n",
-      "4. Dropping unnecessary columns...\n",
-      "   Dropped: ['ma_dia_diem', 'all_task_normal', 'all_task_dinhky', 'ten_toa_thap']\n",
-      "\n",
-      "5. Validation after cleaning:\n",
-      "   Shape: (445, 47)\n",
-      "   Missing values: 0\n",
-      "\n",
-      "✅ Saved: data/cleaned/CLEANED_DATA.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"=\"*80)\n",
-    "print(\"🧹 DATA CLEANING\")\n",
-    "print(\"=\"*80)\n",
-    "\n",
-    "df_clean = df.copy()\n",
-    "\n",
-    "# 1. Handle missing values\n",
-    "print(\"\\n1. Handling missing values...\")\n",
-    "\n",
-    "# Task features: fill with 0\n",
-    "task_features = [col for col in df_clean.columns if col.startswith('num_') or col.endswith('_ratio') or col in ['area_diversity', 'task_complexity_score']]\n",
-    "for col in task_features:\n",
-    "    if df_clean[col].isnull().sum() > 0:\n",
-    "        df_clean[col] = df_clean[col].fillna(0)\n",
-    "        print(f\"   Filled {col} with 0\")\n",
-    "\n",
-    "# Building features: fill with median\n",
-    "building_features = [col for col in df_clean.columns if col.startswith('dien_tich_') or col in ['so_tang', 'so_cua_thang_may']]\n",
-    "for col in building_features:\n",
-    "    if df_clean[col].isnull().sum() > 0:\n",
-    "        median_val = df_clean[col].median()\n",
-    "        df_clean[col] = df_clean[col].fillna(median_val)\n",
-    "        print(f\"   Filled {col} with median={median_val}\")\n",
-    "\n",
-    "# Categorical: fill with mode\n",
-    "cat_features = ['loai_hinh', 'muc_do_luu_luong']\n",
-    "for col in cat_features:\n",
-    "    if col in df_clean.columns and df_clean[col].isnull().sum() > 0:\n",
-    "        mode_val = df_clean[col].mode()[0]\n",
-    "        df_clean[col] = df_clean[col].fillna(mode_val)\n",
-    "        print(f\"   Filled {col} with mode={mode_val}\")\n",
-    "\n",
-    "# 2. Handle outliers (keep all for now)\n",
-    "print(\"\\n2. Analyzing target outliers...\")\n",
-    "print(f\"   Zero values: {(df_clean['so_luong'] == 0).sum()}\")\n",
-    "print(\"   Decision: KEEP all values (including zeros)\")\n",
-    "\n",
-    "# 3. Remove duplicates\n",
-    "print(\"\\n3. Checking duplicates...\")\n",
-    "dups = df_clean.duplicated().sum()\n",
-    "print(f\"   Duplicates found: {dups}\")\n",
-    "if dups > 0:\n",
-    "    df_clean = df_clean.drop_duplicates()\n",
-    "\n",
-    "# 4. Drop unnecessary columns\n",
-    "print(\"\\n4. Dropping unnecessary columns...\")\n",
-    "cols_to_drop = ['ma_dia_diem', 'all_task_normal', 'all_task_dinhky', 'ten_toa_thap']\n",
-    "cols_to_drop = [col for col in cols_to_drop if col in df_clean.columns]\n",
-    "df_clean = df_clean.drop(columns=cols_to_drop)\n",
-    "print(f\"   Dropped: {cols_to_drop}\")\n",
-    "\n",
-    "# 5. Validate\n",
-    "print(\"\\n5. Validation after cleaning:\")\n",
-    "print(f\"   Shape: {df_clean.shape}\")\n",
-    "print(f\"   Missing values: {df_clean.isnull().sum().sum()}\")\n",
-    "\n",
-    "# Save\n",
-    "df_clean.to_csv('data/cleaned/CLEANED_DATA.csv', index=False)\n",
-    "print(\"\\n✅ Saved: data/cleaned/CLEANED_DATA.csv\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c6468736",
-   "metadata": {},
-   "source": [
-    "# 5️⃣ Feature Engineering"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "4b5fe9a8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "⚙️ FEATURE ENGINEERING\n",
-      "================================================================================\n",
-      "\n",
-      "1. Creating time features...\n",
-      "   ✅ Created time features\n",
-      "\n",
-      "2. Creating interaction features...\n",
-      "   ✅ Created interaction features\n",
-      "\n",
-      "3. Creating aggregation features...\n",
-      "   ✅ Created aggregation features\n",
-      "\n",
-      "4. Encoding categorical features...\n",
-      "   ✅ One-hot encoded loai_ca\n",
-      "   ✅ Label encoded loai_hinh\n",
-      "   ✅ Label encoded muc_do_luu_luong\n",
-      "\n",
-      "✅ Feature engineering complete! Shape: (445, 64)\n",
-      "✅ Saved: data/cleaned/ENGINEERED_DATA.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"=\"*80)\n",
-    "print(\"⚙️ FEATURE ENGINEERING\")\n",
-    "print(\"=\"*80)\n",
-    "\n",
-    "df_eng = df_clean.copy()\n",
-    "\n",
-    "# A. TIME FEATURES\n",
-    "print(\"\\n1. Creating time features...\")\n",
-    "\n",
-    "def parse_time(time_val):\n",
-    "    if pd.isna(time_val):\n",
-    "        return 0\n",
-    "    \n",
-    "    # If it's already a datetime object\n",
-    "    if isinstance(time_val, pd.Timestamp) or hasattr(time_val, 'hour'):\n",
-    "        return time_val.hour\n",
-    "    \n",
-    "    # If it's a string\n",
-    "    if isinstance(time_val, str):\n",
-    "        time_val = time_val.strip()\n",
-    "        \n",
-    "        # Format: \"2025-01-01 22:00:00\" or \"2025-01-01 22\"\n",
-    "        if ' ' in time_val:\n",
-    "            # Split by space and get the time part\n",
-    "            parts = time_val.split(' ')\n",
-    "            if len(parts) >= 2:\n",
-    "                time_part = parts[1]  # Get time part after space\n",
-    "                if ':' in time_part:\n",
-    "                    return int(time_part.split(':')[0])\n",
-    "                else:\n",
-    "                    return int(time_part)\n",
-    "        \n",
-    "        # Format: \"22:00:00\" or \"22:00\"\n",
-    "        elif ':' in time_val:\n",
-    "            return int(time_val.split(':')[0])\n",
-    "        \n",
-    "        # Try to parse as integer\n",
-    "        try:\n",
-    "            return int(float(time_val))\n",
-    "        except:\n",
-    "            return 0\n",
-    "    \n",
-    "    # If it's a number\n",
-    "    if isinstance(time_val, (int, float)):\n",
-    "        return int(time_val) if not np.isnan(time_val) else 0\n",
-    "    \n",
-    "    return 0\n",
-    "\n",
-    "df_eng['hour_start'] = df_eng['bat_dau'].apply(parse_time)\n",
-    "df_eng['hour_end'] = df_eng['ket_thuc'].apply(parse_time)\n",
-    "\n",
-    "def parse_work_hours(time_str):\n",
-    "    if pd.isna(time_str):\n",
-    "        return 8.0\n",
-    "    if isinstance(time_str, str) and ':' in time_str:\n",
-    "        parts = time_str.split(':')\n",
-    "        return float(parts[0]) + float(parts[1])/60 if len(parts) > 1 else float(parts[0])\n",
-    "    return 8.0\n",
-    "\n",
-    "df_eng['work_hours_numeric'] = df_eng['tong_gio_lam'].apply(parse_work_hours)\n",
-    "\n",
-    "# Shift indicators\n",
-    "df_eng['is_morning_shift'] = ((df_eng['hour_start'] >= 6) & (df_eng['hour_start'] < 12)).astype(int)\n",
-    "df_eng['is_afternoon_shift'] = ((df_eng['hour_start'] >= 12) & (df_eng['hour_start'] < 18)).astype(int)\n",
-    "df_eng['is_evening_shift'] = ((df_eng['hour_start'] >= 18) & (df_eng['hour_start'] < 24)).astype(int)\n",
-    "df_eng['is_night_shift'] = ((df_eng['hour_start'] >= 0) & (df_eng['hour_start'] < 6)).astype(int)\n",
-    "\n",
-    "print(\"   ✅ Created time features\")\n",
-    "\n",
-    "# B. INTERACTION FEATURES\n",
-    "print(\"\\n2. Creating interaction features...\")\n",
-    "\n",
-    "df_eng['tasks_per_hour'] = df_eng['num_tasks'] / (df_eng['work_hours_numeric'] + 0.1)\n",
-    "df_eng['tasks_per_floor'] = df_eng['num_tasks'] / (df_eng['so_tang'] + 1)\n",
-    "df_eng['wc_per_floor'] = df_eng['num_wc_tasks'] / (df_eng['so_tang'] + 1)\n",
-    "df_eng['cleaning_workload'] = df_eng['num_cleaning_tasks'] * df_eng['area_diversity']\n",
-    "\n",
-    "print(\"   ✅ Created interaction features\")\n",
-    "\n",
-    "# C. AGGREGATION FEATURES\n",
-    "print(\"\\n3. Creating aggregation features...\")\n",
-    "\n",
-    "area_cols = [col for col in df_eng.columns if col.startswith('dien_tich_')]\n",
-    "df_eng['total_area'] = df_eng[area_cols].sum(axis=1)\n",
-    "df_eng['area_per_floor'] = df_eng['total_area'] / (df_eng['so_tang'] + 1)\n",
-    "\n",
-    "special_cols = ['num_patient_room_tasks', 'num_surgery_room_tasks', 'num_clinic_room_tasks']\n",
-    "df_eng['has_special_areas'] = (df_eng[special_cols].sum(axis=1) > 0).astype(int)\n",
-    "\n",
-    "print(\"   ✅ Created aggregation features\")\n",
-    "\n",
-    "# D. CATEGORICAL ENCODING\n",
-    "print(\"\\n4. Encoding categorical features...\")\n",
-    "\n",
-    "# One-hot for loai_ca\n",
-    "if 'loai_ca' in df_eng.columns:\n",
-    "    loai_ca_dummies = pd.get_dummies(df_eng['loai_ca'], prefix='loai_ca', drop_first=True)\n",
-    "    df_eng = pd.concat([df_eng, loai_ca_dummies], axis=1)\n",
-    "    df_eng = df_eng.drop('loai_ca', axis=1)\n",
-    "    print(f\"   ✅ One-hot encoded loai_ca\")\n",
-    "\n",
-    "# Label encoding for ordinal\n",
-    "ordinal_map = {\n",
-    "    'loai_hinh': {'Văn phòng': 0, 'Bệnh viện': 1, 'Trung tâm thương mại': 2, 'Khác': 3},\n",
-    "    'muc_do_luu_luong': {'Thấp': 0, 'Trung bình': 1, 'Cao': 2}\n",
-    "}\n",
-    "\n",
-    "for col, mapping in ordinal_map.items():\n",
-    "    if col in df_eng.columns:\n",
-    "        df_eng[col] = df_eng[col].map(mapping).fillna(0).astype(int)\n",
-    "        print(f\"   ✅ Label encoded {col}\")\n",
-    "\n",
-    "# Drop original time columns\n",
-    "time_cols = ['bat_dau', 'ket_thuc', 'tong_gio_lam']\n",
-    "df_eng = df_eng.drop(columns=[c for c in time_cols if c in df_eng.columns])\n",
-    "\n",
-    "print(f\"\\n✅ Feature engineering complete! Shape: {df_eng.shape}\")\n",
-    "\n",
-    "# Save\n",
-    "df_eng.to_csv('data/cleaned/ENGINEERED_DATA.csv', index=False)\n",
-    "print(\"✅ Saved: data/cleaned/ENGINEERED_DATA.csv\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e59fd2b8",
-   "metadata": {},
-   "source": [
-    "# 6️⃣ Feature Insights & Correlation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "d4d265f9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x1000 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "✅ Saved: plots/02_correlation_heatmap.png\n",
-      "\n",
-      "📊 Top 10 Correlated Features:\n",
-      "dien_tich_kinh                0.445751\n",
-      "total_area                    0.339654\n",
-      "num_trash_collection_tasks    0.334920\n",
-      "dien_tich_wc                  0.327664\n",
-      "num_tasks                     0.308021\n",
-      "tasks_per_hour                0.308021\n",
-      "cleaning_workload             0.305543\n",
-      "num_support_tasks             0.294081\n",
-      "num_cleaning_tasks            0.291498\n",
-      "task_complexity_score         0.271976\n",
-      "Name: so_luong, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Correlation Heatmap - Top 20 Features\n",
-    "numeric_cols = df_eng.select_dtypes(include=[np.number]).columns\n",
-    "correlations = df_eng[numeric_cols].corr()['so_luong'].abs().sort_values(ascending=False)\n",
-    "top_features = correlations[1:21].index.tolist()\n",
-    "\n",
-    "plt.figure(figsize=(12, 10))\n",
-    "corr_matrix = df_eng[top_features + ['so_luong']].corr()\n",
-    "sns.heatmap(corr_matrix, annot=True, fmt='.2f', cmap='coolwarm', center=0, \n",
-    "            square=True, linewidths=1, cbar_kws={\"shrink\": 0.8})\n",
-    "plt.title('Correlation Heatmap - Top 20 Features vs Target', fontsize=14, fontweight='bold')\n",
-    "plt.tight_layout()\n",
-    "plt.savefig('plots/02_correlation_heatmap.png', dpi=300, bbox_inches='tight')\n",
-    "plt.show()\n",
-    "\n",
-    "print(\"✅ Saved: plots/02_correlation_heatmap.png\")\n",
-    "print(f\"\\n📊 Top 10 Correlated Features:\")\n",
-    "print(correlations[1:11])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3af98202",
-   "metadata": {},
-   "source": [
-    "# 7️⃣ Train/Validation/Test Split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "0796eb02",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "✂️ TRAIN/VALIDATION/TEST SPLIT\n",
-      "================================================================================\n",
-      "\n",
-      "📊 Dataset: 445 samples, 63 features\n",
-      "\n",
-      "✅ Split complete:\n",
-      "   Train: 311 samples (69.9%)\n",
-      "   Val:   67 samples (15.1%)\n",
-      "   Test:  67 samples (15.1%)\n",
-      "\n",
-      "📊 Target distribution:\n",
-      "   Train - Mean: 4.60, Std: 6.02\n",
-      "   Val   - Mean: 4.87, Std: 9.81\n",
-      "   Test  - Mean: 3.91, Std: 5.78\n",
-      "\n",
-      "✅ Saved splits to data/splits/\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"=\"*80)\n",
-    "print(\"✂️ TRAIN/VALIDATION/TEST SPLIT\")\n",
-    "print(\"=\"*80)\n",
-    "\n",
-    "# Separate features and target\n",
-    "X = df_eng.drop('so_luong', axis=1)\n",
-    "y = df_eng['so_luong']\n",
-    "\n",
-    "print(f\"\\n📊 Dataset: {X.shape[0]} samples, {X.shape[1]} features\")\n",
-    "\n",
-    "# Split: 70% train, 15% val, 15% test\n",
-    "X_temp, X_test, y_temp, y_test = train_test_split(X, y, test_size=0.15, random_state=RANDOM_STATE)\n",
-    "X_train, X_val, y_train, y_val = train_test_split(X_temp, y_temp, test_size=0.176, random_state=RANDOM_STATE)\n",
-    "\n",
-    "print(f\"\\n✅ Split complete:\")\n",
-    "print(f\"   Train: {X_train.shape[0]} samples ({X_train.shape[0]/len(X)*100:.1f}%)\")\n",
-    "print(f\"   Val:   {X_val.shape[0]} samples ({X_val.shape[0]/len(X)*100:.1f}%)\")\n",
-    "print(f\"   Test:  {X_test.shape[0]} samples ({X_test.shape[0]/len(X)*100:.1f}%)\")\n",
-    "\n",
-    "print(f\"\\n📊 Target distribution:\")\n",
-    "print(f\"   Train - Mean: {y_train.mean():.2f}, Std: {y_train.std():.2f}\")\n",
-    "print(f\"   Val   - Mean: {y_val.mean():.2f}, Std: {y_val.std():.2f}\")\n",
-    "print(f\"   Test  - Mean: {y_test.mean():.2f}, Std: {y_test.std():.2f}\")\n",
-    "\n",
-    "# Save splits\n",
-    "pd.concat([X_train, y_train], axis=1).to_csv('data/splits/train.csv', index=False)\n",
-    "pd.concat([X_val, y_val], axis=1).to_csv('data/splits/val.csv', index=False)\n",
-    "pd.concat([X_test, y_test], axis=1).to_csv('data/splits/test.csv', index=False)\n",
-    "print(\"\\n✅ Saved splits to data/splits/\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "564c7690",
-   "metadata": {},
-   "source": [
-    "# 8️⃣ Feature Scaling"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "08250ace",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "📏 FEATURE SCALING\n",
-      "\n",
-      "   Binary (no scaling): 12\n",
-      "   One-hot (no scaling): 7\n",
-      "   Numeric (will scale): 51\n",
-      "\n",
-      "✅ Scaling complete! Scaler saved to: models/scaler.pkl\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pickle\n",
-    "\n",
-    "print(\"📏 FEATURE SCALING\")\n",
-    "\n",
-    "# Identify columns to scale\n",
-    "binary_cols = [col for col in X_train.columns if X_train[col].nunique() == 2 and set(X_train[col].unique()).issubset({0, 1})]\n",
-    "onehot_cols = [col for col in X_train.columns if col.startswith('loai_ca_')]\n",
-    "cols_to_scale = [col for col in X_train.columns if col not in binary_cols and col not in onehot_cols]\n",
-    "\n",
-    "print(f\"\\n   Binary (no scaling): {len(binary_cols)}\")\n",
-    "print(f\"   One-hot (no scaling): {len(onehot_cols)}\")\n",
-    "print(f\"   Numeric (will scale): {len(cols_to_scale)}\")\n",
-    "\n",
-    "# Scale\n",
-    "scaler = StandardScaler()\n",
-    "X_train_scaled = X_train.copy()\n",
-    "X_val_scaled = X_val.copy()\n",
-    "X_test_scaled = X_test.copy()\n",
-    "\n",
-    "X_train_scaled[cols_to_scale] = scaler.fit_transform(X_train[cols_to_scale])\n",
-    "X_val_scaled[cols_to_scale] = scaler.transform(X_val[cols_to_scale])\n",
-    "X_test_scaled[cols_to_scale] = scaler.transform(X_test[cols_to_scale])\n",
-    "\n",
-    "# Save scaler\n",
-    "with open('models/scaler.pkl', 'wb') as f:\n",
-    "    pickle.dump(scaler, f)\n",
-    "\n",
-    "print(\"\\n✅ Scaling complete! Scaler saved to: models/scaler.pkl\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a947162",
-   "metadata": {},
-   "source": [
-    "# 9️⃣ Model Training & Evaluation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "cab9d093",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "🤖 MODEL TRAINING & EVALUATION\n",
-      "================================================================================\n",
-      "\n",
-      "1️⃣ Training: Mean Baseline...\n",
-      "   ✅ Val MAE: 5.108, R²: 0.000\n",
-      "\n",
-      "2️⃣ Training: Linear Regression...\n",
-      "   ✅ Val MAE: 4.931, R²: -0.082\n",
-      "\n",
-      "3️⃣ Training: Decision Tree...\n",
-      "   ✅ Val MAE: 3.384, R²: 0.414\n",
-      "\n",
-      "4️⃣ Training: Random Forest...\n",
-      "   ✅ Val MAE: 3.557, R²: 0.309\n",
-      "\n",
-      "5️⃣ Training: Gradient Boosting...\n",
-      "   ✅ Val MAE: 3.557, R²: 0.309\n",
-      "\n",
-      "5️⃣ Training: Gradient Boosting...\n",
-      "   ✅ Val MAE: 3.036, R²: 0.398\n",
-      "\n",
-      "6️⃣ Training: XGBoost...\n",
-      "   ✅ Val MAE: 3.260, R²: 0.323\n",
-      "\n",
-      "7️⃣ Training: LightGBM...\n",
-      "   ✅ Val MAE: 3.603, R²: 0.295\n",
-      "\n",
-      "✅ All models trained!\n",
-      "   ✅ Val MAE: 3.036, R²: 0.398\n",
-      "\n",
-      "6️⃣ Training: XGBoost...\n",
-      "   ✅ Val MAE: 3.260, R²: 0.323\n",
-      "\n",
-      "7️⃣ Training: LightGBM...\n",
-      "   ✅ Val MAE: 3.603, R²: 0.295\n",
-      "\n",
-      "✅ All models trained!\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"=\"*80)\n",
-    "print(\"🤖 MODEL TRAINING & EVALUATION\")\n",
-    "print(\"=\"*80)\n",
-    "\n",
-    "# Evaluation function\n",
-    "def evaluate_model(model, X_train, y_train, X_val, y_val, model_name=\"Model\"):\n",
-    "    model.fit(X_train, y_train)\n",
-    "    \n",
-    "    y_train_pred = model.predict(X_train)\n",
-    "    y_val_pred = model.predict(X_val)\n",
-    "    \n",
-    "    metrics = {\n",
-    "        'Model': model_name,\n",
-    "        'Train_MAE': mean_absolute_error(y_train, y_train_pred),\n",
-    "        'Train_RMSE': np.sqrt(mean_squared_error(y_train, y_train_pred)),\n",
-    "        'Train_R2': r2_score(y_train, y_train_pred),\n",
-    "        'Val_MAE': mean_absolute_error(y_val, y_val_pred),\n",
-    "        'Val_RMSE': np.sqrt(mean_squared_error(y_val, y_val_pred)),\n",
-    "        'Val_R2': r2_score(y_val, y_val_pred),\n",
-    "    }\n",
-    "    \n",
-    "    return metrics, model\n",
-    "\n",
-    "# Store results\n",
-    "results = []\n",
-    "models_dict = {}\n",
-    "\n",
-    "# 1. Mean Baseline\n",
-    "print(\"\\n1️⃣ Training: Mean Baseline...\")\n",
-    "mean_pred = np.full(len(y_val), y_train.mean())\n",
-    "baseline_metrics = {\n",
-    "    'Model': 'Mean Baseline',\n",
-    "    'Train_MAE': mean_absolute_error(y_train, [y_train.mean()]*len(y_train)),\n",
-    "    'Train_RMSE': np.sqrt(mean_squared_error(y_train, [y_train.mean()]*len(y_train))),\n",
-    "    'Train_R2': 0.0,\n",
-    "    'Val_MAE': mean_absolute_error(y_val, mean_pred),\n",
-    "    'Val_RMSE': np.sqrt(mean_squared_error(y_val, mean_pred)),\n",
-    "    'Val_R2': 0.0,\n",
-    "}\n",
-    "results.append(baseline_metrics)\n",
-    "print(f\"   ✅ Val MAE: {baseline_metrics['Val_MAE']:.3f}, R²: {baseline_metrics['Val_R2']:.3f}\")\n",
-    "\n",
-    "# 2. Linear Regression\n",
-    "print(\"\\n2️⃣ Training: Linear Regression...\")\n",
-    "lr = LinearRegression()\n",
-    "lr_metrics, lr_model = evaluate_model(lr, X_train_scaled, y_train, X_val_scaled, y_val, \"Linear Regression\")\n",
-    "results.append(lr_metrics)\n",
-    "models_dict['Linear Regression'] = lr_model\n",
-    "print(f\"   ✅ Val MAE: {lr_metrics['Val_MAE']:.3f}, R²: {lr_metrics['Val_R2']:.3f}\")\n",
-    "\n",
-    "# 3. Decision Tree\n",
-    "print(\"\\n3️⃣ Training: Decision Tree...\")\n",
-    "dt = DecisionTreeRegressor(max_depth=10, min_samples_split=10, random_state=RANDOM_STATE)\n",
-    "dt_metrics, dt_model = evaluate_model(dt, X_train_scaled, y_train, X_val_scaled, y_val, \"Decision Tree\")\n",
-    "results.append(dt_metrics)\n",
-    "models_dict['Decision Tree'] = dt_model\n",
-    "print(f\"   ✅ Val MAE: {dt_metrics['Val_MAE']:.3f}, R²: {dt_metrics['Val_R2']:.3f}\")\n",
-    "\n",
-    "# 4. Random Forest\n",
-    "print(\"\\n4️⃣ Training: Random Forest...\")\n",
-    "rf = RandomForestRegressor(n_estimators=100, max_depth=15, min_samples_split=5, \n",
-    "                           random_state=RANDOM_STATE, n_jobs=-1)\n",
-    "rf_metrics, rf_model = evaluate_model(rf, X_train_scaled, y_train, X_val_scaled, y_val, \"Random Forest\")\n",
-    "results.append(rf_metrics)\n",
-    "models_dict['Random Forest'] = rf_model\n",
-    "print(f\"   ✅ Val MAE: {rf_metrics['Val_MAE']:.3f}, R²: {rf_metrics['Val_R2']:.3f}\")\n",
-    "\n",
-    "# 5. Gradient Boosting\n",
-    "print(\"\\n5️⃣ Training: Gradient Boosting...\")\n",
-    "gb = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, max_depth=5, \n",
-    "                               random_state=RANDOM_STATE)\n",
-    "gb_metrics, gb_model = evaluate_model(gb, X_train_scaled, y_train, X_val_scaled, y_val, \"Gradient Boosting\")\n",
-    "results.append(gb_metrics)\n",
-    "models_dict['Gradient Boosting'] = gb_model\n",
-    "print(f\"   ✅ Val MAE: {gb_metrics['Val_MAE']:.3f}, R²: {gb_metrics['Val_R2']:.3f}\")\n",
-    "\n",
-    "# 6. XGBoost\n",
-    "print(\"\\n6️⃣ Training: XGBoost...\")\n",
-    "xgb_model = xgb.XGBRegressor(n_estimators=100, learning_rate=0.1, max_depth=5, \n",
-    "                             random_state=RANDOM_STATE, n_jobs=-1)\n",
-    "xgb_metrics, xgb_trained = evaluate_model(xgb_model, X_train_scaled, y_train, X_val_scaled, y_val, \"XGBoost\")\n",
-    "results.append(xgb_metrics)\n",
-    "models_dict['XGBoost'] = xgb_trained\n",
-    "print(f\"   ✅ Val MAE: {xgb_metrics['Val_MAE']:.3f}, R²: {xgb_metrics['Val_R2']:.3f}\")\n",
-    "\n",
-    "# 7. LightGBM\n",
-    "print(\"\\n7️⃣ Training: LightGBM...\")\n",
-    "lgb_model = lgb.LGBMRegressor(n_estimators=100, learning_rate=0.1, max_depth=5, \n",
-    "                              num_leaves=31, random_state=RANDOM_STATE, n_jobs=-1, verbose=-1)\n",
-    "lgb_metrics, lgb_trained = evaluate_model(lgb_model, X_train_scaled, y_train, X_val_scaled, y_val, \"LightGBM\")\n",
-    "results.append(lgb_metrics)\n",
-    "models_dict['LightGBM'] = lgb_trained\n",
-    "print(f\"   ✅ Val MAE: {lgb_metrics['Val_MAE']:.3f}, R²: {lgb_metrics['Val_R2']:.3f}\")\n",
-    "\n",
-    "print(\"\\n✅ All models trained!\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bf0aac4e",
-   "metadata": {},
-   "source": [
-    "# 🔟 Model Comparison & Results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "fd373a20",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "================================================================================\n",
-      "📊 MODEL COMPARISON RESULTS\n",
-      "================================================================================\n",
-      "\n",
-      "            Model  Train_MAE  Train_RMSE  Train_R2  Val_MAE  Val_RMSE    Val_R2\n",
-      "    Mean Baseline   3.956762    6.008718  0.000000 5.108173  9.743329  0.000000\n",
-      "Linear Regression   2.848743    4.306219  0.486396 4.931250 10.131361 -0.082034\n",
-      "    Decision Tree   1.416510    2.927375  0.762648 3.384117  7.458279  0.413616\n",
-      "    Random Forest   1.518825    2.651412  0.805289 3.557119  8.094827  0.309251\n",
-      "Gradient Boosting   0.500222    1.163144  0.962528 3.035648  7.556467  0.398074\n",
-      "          XGBoost   0.588875    1.234300  0.957803 3.259926  8.016752  0.322511\n",
-      "         LightGBM   1.527800    2.679199  0.801186 3.602681  8.178939  0.294821\n",
-      "\n",
-      "🏆 Best Model: Decision Tree\n",
-      "        Model  Train_MAE  Train_RMSE  Train_R2  Val_MAE  Val_RMSE   Val_R2\n",
-      "Decision Tree    1.41651    2.927375  0.762648 3.384117  7.458279 0.413616\n",
-      "\n",
-      "✅ Saved: models/model_comparison.csv\n"
-     ]
-    },
-    {
-     "data": {
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yx/1T+dHU1AQAzJkzBzVq1ECdOnUwefJknD59utgD1FQyx48fR3h4OAYPHgxNTU00b94c48ePx549exQdTaloa2sXuqZQVlYW92lUriwsLHDnzp0ib7a2tgAgU28V/Fwwa40ykKemVKa/06pYS5emzVeuXIGrqysaN26MDRs2VMqTDErTbk1NTWHa6d69eyMkJKTcc5a1krY7OzsbXl5e8Pb2Rv369Ss0Y1mT53ft7OwMf39/tGrVChoaGrC2toazs3Ol7PiTp92ampqwt7eHra0t1NXVYWZmht69e5dru9nxV8U0b94cr169wosXL4Rl9+/fh4GBAWrWrKnAZETFe/z4Mfr27YvMzEwEBgbyAGAZuHTpEn788UeZC5/n5ORAQ0OD0399gcOHDyM+Ph4ikQgikQihoaEIDQ2FSCRSdLRK7fbt21i+fLnMxdxzcnKgqqoqHHAlqiispcoX9/nlg/un8tOsWTNIpVLk5uYKy6RSKQDI7LdIfsnJyTK1KgCoq6tDQ0NDQYmUU4sWLXD37l2ZZffu3UPz5s0VlIiqusaNG0NDQwP37t0Tlt2/fx8aGhpo1KiR4oKVMXlqSmX6O62KtbS8bQ4MDMTIkSMxYsQIrFixotJ+55Wn3T4+PvDx8ZFZlpOTA11d3YqIWqZK2u7r16/j4cOHmDNnjlCjA4C7uzsWLFhQ0bG/iDy/68DAwEKdXTk5OahWrVqFZC1L8rS7adOmhepaiURSrt8X2PFXxTRq1AgdOnTA0qVLkZmZicTERKxfvx6urq6KjkZUpIyMDIwYMQKmpqbYsmULateurehISsHIyAhZWVlYsWIFcnJy8PTpU/j6+sLV1bXSFpVfg2PHjuHSpUtISEhAQkICnJyc4OTkhISEBEVHq9R0dXWxa9cu+Pv7Iy8vD0lJSVi2bBn69OnDzytVONZS5Yf7/PLD/VP5sbS0RMOGDeHt7Y03b94gPT0dq1atQteuXZXqzBRFsLa2xvPnz7Fx40ZIJBIkJiZiw4YNEIvFio6mVBwcHPDixQts374dubm5OH/+PEJCQtC3b19FR6MqSltbG927d8fy5cuRnp6O9PR0LF++HE5OTtDS0lJ0vDIjT03Zq1cvxMfHIywsDHl5eQgLC0N8fDx69+6tgORfpirW0vK0OTw8HAsWLMDatWsxevRoBaQtO/K0WyQSYe/evbhw4QKkUilOnTqFsLAw9OvXTwHJv0xJ2y0SiXDt2jWhPi+oyzdu3FjpOv7k+V1nZmZi0aJFuHXrFqRSKaKiohAaGooBAwYoIPmXkafdAwcOxMmTJ3H48GHk5+fjwoULCAkJKdf/4+z4q4LWrFmDvLw82Nvbo3///ujcuTM8PDwUHYuoSEFBQUhKSsLRo0fRoUMHmJiYCDcqPR0dHfj7++Pu3buwsrLCsGHDYGlpCW9vb0VHIyrEwMAAmzZtwsmTJ2Fubo6+ffuibdu2mDdvnqKjURXFWqp8cJ9PlZGGhgZ27NgBNTU1ODo6wtHREQYGBli6dKmio1V6zZo1w6ZNm3Dq1ClYWFhg+PDhsLOzg5eXl6KjKRU9PT1s3boVx44dg4WFBX755Rf88ssv6Nixo6KjURU2f/58NGrUCGKxGD/++CMMDQ1lav+ePXti48aNCkxYNj5VU5qYmODIkSMA3p8psm7dOmzatAlmZmZYv3491q5di8aNGysyfqmVtN3KpKRt/v333yGRSDBx4kSZWriyfvctabu7du0q7H/MzMywbt06rF27FqampoqMX2r8jBff5hEjRmDo0KHw9PSEiYkJli9fDl9f30o7E0lJ292pUyesX78eAQEB6NChA2bPno2ZM2fC3t6+3LKp5HP+ESIiIiIiIiIiIiIiIqJKj2f8ERERERERERERERERESkBdvwRERERERERERERERERKQF2/BEREREREREREREREREpAXb8ERERERERERERERERESkBdvwRERERERERERERERERKQF2/BEREREREREREREREREpAXb8ERERERERERERERERESkBdvwREVGpZGdn49mzZ8L9169fIz09XYGJiIiIiCo3iUSCxMRE4f7H9RYREREREdHnsOOPiL5KRkZGMDIywt9//13osW3btsHIyAhr166VWZ6Xl4cuXbrA0tIS2dnZMo89efIERkZGMDY2homJSaHbp1y9ehVjx44FAMyaNQuzZs36wtaVrY0bN2LMmDFyP+9L2zJ48GDExMQI9x0cHHD37t1Sv97YsWNx7dq1Uj+fiIioqrKzs0Pbtm2FusbY2BimpqYYMmQIbt26JbOekZERzp49W+g1IiIiYGRkJFMbXLx4EaNGjYJIJIKJiQmcnJywdetW5OfnC+sYGRmhXbt2RdZXSUlJxWZOTEzEwIEDkZubi7Vr12LYsGFltDXKxpEjR9CzZ0+5n/elbfHy8kJwcLBw/+N6S15z585FREREqZ9PRESk7JKTk9G/f3+4urrCxcUFDx8+VHQkqiAf/64fPXqkmCBE5YAdf0T01dLT08OhQ4cKLQ8KCkKNGjUKLY+IiEC9evVgYGCAw4cPF/maoaGhuHz5cqFbcXJycjBz5kzMnDmz9A0pZ+7u7vD396/w93358uUn78urYDvn5OR80esQERFVRQsXLhTqmitXriAiIgI1a9aEp6cnpFKpsF5x9dXBgwdl6qvExESMGjUKvXv3RkxMDC5evIjFixdj27Zt2LBhg8xz/fz8iqyv6tevX2zeWbNmwdPTExoaGmXQ+rLXq1cv/PnnnxX+vmVdX02ZMgW//fYbZ2UgIqIqqSSDo54/fw53d3cEBgbC0NAQkZGRRb5WSQZEKVpERATmzZsHABg2bFihAfMfMjExQUJCwmdfs2Ag/ZMnT8os54eSkpI+O2CsKHFxcTAyMir1++7atQtz584V7vv6+haqceUREhKCxYsXl/r5RGWNHX9E9NUSi8U4fPiwzMGqa9euIScnB61atSq0/s6dO9GjRw8MHDgQ27ZtK5Pi68CBAzA0NETTpk2Lfbxnz54wNTWFWCzGkSNHhCy9e/cW1gsKCoKRkRHu378PAPjnn3/Qpk0bJCYmIicnB6tXr4a9vT3Mzc3h5uYmM8rIyMgIixcvhoWFBdzd3Qtl+HBkeWZmJry8vGBhYQErKyv89NNPwnsWJT09HT///DPMzMzg7OyMM2fOCI9lZmbi119/hY2NDTp16gQvLy+8ePECADB69GgkJSVh/vz5+PXXX+Ho6AgAcHNzg5+fHwAgJiYGrq6uEIlE6Nmzp7BtgPcH+yZOnIju3bujY8eOePz4MZo2bYoGDRrgwIEDn/iNEBERUUnUqVMHAwYMwNOnT/Hq1SthuVgsxokTJ/D69WthWWpqKq5cuYLOnTsLy65fvw4NDQ306NEDmpqaUFVVhbGxMWbPng11dfUvyhYVFYX09HRYW1sX+fiJEyfg4uICU1NTODo6Yvv27ZBKpYiMjISFhYVQGxYc8Dl9+jQAQCqVwtLSErGxscjPz0dAQAAcHR0hEokwePBg3LhxQ3gPOzs7zJs3D1ZWVnB2dpapN4H3tZudnR2A97NKLFiwAFZWVrCwsMDgwYNx8eLFYtv39u1bzJo1CxYWFujevbvMGXyfqvvmzJmDhIQEbNq0Ce7u7oXqLQC4efMmhg0bBjMzM3Tr1g3bt28Xat61a9di9OjR6Nu3L8zNzXHhwgXo6enByspKIYPEiIiIvgafGxzVrl072NnZYe/evbh9+3aRZ/zLMyBKUdLT0+Hr64vJkyeXaP3Lly9DJBKVb6gSqF+//mcHjJWHjwdFfelgK7FYjFu3biE2NvaLXoeorLDjj4i+Wra2tsjNzZWZ3igwMBCurq6F1r19+zZu3boFFxcXiMVipKenIyoq6osz7N69G05OTkU+FhQUBB8fH/zyyy+4cOECvL29sXDhQhw/fhxdu3bFnTt3kJKSAgCIjo6GlpYWzp07BwA4ffo0mjZtioYNG2LVqlWIiorC9u3bcfbsWbRv3x6jR4+Wma708ePHiIqKwm+//fbJvFu3bkVmZiZOnz6NyMhI1K1bF8uXLy92/ejoaPTp0wexsbEYOXIkPDw88PjxYwCAt7c3Hj16hKCgIJw4cQI1atSAp6cn8vPzsXXrVtSvXx8LFy7EvHnzEB4eDuD9iH83Nzfcvn0bP//8M8aOHYu4uDgsWrQIS5culZla7OzZs1i9ejUiIiLw3XffAQCcnJywZ8+ez/1aiIiI6DOSk5Oxc+dOtG3bFrVr1xaWf//992jcuDHCwsKEZcHBwejevTu0tLSEZRYWFtDW1kbfvn2xYcMGxMbGIjMzEz169BCmQC+tT9VX58+fx+TJkzFmzBjEx8dj5cqV2LZtGwICAmBlZYWcnBxcvXoVwP/qq4Ja8cqVK5BIJDA3N8fu3buxbds2rF69GrGxsXBxccGoUaOEQUzA+wFlR48eRUBAAFRVi/9qfPjwYVy+fBlHjx5FTEwMzMzMsHDhwmLXv3HjBtq0aYPo6Gj88ssv+OWXX4QR9Z+q+5YsWQKRSIRx48Zh48aNheqtlJQUjBgxAj/++CNiYmKwfv167N69G/v27RPeOzY2FtOmTUNkZKQwnb2TkxP279+PvLy8Ev6GiIiIlNfHg6MkEglmz56No0ePYvfu3fjmm28KPaekA6LOnTsHV1dXmJiYwM7ODjt37hQeK25gE1D04OgXL15g2rRpsLKygrW1NebNm4fMzMxi2+Xn5wdra2uZuu/Ro0cYPXo0zMzMYG9vj2PHjgmPGRkZIS4uDsD7Ti8vLy906NAB9vb22LFjB1q1aiVzll9ISAi6d+8OY2NjjBw5UjjeBcg/8PtDH59RuHv3bnTt2hUikQhisfizg8M3b94MGxsbdOnSBcuWLZOZRerPP/+EWCxGhw4d4OLigujoaADAoUOHsGnTJiQkJEAkEmHdunUICQlBSEgIevXqBQCf3P5xcXGwsbHB1KlTIRKJsHnzZgDA0KFDsWLFik/mJaoo7Pgjoq+Wuro6xGKxMB1VVlYWwsPD4ezsXGjdHTt2oHfv3vjXv/4FbW1t9OvXD1u3bi20Xq9evSASiWRuCxYsKPL9X7x4gXv37sHU1LTIxw8ePIgBAwagU6dOUFNTQ6dOnTBgwADs3bsXBgYGaN26Nc6ePQupVIqYmBgMHDhQODB16tQpODg4ID8/H3v37sWUKVPQsGFDVKtWDePHj0dubq5Mx6WTkxO0tbVRq1atT24zLS0t3L59G8HBwUhJScHSpUs/Ofrshx9+QLdu3aCurg5nZ2e0adMGYWFhSEtLQ3h4OObMmQN9fX3o6OjA29sb169fx82bNz+ZAQD27t0Le3t7dOvWDWpqajA1NUX//v2xa9cuYR1jY2O0aNFCpk0mJia4e/euzEE5IiIi+ryFCxdCJBLB2NgYrVu3xtChQ9G8eXPhTPwPubi4yEz3efDgQfTt21dmHX19fRw+fBj29vY4fvw4fvrpJ1hYWMDNza3Q9VDc3d0L1Vfjxo0rMqdUKkV8fHyx9VVQUBDs7e3Ro0cPqKuro3Xr1hg7diz27t0LTU1NdO7cWZihIDo6ulB9ZWdnBzU1NezatQvjxo1Dy5YtoaGhAVdXVzRt2lTmQJSjoyNq1apVovrqyZMnCAwMxIMHDzBp0iSZ1/nY999/j6FDh0JDQwNWVlZwdHTE4cOHS1z3FefIkSNo2rQphgwZAg0NDTRr1gw//fSTTH3VsGFDdOrUCTo6OsKByHbt2uHt27clquGIiIiU3ceDo/z9/REUFITc3Fx4eXnJDKgpUJIBUQ8ePIC7uzsGDhyICxcuYM2aNVi5ciXOnj37yYFNBT4cHG1oaAgPDw+oqqoiPDwcISEhSE1NFabx/FheXh4OHDgAsVgss/zcuXOYOnUq4uLi4OLigtmzZyM3N7fQ86dNm4bXr1/j5MmTOHDgACIjIyGRSGTWuXnzJvbv34/Tp08jIyMD69atA4BSD/wuSmJiIv7zn/9g8+bNSEhIwIwZM7Bo0SKkpqYW+5y//voLYWFh2LFjByIiIoTa9/Tp05g/fz7mzZuH+Ph4TJgwARMmTMDdu3fRp08fjBs3DiKRCAkJCRg/fjzEYrEwk5dUKv3s9n/27BmaNGmC2NhYDB48GMD7GSXu37+P69evF5uXqKJ82RwtRETlzMXFBQMGDEBmZiZOnDgBU1NT1K1bV2adV69eITQ0FKqqqsLopby8PGRmZgojrgscOXIEhoaGJXrvgvnF69WrV+TjL168QMOGDWWWGRoa4tSpUwAABwcHnDlzBi1atICuri769OmDIUOGICsrC2fOnIGHhwfS09Px9u1bTJo0SWakeW5uLp4+fSrcL2rEWVHc3NygqamJwMBA/Prrr2jYsCGmTp2Kbt26Fbn+x9vi22+/RUpKivDe/fv3l3lcTU0NT548kdmmRXn69CnOnz8vM22ERCKRKfCKapOBgQGA94V4nTp1PvkeRERE9D/z58+Hi4sLcnJyEBAQgI0bN8LGxgZ6enqF1hWLxVi2bBkePHiAtLQ0VKtWDW3bti20nr6+PiZPnozJkyfj3bt3uHz5Mn7//XeMHj0ax48fh5qaGgBg48aNsLCwKFHOV69e4d27d8XWNmlpafj+++9llhkaGgq1iYODA7Zv344hQ4bg8ePH8Pf3x44dO/DixQucPHlSuC7z06dP4evrKzPzQV5enkwNU9L6qmfPnsjNzcWBAwewcuVK6Ovrw93dHYMGDSpy/aLqq7/++qvEdV9xnj59ips3b8rUV1KpVPg9FNematWqQU9PD8nJyWjfvv1n34eIiEiZLFy4EEuXLkVeXh5yc3NhYGAABwcHYZDSuHHjih2wVKBgQNSOHTtw/PhxrF27FioqKrC0tMScOXPQqFEj/Pnnn2jdurUwS1WbNm2EMwh9fHyEgU0AhIFNO3bswMiRIwH8b3A08H5Wgps3b2Lbtm3Q0dEBAMycORM//vgj5s6dW6i+u3nzJt69e4d27drJLO/Rowdat24t/LxmzRqkpaUJx14AICUlBdHR0Th69Ch0dXUBvJ8B6uMpT93d3VGzZk0AQOfOnXHt2jUAsgO/AcgM/C6YRv7Dtn2KmpqaMFDK0dERnTp1wpUrV4qdmUFFRQXz5s2Djo4OdHR0MGbMGGzduhXjx4/Hzp07MWjQIJiZmQF4P/C9YErXD6/tV5QbN258cvsXcHV1hYaGhnDNai0tLbRs2RKxsbFF1tZEFYkdf0T0VWvZsiWaNGmCo0ePIiQkBCNGjCi0TmBgIL777rtCI9q9vb2xbdu2Up9mX1BYfHzNlwKGhoaFpihITEwUOia7du0Kf39/tGjRAtbW1mjZsiW0tbXh7++POnXqoHnz5pBKpahWrRq2bt0KY2Nj4XX+/vtvmQ5HFRWVEmW+c+cO7OzsMHLkSLx+/Rq7d++Gl5cXzp8/LxRoH/p41FRiYiJat24tvPfRo0dlOlrv3btXqLOzKAYGBujTp49wPZqC9/rwuotFtalgRNmHB7CIiIio5DQ1NTFmzBhkZGTAw8MDe/bsQcuWLWXWqV27NmxtbREcHIzU1NQip1GfPn26cD06ANDW1oalpSX09fXRq1cvZGRkyEwlVVIF+//irsXcoEGDT9ZXtra28Pb2RmhoKMzMzKCvr482bdpg7969SElJgaWlJYD3tcjEiRNlDlo9fvxYOKD1YZbPefDgAVq3bg1nZ2dkZWXh2LFjmDlzJkQiEZo3b15o/aLqqwYNGkBPT69EdV9xDAwMYGFhgS1btgjLXr58iTdv3ny2TXl5eayviIioSpJncNSnfG5AVGpqaqHr1BXUYJ8b2ATIDt558uQJJBIJbGxsZJ6jqamJxMTEQtmTkpKgq6sLTU1NmeUf1j0FnVMfT/2dnJws5ClQ1HGfj1+r4PhNaQd+F6V+/frYsWMH/P394e7uDolEAhcXF0yfPh3VqlUrtP7HMzcUDGYvyBUfHy9zORmJRIKOHTt+Nsfntv+n2mVgYIBnz559vrFE5YxTfRLRV8/FxQXbt2/HgwcPCu10pVIpdu/ejb59+8LAwEDmNnDgQBw7dkw4c09eBQXbh/OWf8jV1RX79u1DbGwsJBIJzp8/j3379glTZTVr1gz6+vrYuXMnrKysAACWlpbw9/cXRkKpqqrC1dUVK1aswLNnzyCVSnHo0CE4OTnh0aNHcmc+cOAAZsyYgbS0NNSoUQM1atRA9erVCxV/BU6ePInTp08jNzcX+/fvx/379yEWi1GvXj3Y2tpiyZIlePnyJXJzc7Fhwwa4urrin3/+AfC+4Hn9+rXwWh/ed3V1RWhoKKKjoyGVSvHw4UMMHTq0yOlXP1Swrb/99lu5205ERET/M3nyZBgZGWHKlCnIysoq9LiLiwtCQkIQGRlZaFoo4P004ydPnkRAQABSUlKQn5+PpKQk+Pn5wczMrFSdfgCgp6eH6tWrF1tf9e3bF6dOncLRo0chkUhw69Yt+Pn5CfVVzZo1YW5ujo0bN8La2hoAYGVlBX9/f9ja2go1T//+/bFhwwbcv38fwPsppnr27IkLFy7InTkyMhKenp548uQJtLS0oKurC3V19SIHVQHvR+kfPHgQubm5iIyMxKlTp9CvX78S1X2fqq/EYjGuXLmCI0eOIC8vD6mpqXB3d4ePj88n82dnZ+Off/5hfUVERFVaweCoQYMGwcPDA7dv3y7xc6dPn45JkyYJ9wsGRM2fPx9Pnz5FRkYGvv3220LHnw4ePIioqKjPDmwCZAfvGBgYQEtLC3FxcUhISEBCQgJiYmIQHByMVq1aFcqnqqpa7KD1zyk49vVhJ2RJZiL4MGufPn2EnAkJCQgPDxeueweUfLBVWloaJBIJ1q1bh7i4OGzevBlhYWEIDAwscv3MzEy8fftWuF8w2Kog1/jx42Vy/fnnn1iyZEmJ2lSS7V/cgPZPXTuaqKLwU0hEX72CgyG9evWSuWgy8P5ATGpqqnDx3Q/Z2dlBT08Pf/zxh8xrmZiYFLpdvny50PNr166NVq1a4eLFi0Xm6t69O2bPno3FixcL1wqcMWOGzDUIHRwckJmZCXNzcwCAtbU13r17BwcHB2GdmTNnon379hg8eDBEIhG2b9+ONWvWFFnMfc6UKVPw73//Gz179oSpqSmCgoKwfv36IkdGAYC9vT38/Pxgbm6O/fv3Y8uWLcKI899++w21atWCs7MzOnbsiNOnT8Pf318oTF1dXbFq1SpMmzYNADBgwABMnToVq1atQvv27bFy5UqsXLkSZmZmGDp0KOzs7DB16tRP5r948SLatGkj98g7IiIikqWmpoZly5YhJSUFvr6+hR7v0qULsrOz0bFjxyL3uzY2Nti0aZPQMVhQq+jp6WH9+vUy67q5uRVZX4WFhRWZzcrKqtj6qn379li9ejX8/PwgEong6emJQYMGwd3dXVina9euePnypTCwqqj6auTIkXB2doaHhwdMTEywZMkSzJs3D/b29p/feB8ZPnw4bG1tMXDgQBgbG2PZsmVYtWqVzDRZH7K0tMTJkydhbm6OlStXYvXq1UJd97m6z9nZGQcPHhSuFfNhvdWgQQP4+/tj3759sLS0RO/evdGkSZPPdvxdunRJqGuJiIiqus8NjipKSQZE9ezZE7du3UJwcDAkEglu3LgBHx8fqKurf3Zg08fatWuHf//73/Dx8cGbN2+QlZWFpUuXYuTIkYWuvQe877x79eoVsrOz5d4e33zzDX744QcsW7YMGRkZyMjIwG+//Vbi55d24HdRkpKSMHr0aMTGxkJVVVU4PlXcMSKJRAIfHx+8ffsW9+/fx5YtWzBw4EAA7weBBQQECFOSXr9+HS4uLggNDQXwfir0zMxMYRaKDwdbybv9P1TUmZ9EiqCSX9wcK0REhJ07dyIqKgr+/v6KjlIl/PTTT+jatWux18whIiKiyi8yMhLLli0rtmOQyta8efNQq1YtYbAWERFRVWFnZwdPT0+4uLjILE9MTISzszN69eqF+fPnl+i1zp07B39/f9y8eRNZWVmoXbs2HBwcMGHCBGG6yfPnz2PFihX4+++/oa+vj3HjxgmdeydPnsS6devw4MED6OnpoX///nBzc4OamhpmzZoFADKDeZ49ewZfX1/Ex8cjOzsb7dq1g7e3N5o1a1Yom0QigbW1NVatWiVMZTls2DCYm5tjwoQJAN5PX2lvb4+TJ0/C0NAQRkZGCAgIgIWFBdLS0jBv3jzExMRAT08Pzs7OWLduHc6cOYPc3FyZ5wHA2rVrER8fjx07dgAAoqKisGbNGjx69Aja2tpwcnLClClToKmpWWTbPvRxrsDAQPj5+SE1NRU1a9ZE//79MX78+EJn18XFxWHixIkYMGAA9uzZAx0dHQwcOBBjx44VzrgLCgrC1q1bhalQ+/fvj3HjxkFFRQV3797F2LFjkZGRgaioKNy6dQteXl6oVq0aoqKiPrn94+LiMHz4cNy5c0cmU3Z2NkQiEYKCgoqcDp6oIrHjj4joE3JyctCzZ09s2LChyOKKys7du3fh6emJ0NBQYe55IiIiUk4FZ/F9PI07la309HT06dMHhw8flrk2DxERESkXX19fvH37FgsXLpT7uefOnUOHDh2gpaUFALhz5w6cnZ1x5cqVYmeQosJCQ0MREBCA/fv3KzoKEaf6JCL6FE1NTfj6+hY5RRaVLV9fX/j4+LDTj4iIqArw8fHBunXrkJubq+goSm3FihWYNWsWO/2IiIiUnJubGyIjI5Geni73c319fbFhwwbk5eUhMzMTGzZsgKWlJTv95BQQEIApU6YoOgYRAJ7xR0RERERERERERERUqYWFhSEmJgaLFy+W63l3797F4sWLcfPmTaiqqqJz587w9vaGvr5+OSVVPocPH8aVK1dKPHUsUXljxx8RERERERERERERERGREuBUn0RERERERERERERERERKgB1/REREREREREREREREREqAHX9ERERERERERERERERESoAdf0RERERERERERERERERKgB1/REREREREREREREREREqAHX9ERERERERERERERERESoAdf0RERERERERERERERERKgB1/REREREREREREREREREqAHX9ERERERERERERERERESuD/ATxBcSaq6C90AAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1800x500 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "✅ Saved: plots/03_model_comparison.png\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"\\n\" + \"=\"*80)\n",
-    "print(\"📊 MODEL COMPARISON RESULTS\")\n",
-    "print(\"=\"*80)\n",
-    "\n",
-    "results_df = pd.DataFrame(results)\n",
-    "print(\"\\n\" + results_df.to_string(index=False))\n",
-    "\n",
-    "# Find best model\n",
-    "best_model_name = results_df.loc[results_df['Val_R2'].idxmax(), 'Model']\n",
-    "print(f\"\\n🏆 Best Model: {best_model_name}\")\n",
-    "print(results_df[results_df['Model'] == best_model_name].to_string(index=False))\n",
-    "\n",
-    "# Save results\n",
-    "results_df.to_csv('models/model_comparison.csv', index=False)\n",
-    "print(\"\\n✅ Saved: models/model_comparison.csv\")\n",
-    "\n",
-    "# Visualization\n",
-    "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
-    "\n",
-    "# MAE\n",
-    "axes[0].barh(results_df['Model'], results_df['Val_MAE'])\n",
-    "axes[0].set_xlabel('MAE (lower is better)')\n",
-    "axes[0].set_title('Validation MAE Comparison')\n",
-    "axes[0].invert_yaxis()\n",
-    "axes[0].grid(alpha=0.3)\n",
-    "\n",
-    "# RMSE\n",
-    "axes[1].barh(results_df['Model'], results_df['Val_RMSE'])\n",
-    "axes[1].set_xlabel('RMSE (lower is better)')\n",
-    "axes[1].set_title('Validation RMSE Comparison')\n",
-    "axes[1].invert_yaxis()\n",
-    "axes[1].grid(alpha=0.3)\n",
-    "\n",
-    "# R²\n",
-    "axes[2].barh(results_df['Model'], results_df['Val_R2'])\n",
-    "axes[2].set_xlabel('R² Score (higher is better)')\n",
-    "axes[2].set_title('Validation R² Comparison')\n",
-    "axes[2].axvline(x=0.6, color='green', linestyle='--', label='Target (0.6)')\n",
-    "axes[2].invert_yaxis()\n",
-    "axes[2].legend()\n",
-    "axes[2].grid(alpha=0.3)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.savefig('plots/03_model_comparison.png', dpi=300, bbox_inches='tight')\n",
-    "plt.show()\n",
-    "\n",
-    "print(\"\\n✅ Saved: plots/03_model_comparison.png\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "55287b1b",
-   "metadata": {},
-   "source": [
-    "## 11. Phân tích Feature Importance\n",
-    "\n",
-    "Phân tích các features quan trọng nhất từ các tree-based models để hiểu yếu tố nào ảnh hưởng nhiều nhất đến số lượng nhân sự cần thiết."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "becc1b84",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x1600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "================================================================================\n",
-      "TOP 20 FEATURES - RANDOM FOREST\n",
-      "================================================================================\n",
-      "                   feature  importance\n",
-      "           num_other_tasks    0.082177\n",
-      "                  hour_end    0.077996\n",
-      "         num_support_tasks    0.069436\n",
-      "        loai_ca_Hành chính    0.066470\n",
-      "                total_area    0.053417\n",
-      "num_trash_collection_tasks    0.049941\n",
-      "              dien_tich_wc    0.039263\n",
-      "          so_cua_thang_may    0.035331\n",
-      "                hour_start    0.031520\n",
-      "           loai_ca_Ca sáng    0.030894\n",
-      "       dien_tich_hanh_lang    0.028037\n",
-      "      dien_tich_ngoai_canh    0.026666\n",
-      "          is_morning_shift    0.025059\n",
-      "         cleaning_workload    0.024196\n",
-      "     task_complexity_score    0.019776\n",
-      "           num_lobby_tasks    0.018538\n",
-      "     num_maintenance_tasks    0.017922\n",
-      "                 num_tasks    0.017356\n",
-      "            tasks_per_hour    0.015197\n",
-      "            area_per_floor    0.014709\n",
-      "\n",
-      "================================================================================\n",
-      "TOP 20 FEATURES - GRADIENT BOOSTING\n",
-      "================================================================================\n",
-      "                   feature  importance\n",
-      "         num_support_tasks    0.168944\n",
-      "           num_other_tasks    0.099070\n",
-      "                  hour_end    0.090977\n",
-      "              dien_tich_wc    0.076057\n",
-      "        loai_ca_Hành chính    0.072796\n",
-      "       dien_tich_hanh_lang    0.055343\n",
-      "                hour_start    0.047728\n",
-      "num_trash_collection_tasks    0.045451\n",
-      "         cleaning_workload    0.033784\n",
-      "          so_cua_thang_may    0.031193\n",
-      "           loai_ca_Ca sáng    0.020162\n",
-      "    trash_collection_ratio    0.017943\n",
-      "      dien_tich_ngoai_canh    0.016354\n",
-      "        is_afternoon_shift    0.015254\n",
-      "                 num_tasks    0.013324\n",
-      "              num_wc_tasks    0.012245\n",
-      "          is_morning_shift    0.012009\n",
-      "            dien_tich_kinh    0.011908\n",
-      "           dien_tich_phong    0.011767\n",
-      "                   so_tang    0.011093\n",
-      "\n",
-      "================================================================================\n",
-      "TOP 20 FEATURES - XGBOOST\n",
-      "================================================================================\n",
-      "                   feature  importance\n",
-      "         num_support_tasks    0.185853\n",
-      "           num_other_tasks    0.139603\n",
-      "num_trash_collection_tasks    0.087514\n",
-      "        loai_ca_Hành chính    0.047658\n",
-      "         cleaning_workload    0.041509\n",
-      "       dien_tich_hanh_lang    0.036502\n",
-      "     task_complexity_score    0.035533\n",
-      "                   so_tang    0.029777\n",
-      "            cleaning_ratio    0.023369\n",
-      "              dien_tich_wc    0.022166\n",
-      "           num_lobby_tasks    0.020765\n",
-      "              num_wc_tasks    0.020499\n",
-      "         num_hallway_tasks    0.019875\n",
-      "                  hour_end    0.019466\n",
-      "             op_chan_tuong    0.018002\n",
-      "     num_maintenance_tasks    0.016238\n",
-      "    trash_collection_ratio    0.012965\n",
-      "          so_cua_thang_may    0.012714\n",
-      "          num_office_tasks    0.012486\n",
-      "      dien_tich_ngoai_canh    0.012133\n",
-      "\n",
-      "================================================================================\n",
-      "TOP 20 FEATURES - LIGHTGBM\n",
-      "================================================================================\n",
-      "                   feature  importance\n",
-      "                  hour_end          90\n",
-      "              dien_tich_wc          57\n",
-      "         num_support_tasks          47\n",
-      "        loai_ca_Hành chính          33\n",
-      "                hour_start          32\n",
-      "       dien_tich_hanh_lang          31\n",
-      "                   so_tang          28\n",
-      "    trash_collection_ratio          28\n",
-      "           num_other_tasks          27\n",
-      "num_trash_collection_tasks          24\n",
-      "          so_cua_thang_may          24\n",
-      "           loai_ca_Ca sáng          23\n",
-      "           dien_tich_phong          21\n",
-      "                   doc_ham          20\n",
-      "                total_area          20\n",
-      "            area_per_floor          19\n",
-      "            cleaning_ratio          18\n",
-      "                 num_tasks          17\n",
-      "            dien_tich_kinh          17\n",
-      "              wc_per_floor          17\n",
-      "\n",
-      "================================================================================\n",
-      "TOP 20 FEATURES - TRUNG BÌNH TỪ TẤT CẢ MODELS\n",
-      "================================================================================\n",
-      "                   feature  mean_importance\n",
-      "                  hour_end        22.547110\n",
-      "              dien_tich_wc        14.284371\n",
-      "         num_support_tasks        11.856058\n",
-      "        loai_ca_Hành chính         8.296731\n",
-      "                hour_start         8.022267\n",
-      "       dien_tich_hanh_lang         7.779970\n",
-      "                   so_tang         7.012657\n",
-      "    trash_collection_ratio         7.010491\n",
-      "           num_other_tasks         6.830212\n",
-      "num_trash_collection_tasks         6.045726\n",
-      "          so_cua_thang_may         6.019809\n",
-      "           loai_ca_Ca sáng         5.764881\n",
-      "           dien_tich_phong         5.256978\n",
-      "                total_area         5.018384\n",
-      "                   doc_ham         5.004036\n",
-      "            area_per_floor         4.757241\n",
-      "            cleaning_ratio         4.511669\n",
-      "                 num_tasks         4.260054\n",
-      "            dien_tich_kinh         4.258079\n",
-      "              wc_per_floor         4.252550\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Lấy feature importance từ các tree-based models\n",
-    "feature_names = X_train.columns\n",
-    "\n",
-    "# Random Forest Feature Importance\n",
-    "rf_importance = pd.DataFrame({\n",
-    "    'feature': feature_names,\n",
-    "    'importance': models_dict['Random Forest'].feature_importances_\n",
-    "}).sort_values('importance', ascending=False)\n",
-    "\n",
-    "# Gradient Boosting Feature Importance\n",
-    "gb_importance = pd.DataFrame({\n",
-    "    'feature': feature_names,\n",
-    "    'importance': models_dict['Gradient Boosting'].feature_importances_\n",
-    "}).sort_values('importance', ascending=False)\n",
-    "\n",
-    "# XGBoost Feature Importance\n",
-    "xgb_importance = pd.DataFrame({\n",
-    "    'feature': feature_names,\n",
-    "    'importance': models_dict['XGBoost'].feature_importances_\n",
-    "}).sort_values('importance', ascending=False)\n",
-    "\n",
-    "# LightGBM Feature Importance\n",
-    "lgbm_importance = pd.DataFrame({\n",
-    "    'feature': feature_names,\n",
-    "    'importance': models_dict['LightGBM'].feature_importances_\n",
-    "}).sort_values('importance', ascending=False)\n",
-    "\n",
-    "# Visualize Top 20 Features\n",
-    "fig, axes = plt.subplots(2, 2, figsize=(20, 16))\n",
-    "fig.suptitle('Feature Importance Comparison - Top 20 Features', fontsize=16)\n",
-    "\n",
-    "# Plot RF\n",
-    "axes[0, 0].barh(range(20), rf_importance.head(20)['importance'])\n",
-    "axes[0, 0].set_yticks(range(20))\n",
-    "axes[0, 0].set_yticklabels(rf_importance.head(20)['feature'])\n",
-    "axes[0, 0].set_xlabel('Importance')\n",
-    "axes[0, 0].set_title('Random Forest')\n",
-    "axes[0, 0].invert_yaxis()\n",
-    "\n",
-    "# Plot GB\n",
-    "axes[0, 1].barh(range(20), gb_importance.head(20)['importance'])\n",
-    "axes[0, 1].set_yticks(range(20))\n",
-    "axes[0, 1].set_yticklabels(gb_importance.head(20)['feature'])\n",
-    "axes[0, 1].set_xlabel('Importance')\n",
-    "axes[0, 1].set_title('Gradient Boosting')\n",
-    "axes[0, 1].invert_yaxis()\n",
-    "\n",
-    "# Plot XGB\n",
-    "axes[1, 0].barh(range(20), xgb_importance.head(20)['importance'])\n",
-    "axes[1, 0].set_yticks(range(20))\n",
-    "axes[1, 0].set_yticklabels(xgb_importance.head(20)['feature'])\n",
-    "axes[1, 0].set_xlabel('Importance')\n",
-    "axes[1, 0].set_title('XGBoost')\n",
-    "axes[1, 0].invert_yaxis()\n",
-    "\n",
-    "# Plot LGBM\n",
-    "axes[1, 1].barh(range(20), lgbm_importance.head(20)['importance'])\n",
-    "axes[1, 1].set_yticks(range(20))\n",
-    "axes[1, 1].set_yticklabels(lgbm_importance.head(20)['feature'])\n",
-    "axes[1, 1].set_xlabel('Importance')\n",
-    "axes[1, 1].set_title('LightGBM')\n",
-    "axes[1, 1].invert_yaxis()\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.savefig('plots/feature_importance_comparison.png', dpi=300, bbox_inches='tight')\n",
-    "plt.show()\n",
-    "\n",
-    "# Tổng hợp Top 20 Features chung\n",
-    "print(\"\\n\" + \"=\"*80)\n",
-    "print(\"TOP 20 FEATURES - RANDOM FOREST\")\n",
-    "print(\"=\"*80)\n",
-    "print(rf_importance.head(20).to_string(index=False))\n",
-    "\n",
-    "print(\"\\n\" + \"=\"*80)\n",
-    "print(\"TOP 20 FEATURES - GRADIENT BOOSTING\")\n",
-    "print(\"=\"*80)\n",
-    "print(gb_importance.head(20).to_string(index=False))\n",
-    "\n",
-    "print(\"\\n\" + \"=\"*80)\n",
-    "print(\"TOP 20 FEATURES - XGBOOST\")\n",
-    "print(\"=\"*80)\n",
-    "print(xgb_importance.head(20).to_string(index=False))\n",
-    "\n",
-    "print(\"\\n\" + \"=\"*80)\n",
-    "print(\"TOP 20 FEATURES - LIGHTGBM\")\n",
-    "print(\"=\"*80)\n",
-    "print(lgbm_importance.head(20).to_string(index=False))\n",
-    "\n",
-    "# Tính trung bình importance từ tất cả các models\n",
-    "all_importance = pd.DataFrame({\n",
-    "    'feature': feature_names,\n",
-    "    'rf_imp': models_dict['Random Forest'].feature_importances_,\n",
-    "    'gb_imp': models_dict['Gradient Boosting'].feature_importances_,\n",
-    "    'xgb_imp': models_dict['XGBoost'].feature_importances_,\n",
-    "    'lgbm_imp': models_dict['LightGBM'].feature_importances_\n",
-    "})\n",
-    "\n",
-    "all_importance['mean_importance'] = all_importance[['rf_imp', 'gb_imp', 'xgb_imp', 'lgbm_imp']].mean(axis=1)\n",
-    "all_importance = all_importance.sort_values('mean_importance', ascending=False)\n",
-    "\n",
-    "print(\"\\n\" + \"=\"*80)\n",
-    "print(\"TOP 20 FEATURES - TRUNG BÌNH TỪ TẤT CẢ MODELS\")\n",
-    "print(\"=\"*80)\n",
-    "print(all_importance[['feature', 'mean_importance']].head(20).to_string(index=False))\n",
-    "\n",
-    "# Visualize average importance\n",
-    "plt.figure(figsize=(12, 8))\n",
-    "plt.barh(range(20), all_importance.head(20)['mean_importance'])\n",
-    "plt.yticks(range(20), all_importance.head(20)['feature'])\n",
-    "plt.xlabel('Average Importance', fontsize=12)\n",
-    "plt.title('Top 20 Features - Average Importance Across All Models', fontsize=14)\n",
-    "plt.gca().invert_yaxis()\n",
-    "plt.tight_layout()\n",
-    "plt.savefig('plots/average_feature_importance.png', dpi=300, bbox_inches='tight')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d9691c38",
-   "metadata": {},
-   "source": [
-    "## 12. Đánh Giá Chi Tiết Trên Test Set\n",
-    "\n",
-    "Đánh giá hiệu suất của best model trên test set và phân tích các trường hợp dự đoán sai."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "e0e2e443",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "BEST MODEL: Gradient Boosting\n",
-      "================================================================================\n",
-      "\n",
-      "Test Set Performance:\n",
-      "  MAE:  2.0764\n",
-      "  MSE:  11.8555\n",
-      "  RMSE: 3.4432\n",
-      "  R²:   0.6402\n",
-      "\n",
-      "Comparison with Validation Set:\n",
-      "  Validation MAE: 3.0356 | Test MAE: 2.0764 | Diff: 0.9592\n",
-      "  Validation R²:  0.3981 | Test R²:  0.6402 | Diff: 0.2421\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x600 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "================================================================================\n",
-      "TOP 20 TRƯỜNG HỢP DỰ ĐOÁN SAI NHẤT\n",
-      "================================================================================\n",
-      "     actual  predicted      error  abs_error\n",
-      "133      16   3.233567  12.766433  12.766433\n",
-      "31        5  16.012736 -11.012736  11.012736\n",
-      "57        0   8.738258  -8.738258   8.738258\n",
-      "15       16   7.741727   8.258273   8.258273\n",
-      "56       26  18.073003   7.926997   7.926997\n",
-      "11       29  21.558546   7.441454   7.441454\n",
-      "76       13   7.052550   5.947450   5.947450\n",
-      "245      10   4.987020   5.012980   5.012980\n",
-      "441       1   5.795453  -4.795453   4.795453\n",
-      "369      20  15.401711   4.598289   4.598289\n",
-      "149       1   5.097772  -4.097772   4.097772\n",
-      "397       4   7.956151  -3.956151   3.956151\n",
-      "9         1   4.872358  -3.872358   3.872358\n",
-      "366       2   5.717268  -3.717268   3.717268\n",
-      "285       2   5.616980  -3.616980   3.616980\n",
-      "430       1   4.032095  -3.032095   3.032095\n",
-      "79       11   8.008475   2.991525   2.991525\n",
-      "91        3   5.866139  -2.866139   2.866139\n",
-      "77        2   4.692245  -2.692245   2.692245\n",
-      "447       1   2.888733  -1.888733   1.888733\n",
-      "\n",
-      "================================================================================\n",
-      "PHÂN TÍCH LỖI\n",
-      "================================================================================\n",
-      "Mean Absolute Error: 2.0764\n",
-      "Median Absolute Error: 0.9094\n",
-      "Max Absolute Error: 12.7664\n",
-      "Min Absolute Error: 0.0063\n",
-      "Std of Absolute Error: 2.7674\n",
-      "\n",
-      "Phân vị lỗi tuyệt đối:\n",
-      "  25th percentile: 0.2435\n",
-      "  50th percentile: 0.9094\n",
-      "  75th percentile: 2.9288\n",
-      "  90th percentile: 5.3868\n",
-      "  95th percentile: 8.1589\n",
-      "\n",
-      "✓ Kết quả test set đã được lưu vào 'data/test_set_predictions.csv'\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Chọn best model (theo MAE thấp nhất)\n",
-    "best_model_name = results_df.loc[results_df['Val_MAE'].idxmin(), 'Model']\n",
-    "best_model = models_dict[best_model_name]\n",
-    "\n",
-    "print(\"=\"*80)\n",
-    "print(f\"BEST MODEL: {best_model_name}\")\n",
-    "print(\"=\"*80)\n",
-    "\n",
-    "# Dự đoán trên test set\n",
-    "y_test_pred = best_model.predict(X_test_scaled)\n",
-    "\n",
-    "# Tính toán metrics trên test set\n",
-    "test_mae = mean_absolute_error(y_test, y_test_pred)\n",
-    "test_mse = mean_squared_error(y_test, y_test_pred)\n",
-    "test_rmse = np.sqrt(test_mse)\n",
-    "test_r2 = r2_score(y_test, y_test_pred)\n",
-    "\n",
-    "print(f\"\\nTest Set Performance:\")\n",
-    "print(f\"  MAE:  {test_mae:.4f}\")\n",
-    "print(f\"  MSE:  {test_mse:.4f}\")\n",
-    "print(f\"  RMSE: {test_rmse:.4f}\")\n",
-    "print(f\"  R²:   {test_r2:.4f}\")\n",
-    "\n",
-    "# So sánh với validation set\n",
-    "val_mae = results_df.loc[results_df['Model'] == best_model_name, 'Val_MAE'].values[0]\n",
-    "val_r2 = results_df.loc[results_df['Model'] == best_model_name, 'Val_R2'].values[0]\n",
-    "\n",
-    "print(f\"\\nComparison with Validation Set:\")\n",
-    "print(f\"  Validation MAE: {val_mae:.4f} | Test MAE: {test_mae:.4f} | Diff: {abs(test_mae - val_mae):.4f}\")\n",
-    "print(f\"  Validation R²:  {val_r2:.4f} | Test R²:  {test_r2:.4f} | Diff: {abs(test_r2 - val_r2):.4f}\")\n",
-    "\n",
-    "# Visualize predictions vs actual\n",
-    "fig, axes = plt.subplots(1, 3, figsize=(20, 6))\n",
-    "\n",
-    "# Scatter plot\n",
-    "axes[0].scatter(y_test, y_test_pred, alpha=0.5, edgecolors='k')\n",
-    "axes[0].plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=2)\n",
-    "axes[0].set_xlabel('Actual', fontsize=12)\n",
-    "axes[0].set_ylabel('Predicted', fontsize=12)\n",
-    "axes[0].set_title(f'{best_model_name} - Test Set\\nPredicted vs Actual', fontsize=14)\n",
-    "axes[0].grid(True, alpha=0.3)\n",
-    "\n",
-    "# Residuals plot\n",
-    "residuals = y_test - y_test_pred\n",
-    "axes[1].scatter(y_test_pred, residuals, alpha=0.5, edgecolors='k')\n",
-    "axes[1].axhline(y=0, color='r', linestyle='--', lw=2)\n",
-    "axes[1].set_xlabel('Predicted', fontsize=12)\n",
-    "axes[1].set_ylabel('Residuals (Actual - Predicted)', fontsize=12)\n",
-    "axes[1].set_title('Residual Plot', fontsize=14)\n",
-    "axes[1].grid(True, alpha=0.3)\n",
-    "\n",
-    "# Residuals distribution\n",
-    "axes[2].hist(residuals, bins=50, edgecolor='black', alpha=0.7)\n",
-    "axes[2].axvline(x=0, color='r', linestyle='--', lw=2)\n",
-    "axes[2].set_xlabel('Residuals', fontsize=12)\n",
-    "axes[2].set_ylabel('Frequency', fontsize=12)\n",
-    "axes[2].set_title('Distribution of Residuals', fontsize=14)\n",
-    "axes[2].grid(True, alpha=0.3)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.savefig('plots/test_set_evaluation.png', dpi=300, bbox_inches='tight')\n",
-    "plt.show()\n",
-    "\n",
-    "# Phân tích các trường hợp dự đoán sai nhất\n",
-    "test_results = pd.DataFrame({\n",
-    "    'actual': y_test,\n",
-    "    'predicted': y_test_pred,\n",
-    "    'error': residuals,\n",
-    "    'abs_error': np.abs(residuals)\n",
-    "})\n",
-    "test_results = test_results.sort_values('abs_error', ascending=False)\n",
-    "\n",
-    "print(\"\\n\" + \"=\"*80)\n",
-    "print(\"TOP 20 TRƯỜNG HỢP DỰ ĐOÁN SAI NHẤT\")\n",
-    "print(\"=\"*80)\n",
-    "print(test_results.head(20))\n",
-    "\n",
-    "print(\"\\n\" + \"=\"*80)\n",
-    "print(\"PHÂN TÍCH LỖI\")\n",
-    "print(\"=\"*80)\n",
-    "print(f\"Mean Absolute Error: {test_mae:.4f}\")\n",
-    "print(f\"Median Absolute Error: {np.median(test_results['abs_error']):.4f}\")\n",
-    "print(f\"Max Absolute Error: {test_results['abs_error'].max():.4f}\")\n",
-    "print(f\"Min Absolute Error: {test_results['abs_error'].min():.4f}\")\n",
-    "print(f\"Std of Absolute Error: {test_results['abs_error'].std():.4f}\")\n",
-    "\n",
-    "# Phân tích lỗi theo phân vị\n",
-    "print(f\"\\nPhân vị lỗi tuyệt đối:\")\n",
-    "print(f\"  25th percentile: {test_results['abs_error'].quantile(0.25):.4f}\")\n",
-    "print(f\"  50th percentile: {test_results['abs_error'].quantile(0.50):.4f}\")\n",
-    "print(f\"  75th percentile: {test_results['abs_error'].quantile(0.75):.4f}\")\n",
-    "print(f\"  90th percentile: {test_results['abs_error'].quantile(0.90):.4f}\")\n",
-    "print(f\"  95th percentile: {test_results['abs_error'].quantile(0.95):.4f}\")\n",
-    "\n",
-    "# Lưu kết quả test set\n",
-    "test_results.to_csv('data/test_set_predictions.csv', index=False)\n",
-    "print(\"\\n✓ Kết quả test set đã được lưu vào 'data/test_set_predictions.csv'\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "base",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/ML_Training_Pipeline_Complete.ipynb b/ML_Training_Pipeline_Complete.ipynb
deleted file mode 100644
index 51c5806..0000000
--- a/ML_Training_Pipeline_Complete.ipynb
+++ /dev/null
@@ -1,2224 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "1432970a",
-   "metadata": {},
-   "source": [
-    "# 🤖 ML Pipeline HOÀN CHỈNH: Dự Đoán Số Lượng Nhân Sự\n",
-    "\n",
-    "**Mục tiêu:** Dự đoán số lượng nhân sự (`so_luong`) cần thiết cho mỗi ca làm việc\n",
-    "\n",
-    "**Dataset:** FINAL_DATASET_WITH_TEXT.xlsx (454 rows × 51 columns)\n",
-    "\n",
-    "**Features:** 47 features (không bao gồm 2 cột text)\n",
-    "\n",
-    "**Target:** `so_luong` (0-64 nhân sự)\n",
-    "\n",
-    "---\n",
-    "\n",
-    "## 📋 Pipeline Hoàn Chỉnh:\n",
-    "\n",
-    "1. ✅ **Import Libraries & Load Data** - Thiết lập môi trường\n",
-    "2. ✅ **Data Cleaning & Preprocessing** - Làm sạch và chuẩn hóa dữ liệu\n",
-    "3. ✅ **Feature Engineering** - Tạo features mới\n",
-    "4. ✅ **Train/Val/Test Split** - Chia dữ liệu (60/20/20)\n",
-    "5. ✅ **K-Fold Cross-Validation** - Đánh giá độ ổn định model\n",
-    "6. ✅ **Feature Selection** - Tìm best features (RF + MI + Correlation)\n",
-    "7. ✅ **Final Training** - Train model với best features\n",
-    "8. ✅ **Test Evaluation** - Đánh giá trên test set\n",
-    "9. ✅ **Save Model** - Lưu model + artifacts\n",
-    "\n",
-    "---\n",
-    "\n",
-    "**Version:** 2.0 - Complete Pipeline  \n",
-    "**Date:** January 8, 2026"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "552b4010",
-   "metadata": {},
-   "source": [
-    "# 1️⃣ Import Libraries & Load Data\n",
-    "\n",
-    "**Mục đích:** Thiết lập môi trường, import các thư viện cần thiết, và load dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "id": "10157e9c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "✅ Libraries imported successfully!\n",
-      "📅 Date: 2026-01-08 17:52:31\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Import essential libraries\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "import warnings\n",
-    "import os\n",
-    "import pickle\n",
-    "import json\n",
-    "from datetime import datetime\n",
-    "\n",
-    "# Machine Learning libraries\n",
-    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, KFold\n",
-    "from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.tree import DecisionTreeRegressor\n",
-    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
-    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
-    "from sklearn.feature_selection import SelectKBest, mutual_info_regression, RFE\n",
-    "import xgboost as xgb\n",
-    "import lightgbm as lgb\n",
-    "\n",
-    "# Configuration\n",
-    "warnings.filterwarnings('ignore')\n",
-    "plt.style.use('seaborn-v0_8-darkgrid')\n",
-    "sns.set_palette(\"husl\")\n",
-    "pd.set_option('display.max_columns', None)\n",
-    "pd.set_option('display.max_rows', 100)\n",
-    "\n",
-    "# Random seed for reproducibility\n",
-    "RANDOM_STATE = 42\n",
-    "np.random.seed(RANDOM_STATE)\n",
-    "\n",
-    "# Create folders for outputs\n",
-    "os.makedirs('data/cleaned', exist_ok=True)\n",
-    "os.makedirs('data/splits', exist_ok=True)\n",
-    "os.makedirs('models', exist_ok=True)\n",
-    "os.makedirs('plots', exist_ok=True)\n",
-    "\n",
-    "print(\"✅ Libraries imported successfully!\")\n",
-    "print(f\"📅 Date: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "id": "8ecb54a2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "📂 Loading dataset...\n",
-      "✅ Dataset loaded successfully!\n",
-      "📊 Shape: (454, 51)\n",
-      "📋 Columns: ['ma_dia_diem', 'all_task_normal', 'all_task_dinhky', 'loai_ca', 'bat_dau', 'ket_thuc', 'tong_gio_lam', 'so_ca_cua_toa', 'so_luong', 'num_tasks']... (showing first 10)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ma_dia_diem</th>\n",
-       "      <th>all_task_normal</th>\n",
-       "      <th>all_task_dinhky</th>\n",
-       "      <th>loai_ca</th>\n",
-       "      <th>bat_dau</th>\n",
-       "      <th>ket_thuc</th>\n",
-       "      <th>tong_gio_lam</th>\n",
-       "      <th>so_ca_cua_toa</th>\n",
-       "      <th>so_luong</th>\n",
-       "      <th>num_tasks</th>\n",
-       "      <th>num_cleaning_tasks</th>\n",
-       "      <th>num_trash_collection_tasks</th>\n",
-       "      <th>num_monitoring_tasks</th>\n",
-       "      <th>num_room_cleaning_tasks</th>\n",
-       "      <th>num_deep_cleaning_tasks</th>\n",
-       "      <th>num_maintenance_tasks</th>\n",
-       "      <th>num_support_tasks</th>\n",
-       "      <th>num_other_tasks</th>\n",
-       "      <th>num_wc_tasks</th>\n",
-       "      <th>num_hallway_tasks</th>\n",
-       "      <th>num_lobby_tasks</th>\n",
-       "      <th>num_patient_room_tasks</th>\n",
-       "      <th>num_clinic_room_tasks</th>\n",
-       "      <th>num_surgery_room_tasks</th>\n",
-       "      <th>num_outdoor_tasks</th>\n",
-       "      <th>num_elevator_tasks</th>\n",
-       "      <th>num_office_tasks</th>\n",
-       "      <th>num_technical_room_tasks</th>\n",
-       "      <th>cleaning_ratio</th>\n",
-       "      <th>trash_collection_ratio</th>\n",
-       "      <th>monitoring_ratio</th>\n",
-       "      <th>room_cleaning_ratio</th>\n",
-       "      <th>area_diversity</th>\n",
-       "      <th>task_complexity_score</th>\n",
-       "      <th>loai_hinh</th>\n",
-       "      <th>ten_toa_thap</th>\n",
-       "      <th>muc_do_luu_luong</th>\n",
-       "      <th>so_tang</th>\n",
-       "      <th>so_cua_thang_may</th>\n",
-       "      <th>dien_tich_ngoai_canh</th>\n",
-       "      <th>dien_tich_sanh</th>\n",
-       "      <th>dien_tich_hanh_lang</th>\n",
-       "      <th>dien_tich_wc</th>\n",
-       "      <th>dien_tich_phong</th>\n",
-       "      <th>dien_tich_tham</th>\n",
-       "      <th>doc_ham</th>\n",
-       "      <th>vien_phan_quang</th>\n",
-       "      <th>op_tuong</th>\n",
-       "      <th>op_chan_tuong</th>\n",
-       "      <th>ranh_thoat_nuoc</th>\n",
-       "      <th>dien_tich_kinh</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>115-2</td>\n",
-       "      <td>Làm sạch toàn bộ phòng giao dịch tầng 1 (kể cả...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Part time</td>\n",
-       "      <td>06:30:00</td>\n",
-       "      <td>10:30:00</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0.1429</td>\n",
-       "      <td>0.2857</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1.38</td>\n",
-       "      <td>0</td>\n",
-       "      <td>AGRIBANK CHI NHÁNH MỸ ĐÌNH</td>\n",
-       "      <td>Trung bình (11–20 người)</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>290.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>20.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>115-5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Hành chính</td>\n",
-       "      <td>06:30:00</td>\n",
-       "      <td>16:00:00</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>AGRIBANK PGD SỐ 05</td>\n",
-       "      <td>Trung bình (11–20 người)</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>300.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>30.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>101-1</td>\n",
-       "      <td>Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...</td>\n",
-       "      <td>Lau bảng biển, bình cứu hỏa , cây nước hành la...</td>\n",
-       "      <td>Hành chính</td>\n",
-       "      <td>06:30:00</td>\n",
-       "      <td>16:00:00</td>\n",
-       "      <td>7.5</td>\n",
-       "      <td>6</td>\n",
-       "      <td>24</td>\n",
-       "      <td>441.0</td>\n",
-       "      <td>258.0</td>\n",
-       "      <td>145.0</td>\n",
-       "      <td>134.0</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>39.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>0.585</td>\n",
-       "      <td>0.3288</td>\n",
-       "      <td>0.3039</td>\n",
-       "      <td>0.1474</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>10.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Tòa 5 tầng</td>\n",
-       "      <td>Rất cao (Trên 40 người)</td>\n",
-       "      <td>10</td>\n",
-       "      <td>18</td>\n",
-       "      <td>1700.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2600.0</td>\n",
-       "      <td>348.0</td>\n",
-       "      <td>6825.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9176.0</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>25</td>\n",
-       "      <td>894.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>101-1</td>\n",
-       "      <td>Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...</td>\n",
-       "      <td>Lau bảng biển, bình cứu hỏa , cây nước hành la...</td>\n",
-       "      <td>Ca sáng</td>\n",
-       "      <td>06:00:00</td>\n",
-       "      <td>14:00:00</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>3</td>\n",
-       "      <td>441.0</td>\n",
-       "      <td>258.0</td>\n",
-       "      <td>145.0</td>\n",
-       "      <td>134.0</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>39.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>0.585</td>\n",
-       "      <td>0.3288</td>\n",
-       "      <td>0.3039</td>\n",
-       "      <td>0.1474</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>10.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Tòa 5 tầng</td>\n",
-       "      <td>Rất cao (Trên 40 người)</td>\n",
-       "      <td>10</td>\n",
-       "      <td>18</td>\n",
-       "      <td>1700.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2600.0</td>\n",
-       "      <td>348.0</td>\n",
-       "      <td>6825.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9176.0</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>25</td>\n",
-       "      <td>894.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>101-1</td>\n",
-       "      <td>Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...</td>\n",
-       "      <td>Lau bảng biển, bình cứu hỏa , cây nước hành la...</td>\n",
-       "      <td>Ca chiều</td>\n",
-       "      <td>14:00:00</td>\n",
-       "      <td>22:00:00</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>5</td>\n",
-       "      <td>441.0</td>\n",
-       "      <td>258.0</td>\n",
-       "      <td>145.0</td>\n",
-       "      <td>134.0</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>39.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>0.585</td>\n",
-       "      <td>0.3288</td>\n",
-       "      <td>0.3039</td>\n",
-       "      <td>0.1474</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>10.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Tòa 5 tầng</td>\n",
-       "      <td>Rất cao (Trên 40 người)</td>\n",
-       "      <td>10</td>\n",
-       "      <td>18</td>\n",
-       "      <td>1700.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2600.0</td>\n",
-       "      <td>348.0</td>\n",
-       "      <td>6825.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9176.0</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>25</td>\n",
-       "      <td>894.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  ma_dia_diem                                    all_task_normal  \\\n",
-       "0       115-2  Làm sạch toàn bộ phòng giao dịch tầng 1 (kể cả...   \n",
-       "1       115-5                                                NaN   \n",
-       "2       101-1  Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...   \n",
-       "3       101-1  Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...   \n",
-       "4       101-1  Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...   \n",
-       "\n",
-       "                                     all_task_dinhky     loai_ca   bat_dau  \\\n",
-       "0                                                NaN   Part time  06:30:00   \n",
-       "1                                                NaN  Hành chính  06:30:00   \n",
-       "2  Lau bảng biển, bình cứu hỏa , cây nước hành la...  Hành chính  06:30:00   \n",
-       "3  Lau bảng biển, bình cứu hỏa , cây nước hành la...     Ca sáng  06:00:00   \n",
-       "4  Lau bảng biển, bình cứu hỏa , cây nước hành la...    Ca chiều  14:00:00   \n",
-       "\n",
-       "   ket_thuc  tong_gio_lam  so_ca_cua_toa  so_luong  num_tasks  \\\n",
-       "0  10:30:00           4.0              1         1        7.0   \n",
-       "1  16:00:00           8.0              1         1        NaN   \n",
-       "2  16:00:00           7.5              6        24      441.0   \n",
-       "3  14:00:00           8.0              6         3      441.0   \n",
-       "4  22:00:00           8.0              6         5      441.0   \n",
-       "\n",
-       "   num_cleaning_tasks  num_trash_collection_tasks  num_monitoring_tasks  \\\n",
-       "0                 7.0                         1.0                   2.0   \n",
-       "1                 NaN                         NaN                   NaN   \n",
-       "2               258.0                       145.0                 134.0   \n",
-       "3               258.0                       145.0                 134.0   \n",
-       "4               258.0                       145.0                 134.0   \n",
-       "\n",
-       "   num_room_cleaning_tasks  num_deep_cleaning_tasks  num_maintenance_tasks  \\\n",
-       "0                      0.0                      1.0                    1.0   \n",
-       "1                      NaN                      NaN                    NaN   \n",
-       "2                     65.0                     75.0                   62.0   \n",
-       "3                     65.0                     75.0                   62.0   \n",
-       "4                     65.0                     75.0                   62.0   \n",
-       "\n",
-       "   num_support_tasks  num_other_tasks  num_wc_tasks  num_hallway_tasks  \\\n",
-       "0                0.0              0.0           4.0                0.0   \n",
-       "1                NaN              NaN           NaN                NaN   \n",
-       "2               57.0             45.0          89.0               90.0   \n",
-       "3               57.0             45.0          89.0               90.0   \n",
-       "4               57.0             45.0          89.0               90.0   \n",
-       "\n",
-       "   num_lobby_tasks  num_patient_room_tasks  num_clinic_room_tasks  \\\n",
-       "0              0.0                     0.0                    0.0   \n",
-       "1              NaN                     NaN                    NaN   \n",
-       "2              5.0                    41.0                   25.0   \n",
-       "3              5.0                    41.0                   25.0   \n",
-       "4              5.0                    41.0                   25.0   \n",
-       "\n",
-       "   num_surgery_room_tasks  num_outdoor_tasks  num_elevator_tasks  \\\n",
-       "0                     0.0                2.0                 3.0   \n",
-       "1                     NaN                NaN                 NaN   \n",
-       "2                    30.0                4.0                12.0   \n",
-       "3                    30.0                4.0                12.0   \n",
-       "4                    30.0                4.0                12.0   \n",
-       "\n",
-       "   num_office_tasks  num_technical_room_tasks  cleaning_ratio  \\\n",
-       "0               1.0                       0.0           1.000   \n",
-       "1               NaN                       NaN             NaN   \n",
-       "2              39.0                      16.0           0.585   \n",
-       "3              39.0                      16.0           0.585   \n",
-       "4              39.0                      16.0           0.585   \n",
-       "\n",
-       "   trash_collection_ratio  monitoring_ratio  room_cleaning_ratio  \\\n",
-       "0                  0.1429            0.2857               0.0000   \n",
-       "1                     NaN               NaN                  NaN   \n",
-       "2                  0.3288            0.3039               0.1474   \n",
-       "3                  0.3288            0.3039               0.1474   \n",
-       "4                  0.3288            0.3039               0.1474   \n",
-       "\n",
-       "   area_diversity  task_complexity_score  loai_hinh  \\\n",
-       "0             4.0                   1.38          0   \n",
-       "1             NaN                    NaN          0   \n",
-       "2            10.0                  10.00          0   \n",
-       "3            10.0                  10.00          0   \n",
-       "4            10.0                  10.00          0   \n",
-       "\n",
-       "                 ten_toa_thap          muc_do_luu_luong  so_tang  \\\n",
-       "0  AGRIBANK CHI NHÁNH MỸ ĐÌNH  Trung bình (11–20 người)        4   \n",
-       "1          AGRIBANK PGD SỐ 05  Trung bình (11–20 người)        1   \n",
-       "2                  Tòa 5 tầng   Rất cao (Trên 40 người)       10   \n",
-       "3                  Tòa 5 tầng   Rất cao (Trên 40 người)       10   \n",
-       "4                  Tòa 5 tầng   Rất cao (Trên 40 người)       10   \n",
-       "\n",
-       "   so_cua_thang_may  dien_tich_ngoai_canh  dien_tich_sanh  \\\n",
-       "0                 0                   0.0             0.0   \n",
-       "1                 0                   0.0             0.0   \n",
-       "2                18                1700.0             0.0   \n",
-       "3                18                1700.0             0.0   \n",
-       "4                18                1700.0             0.0   \n",
-       "\n",
-       "   dien_tich_hanh_lang  dien_tich_wc  dien_tich_phong  dien_tich_tham  \\\n",
-       "0                  0.0          15.0            290.0             0.0   \n",
-       "1                  0.0          15.0            300.0             0.0   \n",
-       "2               2600.0         348.0           6825.0             0.0   \n",
-       "3               2600.0         348.0           6825.0             0.0   \n",
-       "4               2600.0         348.0           6825.0             0.0   \n",
-       "\n",
-       "   doc_ham  vien_phan_quang  op_tuong  op_chan_tuong  ranh_thoat_nuoc  \\\n",
-       "0        0                0       0.0            0.0                0   \n",
-       "1        0                0       0.0            0.0                0   \n",
-       "2       70                0    9176.0           89.0               25   \n",
-       "3       70                0    9176.0           89.0               25   \n",
-       "4       70                0    9176.0           89.0               25   \n",
-       "\n",
-       "   dien_tich_kinh  \n",
-       "0            20.0  \n",
-       "1            30.0  \n",
-       "2           894.0  \n",
-       "3           894.0  \n",
-       "4           894.0  "
-      ]
-     },
-     "execution_count": 99,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Load dataset\n",
-    "print(\"📂 Loading dataset...\")\n",
-    "df = pd.read_excel('FINAL_DATASET_WITH_TEXT_BACKUP_20260105_213507.xlsx')\n",
-    "\n",
-    "print(f\"✅ Dataset loaded successfully!\")\n",
-    "print(f\"📊 Shape: {df.shape}\")\n",
-    "print(f\"📋 Columns: {df.columns.tolist()[:10]}... (showing first 10)\")\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9e49dd58",
-   "metadata": {},
-   "source": [
-    "# 2️⃣ Exploratory Data Analysis (EDA)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "id": "b277092d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "📊 EXPLORATORY DATA ANALYSIS\n",
-      "================================================================================\n",
-      "\n",
-      "1. DATA INFO:\n",
-      "   Shape: (454, 51)\n",
-      "   Memory: 9.10 MB\n",
-      "\n",
-      "2. DATA TYPES:\n",
-      "float64    35\n",
-      "object      8\n",
-      "int64       8\n",
-      "Name: count, dtype: int64\n",
-      "\n",
-      "3. MISSING VALUES:\n",
-      "                            Missing  Percentage\n",
-      "all_task_dinhky                 168       37.00\n",
-      "all_task_normal                  27        5.95\n",
-      "num_patient_room_tasks           22        4.85\n",
-      "area_diversity                   22        4.85\n",
-      "room_cleaning_ratio              22        4.85\n",
-      "monitoring_ratio                 22        4.85\n",
-      "trash_collection_ratio           22        4.85\n",
-      "cleaning_ratio                   22        4.85\n",
-      "num_technical_room_tasks         22        4.85\n",
-      "num_office_tasks                 22        4.85\n",
-      "num_elevator_tasks               22        4.85\n",
-      "num_outdoor_tasks                22        4.85\n",
-      "num_surgery_room_tasks           22        4.85\n",
-      "num_clinic_room_tasks            22        4.85\n",
-      "num_lobby_tasks                  22        4.85\n",
-      "num_hallway_tasks                22        4.85\n",
-      "num_wc_tasks                     22        4.85\n",
-      "num_other_tasks                  22        4.85\n",
-      "num_support_tasks                22        4.85\n",
-      "num_maintenance_tasks            22        4.85\n",
-      "num_deep_cleaning_tasks          22        4.85\n",
-      "num_room_cleaning_tasks          22        4.85\n",
-      "num_monitoring_tasks             22        4.85\n",
-      "num_trash_collection_tasks       22        4.85\n",
-      "num_cleaning_tasks               22        4.85\n",
-      "num_tasks                        22        4.85\n",
-      "task_complexity_score            22        4.85\n",
-      "\n",
-      "4. TARGET VARIABLE (so_luong):\n",
-      "count    454.000000\n",
-      "mean       4.484581\n",
-      "std        6.626822\n",
-      "min        0.000000\n",
-      "25%        1.000000\n",
-      "50%        2.000000\n",
-      "75%        5.000000\n",
-      "max       64.000000\n",
-      "Name: so_luong, dtype: float64\n",
-      "   Zero values: 16\n",
-      "   Skewness: 3.69\n",
-      "   Kurtosis: 19.63\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"=\"*80)\n",
-    "print(\"📊 EXPLORATORY DATA ANALYSIS\")\n",
-    "print(\"=\"*80)\n",
-    "\n",
-    "print(\"\\n1. DATA INFO:\")\n",
-    "print(f\"   Shape: {df.shape}\")\n",
-    "print(f\"   Memory: {df.memory_usage(deep=True).sum() / 1024**2:.2f} MB\")\n",
-    "\n",
-    "print(\"\\n2. DATA TYPES:\")\n",
-    "print(df.dtypes.value_counts())\n",
-    "\n",
-    "print(\"\\n3. MISSING VALUES:\")\n",
-    "missing = df.isnull().sum()\n",
-    "missing_pct = (missing / len(df) * 100).round(2)\n",
-    "missing_df = pd.DataFrame({\n",
-    "    'Missing': missing[missing > 0],\n",
-    "    'Percentage': missing_pct[missing > 0]\n",
-    "}).sort_values('Missing', ascending=False)\n",
-    "print(missing_df)\n",
-    "\n",
-    "print(\"\\n4. TARGET VARIABLE (so_luong):\")\n",
-    "print(df['so_luong'].describe())\n",
-    "print(f\"   Zero values: {(df['so_luong'] == 0).sum()}\")\n",
-    "print(f\"   Skewness: {df['so_luong'].skew():.2f}\")\n",
-    "print(f\"   Kurtosis: {df['so_luong'].kurt():.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "234dfda5",
-   "metadata": {},
-   "source": [
-    "# 4️⃣ Data Cleaning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "cda6f5fc",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "🧹 DATA CLEANING\n",
-      "================================================================================\n",
-      "\n",
-      "1. Handling missing values...\n",
-      "   Filled num_tasks with 0\n",
-      "   Filled num_cleaning_tasks with 0\n",
-      "   Filled num_trash_collection_tasks with 0\n",
-      "   Filled num_monitoring_tasks with 0\n",
-      "   Filled num_room_cleaning_tasks with 0\n",
-      "   Filled num_deep_cleaning_tasks with 0\n",
-      "   Filled num_maintenance_tasks with 0\n",
-      "   Filled num_support_tasks with 0\n",
-      "   Filled num_other_tasks with 0\n",
-      "   Filled num_wc_tasks with 0\n",
-      "   Filled num_hallway_tasks with 0\n",
-      "   Filled num_lobby_tasks with 0\n",
-      "   Filled num_patient_room_tasks with 0\n",
-      "   Filled num_clinic_room_tasks with 0\n",
-      "   Filled num_surgery_room_tasks with 0\n",
-      "   Filled num_outdoor_tasks with 0\n",
-      "   Filled num_elevator_tasks with 0\n",
-      "   Filled num_office_tasks with 0\n",
-      "   Filled num_technical_room_tasks with 0\n",
-      "   Filled cleaning_ratio with 0\n",
-      "   Filled trash_collection_ratio with 0\n",
-      "   Filled monitoring_ratio with 0\n",
-      "   Filled room_cleaning_ratio with 0\n",
-      "   Filled area_diversity with 0\n",
-      "   Filled task_complexity_score with 0\n",
-      "\n",
-      "2. Analyzing target outliers...\n",
-      "   Zero values: 16\n",
-      "   Decision: KEEP all values (including zeros)\n",
-      "\n",
-      "3. Checking duplicates...\n",
-      "   Duplicates found: 9\n",
-      "\n",
-      "4. Dropping unnecessary columns...\n",
-      "   Dropped: ['ma_dia_diem', 'all_task_normal', 'all_task_dinhky', 'ten_toa_thap']\n",
-      "\n",
-      "5. Validation after cleaning:\n",
-      "   Shape: (445, 47)\n",
-      "   Missing values: 0\n",
-      "\n",
-      "✅ Saved: data/cleaned/CLEANED_DATA.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"=\"*80)\n",
-    "print(\"🧹 DATA CLEANING\")\n",
-    "print(\"=\"*80)\n",
-    "\n",
-    "df_clean = df.copy()\n",
-    "\n",
-    "# 1. Handle missing values\n",
-    "print(\"\\n1. Handling missing values...\")\n",
-    "\n",
-    "# Task features: fill with 0\n",
-    "task_features = [col for col in df_clean.columns if col.startswith('num_') or col.endswith('_ratio') or col in ['area_diversity', 'task_complexity_score']]\n",
-    "for col in task_features:\n",
-    "    if df_clean[col].isnull().sum() > 0:\n",
-    "        df_clean[col] = df_clean[col].fillna(0)\n",
-    "        print(f\"   Filled {col} with 0\")\n",
-    "\n",
-    "# Building features: fill with median\n",
-    "building_features = [col for col in df_clean.columns if col.startswith('dien_tich_') or col in ['so_tang', 'so_cua_thang_may']]\n",
-    "for col in building_features:\n",
-    "    if df_clean[col].isnull().sum() > 0:\n",
-    "        median_val = df_clean[col].median()\n",
-    "        df_clean[col] = df_clean[col].fillna(median_val)\n",
-    "        print(f\"   Filled {col} with median={median_val}\")\n",
-    "\n",
-    "# Categorical: fill with mode\n",
-    "cat_features = ['loai_hinh', 'muc_do_luu_luong']\n",
-    "for col in cat_features:\n",
-    "    if col in df_clean.columns and df_clean[col].isnull().sum() > 0:\n",
-    "        mode_val = df_clean[col].mode()[0]\n",
-    "        df_clean[col] = df_clean[col].fillna(mode_val)\n",
-    "        print(f\"   Filled {col} with mode={mode_val}\")\n",
-    "\n",
-    "# 2. Handle outliers (keep all for now)\n",
-    "print(\"\\n2. Analyzing target outliers...\")\n",
-    "print(f\"   Zero values: {(df_clean['so_luong'] == 0).sum()}\")\n",
-    "print(\"   Decision: KEEP all values (including zeros)\")\n",
-    "\n",
-    "# 3. Remove duplicates\n",
-    "print(\"\\n3. Checking duplicates...\")\n",
-    "dups = df_clean.duplicated().sum()\n",
-    "print(f\"   Duplicates found: {dups}\")\n",
-    "if dups > 0:\n",
-    "    df_clean = df_clean.drop_duplicates()\n",
-    "\n",
-    "# 4. Drop unnecessary columns\n",
-    "print(\"\\n4. Dropping unnecessary columns...\")\n",
-    "cols_to_drop = ['ma_dia_diem', 'all_task_normal', 'all_task_dinhky', 'ten_toa_thap']\n",
-    "cols_to_drop = [col for col in cols_to_drop if col in df_clean.columns]\n",
-    "df_clean = df_clean.drop(columns=cols_to_drop)\n",
-    "print(f\"   Dropped: {cols_to_drop}\")\n",
-    "\n",
-    "# 5. Validate\n",
-    "print(\"\\n5. Validation after cleaning:\")\n",
-    "print(f\"   Shape: {df_clean.shape}\")\n",
-    "print(f\"   Missing values: {df_clean.isnull().sum().sum()}\")\n",
-    "\n",
-    "# Save\n",
-    "df_clean.to_csv('data/cleaned/CLEANED_DATA.csv', index=False)\n",
-    "print(\"\\n✅ Saved: data/cleaned/CLEANED_DATA.csv\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ff3cc30f",
-   "metadata": {},
-   "source": [
-    "# 5️⃣ Feature Engineering"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "id": "83c1bbba",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "⚙️ FEATURE ENGINEERING\n",
-      "================================================================================\n",
-      "\n",
-      "1. Creating time features...\n",
-      "   ✅ Created time features\n",
-      "\n",
-      "2. Creating interaction features...\n",
-      "   ✅ Created interaction features\n",
-      "\n",
-      "3. Creating aggregation features...\n",
-      "   ✅ Created aggregation features\n",
-      "\n",
-      "4. Encoding categorical features...\n",
-      "   ✅ One-hot encoded loai_ca\n",
-      "   ✅ Label encoded loai_hinh\n",
-      "   ✅ Label encoded muc_do_luu_luong\n",
-      "\n",
-      "✅ Feature engineering complete! Shape: (445, 64)\n",
-      "✅ Saved: data/cleaned/ENGINEERED_DATA.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"=\"*80)\n",
-    "print(\"⚙️ FEATURE ENGINEERING\")\n",
-    "print(\"=\"*80)\n",
-    "\n",
-    "df_eng = df_clean.copy()\n",
-    "\n",
-    "# A. TIME FEATURES\n",
-    "print(\"\\n1. Creating time features...\")\n",
-    "\n",
-    "def parse_time(time_val):\n",
-    "    if pd.isna(time_val):\n",
-    "        return 0\n",
-    "    \n",
-    "    # If it's already a datetime object\n",
-    "    if isinstance(time_val, pd.Timestamp) or hasattr(time_val, 'hour'):\n",
-    "        return time_val.hour\n",
-    "    \n",
-    "    # If it's a string\n",
-    "    if isinstance(time_val, str):\n",
-    "        time_val = time_val.strip()\n",
-    "        \n",
-    "        # Format: \"2025-01-01 22:00:00\" or \"2025-01-01 22\"\n",
-    "        if ' ' in time_val:\n",
-    "            # Split by space and get the time part\n",
-    "            parts = time_val.split(' ')\n",
-    "            if len(parts) >= 2:\n",
-    "                time_part = parts[1]  # Get time part after space\n",
-    "                if ':' in time_part:\n",
-    "                    return int(time_part.split(':')[0])\n",
-    "                else:\n",
-    "                    return int(time_part)\n",
-    "        \n",
-    "        # Format: \"22:00:00\" or \"22:00\"\n",
-    "        elif ':' in time_val:\n",
-    "            return int(time_val.split(':')[0])\n",
-    "        \n",
-    "        # Try to parse as integer\n",
-    "        try:\n",
-    "            return int(float(time_val))\n",
-    "        except:\n",
-    "            return 0\n",
-    "    \n",
-    "    # If it's a number\n",
-    "    if isinstance(time_val, (int, float)):\n",
-    "        return int(time_val) if not np.isnan(time_val) else 0\n",
-    "    \n",
-    "    return 0\n",
-    "\n",
-    "df_eng['hour_start'] = df_eng['bat_dau'].apply(parse_time)\n",
-    "df_eng['hour_end'] = df_eng['ket_thuc'].apply(parse_time)\n",
-    "\n",
-    "def parse_work_hours(time_str):\n",
-    "    if pd.isna(time_str):\n",
-    "        return 8.0\n",
-    "    if isinstance(time_str, str) and ':' in time_str:\n",
-    "        parts = time_str.split(':')\n",
-    "        return float(parts[0]) + float(parts[1])/60 if len(parts) > 1 else float(parts[0])\n",
-    "    return 8.0\n",
-    "\n",
-    "df_eng['work_hours_numeric'] = df_eng['tong_gio_lam'].apply(parse_work_hours)\n",
-    "\n",
-    "# Shift indicators\n",
-    "df_eng['is_morning_shift'] = ((df_eng['hour_start'] >= 6) & (df_eng['hour_start'] < 12)).astype(int)\n",
-    "df_eng['is_afternoon_shift'] = ((df_eng['hour_start'] >= 12) & (df_eng['hour_start'] < 18)).astype(int)\n",
-    "df_eng['is_evening_shift'] = ((df_eng['hour_start'] >= 18) & (df_eng['hour_start'] < 24)).astype(int)\n",
-    "df_eng['is_night_shift'] = ((df_eng['hour_start'] >= 0) & (df_eng['hour_start'] < 6)).astype(int)\n",
-    "\n",
-    "print(\"   ✅ Created time features\")\n",
-    "\n",
-    "# B. INTERACTION FEATURES\n",
-    "print(\"\\n2. Creating interaction features...\")\n",
-    "\n",
-    "df_eng['tasks_per_hour'] = df_eng['num_tasks'] / (df_eng['work_hours_numeric'] + 0.1)\n",
-    "df_eng['tasks_per_floor'] = df_eng['num_tasks'] / (df_eng['so_tang'] + 1)\n",
-    "df_eng['wc_per_floor'] = df_eng['num_wc_tasks'] / (df_eng['so_tang'] + 1)\n",
-    "df_eng['cleaning_workload'] = df_eng['num_cleaning_tasks'] * df_eng['area_diversity']\n",
-    "\n",
-    "print(\"   ✅ Created interaction features\")\n",
-    "\n",
-    "# C. AGGREGATION FEATURES\n",
-    "print(\"\\n3. Creating aggregation features...\")\n",
-    "\n",
-    "area_cols = [col for col in df_eng.columns if col.startswith('dien_tich_')]\n",
-    "df_eng['total_area'] = df_eng[area_cols].sum(axis=1)\n",
-    "df_eng['area_per_floor'] = df_eng['total_area'] / (df_eng['so_tang'] + 1)\n",
-    "\n",
-    "special_cols = ['num_patient_room_tasks', 'num_surgery_room_tasks', 'num_clinic_room_tasks']\n",
-    "df_eng['has_special_areas'] = (df_eng[special_cols].sum(axis=1) > 0).astype(int)\n",
-    "\n",
-    "print(\"   ✅ Created aggregation features\")\n",
-    "\n",
-    "# D. CATEGORICAL ENCODING\n",
-    "print(\"\\n4. Encoding categorical features...\")\n",
-    "\n",
-    "# One-hot for loai_ca\n",
-    "if 'loai_ca' in df_eng.columns:\n",
-    "    loai_ca_dummies = pd.get_dummies(df_eng['loai_ca'], prefix='loai_ca', drop_first=True)\n",
-    "    df_eng = pd.concat([df_eng, loai_ca_dummies], axis=1)\n",
-    "    df_eng = df_eng.drop('loai_ca', axis=1)\n",
-    "    print(f\"   ✅ One-hot encoded loai_ca\")\n",
-    "\n",
-    "# Label encoding for ordinal\n",
-    "ordinal_map = {\n",
-    "    'loai_hinh': {'Văn phòng': 0, 'Bệnh viện': 1, 'Trung tâm thương mại': 2, 'Khác': 3},\n",
-    "    'muc_do_luu_luong': {'Thấp': 0, 'Trung bình': 1, 'Cao': 2}\n",
-    "}\n",
-    "\n",
-    "for col, mapping in ordinal_map.items():\n",
-    "    if col in df_eng.columns:\n",
-    "        df_eng[col] = df_eng[col].map(mapping).fillna(0).astype(int)\n",
-    "        print(f\"   ✅ Label encoded {col}\")\n",
-    "\n",
-    "# Drop original time columns\n",
-    "time_cols = ['bat_dau', 'ket_thuc', 'tong_gio_lam']\n",
-    "df_eng = df_eng.drop(columns=[c for c in time_cols if c in df_eng.columns])\n",
-    "\n",
-    "print(f\"\\n✅ Feature engineering complete! Shape: {df_eng.shape}\")\n",
-    "\n",
-    "# Save\n",
-    "df_eng.to_csv('data/cleaned/ENGINEERED_DATA.csv', index=False)\n",
-    "print(\"✅ Saved: data/cleaned/ENGINEERED_DATA.csv\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a1031a3",
-   "metadata": {},
-   "source": [
-    "# 6️⃣ Train/Val/Test Split\n",
-    "\n",
-    "**Mục đích:** Chia dữ liệu thành 3 tập: Train (60%), Validation (20%), Test (20%)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "id": "5edc1e37",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "✂️ TRAIN/VAL/TEST SPLIT\n",
-      "================================================================================\n",
-      "\n",
-      "📊 Dataset:\n",
-      "   Total samples: 445\n",
-      "   Features: 63\n",
-      "   Target range: [0, 64]\n",
-      "\n",
-      "✅ Split completed:\n",
-      "   Train: 267 samples (60.0%)\n",
-      "   Val:   89 samples (20.0%)\n",
-      "   Test:  89 samples (20.0%)\n",
-      "\n",
-      "✅ Saved splits to data/splits/\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"=\"*80)\n",
-    "print(\"✂️ TRAIN/VAL/TEST SPLIT\")\n",
-    "print(\"=\"*80)\n",
-    "\n",
-    "# Separate features and target\n",
-    "y = df_eng['so_luong']\n",
-    "X = df_eng.drop('so_luong', axis=1)\n",
-    "\n",
-    "print(f\"\\n📊 Dataset:\")\n",
-    "print(f\"   Total samples: {len(X)}\")\n",
-    "print(f\"   Features: {X.shape[1]}\")\n",
-    "print(f\"   Target range: [{y.min()}, {y.max()}]\")\n",
-    "\n",
-    "# Split train+val (80%) and test (20%)\n",
-    "X_temp, X_test, y_temp, y_test = train_test_split(\n",
-    "    X, y, test_size=0.2, random_state=RANDOM_STATE\n",
-    ")\n",
-    "\n",
-    "# Split train (75% of 80% = 60%) and val (25% of 80% = 20%)\n",
-    "X_train, X_val, y_train, y_val = train_test_split(\n",
-    "    X_temp, y_temp, test_size=0.25, random_state=RANDOM_STATE\n",
-    ")\n",
-    "\n",
-    "print(f\"\\n✅ Split completed:\")\n",
-    "print(f\"   Train: {X_train.shape[0]} samples ({X_train.shape[0]/len(X)*100:.1f}%)\")\n",
-    "print(f\"   Val:   {X_val.shape[0]} samples ({X_val.shape[0]/len(X)*100:.1f}%)\")\n",
-    "print(f\"   Test:  {X_test.shape[0]} samples ({X_test.shape[0]/len(X)*100:.1f}%)\")\n",
-    "\n",
-    "# Save splits\n",
-    "X_train.to_csv('data/splits/X_train.csv', index=False)\n",
-    "X_val.to_csv('data/splits/X_val.csv', index=False)\n",
-    "X_test.to_csv('data/splits/X_test.csv', index=False)\n",
-    "y_train.to_csv('data/splits/y_train.csv', index=False)\n",
-    "y_val.to_csv('data/splits/y_val.csv', index=False)\n",
-    "y_test.to_csv('data/splits/y_test.csv', index=False)\n",
-    "\n",
-    "print(\"\\n✅ Saved splits to data/splits/\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8ed0b48c",
-   "metadata": {},
-   "source": [
-    "# 7️⃣ K-Fold Cross-Validation\n",
-    "\n",
-    "**Mục đích:** Đánh giá độ ổn định của các models bằng 5-Fold CV trên training set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "d1d120e4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "🔄 K-FOLD CROSS-VALIDATION\n",
-      "================================================================================\n",
-      "\n",
-      "📏 Preparing data for K-Fold CV...\n",
-      "   ⚠️  Dropping 14 non-numeric columns: ['loai_hinh', 'muc_do_luu_luong', 'is_morning_shift', 'is_afternoon_shift', 'is_evening_shift', 'is_night_shift', 'has_special_areas', 'loai_ca_24/24', 'loai_ca_Ca chiều', 'loai_ca_Ca gãy', 'loai_ca_Ca sáng', 'loai_ca_Ca đêm', 'loai_ca_Hành chính', 'loai_ca_Part time']\n",
-      "   Binary columns (no scaling): 0\n",
-      "   One-hot columns (no scaling): 0\n",
-      "   Numeric columns (will scale): 49\n",
-      "   ✅ Scaled 49 numeric features\n",
-      "\n",
-      "🤖 Running 5-Fold CV...\n",
-      "\n",
-      "   Testing Linear Regression...\n",
-      "      MAE: 3.7654 ± 0.8861\n",
-      "      R²:  -0.1780 ± 0.5737\n",
-      "\n",
-      "   Testing Decision Tree...\n",
-      "      MAE: 3.8242 ± 0.8261\n",
-      "      R²:  -0.7565 ± 1.4466\n",
-      "\n",
-      "   Testing Random Forest...\n",
-      "      MAE: 3.3523 ± 0.8554\n",
-      "      R²:  -0.0652 ± 0.3507\n",
-      "\n",
-      "   Testing Gradient Boosting...\n",
-      "      MAE: 3.3027 ± 0.8887\n",
-      "      R²:  -0.1023 ± 0.5154\n",
-      "\n",
-      "   Testing XGBoost...\n",
-      "      MAE: 3.3237 ± 0.7237\n",
-      "      R²:  -0.1420 ± 0.5722\n",
-      "\n",
-      "   Testing LightGBM...\n",
-      "      MAE: 3.4016 ± 0.8549\n",
-      "      R²:  -0.0064 ± 0.2486\n",
-      "\n",
-      "================================================================================\n",
-      "📊 K-FOLD CV RESULTS SUMMARY\n",
-      "================================================================================\n",
-      "            Model  Mean_MAE  Std_MAE   Mean_R2   Std_R2\n",
-      "Gradient Boosting  3.302737 0.888736 -0.102337 0.515448\n",
-      "          XGBoost  3.323695 0.723653 -0.142027 0.572242\n",
-      "    Random Forest  3.352265 0.855390 -0.065234 0.350683\n",
-      "         LightGBM  3.401601 0.854854 -0.006448 0.248615\n",
-      "Linear Regression  3.765361 0.886077 -0.177970 0.573717\n",
-      "    Decision Tree  3.824216 0.826131 -0.756537 1.446579\n",
-      "\n",
-      "🏆 Best Model by K-Fold CV: Gradient Boosting\n",
-      "   MAE: 3.3027 ± 0.8887\n",
-      "   R²:  -0.1023 ± 0.5154\n",
-      "\n",
-      "✅ Saved: models/kfold_cv_results.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"=\"*80)\n",
-    "print(\"🔄 K-FOLD CROSS-VALIDATION\")\n",
-    "print(\"=\"*80)\n",
-    "\n",
-    "# Prepare data for K-Fold CV\n",
-    "print(\"\\n📏 Preparing data for K-Fold CV...\")\n",
-    "\n",
-    "# Check for non-numeric columns\n",
-    "non_numeric_cols = X_train.select_dtypes(exclude=['int64', 'float64']).columns.tolist()\n",
-    "if non_numeric_cols:\n",
-    "    print(f\"   ⚠️  Dropping {len(non_numeric_cols)} non-numeric columns: {non_numeric_cols}\")\n",
-    "    X_train_cv = X_train.drop(columns=non_numeric_cols).copy()\n",
-    "else:\n",
-    "    X_train_cv = X_train.copy()\n",
-    "\n",
-    "# Identify columns to scale\n",
-    "binary_cols = [col for col in X_train_cv.columns if X_train_cv[col].nunique() == 2 and set(X_train_cv[col].unique()).issubset({0, 1})]\n",
-    "onehot_cols = [col for col in X_train_cv.columns if col.startswith('loai_ca_')]\n",
-    "cols_to_scale = [col for col in X_train_cv.columns if col not in binary_cols and col not in onehot_cols]\n",
-    "\n",
-    "print(f\"   Binary columns (no scaling): {len(binary_cols)}\")\n",
-    "print(f\"   One-hot columns (no scaling): {len(onehot_cols)}\")\n",
-    "print(f\"   Numeric columns (will scale): {len(cols_to_scale)}\")\n",
-    "\n",
-    "# Scale numeric columns\n",
-    "scaler_cv = StandardScaler()\n",
-    "if cols_to_scale:\n",
-    "    X_train_cv[cols_to_scale] = scaler_cv.fit_transform(X_train_cv[cols_to_scale])\n",
-    "    print(f\"   ✅ Scaled {len(cols_to_scale)} numeric features\")\n",
-    "\n",
-    "# Initialize KFold\n",
-    "kf = KFold(n_splits=5, shuffle=True, random_state=RANDOM_STATE)\n",
-    "\n",
-    "# Models to test\n",
-    "cv_models = {\n",
-    "    'Linear Regression': LinearRegression(),\n",
-    "    'Decision Tree': DecisionTreeRegressor(max_depth=10, min_samples_split=5, random_state=RANDOM_STATE),\n",
-    "    'Random Forest': RandomForestRegressor(n_estimators=100, max_depth=15, min_samples_split=5, random_state=RANDOM_STATE, n_jobs=-1),\n",
-    "    'Gradient Boosting': GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, max_depth=5, random_state=RANDOM_STATE),\n",
-    "    'XGBoost': xgb.XGBRegressor(n_estimators=100, learning_rate=0.1, max_depth=5, random_state=RANDOM_STATE, n_jobs=-1),\n",
-    "    'LightGBM': lgb.LGBMRegressor(n_estimators=100, learning_rate=0.1, max_depth=5, num_leaves=31, random_state=RANDOM_STATE, n_jobs=-1, verbose=-1)\n",
-    "}\n",
-    "\n",
-    "# Run K-Fold CV\n",
-    "cv_results = []\n",
-    "print(\"\\n🤖 Running 5-Fold CV...\")\n",
-    "\n",
-    "for model_name, model in cv_models.items():\n",
-    "    print(f\"\\n   Testing {model_name}...\")\n",
-    "    \n",
-    "    fold_maes = []\n",
-    "    fold_r2s = []\n",
-    "    \n",
-    "    for fold, (train_idx, val_idx) in enumerate(kf.split(X_train_cv), 1):\n",
-    "        X_fold_train = X_train_cv.iloc[train_idx]\n",
-    "        y_fold_train = y_train.iloc[train_idx]\n",
-    "        X_fold_val = X_train_cv.iloc[val_idx]\n",
-    "        y_fold_val = y_train.iloc[val_idx]\n",
-    "        \n",
-    "        model.fit(X_fold_train, y_fold_train)\n",
-    "        y_pred = model.predict(X_fold_val)\n",
-    "        \n",
-    "        mae = mean_absolute_error(y_fold_val, y_pred)\n",
-    "        r2 = r2_score(y_fold_val, y_pred)\n",
-    "        \n",
-    "        fold_maes.append(mae)\n",
-    "        fold_r2s.append(r2)\n",
-    "    \n",
-    "    mean_mae = np.mean(fold_maes)\n",
-    "    std_mae = np.std(fold_maes)\n",
-    "    mean_r2 = np.mean(fold_r2s)\n",
-    "    std_r2 = np.std(fold_r2s)\n",
-    "    \n",
-    "    cv_results.append({\n",
-    "        'Model': model_name,\n",
-    "        'Mean_MAE': mean_mae,\n",
-    "        'Std_MAE': std_mae,\n",
-    "        'Mean_R2': mean_r2,\n",
-    "        'Std_R2': std_r2\n",
-    "    })\n",
-    "    \n",
-    "    print(f\"      MAE: {mean_mae:.4f} ± {std_mae:.4f}\")\n",
-    "    print(f\"      R²:  {mean_r2:.4f} ± {std_r2:.4f}\")\n",
-    "\n",
-    "# Create results DataFrame\n",
-    "cv_results_df = pd.DataFrame(cv_results).sort_values('Mean_MAE')\n",
-    "\n",
-    "print(\"\\n\" + \"=\"*80)\n",
-    "print(\"📊 K-FOLD CV RESULTS SUMMARY\")\n",
-    "print(\"=\"*80)\n",
-    "print(cv_results_df.to_string(index=False))\n",
-    "\n",
-    "best_cv_model = cv_results_df.iloc[0]['Model']\n",
-    "print(f\"\\n🏆 Best Model by K-Fold CV: {best_cv_model}\")\n",
-    "print(f\"   MAE: {cv_results_df.iloc[0]['Mean_MAE']:.4f} ± {cv_results_df.iloc[0]['Std_MAE']:.4f}\")\n",
-    "print(f\"   R²:  {cv_results_df.iloc[0]['Mean_R2']:.4f} ± {cv_results_df.iloc[0]['Std_R2']:.4f}\")\n",
-    "\n",
-    "# Save results\n",
-    "cv_results_df.to_csv('models/kfold_cv_results.csv', index=False)\n",
-    "print(\"\\n✅ Saved: models/kfold_cv_results.csv\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d69b6c4e",
-   "metadata": {},
-   "source": [
-    "# 8️⃣ Feature Selection\n",
-    "\n",
-    "**Mục đích:** Tìm best features sử dụng 3 phương pháp: Random Forest, Mutual Information, Correlation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "id": "e017e7db",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "🔬 FEATURE SELECTION\n",
-      "================================================================================\n",
-      "\n",
-      "📏 Preparing data for feature selection...\n",
-      "   Dropping 14 non-numeric columns: ['loai_hinh', 'muc_do_luu_luong', 'is_morning_shift', 'is_afternoon_shift', 'is_evening_shift', 'is_night_shift', 'has_special_areas', 'loai_ca_24/24', 'loai_ca_Ca chiều', 'loai_ca_Ca gãy', 'loai_ca_Ca sáng', 'loai_ca_Ca đêm', 'loai_ca_Hành chính', 'loai_ca_Part time']\n",
-      "   ✅ Scaled 49 numeric features\n",
-      "\n",
-      "🌲 Method 1: Random Forest Feature Importance\n",
-      "   Top 10 features:\n",
-      "      hour_end                                : 0.1723\n",
-      "      hour_start                              : 0.0724\n",
-      "      num_other_tasks                         : 0.0590\n",
-      "      num_trash_collection_tasks              : 0.0461\n",
-      "      tasks_per_hour                          : 0.0430\n",
-      "      cleaning_ratio                          : 0.0391\n",
-      "      tasks_per_floor                         : 0.0354\n",
-      "      dien_tich_wc                            : 0.0352\n",
-      "      num_tasks                               : 0.0332\n",
-      "      total_area                              : 0.0308\n",
-      "\n",
-      "📊 Method 2: Mutual Information\n",
-      "   Top 10 features:\n",
-      "      cleaning_workload                       : 0.2850\n",
-      "      num_cleaning_tasks                      : 0.2775\n",
-      "      num_trash_collection_tasks              : 0.2717\n",
-      "      tasks_per_hour                          : 0.2396\n",
-      "      num_tasks                               : 0.2319\n",
-      "      num_other_tasks                         : 0.2167\n",
-      "      task_complexity_score                   : 0.2143\n",
-      "      hour_start                              : 0.2017\n",
-      "      total_area                              : 0.1944\n",
-      "      num_support_tasks                       : 0.1777\n",
-      "\n",
-      "📈 Method 3: Correlation with Target\n",
-      "   Top 10 features:\n",
-      "      num_trash_collection_tasks              : 0.3996\n",
-      "      tasks_per_hour                          : 0.3622\n",
-      "      num_tasks                               : 0.3622\n",
-      "      cleaning_workload                       : 0.3603\n",
-      "      num_cleaning_tasks                      : 0.3483\n",
-      "      num_monitoring_tasks                    : 0.3214\n",
-      "      num_technical_room_tasks                : 0.3189\n",
-      "      num_support_tasks                       : 0.3113\n",
-      "      task_complexity_score                   : 0.3106\n",
-      "      num_other_tasks                         : 0.3059\n",
-      "\n",
-      "🎯 Aggregating Feature Scores...\n",
-      "\n",
-      "📋 Top 20 Features (by average score):\n",
-      "   dien_tich_phong                         : 0.7402\n",
-      "   tasks_per_hour                          : 0.6650\n",
-      "   num_trash_collection_tasks              : 0.6648\n",
-      "   num_surgery_room_tasks                  : 0.6385\n",
-      "   dien_tich_hanh_lang                     : 0.6369\n",
-      "   area_diversity                          : 0.6214\n",
-      "   num_lobby_tasks                         : 0.6026\n",
-      "   hour_end                                : 0.5416\n",
-      "   dien_tich_ngoai_canh                    : 0.4777\n",
-      "   monitoring_ratio                        : 0.4716\n",
-      "   num_patient_room_tasks                  : 0.4620\n",
-      "   num_monitoring_tasks                    : 0.4545\n",
-      "   tasks_per_floor                         : 0.4460\n",
-      "   work_hours_numeric                      : 0.4020\n",
-      "   trash_collection_ratio                  : 0.3944\n",
-      "   dien_tich_tham                          : 0.3927\n",
-      "   so_tang                                 : 0.3817\n",
-      "   so_ca_cua_toa                           : 0.3730\n",
-      "   dien_tich_wc                            : 0.3634\n",
-      "   num_wc_tasks                            : 0.3597\n",
-      "\n",
-      "================================================================================\n",
-      "🔬 TESTING WITH DIFFERENT NUMBERS OF FEATURES\n",
-      "================================================================================\n",
-      "\n",
-      "📌 BASELINE (All 49 Features):\n",
-      "   Val MAE: 3.6346, R²: -0.0129\n",
-      "\n",
-      "================================================================================\n",
-      "🔬 Testing with Top 20 Features\n",
-      "================================================================================\n",
-      "\n",
-      "📋 Selected features:\n",
-      "    1. dien_tich_phong                          (score: 0.7402)\n",
-      "    2. tasks_per_hour                           (score: 0.6650)\n",
-      "    3. num_trash_collection_tasks               (score: 0.6648)\n",
-      "    4. num_surgery_room_tasks                   (score: 0.6385)\n",
-      "    5. dien_tich_hanh_lang                      (score: 0.6369)\n",
-      "    6. area_diversity                           (score: 0.6214)\n",
-      "    7. num_lobby_tasks                          (score: 0.6026)\n",
-      "    8. hour_end                                 (score: 0.5416)\n",
-      "    9. dien_tich_ngoai_canh                     (score: 0.4777)\n",
-      "   10. monitoring_ratio                         (score: 0.4716)\n",
-      "   ... and 10 more\n",
-      "\n",
-      "🤖 Training models...\n",
-      "   Random Forest       : MAE=3.6661 (-0.0315), R²=0.0374\n",
-      "   Gradient Boosting   : MAE=3.7772 (-0.1426), R²=-0.0601\n",
-      "   XGBoost             : MAE=3.6416 (-0.0071), R²=0.0158\n",
-      "   LightGBM            : MAE=3.7484 (-0.1138), R²=0.0495\n",
-      "\n",
-      "================================================================================\n",
-      "🔬 Testing with Top 25 Features\n",
-      "================================================================================\n",
-      "\n",
-      "📋 Selected features:\n",
-      "    1. dien_tich_phong                          (score: 0.7402)\n",
-      "    2. tasks_per_hour                           (score: 0.6650)\n",
-      "    3. num_trash_collection_tasks               (score: 0.6648)\n",
-      "    4. num_surgery_room_tasks                   (score: 0.6385)\n",
-      "    5. dien_tich_hanh_lang                      (score: 0.6369)\n",
-      "    6. area_diversity                           (score: 0.6214)\n",
-      "    7. num_lobby_tasks                          (score: 0.6026)\n",
-      "    8. hour_end                                 (score: 0.5416)\n",
-      "    9. dien_tich_ngoai_canh                     (score: 0.4777)\n",
-      "   10. monitoring_ratio                         (score: 0.4716)\n",
-      "   ... and 15 more\n",
-      "\n",
-      "🤖 Training models...\n",
-      "   Random Forest       : MAE=3.6149 (+0.0197), R²=0.0681\n",
-      "   Gradient Boosting   : MAE=3.7803 (-0.1457), R²=-0.0520\n",
-      "   XGBoost             : MAE=3.7601 (-0.1255), R²=-0.0180\n",
-      "   LightGBM            : MAE=3.7434 (-0.1088), R²=0.0423\n",
-      "\n",
-      "================================================================================\n",
-      "🔬 Testing with Top 30 Features\n",
-      "================================================================================\n",
-      "\n",
-      "📋 Selected features:\n",
-      "    1. dien_tich_phong                          (score: 0.7402)\n",
-      "    2. tasks_per_hour                           (score: 0.6650)\n",
-      "    3. num_trash_collection_tasks               (score: 0.6648)\n",
-      "    4. num_surgery_room_tasks                   (score: 0.6385)\n",
-      "    5. dien_tich_hanh_lang                      (score: 0.6369)\n",
-      "    6. area_diversity                           (score: 0.6214)\n",
-      "    7. num_lobby_tasks                          (score: 0.6026)\n",
-      "    8. hour_end                                 (score: 0.5416)\n",
-      "    9. dien_tich_ngoai_canh                     (score: 0.4777)\n",
-      "   10. monitoring_ratio                         (score: 0.4716)\n",
-      "   ... and 20 more\n",
-      "\n",
-      "🤖 Training models...\n",
-      "   Random Forest       : MAE=3.6134 (+0.0212), R²=0.0712\n",
-      "   Gradient Boosting   : MAE=3.8167 (-0.1821), R²=-0.0845\n",
-      "   XGBoost             : MAE=3.8264 (-0.1918), R²=-0.0368\n",
-      "   LightGBM            : MAE=3.6914 (-0.0568), R²=0.0629\n",
-      "\n",
-      "================================================================================\n",
-      "🔬 Testing with Top 35 Features\n",
-      "================================================================================\n",
-      "\n",
-      "📋 Selected features:\n",
-      "    1. dien_tich_phong                          (score: 0.7402)\n",
-      "    2. tasks_per_hour                           (score: 0.6650)\n",
-      "    3. num_trash_collection_tasks               (score: 0.6648)\n",
-      "    4. num_surgery_room_tasks                   (score: 0.6385)\n",
-      "    5. dien_tich_hanh_lang                      (score: 0.6369)\n",
-      "    6. area_diversity                           (score: 0.6214)\n",
-      "    7. num_lobby_tasks                          (score: 0.6026)\n",
-      "    8. hour_end                                 (score: 0.5416)\n",
-      "    9. dien_tich_ngoai_canh                     (score: 0.4777)\n",
-      "   10. monitoring_ratio                         (score: 0.4716)\n",
-      "   ... and 25 more\n",
-      "\n",
-      "🤖 Training models...\n",
-      "   Random Forest       : MAE=3.6404 (-0.0058), R²=0.0730\n",
-      "   Gradient Boosting   : MAE=3.9151 (-0.2805), R²=-0.1125\n",
-      "   XGBoost             : MAE=3.8845 (-0.2499), R²=-0.0521\n",
-      "   LightGBM            : MAE=3.8490 (-0.2144), R²=0.0009\n",
-      "\n",
-      "================================================================================\n",
-      "🔬 Testing with Top 40 Features\n",
-      "================================================================================\n",
-      "\n",
-      "📋 Selected features:\n",
-      "    1. dien_tich_phong                          (score: 0.7402)\n",
-      "    2. tasks_per_hour                           (score: 0.6650)\n",
-      "    3. num_trash_collection_tasks               (score: 0.6648)\n",
-      "    4. num_surgery_room_tasks                   (score: 0.6385)\n",
-      "    5. dien_tich_hanh_lang                      (score: 0.6369)\n",
-      "    6. area_diversity                           (score: 0.6214)\n",
-      "    7. num_lobby_tasks                          (score: 0.6026)\n",
-      "    8. hour_end                                 (score: 0.5416)\n",
-      "    9. dien_tich_ngoai_canh                     (score: 0.4777)\n",
-      "   10. monitoring_ratio                         (score: 0.4716)\n",
-      "   ... and 30 more\n",
-      "\n",
-      "🤖 Training models...\n",
-      "   Random Forest       : MAE=3.6265 (+0.0081), R²=0.0785\n",
-      "   Gradient Boosting   : MAE=3.8842 (-0.2496), R²=-0.0892\n",
-      "   XGBoost             : MAE=3.9312 (-0.2966), R²=-0.0725\n",
-      "   LightGBM            : MAE=3.7473 (-0.1127), R²=0.0294\n",
-      "\n",
-      "================================================================================\n",
-      "🏆 BEST FEATURE SELECTION RESULT\n",
-      "================================================================================\n",
-      "   🤖 Model: Random Forest\n",
-      "   📋 Features: 30 selected\n",
-      "\n",
-      "   📈 Best Model Performance:\n",
-      "      Val MAE: 3.6134\n",
-      "      Val R²:  0.0712\n",
-      "\n",
-      "   📊 Comparison with Baseline:\n",
-      "      Baseline MAE: 3.6346 | R²: -0.0129\n",
-      "      Best MAE:     3.6134 | R²: 0.0712\n",
-      "      MAE Improvement: 0.0212 (0.58%)\n",
-      "      R² Improvement:  0.0842 (+8.42%)\n",
-      "\n",
-      "✅ Saved: models/feature_selection_results.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"=\"*80)\n",
-    "print(\"🔬 FEATURE SELECTION\")\n",
-    "print(\"=\"*80)\n",
-    "\n",
-    "# Prepare data\n",
-    "print(\"\\n📏 Preparing data for feature selection...\")\n",
-    "\n",
-    "# Drop non-numeric columns\n",
-    "non_numeric_cols_fs = X_train.select_dtypes(exclude=['int64', 'float64']).columns.tolist()\n",
-    "if non_numeric_cols_fs:\n",
-    "    print(f\"   Dropping {len(non_numeric_cols_fs)} non-numeric columns: {non_numeric_cols_fs}\")\n",
-    "    X_train_fs = X_train.drop(columns=non_numeric_cols_fs).copy()\n",
-    "    X_val_fs_temp = X_val.drop(columns=non_numeric_cols_fs).copy()\n",
-    "else:\n",
-    "    X_train_fs = X_train.copy()\n",
-    "    X_val_fs_temp = X_val.copy()\n",
-    "\n",
-    "# Identify columns to scale\n",
-    "binary_cols_fs = [col for col in X_train_fs.columns if X_train_fs[col].nunique() == 2 and set(X_train_fs[col].unique()).issubset({0, 1})]\n",
-    "onehot_cols_fs = [col for col in X_train_fs.columns if col.startswith('loai_ca_')]\n",
-    "cols_to_scale_fs = [col for col in X_train_fs.columns if col not in binary_cols_fs and col not in onehot_cols_fs]\n",
-    "\n",
-    "# Scale\n",
-    "scaler_fs = StandardScaler()\n",
-    "if cols_to_scale_fs:\n",
-    "    X_train_fs[cols_to_scale_fs] = scaler_fs.fit_transform(X_train_fs[cols_to_scale_fs])\n",
-    "    X_val_fs_temp[cols_to_scale_fs] = scaler_fs.transform(X_val_fs_temp[cols_to_scale_fs])\n",
-    "    print(f\"   ✅ Scaled {len(cols_to_scale_fs)} numeric features\")\n",
-    "\n",
-    "# Method 1: Random Forest Feature Importance\n",
-    "print(\"\\n🌲 Method 1: Random Forest Feature Importance\")\n",
-    "rf_selector = RandomForestRegressor(n_estimators=100, max_depth=15, random_state=RANDOM_STATE, n_jobs=-1)\n",
-    "rf_selector.fit(X_train_fs, y_train)\n",
-    "\n",
-    "rf_importance_df = pd.DataFrame({\n",
-    "    'feature': X_train_fs.columns,\n",
-    "    'importance': rf_selector.feature_importances_\n",
-    "}).sort_values('importance', ascending=False)\n",
-    "\n",
-    "print(f\"   Top 10 features:\")\n",
-    "for i, row in rf_importance_df.head(10).iterrows():\n",
-    "    print(f\"      {row['feature']:40s}: {row['importance']:.4f}\")\n",
-    "\n",
-    "# Method 2: Mutual Information\n",
-    "print(\"\\n📊 Method 2: Mutual Information\")\n",
-    "mi_scores = mutual_info_regression(X_train_fs, y_train, random_state=RANDOM_STATE)\n",
-    "mi_importance_df = pd.DataFrame({\n",
-    "    'feature': X_train_fs.columns,\n",
-    "    'importance': mi_scores\n",
-    "}).sort_values('importance', ascending=False)\n",
-    "\n",
-    "print(f\"   Top 10 features:\")\n",
-    "for i, row in mi_importance_df.head(10).iterrows():\n",
-    "    print(f\"      {row['feature']:40s}: {row['importance']:.4f}\")\n",
-    "\n",
-    "# Method 3: Correlation with target\n",
-    "print(\"\\n📈 Method 3: Correlation with Target\")\n",
-    "correlations = X_train_fs.corrwith(y_train).abs().sort_values(ascending=False)\n",
-    "corr_df = pd.DataFrame({\n",
-    "    'feature': correlations.index,\n",
-    "    'importance': correlations.values\n",
-    "})\n",
-    "\n",
-    "print(f\"   Top 10 features:\")\n",
-    "for i, row in corr_df.head(10).iterrows():\n",
-    "    print(f\"      {row['feature']:40s}: {row['importance']:.4f}\")\n",
-    "\n",
-    "# Aggregate scores\n",
-    "print(\"\\n🎯 Aggregating Feature Scores...\")\n",
-    "aggregated = pd.DataFrame({\n",
-    "    'feature': X_train_fs.columns,\n",
-    "    'rf_score': rf_importance_df.set_index('feature')['importance'],\n",
-    "    'mi_score': mi_importance_df.set_index('feature')['importance'],\n",
-    "    'corr_score': corr_df.set_index('feature')['importance']\n",
-    "})\n",
-    "\n",
-    "# Normalize scores to [0,1]\n",
-    "for col in ['rf_score', 'mi_score', 'corr_score']:\n",
-    "    aggregated[col] = (aggregated[col] - aggregated[col].min()) / (aggregated[col].max() - aggregated[col].min())\n",
-    "\n",
-    "aggregated['avg_score'] = aggregated[['rf_score', 'mi_score', 'corr_score']].mean(axis=1)\n",
-    "aggregated = aggregated.sort_values('avg_score', ascending=False)\n",
-    "\n",
-    "print(f\"\\n📋 Top 20 Features (by average score):\")\n",
-    "for i, row in aggregated.head(20).iterrows():\n",
-    "    print(f\"   {row['feature']:40s}: {row['avg_score']:.4f}\")\n",
-    "\n",
-    "# Test with different numbers of features\n",
-    "print(\"\\n\" + \"=\"*80)\n",
-    "print(\"🔬 TESTING WITH DIFFERENT NUMBERS OF FEATURES\")\n",
-    "print(\"=\"*80)\n",
-    "\n",
-    "X_val_fs = X_val_fs_temp.copy()\n",
-    "\n",
-    "# Test feature counts\n",
-    "top_k_values = [20, 25, 30, 35, 40, 50]\n",
-    "feature_selection_results = []\n",
-    "\n",
-    "# Baseline: all features\n",
-    "baseline_model = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, max_depth=5, random_state=RANDOM_STATE)\n",
-    "baseline_model.fit(X_train_fs, y_train)\n",
-    "baseline_pred = baseline_model.predict(X_val_fs)\n",
-    "baseline_mae = mean_absolute_error(y_val, baseline_pred)\n",
-    "baseline_r2 = r2_score(y_val, baseline_pred)\n",
-    "\n",
-    "feature_selection_results.append({\n",
-    "    'num_features': X_train_fs.shape[1],\n",
-    "    'feature_set': 'All Features',\n",
-    "    'model': 'Gradient Boosting',\n",
-    "    'val_mae': baseline_mae,\n",
-    "    'val_r2': baseline_r2\n",
-    "})\n",
-    "\n",
-    "print(f\"\\n📌 BASELINE (All {X_train_fs.shape[1]} Features):\")\n",
-    "print(f\"   Val MAE: {baseline_mae:.4f}, R²: {baseline_r2:.4f}\")\n",
-    "\n",
-    "# Test with top-k features\n",
-    "for k in top_k_values:\n",
-    "    if k >= X_train_fs.shape[1]:\n",
-    "        continue\n",
-    "        \n",
-    "    print(f\"\\n{'='*80}\")\n",
-    "    print(f\"🔬 Testing with Top {k} Features\")\n",
-    "    print(f\"{'='*80}\")\n",
-    "    \n",
-    "    selected_features = aggregated.head(k)['feature'].tolist()\n",
-    "    X_train_selected = X_train_fs[selected_features]\n",
-    "    X_val_selected = X_val_fs[selected_features]\n",
-    "    \n",
-    "    print(f\"\\n📋 Selected features:\")\n",
-    "    for i, feat in enumerate(selected_features[:10], 1):\n",
-    "        score = aggregated[aggregated['feature'] == feat]['avg_score'].values[0]\n",
-    "        print(f\"   {i:2d}. {feat:40s} (score: {score:.4f})\")\n",
-    "    if k > 10:\n",
-    "        print(f\"   ... and {k-10} more\")\n",
-    "    \n",
-    "    # Test multiple models\n",
-    "    models_to_test = {\n",
-    "        'Random Forest': RandomForestRegressor(n_estimators=100, max_depth=15, min_samples_split=5, random_state=RANDOM_STATE, n_jobs=-1),\n",
-    "        'Gradient Boosting': GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, max_depth=5, random_state=RANDOM_STATE),\n",
-    "        'XGBoost': xgb.XGBRegressor(n_estimators=100, learning_rate=0.1, max_depth=5, random_state=RANDOM_STATE, n_jobs=-1),\n",
-    "        'LightGBM': lgb.LGBMRegressor(n_estimators=100, learning_rate=0.1, max_depth=5, num_leaves=31, random_state=RANDOM_STATE, n_jobs=-1, verbose=-1)\n",
-    "    }\n",
-    "    \n",
-    "    print(\"\\n🤖 Training models...\")\n",
-    "    for model_name, model in models_to_test.items():\n",
-    "        model.fit(X_train_selected, y_train)\n",
-    "        y_val_pred = model.predict(X_val_selected)\n",
-    "        \n",
-    "        val_mae = mean_absolute_error(y_val, y_val_pred)\n",
-    "        val_r2 = r2_score(y_val, y_val_pred)\n",
-    "        \n",
-    "        feature_selection_results.append({\n",
-    "            'num_features': k,\n",
-    "            'feature_set': f'Top {k}',\n",
-    "            'model': model_name,\n",
-    "            'val_mae': val_mae,\n",
-    "            'val_r2': val_r2\n",
-    "        })\n",
-    "        \n",
-    "        improvement = baseline_mae - val_mae\n",
-    "        print(f\"   {model_name:20s}: MAE={val_mae:.4f} ({'+' if improvement > 0 else ''}{improvement:.4f}), R²={val_r2:.4f}\")\n",
-    "\n",
-    "# Find best result\n",
-    "fs_results_df = pd.DataFrame(feature_selection_results)\n",
-    "best_idx = fs_results_df['val_mae'].idxmin()\n",
-    "best_result = fs_results_df.loc[best_idx]\n",
-    "\n",
-    "print(\"\\n\" + \"=\"*80)\n",
-    "print(\"🏆 BEST FEATURE SELECTION RESULT\")\n",
-    "print(\"=\"*80)\n",
-    "print(f\"   🤖 Model: {best_result['model']}\")\n",
-    "print(f\"   📋 Features: {int(best_result['num_features'])} selected\")\n",
-    "print(f\"\\n   📈 Best Model Performance:\")\n",
-    "print(f\"      Val MAE: {best_result['val_mae']:.4f}\")\n",
-    "print(f\"      Val R²:  {best_result['val_r2']:.4f}\")\n",
-    "print(f\"\\n   📊 Comparison with Baseline:\")\n",
-    "print(f\"      Baseline MAE: {baseline_mae:.4f} | R²: {baseline_r2:.4f}\")\n",
-    "print(f\"      Best MAE:     {best_result['val_mae']:.4f} | R²: {best_result['val_r2']:.4f}\")\n",
-    "print(f\"      MAE Improvement: {baseline_mae - best_result['val_mae']:.4f} ({(baseline_mae - best_result['val_mae'])/baseline_mae*100:.2f}%)\")\n",
-    "print(f\"      R² Improvement:  {best_result['val_r2'] - baseline_r2:.4f} ({'+' if best_result['val_r2'] > baseline_r2 else ''}{(best_result['val_r2'] - baseline_r2)*100:.2f}%)\")\n",
-    "\n",
-    "# Save results\n",
-    "fs_results_df.to_csv('models/feature_selection_results.csv', index=False)\n",
-    "print(\"\\n✅ Saved: models/feature_selection_results.csv\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "27452d46",
-   "metadata": {},
-   "source": [
-    "# 9️⃣ Final Training với Best Features\n",
-    "\n",
-    "**Mục đích:** Train model cuối cùng với best model + best features. Combine train+val data để train, evaluate trên test set."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "08e1918d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "🎯 FINAL TRAINING WITH BEST FEATURES\n",
-      "================================================================================\n",
-      "\n",
-      "📋 Best Configuration:\n",
-      "   Model: Random Forest\n",
-      "   Number of Features: 30\n",
-      "\n",
-      "📏 Preparing scaled data...\n",
-      "   Dropping 14 non-numeric columns\n",
-      "   ✅ Scaled 49 numeric features\n",
-      "\n",
-      "📋 Using Top 30 Selected Features:\n",
-      "    1. dien_tich_phong                          (score: 0.7402)\n",
-      "    2. tasks_per_hour                           (score: 0.6650)\n",
-      "    3. num_trash_collection_tasks               (score: 0.6648)\n",
-      "    4. num_surgery_room_tasks                   (score: 0.6385)\n",
-      "    5. dien_tich_hanh_lang                      (score: 0.6369)\n",
-      "    6. area_diversity                           (score: 0.6214)\n",
-      "    7. num_lobby_tasks                          (score: 0.6026)\n",
-      "    8. hour_end                                 (score: 0.5416)\n",
-      "    9. dien_tich_ngoai_canh                     (score: 0.4777)\n",
-      "   10. monitoring_ratio                         (score: 0.4716)\n",
-      "   11. num_patient_room_tasks                   (score: 0.4620)\n",
-      "   12. num_monitoring_tasks                     (score: 0.4545)\n",
-      "   13. tasks_per_floor                          (score: 0.4460)\n",
-      "   14. work_hours_numeric                       (score: 0.4020)\n",
-      "   15. trash_collection_ratio                   (score: 0.3944)\n",
-      "   ... and 15 more\n",
-      "\n",
-      "📊 Dataset Shapes:\n",
-      "   Train+Val: (356, 30) (356 samples)\n",
-      "   Test: (89, 30) (89 samples)\n",
-      "\n",
-      "🤖 Initializing Random Forest...\n",
-      "🔄 Training on Train+Val data...\n",
-      "   ✅ Training completed!\n",
-      "\n",
-      "🔮 Predicting on Test set...\n",
-      "\n",
-      "================================================================================\n",
-      "📊 FINAL TEST SET RESULTS\n",
-      "================================================================================\n",
-      "\n",
-      "🎯 Model: Random Forest\n",
-      "📋 Features: 30 selected features\n",
-      "\n",
-      "📈 Test Set Performance:\n",
-      "   MAE:  2.9597\n",
-      "   MSE:  22.5281\n",
-      "   RMSE: 4.7464\n",
-      "   R²:   0.4147\n",
-      "\n",
-      "📊 Comparison with Baseline:\n",
-      "   ⚪ Baseline (All features):   MAE=3.6346 | R²=-0.0129\n",
-      "   🎯 Final Model (30 features): MAE=2.9597 | R²=0.4147\n",
-      "\n",
-      "   📈 Improvement:\n",
-      "      MAE giảm: 0.6749 (18.57%)\n",
-      "      R² tăng:  0.4276 (+42.76%)\n",
-      "\n",
-      "📊 Creating visualizations...\n"
-     ]
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 1500x1200 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "✅ Saved: plts/final_model_test_results.png\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"=\"*80)\n",
-    "print(\"🎯 FINAL TRAINING WITH BEST FEATURES\")\n",
-    "print(\"=\"*80)\n",
-    "\n",
-    "# Get best configuration\n",
-    "best_num_features = int(best_result['num_features'])\n",
-    "best_model_name = best_result['model']\n",
-    "\n",
-    "print(f\"\\n📋 Best Configuration:\")\n",
-    "print(f\"   Model: {best_model_name}\")\n",
-    "print(f\"   Number of Features: {best_num_features}\")\n",
-    "\n",
-    "# Prepare scaled data for all sets\n",
-    "print(\"\\n📏 Preparing scaled data...\")\n",
-    "\n",
-    "# Drop non-numeric columns\n",
-    "non_numeric_cols_final = X_train.select_dtypes(exclude=['int64', 'float64']).columns.tolist()\n",
-    "if non_numeric_cols_final:\n",
-    "    print(f\"   Dropping {len(non_numeric_cols_final)} non-numeric columns\")\n",
-    "    X_train_final_scaled = X_train.drop(columns=non_numeric_cols_final).copy()\n",
-    "    X_val_final_scaled = X_val.drop(columns=non_numeric_cols_final).copy()\n",
-    "    X_test_final_scaled = X_test.drop(columns=non_numeric_cols_final).copy()\n",
-    "else:\n",
-    "    X_train_final_scaled = X_train.copy()\n",
-    "    X_val_final_scaled = X_val.copy()\n",
-    "    X_test_final_scaled = X_test.copy()\n",
-    "\n",
-    "# Identify columns to scale\n",
-    "binary_cols_final = [col for col in X_train_final_scaled.columns if X_train_final_scaled[col].nunique() == 2 and set(X_train_final_scaled[col].unique()).issubset({0, 1})]\n",
-    "onehot_cols_final = [col for col in X_train_final_scaled.columns if col.startswith('loai_ca_')]\n",
-    "cols_to_scale_final = [col for col in X_train_final_scaled.columns if col not in binary_cols_final and col not in onehot_cols_final]\n",
-    "\n",
-    "scaler_final = StandardScaler()\n",
-    "if cols_to_scale_final:\n",
-    "    X_train_final_scaled[cols_to_scale_final] = scaler_final.fit_transform(X_train_final_scaled[cols_to_scale_final])\n",
-    "    X_val_final_scaled[cols_to_scale_final] = scaler_final.transform(X_val_final_scaled[cols_to_scale_final])\n",
-    "    X_test_final_scaled[cols_to_scale_final] = scaler_final.transform(X_test_final_scaled[cols_to_scale_final])\n",
-    "    print(f\"   ✅ Scaled {len(cols_to_scale_final)} numeric features\")\n",
-    "\n",
-    "# Select best features\n",
-    "if best_num_features < X_train_final_scaled.shape[1]:\n",
-    "    selected_features = aggregated.head(best_num_features)['feature'].tolist()\n",
-    "    \n",
-    "    print(f\"\\n📋 Using Top {best_num_features} Selected Features\")\n",
-    "    print(f\"   Top 5: {', '.join(selected_features[:5])}\")\n",
-    "    if best_num_features > 5:\n",
-    "        print(f\"   ... and {best_num_features - 5} more features\")\n",
-    "    \n",
-    "    X_train_final = X_train_final_scaled[selected_features]\n",
-    "    X_val_final = X_val_final_scaled[selected_features]\n",
-    "    X_test_final = X_test_final_scaled[selected_features]\n",
-    "else:\n",
-    "    print(f\"\\n📋 Using ALL {X_train_final_scaled.shape[1]} Features\")\n",
-    "    selected_features = X_train_final_scaled.columns.tolist()\n",
-    "    X_train_final = X_train_final_scaled\n",
-    "    X_val_final = X_val_final_scaled\n",
-    "    X_test_final = X_test_final_scaled\n",
-    "\n",
-    "# Combine train + validation for final training\n",
-    "X_train_val = pd.concat([X_train_final, X_val_final])\n",
-    "y_train_val = pd.concat([y_train, y_val])\n",
-    "\n",
-    "# Initialize best model\n",
-    "if best_model_name == 'Random Forest':\n",
-    "    final_model = RandomForestRegressor(n_estimators=150, max_depth=20, min_samples_split=5, random_state=RANDOM_STATE, n_jobs=-1)\n",
-    "elif best_model_name == 'Gradient Boosting':\n",
-    "    final_model = GradientBoostingRegressor(n_estimators=150, learning_rate=0.05, max_depth=7, random_state=RANDOM_STATE)\n",
-    "elif best_model_name == 'XGBoost':\n",
-    "    final_model = xgb.XGBRegressor(n_estimators=150, learning_rate=0.05, max_depth=7, random_state=RANDOM_STATE, n_jobs=-1)\n",
-    "elif best_model_name == 'LightGBM':\n",
-    "    final_model = lgb.LGBMRegressor(n_estimators=150, learning_rate=0.05, max_depth=7, num_leaves=31, random_state=RANDOM_STATE, n_jobs=-1, verbose=-1)\n",
-    "else:\n",
-    "    final_model = GradientBoostingRegressor(n_estimators=150, learning_rate=0.05, max_depth=7, random_state=RANDOM_STATE)\n",
-    "\n",
-    "# Train on combined train+val\n",
-    "print(f\"\\n🔄 Training {best_model_name} on {len(X_train_val)} samples...\")\n",
-    "final_model.fit(X_train_val, y_train_val)\n",
-    "\n",
-    "# Predict on test set\n",
-    "print(f\"🔮 Predicting on {len(X_test_final)} test samples...\")\n",
-    "y_test_pred = final_model.predict(X_test_final)\n",
-    "\n",
-    "# Calculate metrics\n",
-    "test_mae = mean_absolute_error(y_test, y_test_pred)\n",
-    "test_mse = mean_squared_error(y_test, y_test_pred)\n",
-    "test_rmse = np.sqrt(test_mse)\n",
-    "test_r2 = r2_score(y_test, y_test_pred)\n",
-    "\n",
-    "print(\"\\n\" + \"=\"*80)\n",
-    "print(\"📊 FINAL TEST SET RESULTS\")\n",
-    "print(\"=\"*80)\n",
-    "print(f\"🎯 Model: {best_model_name} | Features: {best_num_features}\")\n",
-    "print(f\"\\n📈 Test Performance: MAE={test_mae:.4f} | RMSE={test_rmse:.4f} | R²={test_r2:.4f}\")\n",
-    "print(f\"📊 Baseline (All):   MAE={baseline_mae:.4f} | R²={baseline_r2:.4f}\")\n",
-    "print(f\"📈 Improvement:      MAE giảm {baseline_mae - test_mae:.4f} ({(baseline_mae - test_mae)/baseline_mae*100:.1f}%) | R² tăng {test_r2 - baseline_r2:.4f} ({(test_r2 - baseline_r2)*100:.1f}%)\")\n",
-    "\n",
-    "# Visualizations\n",
-    "print(f\"\\n📊 Creating visualizations...\")\n",
-    "\n",
-    "fig, axes = plt.subplots(2, 2, figsize=(15, 12))\n",
-    "\n",
-    "# 1. Predicted vs Actual\n",
-    "axes[0, 0].scatter(y_test, y_test_pred, alpha=0.6, edgecolors='k', s=50)\n",
-    "axes[0, 0].plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=2)\n",
-    "axes[0, 0].set_xlabel('Actual Values', fontsize=11)\n",
-    "axes[0, 0].set_ylabel('Predicted Values', fontsize=11)\n",
-    "axes[0, 0].set_title(f'{best_model_name} - Test Set\\nPredicted vs Actual', fontsize=12, fontweight='bold')\n",
-    "axes[0, 0].grid(True, alpha=0.3)\n",
-    "axes[0, 0].text(0.05, 0.95, f'R² = {test_r2:.4f}\\nMAE = {test_mae:.4f}',\n",
-    "                transform=axes[0, 0].transAxes, fontsize=10, verticalalignment='top',\n",
-    "                bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))\n",
-    "\n",
-    "# 2. Residuals plot\n",
-    "residuals = y_test - y_test_pred\n",
-    "axes[0, 1].scatter(y_test_pred, residuals, alpha=0.6, edgecolors='k', s=50)\n",
-    "axes[0, 1].axhline(y=0, color='r', linestyle='--', lw=2)\n",
-    "axes[0, 1].set_xlabel('Predicted Values', fontsize=11)\n",
-    "axes[0, 1].set_ylabel('Residuals', fontsize=11)\n",
-    "axes[0, 1].set_title('Residual Plot', fontsize=12, fontweight='bold')\n",
-    "axes[0, 1].grid(True, alpha=0.3)\n",
-    "\n",
-    "# 3. Residuals distribution\n",
-    "axes[1, 0].hist(residuals, bins=30, edgecolor='black', alpha=0.7, color='skyblue')\n",
-    "axes[1, 0].axvline(x=0, color='r', linestyle='--', lw=2)\n",
-    "axes[1, 0].set_xlabel('Residuals', fontsize=11)\n",
-    "axes[1, 0].set_ylabel('Frequency', fontsize=11)\n",
-    "axes[1, 0].set_title('Residual Distribution', fontsize=12, fontweight='bold')\n",
-    "axes[1, 0].grid(True, alpha=0.3)\n",
-    "\n",
-    "# 4. Feature importance (if available)\n",
-    "if hasattr(final_model, 'feature_importances_'):\n",
-    "    feature_importance = pd.DataFrame({\n",
-    "        'feature': selected_features,\n",
-    "        'importance': final_model.feature_importances_\n",
-    "    }).sort_values('importance', ascending=False).head(15)\n",
-    "    \n",
-    "    axes[1, 1].barh(range(len(feature_importance)), feature_importance['importance'], alpha=0.7)\n",
-    "    axes[1, 1].set_yticks(range(len(feature_importance)))\n",
-    "    axes[1, 1].set_yticklabels(feature_importance['feature'], fontsize=9)\n",
-    "    axes[1, 1].set_xlabel('Importance', fontsize=11)\n",
-    "    axes[1, 1].set_title('Top 15 Feature Importance', fontsize=12, fontweight='bold')\n",
-    "    axes[1, 1].invert_yaxis()\n",
-    "    axes[1, 1].grid(True, alpha=0.3, axis='x')\n",
-    "else:\n",
-    "    axes[1, 1].text(0.5, 0.5, f'Test MAE: {test_mae:.4f}\\nTest R²: {test_r2:.4f}\\n{best_num_features} features used',\n",
-    "                    transform=axes[1, 1].transAxes, fontsize=14, ha='center', va='center',\n",
-    "                    bbox=dict(boxstyle='round', facecolor='lightblue', alpha=0.3))\n",
-    "    axes[1, 1].axis('off')\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.savefig('plots/final_model_test_results.png', dpi=300, bbox_inches='tight')\n",
-    "plt.show()\n",
-    "\n",
-    "print(\"✅ Visualization saved!\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "61aaae99",
-   "metadata": {},
-   "source": [
-    "# 🔟 Save Model & Artifacts\n",
-    "\n",
-    "**Mục đích:** Lưu model, scaler, selected features list, và training report để sử dụng sau này."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "bc3da137",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "💾 SAVING MODEL & ARTIFACTS\n",
-      "================================================================================\n",
-      "\n",
-      "✅ Saved model: models/final_model_random_forest_top15.pkl\n",
-      "✅ Saved scaler: models/scaler_final.pkl\n",
-      "✅ Saved features list: models/selected_features_top15.txt\n",
-      "✅ Saved columns to scale: models/columns_to_scale.txt\n",
-      "✅ Saved training report: models/training_report_final.json\n",
-      "\n",
-      "================================================================================\n",
-      "📋 TRAINING SUMMARY\n",
-      "================================================================================\n",
-      "\n",
-      "🎯 Model Configuration:\n",
-      "   Model Type: Random Forest\n",
-      "   Features Used: 15/57\n",
-      "   Feature Reduction: 73.7%\n",
-      "\n",
-      "📊 Dataset:\n",
-      "   Training Samples: 356 (Train+Val combined)\n",
-      "   Test Samples: 89\n",
-      "\n",
-      "📈 Performance:\n",
-      "   Test MAE:  3.0054\n",
-      "   Test RMSE: 5.0646\n",
-      "   Test R²:   0.3336\n",
-      "\n",
-      "🎯 Improvement:\n",
-      "   Baseline Val MAE: 4.5372\n",
-      "   Final Test MAE:   3.0054\n",
-      "   Improvement:      1.5318 (33.76%)\n",
-      "\n",
-      "💾 Saved Files:\n",
-      "   • models/final_model_random_forest_top15.pkl\n",
-      "   • models/scaler_final.pkl\n",
-      "   • models/selected_features_top15.txt\n",
-      "   • models/columns_to_scale.txt\n",
-      "   • models/training_report_final.json\n",
-      "   • plots/final_model_test_results.png\n",
-      "   • models/feature_selection_results.csv\n",
-      "\n",
-      "================================================================================\n",
-      "✅ PIPELINE COMPLETED SUCCESSFULLY!\n",
-      "================================================================================\n",
-      "\n",
-      "💡 Next Steps:\n",
-      "   1. Review test set predictions and error analysis\n",
-      "   2. Try hyperparameter tuning for further improvement\n",
-      "   3. Deploy model to production\n",
-      "   4. Monitor model performance over time\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"=\"*80)\n",
-    "print(\"💾 SAVING MODEL & ARTIFACTS\")\n",
-    "print(\"=\"*80)\n",
-    "\n",
-    "# 1. Save the trained model\n",
-    "model_filename = f'models/final_model_{best_model_name.lower().replace(\" \", \"_\")}_top{best_num_features}.pkl'\n",
-    "with open(model_filename, 'wb') as f:\n",
-    "    pickle.dump(final_model, f)\n",
-    "print(f\"\\n✅ Saved model: {model_filename}\")\n",
-    "\n",
-    "# 2. Save the scaler\n",
-    "scaler_filename = 'models/scaler_final.pkl'\n",
-    "with open(scaler_filename, 'wb') as f:\n",
-    "    pickle.dump(scaler_final, f)\n",
-    "print(f\"✅ Saved scaler: {scaler_filename}\")\n",
-    "\n",
-    "# 3. Save selected features list\n",
-    "features_filename = f'models/selected_features_top{best_num_features}.txt'\n",
-    "with open(features_filename, 'w', encoding='utf-8') as f:\n",
-    "    f.write('\\n'.join(selected_features))\n",
-    "print(f\"✅ Saved features list: {features_filename}\")\n",
-    "\n",
-    "# 4. Save columns to scale list\n",
-    "cols_to_scale_filename = 'models/columns_to_scale.txt'\n",
-    "with open(cols_to_scale_filename, 'w', encoding='utf-8') as f:\n",
-    "    f.write('\\n'.join(cols_to_scale_final))\n",
-    "print(f\"✅ Saved columns to scale: {cols_to_scale_filename}\")\n",
-    "\n",
-    "# 5. Save training report\n",
-    "report = {\n",
-    "    'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),\n",
-    "    'model_name': best_model_name,\n",
-    "    'num_features': best_num_features,\n",
-    "    'total_features_available': X_train_final_scaled.shape[1],\n",
-    "    'training_samples': len(X_train_val),\n",
-    "    'test_samples': len(y_test),\n",
-    "    'test_mae': float(test_mae),\n",
-    "    'test_mse': float(test_mse),\n",
-    "    'test_rmse': float(test_rmse),\n",
-    "    'test_r2': float(test_r2),\n",
-    "    'baseline_val_mae': float(baseline_mae),\n",
-    "    'improvement_vs_baseline': float(baseline_mae - test_mae),\n",
-    "    'improvement_pct': float((baseline_mae - test_mae) / baseline_mae * 100) if baseline_mae > 0 else 0,\n",
-    "    'k_fold_cv': 'Completed (5-fold)',\n",
-    "    'feature_selection_methods': ['Random Forest', 'Mutual Information', 'Correlation'],\n",
-    "    'selected_features': selected_features,\n",
-    "    'cols_to_scale': cols_to_scale_final\n",
-    "}\n",
-    "\n",
-    "report_filename = 'models/training_report_final.json'\n",
-    "with open(report_filename, 'w', encoding='utf-8') as f:\n",
-    "    json.dump(report, f, indent=4, ensure_ascii=False)\n",
-    "print(f\"✅ Saved training report: {report_filename}\")\n",
-    "\n",
-    "# Print summary\n",
-    "print(\"\\n\" + \"=\"*80)\n",
-    "print(\"📋 TRAINING SUMMARY\")\n",
-    "print(\"=\"*80)\n",
-    "print(f\"\\n🎯 Model Configuration:\")\n",
-    "print(f\"   Model Type: {best_model_name}\")\n",
-    "print(f\"   Features Used: {best_num_features}/{X_train_final_scaled.shape[1]}\")\n",
-    "\n",
-    "print(f\"\\n📊 Dataset:\")\n",
-    "print(f\"   Training Samples: {len(X_train_val)} (Train+Val combined)\")\n",
-    "print(f\"   Test Samples: {len(y_test)}\")\n",
-    "\n",
-    "print(f\"\\n📈 Performance:\")\n",
-    "print(f\"   Test MAE:  {test_mae:.4f}\")\n",
-    "print(f\"   Test RMSE: {test_rmse:.4f}\")\n",
-    "print(f\"   Test R²:   {test_r2:.4f}\")\n",
-    "\n",
-    "print(f\"\\n🎯 Improvement:\")\n",
-    "print(f\"   Baseline Val MAE: {baseline_mae:.4f}\")\n",
-    "print(f\"   Final Test MAE:   {test_mae:.4f}\")\n",
-    "if baseline_mae > 0:\n",
-    "    print(f\"   Improvement:      {baseline_mae - test_mae:.4f} ({(baseline_mae - test_mae)/baseline_mae*100:.2f}%)\")\n",
-    "\n",
-    "print(f\"\\n💾 Saved Files:\")\n",
-    "print(f\"   • {model_filename}\")\n",
-    "print(f\"   • {scaler_filename}\")\n",
-    "print(f\"   • {features_filename}\")\n",
-    "print(f\"   • {cols_to_scale_filename}\")\n",
-    "print(f\"   • {report_filename}\")\n",
-    "print(f\"   • plots/final_model_test_results.png\")\n",
-    "print(f\"   • models/feature_selection_results.csv\")\n",
-    "print(f\"   • models/kfold_cv_results.csv\")\n",
-    "\n",
-    "print(\"\\n\" + \"=\"*80)\n",
-    "print(\"✅ PIPELINE COMPLETED SUCCESSFULLY!\")\n",
-    "print(\"=\"*80)\n",
-    "print(\"\\n💡 Next Steps:\")\n",
-    "print(\"   1. Review test set predictions and error analysis\")\n",
-    "print(\"   2. Try hyperparameter tuning with GridSearchCV\")\n",
-    "print(\"   3. Consider ensemble methods (Voting/Stacking)\")\n",
-    "print(\"   4. Deploy model to production\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8af7c09d",
-   "metadata": {},
-   "source": [
-    "# 🎉 Pipeline Hoàn Thành!\n",
-    "\n",
-    "---\n",
-    "\n",
-    "## ✅ Đã Hoàn Thành:\n",
-    "\n",
-    "1. ✅ **Data Loading & Cleaning** - Load và làm sạch dữ liệu\n",
-    "2. ✅ **Feature Engineering** - Tạo features mới, encode categorical\n",
-    "3. ✅ **Train/Val/Test Split** - Chia dữ liệu 60/20/20\n",
-    "4. ✅ **K-Fold Cross-Validation** - Test độ ổn định với 5-fold CV\n",
-    "5. ✅ **Feature Selection** - Tìm best features với 3 phương pháp\n",
-    "6. ✅ **Final Training** - Train model với best configuration\n",
-    "7. ✅ **Test Evaluation** - Đánh giá trên test set\n",
-    "8. ✅ **Save Artifacts** - Lưu model, scaler, features, report\n",
-    "\n",
-    "---\n",
-    "\n",
-    "## 📊 Kết Quả Cuối Cùng:\n",
-    "\n",
-    "- **Model:** Best model được chọn qua feature selection\n",
-    "- **Features:** Best number of features được tối ưu hóa\n",
-    "- **Performance:** Test MAE, RMSE, R² được đo trên test set\n",
-    "- **Improvement:** So sánh với baseline model\n",
-    "\n",
-    "---\n",
-    "\n",
-    "## 💾 Files Đã Tạo:\n",
-    "\n",
-    "- `models/final_model_*.pkl` - Trained model\n",
-    "- `models/scaler_final.pkl` - Feature scaler\n",
-    "- `models/selected_features_top*.txt` - Danh sách features được chọn\n",
-    "- `models/columns_to_scale.txt` - Danh sách columns cần scale\n",
-    "- `models/training_report_final.json` - Báo cáo chi tiết\n",
-    "- `models/feature_selection_results.csv` - Kết quả feature selection\n",
-    "- `plots/final_model_test_results.png` - Visualizations\n",
-    "\n",
-    "---\n",
-    "\n",
-    "## 🚀 Cách Sử Dụng Model:\n",
-    "\n",
-    "```python\n",
-    "import pickle\n",
-    "import pandas as pd\n",
-    "\n",
-    "# Load model và artifacts\n",
-    "with open('models/final_model_*.pkl', 'rb') as f:\n",
-    "    model = pickle.load(f)\n",
-    "\n",
-    "with open('models/scaler_final.pkl', 'rb') as f:\n",
-    "    scaler = pickle.load(f)\n",
-    "\n",
-    "# Load selected features\n",
-    "with open('models/selected_features_top*.txt', 'r') as f:\n",
-    "    selected_features = [line.strip() for line in f]\n",
-    "\n",
-    "# Load columns to scale\n",
-    "with open('models/columns_to_scale.txt', 'r') as f:\n",
-    "    cols_to_scale = [line.strip() for line in f]\n",
-    "\n",
-    "# Prepare new data\n",
-    "new_data = pd.DataFrame(...)  # Your new data\n",
-    "new_data_scaled = new_data.copy()\n",
-    "new_data_scaled[cols_to_scale] = scaler.transform(new_data[cols_to_scale])\n",
-    "new_data_selected = new_data_scaled[selected_features]\n",
-    "\n",
-    "# Predict\n",
-    "predictions = model.predict(new_data_selected)\n",
-    "```\n",
-    "\n",
-    "---\n",
-    "\n",
-    "## 💡 Recommendations:\n",
-    "\n",
-    "1. **Hyperparameter Tuning:** Sử dụng GridSearchCV hoặc RandomizedSearchCV\n",
-    "2. **Ensemble Methods:** Thử kết hợp nhiều models (Voting, Stacking)\n",
-    "3. **Error Analysis:** Phân tích các predictions sai nhiều nhất\n",
-    "4. **Feature Engineering:** Thêm features mới từ domain knowledge\n",
-    "5. **Data Collection:** Thu thập thêm data để cải thiện model\n",
-    "\n",
-    "---\n",
-    "\n",
-    "**Version:** 2.0 - Complete Pipeline  \n",
-    "**Created:** January 8, 2026  \n",
-    "**Status:** ✅ COMPLETED"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "base",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..b97220b
--- /dev/null
+++ b/README.md
@@ -0,0 +1,491 @@
+# 🏢 Dự Đoán Nhân Sự Ca Làm Việc
+
+> **Hệ thống Machine Learning dự đoán số lượng nhân sự cần thiết cho mỗi ca làm việc dựa trên đặc điểm tòa nhà và công việc**
+
+---
+
+## 📋 Mục Lục
+
+- [Tổng Quan](#-tổng-quan)
+- [Cấu Trúc Dữ Liệu](#-cấu-trúc-dữ-liệu)
+- [Các File Chính](#-các-file-chính)
+- [Pipeline Dự Đoán](#-pipeline-dự-đoán)
+- [Hướng Dẫn Sử Dụng](#-hướng-dẫn-sử-dụng)
+
+---
+
+## 🎯 Tổng Quan
+
+Dự án này sử dụng Machine Learning để tự động dự đoán số lượng nhân sự cần thiết cho mỗi ca làm việc tại các tòa nhà, dựa trên:
+- Đặc điểm vật lý của tòa nhà (diện tích, số tầng, số WC, v.v.)
+- Loại công việc và nội dung công việc (text features)
+- Thông tin ca làm việc (ca sáng, chiều, tối, đêm)
+
+---
+
+## 📊 Cấu Trúc Dữ Liệu
+
+### 🗂️ Dữ Liệu Gốc (`data_raw/`)
+
+#### 1. **`Link LLV 1_5_2025.json`**
+📌 **Mục đích:** Chứa thông tin về các ca làm việc
+
+**Nội dung:**
+- Danh sách các ca làm việc của từng tòa nhà
+- Thời gian làm việc (ca sáng, chiều, tối, đêm)
+- Công việc thường (`all_task_normal`)
+- Công việc định kỳ (`all_task_dinhky`)
+- Số nhân sự thực tế cho mỗi ca
+
+**Vai trò:** Cung cấp dữ liệu về **phân ca** và **nội dung công việc** cho model
+
+---
+
+#### 2. **`Link LLV 2025_clean.json`**
+📌 **Mục đích:** Chứa thông tin về đặc điểm vật lý của các tòa nhà
+
+**Nội dung:**
+- Diện tích từng tầng của mỗi tòa nhà
+- Số lượng WC, phòng, v.v.
+- Thông tin cơ sở hạ tầng
+
+**⚠️ LƯU Ý QUAN TRỌNG:**
+> **Các trường liên quan đến diện tích trong `final_2.csv` là TỔNG diện tích của TẤT CẢ các tầng trong mỗi tòa nhà**, không phải diện tích trung bình hay diện tích một tầng!
+> 
+> Ví dụ: Nếu tòa nhà có 10 tầng, mỗi tầng 500m², thì `tong_dien_tich = 5000m²`
+
+---
+
+### 📈 Dữ Liệu Training
+
+#### **`final_2.csv`** (hoặc `final_2.xlsx`)
+📌 **Mục đích:** Dataset chính để huấn luyện model
+
+**Nguồn dữ liệu:**
+- **Thông tin ca làm việc** ← từ `data_raw/Link LLV 1_5_2025.json`
+- **Thông tin diện tích tòa nhà** ← từ `data_raw/Link LLV 2025_clean.json` (TỔNG diện tích các tầng)
+
+**Các nhóm features chính:**
+
+| Nhóm Features | Nguồn | Ví dụ |
+|---------------|-------|-------|
+| **Thông tin ca** | Link LLV | `ca_sang`, `ca_chieu`, `ca_toi`, `ca_dem` |
+| **Diện tích** | Link LLV 2025 | `tong_dien_tich`, `dien_tich_wc`, `dien_tich_hanh_lang` |
+| **Cơ sở vật chất** | Link LLV 2025 | `so_tang`, `so_wc`, `so_phong` |
+| **Text features** | Sinh ra từ tasks | `num_cleaning_tasks`, `cleaning_ratio`, `area_diversity` |
+| **Target** | Link LLV | `so_nhan_su` (số nhân sự thực tế) |
+
+---
+
+## 📁 Các File Chính
+
+### 1. 📓 **`All_feature_Readme.ipynb`**
+> **Notebook giải thích chi tiết về Text Features**
+
+**Nội dung:**
+- 📖 Giải thích từng loại text feature được trích xuất từ công việc
+- 🔍 Các keyword được sử dụng để nhận diện công việc
+- 📊 Ví dụ minh họa cho từng feature
+- 📈 Phân tích tầm quan trọng của các features
+
+**Khi nào đọc:** Khi bạn muốn hiểu cách hệ thống trích xuất thông tin từ đoạn text công việc
+
+---
+
+### 2. 🤖 **`artifacts/`**
+> **Thư mục chứa các model đã được huấn luyện**
+
+**Các file trong thư mục:**
+
+```
+artifacts/
+├── extratrees_log1p.joblib           # Model chính (Extra Trees với log transform)
+├── extratrees_staff_model.joblib     # Model dự đoán nhân sự
+├── model_meta.joblib                 # Metadata của model (params, metrics)
+└── X_proc_columns.joblib             # Danh sách features và thứ tự columns
+```
+
+**Định dạng:** Joblib (compressed pickle format)
+
+**Cách load:**
+```python
+import joblib
+model = joblib.load('artifacts/extratrees_log1p.joblib')
+```
+
+---
+
+### 3. 📄 **`input.json`**
+> **File input mẫu để dự đoán cho MỘT ca làm việc của MỘT tòa nhà**
+
+**Cấu trúc:**
+```json
+{
+  "ma_dia_diem": "TD-001",
+  "loai_ca": "Hành chính",
+  "bat_dau": "07:00:00",
+  "ket_thuc": "15:00:00",
+  "tong_gio_lam": 8.0,
+  "so_ca_cua_toa": 3,
+  "all_task_normal": "Lau sàn hành lang; Thu gom rác WC; Vệ sinh sảnh chính; Lau kính thang máy",
+  "all_task_dinhky": "Cọ bồn cầu WC tầng 2; Đánh sàn lobby; Trực phát sinh",
+  "so_tang": 12,
+  "so_cua_thang_may": 4,
+  "dien_tich_ngoai_canh": 350.0,
+  "dien_tich_sanh": 220.0,
+  "dien_tich_hanh_lang": 1800.0,
+  "dien_tich_wc": 420.0,
+  "dien_tich_phong": 2600.0,
+  "dien_tich_tham": 800.0,
+  "dien_tich_kinh": 560.0,
+  "doc_ham": 1,
+  "vien_phan_quang": 0,
+  "op_tuong": 1,
+  "op_chan_tuong": 1,
+  "ranh_thoat_nuoc": 1
+}
+```
+
+#### 📝 Giải Thích Chi Tiết Các Trường:
+
+**🏢 Thông tin tòa nhà & ca:**
+- `ma_dia_diem`: Mã định danh tòa nhà
+- `loai_ca`: Loại ca làm việc (Hành chính / Ca sáng / Ca chiều / Ca tối / Ca đêm)
+- `bat_dau`, `ket_thuc`: Thời gian bắt đầu và kết thúc ca (HH:MM:SS)
+- `tong_gio_lam`: Tổng số giờ làm việc trong ca
+- `so_ca_cua_toa`: Tổng số ca làm việc trong ngày của tòa nhà
+
+**📋 Công việc - 2 loại quan trọng:**
+
+> **⭐ `all_task_normal` - CÔNG VIỆC HẰNG NGÀY:**
+> - Các task được thực hiện **MỖI NGÀY** trong ca này
+> - Ví dụ: Lau sàn, Thu rác, Vệ sinh WC (mỗi ngày đều phải làm)
+> - **Tần suất:** Ngày nào cũng làm, ca nào cũng làm
+> - **Format:** Các task cách nhau bởi dấu chấm phẩy (`;`)
+
+> **⭐ `all_task_dinhky` - CÔNG VIỆC ĐỊNH KỲ (TUẦN/THÁNG):**
+> - Các task được thực hiện **THEO TUẦN** hoặc **THEO THÁNG**
+> - Ví dụ: Cọ bồn cầu (1 tuần/lần), Đánh sàn (1 tháng/lần), Trực phát sinh
+> - **Tần suất:** Không làm hàng ngày, chỉ làm định kỳ
+> - **Format:** Các task cách nhau bởi dấu chấm phẩy (`;`)
+
+**Sự khác biệt:**
+| | `all_task_normal` | `all_task_dinhky` |
+|---|---|---|
+| **Tần suất** | Hằng ngày | Theo tuần/tháng |
+| **Ví dụ** | Lau sàn, Thu rác | Cọ rửa, Đánh sàn |
+| **Khối lượng** | Công việc nhẹ, lặp lại | Công việc nặng, ít lần |
+
+**🏗️ Đặc điểm vật lý:**
+- `so_tang`: Tổng số tầng
+- `so_cua_thang_may`: Số cửa thang máy
+
+**📏 Diện tích (m²) - ⚠️ TỔNG TẤT CẢ CÁC TẦNG:**
+- `dien_tich_ngoai_canh`: Khu vực ngoại cảnh (sân, vỉa hè)
+- `dien_tich_sanh`: Sảnh, lobby
+- `dien_tich_hanh_lang`: Hành lang, lối đi
+- `dien_tich_wc`: WC, toilet
+- `dien_tich_phong`: Các phòng (văn phòng, phòng họp)
+- `dien_tich_tham`: Sàn có thảm
+- `dien_tich_kinh`: Kính cần lau
+
+> **⚠️ CHÚ Ý:** Tất cả diện tích là TỔNG của TẤT CẢ các tầng!
+> 
+> Ví dụ: Tòa 12 tầng, mỗi tầng 150m² hành lang → `dien_tich_hanh_lang = 1800m²`
+
+**🔧 Đặc điểm bề mặt (1=Có, 0=Không):**
+- `doc_ham`: Có tầng hầm
+- `vien_phan_quang`: Có viền phản quang
+- `op_tuong`: Có ốp tường
+- `op_chan_tuong`: Có ốp chân tường
+- `ranh_thoat_nuoc`: Có rãnh thoát nước
+
+---
+
+### 4. 🎯 **`all_predict.py`**
+> **Script chính để chạy dự đoán**
+
+**Chức năng:**
+1. 📖 Đọc file `input.json`
+2. 🔄 Trích xuất text features từ công việc
+3. 🏗️ Xây dựng feature vector đầy đủ
+4. 🤖 Load model từ `artifacts/`
+5. 📊 Dự đoán số nhân sú cần thiết
+6. 💾 Xuất kết quả
+
+**Cách chạy:**
+```bash
+python all_predict.py
+```
+
+**Output:** Số nhân sự dự đoán cho ca làm việc trong `input.json`
+
+---
+
+### 5. 🔧 **`predict.py`**
+> **Module chứa hàm trích xuất text features từ công việc**
+
+**Hàm chính:**
+```python
+extract_text_features_to_json(task_normal: str, task_dinhky: str) -> str
+```
+
+**Input:**
+- `task_normal`: Text công việc **HẰNG NGÀY** (các task làm mỗi ngày)
+- `task_dinhky`: Text công việc **ĐỊNH KỲ TUẦN/THÁNG** (các task làm định kỳ)
+
+**Output:** JSON string chứa 18 text features:
+- `task_counts` (7 features): Số lượng từng loại công việc
+- `area_coverage` (7 features): Số công việc theo khu vực
+- `ratios_and_diversity` (4 features): Tỷ lệ và độ đa dạng
+
+**Ví dụ sử dụng:**
+```python
+from predict import extract_text_features_to_json
+
+# Công việc hằng ngày
+task_normal = "Lau sàn WC; Thu gom rác; Vệ sinh sảnh"
+
+# Công việc định kỳ (tuần/tháng)
+task_dinhky = "Cọ bồn cầu; Đánh sàn; Trực phát sinh"
+
+# Trích xuất features
+json_features = extract_text_features_to_json(task_normal, task_dinhky)
+print(json_features)
+```
+
+**Kết quả trả về:**
+```json
+{
+  "task_counts": {
+    "num_tasks": 6,
+    "num_cleaning_tasks": 3,
+    "num_trash_collection_tasks": 1,
+    "num_monitoring_tasks": 1,
+    "num_deep_cleaning_tasks": 2,
+    "num_support_tasks": 0,
+    "num_other_tasks": 0
+  },
+  "area_coverage": {
+    "num_wc_tasks": 2,
+    "num_hallway_tasks": 0,
+    "num_lobby_tasks": 1,
+    ...
+  },
+  ...
+}
+```
+
+---
+
+## 🔄 Pipeline Dự Đoán
+
+```mermaid
+graph LR
+    A[input.json] --> B[all_predict.py]
+    B --> C[predict.py<br/>Text Features]
+    C --> D[Feature Vector]
+    D --> E[Model<br/>artifacts/]
+    E --> F[Dự đoán<br/>Số nhân sự]
+```
+
+### Chi tiết từng bước:
+
+1. **Input** 📥
+   - Đọc `input.json` chứa thông tin ca làm việc
+   - Bao gồm 2 loại công việc: `all_task_normal` (hằng ngày) và `all_task_dinhky` (định kỳ)
+   
+2. **Text Feature Extraction** 🔤
+   - `predict.py` trích xuất 18 features từ text công việc
+   - **Gộp cả 2 loại công việc** (normal + dinhky) để phân tích
+   - Phân loại công việc: cleaning, trash, monitoring, deep cleaning, support, other
+   - Phân vùng khu vực: WC, hallway, lobby, outdoor, elevator, medical, office
+   
+3. **Feature Engineering** 🏗️
+   - Kết hợp text features với building features (diện tích, số tầng, v.v.)
+   - Chuẩn hóa dữ liệu theo chuẩn của training set
+   
+4. **Model Prediction** 🤖
+   - Load model đã được train từ `artifacts/`
+   - Dự đoán số nhân sự dựa trên toàn bộ features
+   
+5. **Output** 📤
+   - Trả về số nhân sự cần thiết cho ca đó
+
+---
+
+## 🚀 Hướng Dẫn Sử Dụng
+
+### Yêu cầu hệ thống
+```bash
+pip install pandas numpy scikit-learn joblib openpyxl
+```
+
+### 1. Dự đoán cho MỘT ca làm việc
+
+**Bước 1:** Chuẩn bị `input.json`
+```json
+{
+  "ma_dia_diem": "TD-001",
+  "loai_ca": "Hành chính",
+  "bat_dau": "07:00:00",
+  "ket_thuc": "15:00:00",
+  "tong_gio_lam": 8.0,
+  "so_ca_cua_toa": 3,
+  "all_task_normal": "Lau sàn hành lang; Thu gom rác WC; Vệ sinh sảnh",
+  "all_task_dinhky": "Cọ bồn cầu WC; Đánh sàn lobby; Trực phát sinh",
+  "so_tang": 12,
+  "so_cua_thang_may": 4,
+  "dien_tich_hanh_lang": 1800.0,
+  "dien_tich_wc": 420.0,
+  ...
+}
+```
+
+> **💡 Lưu ý về 2 trường công việc:**
+> - `all_task_normal`: Các task làm **MỖI NGÀY** (lau sàn, thu rác hàng ngày)
+> - `all_task_dinhky`: Các task làm **THEO TUẦN/THÁNG** (cọ rửa định kỳ, đánh sàn định kỳ)
+
+**Bước 2:** Chạy prediction
+```bash
+python all_predict.py
+```
+
+**Bước 3:** Xem kết quả
+```
+📊 Dự đoán số nhân sự: 8 người
+```
+
+---
+
+### 2. Trích xuất Text Features riêng lẻ
+
+```python
+from predict import extract_text_features_to_json
+
+# Công việc hằng ngày (daily tasks)
+task_normal = "Lau sàn WC tầng 1; Thu gom rác hành lang; Vệ sinh sảnh"
+
+# Công việc định kỳ (weekly/monthly tasks)
+task_dinhky = "Cọ bồn cầu WC; Đánh sàn lobby; Trực phát sinh"
+
+# Trích xuất features (sẽ gộp cả 2 loại công việc để phân tích)
+features_json = extract_text_features_to_json(task_normal, task_dinhky)
+
+# Parse JSON
+import json
+features = json.loads(features_json)
+
+print(f"Tổng số tasks (cả normal + dinhky): {features['task_counts']['num_tasks']}")
+print(f"Số tasks vệ sinh: {features['task_counts']['num_cleaning_tasks']}")
+print(f"Số tasks cọ rửa (deep cleaning): {features['task_counts']['num_deep_cleaning_tasks']}")
+print(f"Tỷ lệ vệ sinh: {features['ratios_and_diversity']['cleaning_ratio']}")
+print(f"Số tasks vệ sinh: {features['task_counts']['num_cleaning_tasks']}")
+print(f"Tỷ lệ vệ sinh: {features['ratios_and_diversity']['cleaning_ratio']}")
+```
+
+---
+
+### 3. Training lại model (nếu cần)
+
+**Bước 1:** Chuẩn bị data
+- Đảm bảo `final_2.csv` có đủ features
+- Đảm bảo diện tích là TỔNG các tầng
+
+**Bước 2:** Chạy notebook training
+```bash
+jupyter notebook ML_Training_Pipeline_Complete.ipynb
+```
+
+**Bước 3:** Model mới sẽ được lưu vào `artifacts/`
+
+---
+
+## 📈 Text Features Chi Tiết
+
+### Nhóm 1: Task Counts (7 features)
+| Feature | Mô tả | Keywords |
+|---------|-------|----------|
+| `num_tasks` | Tổng số công việc | - |
+| `num_cleaning_tasks` | Công việc vệ sinh | vệ sinh, lau, chùi, quét, hút |
+| `num_trash_collection_tasks` | Thu gom rác | thu gom rác, thay rác, đổ rác |
+| `num_monitoring_tasks` | Trực/giám sát | trực, kiểm tra, check, giám sát |
+| `num_deep_cleaning_tasks` | Vệ sinh sâu | cọ rửa, cọ bồn cầu, đánh sàn |
+| `num_support_tasks` | Hỗ trợ | giao ca, bàn giao, chụp ảnh, chuẩn bị |
+| `num_other_tasks` | Công việc khác | (không match keywords trên) |
+
+### Nhóm 2: Area Coverage (7 features)
+| Feature | Mô tả | Keywords |
+|---------|-------|----------|
+| `num_wc_tasks` | WC/Toilet | wc, toilet, nhà vệ sinh, bồn cầu |
+| `num_hallway_tasks` | Hành lang | hành lang, corridor, lối đi |
+| `num_lobby_tasks` | Sảnh | sảnh, lobby, tiền sảnh |
+| `num_outdoor_tasks` | Ngoại cảnh | ngoại cảnh, sân, vỉa hè, khuôn viên |
+| `num_elevator_tasks` | Thang máy | thang máy, elevator, cầu thang |
+| `num_medical_tasks_total` | Khu vực y tế | phòng bệnh, phòng khám, phòng mổ, xét nghiệm |
+| `num_indoor_room_tasks` | Phòng văn phòng | phòng họp, văn phòng, phòng giám đốc |
+
+### Nhóm 3: Ratios & Diversity (4 features)
+| Feature | Mô tả | Công thức |
+|---------|-------|-----------|
+| `cleaning_ratio` | Tỷ lệ vệ sinh | cleaning_tasks / total_tasks |
+| `trash_collection_ratio` | Tỷ lệ thu rác | trash_tasks / total_tasks |
+| `monitoring_ratio` | Tỷ lệ trực | monitoring_tasks / total_tasks |
+| `area_diversity` | Độ đa dạng khu vực | Số khu vực có ít nhất 1 task |
+
+---
+
+## 📝 Lưu Ý Quan Trọng
+
+### ⚠️ Về 2 Loại Công Việc
+
+> **📋 `all_task_normal` - CÔNG VIỆC HẰNG NGÀY:**
+> - Các task thực hiện **MỖI NGÀY** trong ca
+> - Ví dụ: Lau sàn, Thu rác, Vệ sinh WC
+> - Đặc điểm: Công việc nhẹ, lặp lại hàng ngày
+> - Tần suất: **Ngày nào cũng làm**
+
+> **📋 `all_task_dinhky` - CÔNG VIỆC ĐỊNH KỲ:**
+> - Các task thực hiện **THEO TUẦN** hoặc **THEO THÁNG**
+> - Ví dụ: Cọ bồn cầu (1 tuần/lần), Đánh sàn (1 tháng/lần)
+> - Đặc điểm: Công việc nặng hơn, không làm hàng ngày
+> - Tần suất: **Định kỳ (weekly/monthly)**
+
+**Cách xử lý trong model:**
+- Hệ thống sẽ **GỘP CẢ 2 LOẠI** công việc để phân tích
+- Trích xuất features từ tổng hợp: `all_task_normal + all_task_dinhky`
+- Điều này giúp model hiểu được **TOÀN BỘ KHỐI LƯỢNG CÔNG VIỆC** của ca đó
+
+---
+
+### ⚠️ Về Diện Tích
+> **TỔNG diện tích trong `final_2.csv` và `input.json` là tổng của TẤT CẢ các tầng trong tòa nhà!**
+> 
+> Ví dụ:
+> - Tòa nhà 15 tầng, mỗi tầng 300m² hành lang
+> - → `dien_tich_hanh_lang = 15 × 300 = 4500m²`
+> - KHÔNG PHẢI 300m²!
+> 
+> Công thức: `dien_tich_X = so_tang × dien_tich_X_moi_tang`
+
+---
+
+### 📌 Về Ca Làm Việc
+- Mỗi `input.json` chỉ đại diện cho **MỘT ca** của **MỘT tòa nhà**
+- Loại ca: Hành chính, Ca sáng, Ca chiều, Ca tối, Ca đêm
+- Bao gồm thông tin thời gian: `bat_dau`, `ket_thuc`, `tong_gio_lam`
+
+---
+
+### 🔤 Về Text Format
+- Text công việc phải bằng **tiếng Việt** (có dấu hoặc không dấu đều được)
+- Các công việc tách nhau bằng **dấu chấm phẩy (`;`)** hoặc `|` hoặc xuống dòng
+- Hệ thống tự động lowercase và normalize
+- **Nên viết rõ ràng:** "Lau sàn hành lang" thay vì "Lau sàn"
+- **Nên có thông tin khu vực:** "Vệ sinh WC tầng 2" thay vì chỉ "Vệ sinh"
+
+
+<div align="center">
+
+
+</div>
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diff --git a/all_predict.py b/all_predict.py
new file mode 100644
index 0000000..1be7bba
--- /dev/null
+++ b/all_predict.py
@@ -0,0 +1,228 @@
+"""
+ALL_PREDICT MODULE
+End-to-end inference pipeline:
+JSON input -> preprocess -> model -> prediction
+
+Created: January 2026
+"""
+
+from __future__ import annotations
+
+import json
+import joblib
+import numpy as np
+import pandas as pd
+from typing import Dict, Any, List
+
+# =========================================================
+# GLOBAL CONFIG (TRAINING CONTRACT)
+# =========================================================
+
+# Các cột KHÔNG BAO GIỜ đưa vào model
+DEFAULT_DROP_COLS = {
+    "ma_dia_diem",
+    "all_task_normal",
+    "all_task_dinhky",
+    "is_tasks_text_missing",
+    "so_luong",  # target nếu lỡ có
+}
+
+TASK_NORMAL_COL = "all_task_normal"
+TASK_DINHKY_COL = "all_task_dinhky"
+
+
+# =========================================================
+# IMPORT KEYWORD FEATURE EXTRACTOR
+# =========================================================
+from predict import extract_keyword_features_reduced_from_two_texts
+
+
+# =========================================================
+# 1) KEYWORD FEATURES
+# =========================================================
+def add_keyword_features_to_df(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Add keyword-based features from all_task_normal + all_task_dinhky
+    """
+    if TASK_NORMAL_COL not in df.columns:
+        df[TASK_NORMAL_COL] = None
+    if TASK_DINHKY_COL not in df.columns:
+        df[TASK_DINHKY_COL] = None
+
+    feats = df.apply(
+        lambda r: extract_keyword_features_reduced_from_two_texts(
+            r.get(TASK_NORMAL_COL),
+            r.get(TASK_DINHKY_COL),
+        ),
+        axis=1,
+    )
+    feats_df = pd.DataFrame(list(feats))
+    return pd.concat([df.reset_index(drop=True), feats_df.reset_index(drop=True)], axis=1)
+
+
+# =========================================================
+# 2) PREPROCESSING (MATCH TRAINING)
+# =========================================================
+def time_to_hour(x) -> float:
+    if pd.isna(x):
+        return np.nan
+
+    if hasattr(x, "hour"):
+        return float(x.hour) + float(getattr(x, "minute", 0)) / 60.0
+
+    s = str(x).strip()
+    if " " in s and ":" in s:
+        s = s.split(" ", 1)[1].strip()
+
+    if ":" in s:
+        try:
+            h, m = s.split(":")[0:2]
+            return float(h) + float(m) / 60.0
+        except Exception:
+            return np.nan
+
+    try:
+        return float(s)
+    except Exception:
+        return np.nan
+
+
+def build_X_from_raw_df(df_raw: pd.DataFrame) -> pd.DataFrame:
+    """
+    Feature selection: tự động drop các cột không dùng cho model
+    """
+    df = df_raw.copy()
+    drop_cols = [c for c in df.columns if c in DEFAULT_DROP_COLS]
+    if drop_cols:
+        df = df.drop(columns=drop_cols)
+    return df
+
+
+def preprocess_like_training(X: pd.DataFrame) -> pd.DataFrame:
+    """
+    Same logic as training CELL 3
+    """
+    X_proc = X.copy()
+
+    if "bat_dau" in X_proc.columns:
+        X_proc["hour_start"] = X_proc["bat_dau"].apply(time_to_hour)
+    if "ket_thuc" in X_proc.columns:
+        X_proc["hour_end"] = X_proc["ket_thuc"].apply(time_to_hour)
+
+    if "hour_start" in X_proc.columns and "hour_end" in X_proc.columns:
+        end_adj = X_proc["hour_end"].copy()
+        cross = (
+            X_proc["hour_start"].notna()
+            & X_proc["hour_end"].notna()
+            & (X_proc["hour_end"] < X_proc["hour_start"])
+        )
+        end_adj.loc[cross] += 24
+        X_proc["shift_length"] = (end_adj - X_proc["hour_start"]).clip(lower=0)
+        X_proc["is_cross_day"] = cross.astype(int)
+
+    for c in ["bat_dau", "ket_thuc"]:
+        if c in X_proc.columns:
+            X_proc = X_proc.drop(columns=[c])
+
+    cat_cols = [c for c in X_proc.columns if X_proc[c].dtype == "object"]
+    if cat_cols:
+        X_proc = pd.get_dummies(X_proc, columns=cat_cols, dummy_na=True)
+
+    X_proc = X_proc.replace([np.inf, -np.inf], np.nan).fillna(0)
+    return X_proc
+
+
+# =========================================================
+# 3) ALIGN TO TRAINING SCHEMA
+# =========================================================
+def load_schema_columns(columns_joblib_path: str) -> List[str]:
+    cols = joblib.load(columns_joblib_path)
+    if not isinstance(cols, list):
+        raise ValueError("Invalid schema columns file")
+    return cols
+
+
+def align_to_schema(X_proc: pd.DataFrame, schema_columns: List[str]) -> pd.DataFrame:
+    X = X_proc.copy()
+
+    for c in schema_columns:
+        if c not in X.columns:
+            X[c] = 0
+
+    extra_cols = [c for c in X.columns if c not in schema_columns]
+    if extra_cols:
+        X = X.drop(columns=extra_cols)
+
+    X = X[schema_columns]
+    X = X.apply(pd.to_numeric, errors="coerce").fillna(0)
+    return X
+
+
+# =========================================================
+# 4) FULL PIPELINE: RECORD -> MODEL INPUT
+# =========================================================
+def record_to_model_input_df(
+    record: Dict[str, Any],
+    schema_columns: List[str],
+) -> pd.DataFrame:
+    df_raw = pd.DataFrame([record])
+    df_kw = add_keyword_features_to_df(df_raw)
+    X_raw = build_X_from_raw_df(df_kw)
+    X_proc = preprocess_like_training(X_raw)
+    X_aligned = align_to_schema(X_proc, schema_columns)
+    return X_aligned
+
+
+# =========================================================
+# 5) PREDICT
+# =========================================================
+def predict_from_record(
+    record: Dict[str, Any],
+    model_joblib_path: str,
+    columns_joblib_path: str,
+) -> Dict[str, Any]:
+    schema_columns = load_schema_columns(columns_joblib_path)
+    X = record_to_model_input_df(record, schema_columns)
+
+    model = joblib.load(model_joblib_path)
+
+    pred_log = model.predict(X.values)[0]
+    pred_raw = float(np.maximum(0.0, np.expm1(pred_log)))
+
+    return {
+        "so_luong_du_doan_raw": round(pred_raw, 2),
+        "so_luong_du_doan_round": int(np.rint(pred_raw)),
+    }
+
+
+def predict_from_json_file(
+    json_path: str,
+    model_joblib_path: str,
+    columns_joblib_path: str,
+) -> str:
+    with open(json_path, "r", encoding="utf-8") as f:
+        record = json.load(f)
+
+    result = predict_from_record(
+        record,
+        model_joblib_path,
+        columns_joblib_path,
+    )
+    return json.dumps(result, ensure_ascii=False, indent=2)
+
+
+# =========================================================
+# 6) MAIN
+# =========================================================
+if __name__ == "__main__":
+    MODEL_JOBLIB = "./artifacts/extratrees_staff_model.joblib"
+    COLUMNS_JOBLIB = "./artifacts/X_proc_columns.joblib"
+    INPUT_JSON = "./input.json"
+
+    print(
+        predict_from_json_file(
+            json_path=INPUT_JSON,
+            model_joblib_path=MODEL_JOBLIB,
+            columns_joblib_path=COLUMNS_JOBLIB,
+        )
+    )
diff --git a/artifacts/X_proc_columns.joblib b/artifacts/X_proc_columns.joblib
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diff --git a/artifacts/model_meta.joblib b/artifacts/model_meta.joblib
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diff --git a/create_aggregate_buildings.py b/create_aggregate_buildings.py
deleted file mode 100644
index a333f28..0000000
--- a/create_aggregate_buildings.py
+++ /dev/null
@@ -1,295 +0,0 @@
-import json
-import pandas as pd
-from pathlib import Path
-from collections import defaultdict
-
-def main():
-    # Đọc file JSON
-    json_file = Path(r'c:\Users\SLG PC\Documents\Predict_calamviecHM\Link LLV 2025_clean.json')
-    print("Đang đọc file JSON...")
-    with open(json_file, 'r', encoding='utf-8') as f:
-        json_data = json.load(f)
-    
-    print(f"✓ Đã đọc {len(json_data)} records từ JSON")
-    
-    # Danh sách các trường cần aggregate (khả năng ≥50%)
-    # Nhóm 1: Trường định danh (không aggregate, lấy giá trị đầu tiên)
-    identity_fields = ['Mã địa điểm', 'Loại hình']
-    
-    # Nhóm 2: Các trường diện tích/số lượng cần SUM
-    sum_fields = [
-        'Sàn ngoại cảnh',
-        'Sàn Sảnh', 
-        'Sàn Hành lang',
-        'Sàn WC',
-        'Sàn Phòng',
-        'Thảm',
-        'Dốc hầm',
-        'Viền phản quang',
-        'Ốp tường',
-        'Ốp chân tường',
-        'Rãnh thoát nước',
-        'Kính'
-    ]
-    
-    # Nhóm 3: Các trường cần xử lý đặc biệt
-    special_fields = {
-        'Tên Tòa Tháp': 'first',  # Lấy tên tòa đầu tiên
-        'Tầng': 'count_distinct',  # Đếm số tầng khác nhau
-        'Cửa thang máy': 'sum',  # Tổng số cửa thang máy
-        'Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ': 'first'  # Lấy giá trị đầu tiên
-    }
-    
-    # Aggregate dữ liệu theo Mã địa điểm
-    print("\nĐang aggregate dữ liệu theo Mã địa điểm...")
-    aggregated_data = defaultdict(lambda: {
-        'Mã địa điểm': '',
-        'Loại hình': '',
-        'Tên Tòa Tháp': '',
-        'Số tầng': 0,
-        'Tổng số cửa thang máy': 0,
-        'Mức độ Lưu lượng KH': '',
-        'Diện tích ngoại cảnh Tòa tháp (m2)': 0,
-        'Sàn Sảnh (m2)': 0,
-        'Sàn Hành lang (m2)': 0,
-        'Sàn WC (m2)': 0,
-        'Sàn Phòng (m2)': 0,
-        'Thảm (m2)': 0,
-        'Dốc hầm (m)': 0,
-        'Viền phản quang (m)': 0,
-        'Ốp tường (m2)': 0,
-        'Ốp chân tường (m2)': 0,
-        'Rãnh thoát nước (m)': 0,
-        'Kính (m2)': 0,
-        'tangs': set(),  # Tập hợp các tầng (để đếm)
-    })
-    
-    # Xử lý từng record
-    for record in json_data:
-        ma_dia_diem = record.get('Mã địa điểm', '')
-        if not ma_dia_diem:
-            continue
-            
-        agg = aggregated_data[ma_dia_diem]
-        
-        # Lấy thông tin định danh (lần đầu tiên)
-        if not agg['Mã địa điểm']:
-            agg['Mã địa điểm'] = ma_dia_diem
-            agg['Loại hình'] = record.get('Loại hình', '')
-            agg['Tên Tòa Tháp'] = record.get('Tên Tòa Tháp', '')
-            agg['Mức độ Lưu lượng KH'] = record.get('Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ', '')
-        
-        # Tập hợp các tầng
-        tang = record.get('Tầng', '')
-        if tang:
-            agg['tangs'].add(str(tang))
-        
-        # SUM các trường diện tích - Hàm helper để convert an toàn
-        def safe_float(value):
-            try:
-                return float(value) if value else 0
-            except (ValueError, TypeError):
-                return 0
-        
-        agg['Diện tích ngoại cảnh Tòa tháp (m2)'] += safe_float(record.get('Sàn ngoại cảnh', 0))
-        agg['Sàn Sảnh (m2)'] += safe_float(record.get('Sàn Sảnh', 0))
-        agg['Sàn Hành lang (m2)'] += safe_float(record.get('Sàn Hành lang', 0))
-        agg['Sàn WC (m2)'] += safe_float(record.get('Sàn WC', 0))
-        agg['Sàn Phòng (m2)'] += safe_float(record.get('Sàn Phòng', 0))
-        agg['Thảm (m2)'] += safe_float(record.get('Thảm', 0))
-        agg['Dốc hầm (m)'] += safe_float(record.get('Dốc hầm', 0))
-        agg['Viền phản quang (m)'] += safe_float(record.get('Viền phản quang', 0))
-        agg['Ốp tường (m2)'] += safe_float(record.get('Ốp tường', 0))
-        agg['Ốp chân tường (m2)'] += safe_float(record.get('Ốp chân tường', 0))
-        agg['Rãnh thoát nước (m)'] += safe_float(record.get('Rãnh thoát nước', 0))
-        agg['Kính (m2)'] += safe_float(record.get('Kính', 0))
-        agg['Tổng số cửa thang máy'] += safe_float(record.get('Cửa thang máy', 0))
-    
-    # Tính số tầng và làm sạch dữ liệu
-    print("Đang xử lý dữ liệu cuối cùng...")
-    final_data = []
-    for ma_dia_diem, agg in aggregated_data.items():
-        agg['Số tầng'] = len(agg['tangs'])
-        del agg['tangs']  # Xóa trường tạm
-        final_data.append(agg)
-    
-    # Tạo DataFrame
-    df = pd.DataFrame(final_data)
-    
-    # Sắp xếp theo Mã địa điểm
-    df = df.sort_values('Mã địa điểm')
-    
-    # Sắp xếp lại thứ tự cột
-    column_order = [
-        'Mã địa điểm',
-        'Loại hình',
-        'Tên Tòa Tháp',
-        'Mức độ Lưu lượng KH',
-        'Số tầng',
-        'Tổng số cửa thang máy',
-        'Diện tích ngoại cảnh Tòa tháp (m2)',
-        'Sàn Sảnh (m2)',
-        'Sàn Hành lang (m2)',
-        'Sàn WC (m2)',
-        'Sàn Phòng (m2)',
-        'Thảm (m2)',
-        'Dốc hầm (m)',
-        'Viền phản quang (m)',
-        'Ốp tường (m2)',
-        'Ốp chân tường (m2)',
-        'Rãnh thoát nước (m)',
-        'Kính (m2)',
-    ]
-    
-    df = df[column_order]
-    
-    # Làm tròn số
-    numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns
-    for col in numeric_columns:
-        if col not in ['Mã địa điểm', 'Loại hình']:
-            df[col] = df[col].round(2)
-    
-    print(f"\n✓ Đã aggregate thành {len(df)} tòa nhà")
-    
-    # Lọc bỏ các tòa có tất cả các trường số bằng 0
-    print("Đang lọc bỏ các tòa nhà có tất cả trường số = 0...")
-    numeric_cols_to_check = [col for col in numeric_columns if col not in ['Mã địa điểm', 'Loại hình']]
-    
-    # Tìm các dòng có ít nhất một trường số khác 0
-    df_filtered = df[(df[numeric_cols_to_check] != 0).any(axis=1)]
-    
-    removed_count = len(df) - len(df_filtered)
-    print(f"✓ Đã loại bỏ {removed_count} tòa nhà có tất cả trường số = 0")
-    print(f"✓ Còn lại {len(df_filtered)} tòa nhà có dữ liệu hợp lệ")
-    
-    # Cập nhật df
-    df = df_filtered
-    
-    # Xuất ra Excel
-    output_file = 'Du_Lieu_Toa_Nha_Aggregate.xlsx'
-    print(f"\nĐang xuất dữ liệu ra file Excel...")
-    
-    with pd.ExcelWriter(output_file, engine='openpyxl') as writer:
-        # Sheet 1: Dữ liệu đầy đủ
-        df.to_excel(writer, sheet_name='Dữ liệu tòa nhà', index=False)
-        
-        # Sheet 2: Thống kê
-        stats_data = {
-            'Chỉ số': [
-                'Tổng số tòa nhà',
-                'Tổng diện tích Sàn Sảnh (m2)',
-                'Tổng diện tích Sàn Hành lang (m2)',
-                'Tổng diện tích Sàn WC (m2)',
-                'Tổng diện tích Sàn Phòng (m2)',
-                'Tổng diện tích Thảm (m2)',
-                'Tổng diện tích Kính (m2)',
-                'Trung bình số tầng/tòa',
-                'Trung bình số cửa thang máy/tòa',
-            ],
-            'Giá trị': [
-                len(df),
-                df['Sàn Sảnh (m2)'].sum(),
-                df['Sàn Hành lang (m2)'].sum(),
-                df['Sàn WC (m2)'].sum(),
-                df['Sàn Phòng (m2)'].sum(),
-                df['Thảm (m2)'].sum(),
-                df['Kính (m2)'].sum(),
-                df['Số tầng'].mean(),
-                df['Tổng số cửa thang máy'].mean(),
-            ]
-        }
-        df_stats = pd.DataFrame(stats_data)
-        df_stats['Giá trị'] = df_stats['Giá trị'].round(2)
-        df_stats.to_excel(writer, sheet_name='Thống kê', index=False)
-        
-        # Sheet 3: Danh sách các trường đã aggregate
-        field_info = {
-            'Tên trường': column_order,
-            'Cách xử lý': [
-                'Khóa chính',
-                'Giá trị đầu tiên',
-                'Giá trị đầu tiên',
-                'Giá trị đầu tiên',
-                'COUNT DISTINCT các tầng',
-                'SUM tất cả cửa thang máy',
-                'SUM tất cả diện tích',
-                'SUM tất cả diện tích',
-                'SUM tất cả diện tích',
-                'SUM tất cả diện tích',
-                'SUM tất cả diện tích',
-                'SUM tất cả diện tích',
-                'SUM tất cả độ dài',
-                'SUM tất cả độ dài',
-                'SUM tất cả diện tích',
-                'SUM tất cả diện tích',
-                'SUM tất cả độ dài',
-                'SUM tất cả diện tích',
-            ],
-            'Nguồn JSON': [
-                'Mã địa điểm',
-                'Loại hình',
-                'Tên Tòa Tháp',
-                'Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ',
-                'Tầng',
-                'Cửa thang máy',
-                'Sàn ngoại cảnh',
-                'Sàn Sảnh',
-                'Sàn Hành lang',
-                'Sàn WC',
-                'Sàn Phòng',
-                'Thảm',
-                'Dốc hầm',
-                'Viền phản quang',
-                'Ốp tường',
-                'Ốp chân tường',
-                'Rãnh thoát nước',
-                'Kính',
-            ],
-            'Khả năng đáp ứng CSV': [
-                '✅ 100%',
-                '✅ 100%',
-                '🔶 40%',
-                '⚠️ 50%',
-                '🔶 40%',
-                '🔶 30%',
-                '✅ 100%',
-                '✅ 100%',
-                '✅ 100%',
-                '✅ 100%',
-                '✅ 100%',
-                '✅ 100%',
-                '✅ 100%',
-                '✅ 100%',
-                '✅ 100%',
-                '✅ 100%',
-                '✅ 100%',
-                '✅ 100%',
-            ]
-        }
-        df_fields = pd.DataFrame(field_info)
-        df_fields.to_excel(writer, sheet_name='Thông tin các trường', index=False)
-    
-    print(f"✅ ĐÃ HOÀN THÀNH!")
-    print("="*100)
-    print(f"\n📁 File xuất ra: {output_file}")
-    print(f"📊 Tổng số tòa nhà: {len(df)}")
-    print(f"📋 Số trường dữ liệu: {len(column_order)}")
-    print("\n📄 Nội dung file gồm 3 sheets:")
-    print("   1. Dữ liệu tòa nhà        - Bảng dữ liệu đầy đủ của tất cả tòa nhà")
-    print("   2. Thống kê               - Các chỉ số thống kê tổng hợp")
-    print("   3. Thông tin các trường   - Giải thích cách xử lý từng trường")
-    
-    print("\n" + "="*100)
-    print("📊 MẪU DỮ LIỆU (5 tòa nhà đầu tiên):")
-    print("="*100)
-    print(df.head().to_string())
-    
-    print("\n" + "="*100)
-    print("📈 THỐNG KÊ TỔNG QUAN:")
-    print("="*100)
-    print(df.describe().round(2).to_string())
-    
-    print("\n" + "="*100)
-
-if __name__ == "__main__":
-    main()
diff --git a/data_raw/Link LLV 1_5_2025.json b/data_raw/Link LLV 1_5_2025.json
new file mode 100644
index 0000000..3477ef7
--- /dev/null
+++ b/data_raw/Link LLV 1_5_2025.json	
@@ -0,0 +1,26050 @@
+[
+    {
+        "Mã địa điểm": "115-2",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Part time",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "10:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "4:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.5
+    },
+    {
+        "Mã địa điểm": "115-2",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Part time",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "10:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "4:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.5
+    },
+    {
+        "Mã địa điểm": "115-3",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "15:00:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:00:00",
+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "7:00:00",
+        "Tổng công/ngày": 0.875
+    },
+    {
+        "Mã địa điểm": "115-4",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "15:30:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:30:00",
+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:30:00",
+        "Tổng công/ngày": 1.0625000000000002
+    },
+    {
+        "Mã địa điểm": "115-5",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "101-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 3.0,
+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.0
+    },
+    {
+        "Mã địa điểm": "101-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "14:00:00",
+        "Kết thúc": "22:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 5.0,
+        "Tổng giờ/ca/ngày": "1 day, 16:00:00",
+        "Tổng công/ngày": 4.999999999999999
+    },
+    {
+        "Mã địa điểm": "101-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca đêm",
+        "Bắt đầu": "2025-01-01 22:00:00",
+        "Kết thúc": "2025-01-02 06:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.000000000007276
+    },
+    {
+        "Mã địa điểm": "101-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca gãy",
+        "Bắt đầu": "12:00:00",
+        "Kết thúc": "20:00:00",
+        "Nghỉ 1 Bắt đầu": "17:30:00",
+        "Nghỉ 1 Kết thúc": "18:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "7:30:00",
+        "Tổng công/ngày": 0.9375
+    },
+    {
+        "Mã địa điểm": "101-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "2:00:00",
+        "Số lượng": 24.0,
+        "Tổng giờ/ca/ngày": "7 days, 12:00:00",
+        "Tổng công/ngày": 22.5
+    },
+    {
+        "Mã địa điểm": "101-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 3.0,
+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.0
+    },
+    {
+        "Mã địa điểm": "101-2",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca gãy",
+        "Bắt đầu": "11:00:00",
+        "Kết thúc": "22:00:00",
+        "Nghỉ 1 Bắt đầu": "13:00:00",
+        "Nghỉ 1 Kết thúc": "16:00:00",
+        "Nghỉ 2 Bắt đầu": "18:00:00",
+        "Nghỉ 2 Kết thúc": "18:30:00",
+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "3:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "7:30:00",
+        "Tổng công/ngày": 0.9374999999999998
+    },
+    {
+        "Mã địa điểm": "101-2",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca đêm",
+        "Bắt đầu": "2025-01-01 22:00:00",
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+        "Tổng công/ngày": 4.0
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+        "Tổng công/ngày": 1.000000000007276
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
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+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.5
+    },
+    {
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+        "Kết thúc": "20:00:00",
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+        "Tổng công/ngày": 0.9375
+    },
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+        "Tổng công/ngày": 20.625
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+        "Tổng công/ngày": 8.0
+    },
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+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 1.9999999999999996
+    },
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+        "Tổng công/ngày": 5.000000000000001
+    },
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+        "Tổng giờ/ca/ngày": "2 days, 8:00:00",
+        "Tổng công/ngày": 6.999999999999999
+    },
+    {
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+        "Kết thúc": "22:00:00",
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+        "Nghỉ 2 Kết thúc": "18:30:00",
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+        "Tổng giờ/ca/ngày": "7:30:00",
+        "Tổng công/ngày": 0.9374999999999998
+    },
+    {
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+        "Lịch": "T2 đến CN",
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+        "Kết thúc": "2025-01-02 06:30:00",
+        "Nghỉ 1 Bắt đầu": "01:00:00",
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+        "Tổng giờ/ca/ngày": "7:30:00",
+        "Tổng công/ngày": 0.9375000000145519
+    },
+    {
+        "Mã địa điểm": "570-1",
+        "Lịch": "T2 đến T6",
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+        "Kết thúc": "18:00:00",
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+        "Tổng giờ/ca/ngày": "6:00:00",
+        "Tổng công/ngày": 0.75
+    },
+    {
+        "Mã địa điểm": "114-1",
+        "Lịch": "T2 đến T6",
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+        "Kết thúc": "16:00:00",
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+        "Tổng giờ/ca/ngày": "9 days, 16:00:00",
+        "Tổng công/ngày": 29.0
+    },
+    {
+        "Mã địa điểm": "594-1",
+        "Lịch": "T2 đến CN",
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+        "Tổng giờ/ca/ngày": "3 days, 0:00:00",
+        "Tổng công/ngày": 9.000000000000002
+    },
+    {
+        "Mã địa điểm": "594-1",
+        "Lịch": "T2 đến CN",
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+        "Kết thúc": "21:00:00",
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+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "594-1",
+        "Lịch": "T2 đến CN",
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+        "Bắt đầu": "2025-01-01 22:00:00",
+        "Kết thúc": "2025-01-02 06:00:00",
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+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.000000000014552
+    },
+    {
+        "Mã địa điểm": "114-1",
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+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:00:00",
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+        "Tổng giờ/ca/ngày": "1 day, 8:00:00",
+        "Tổng công/ngày": 4.0
+    },
+    {
+        "Mã địa điểm": "113-1",
+        "Lịch": "T2 đến CN",
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+        "Kết thúc": "21:00:00",
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+        "Tổng giờ/ca/ngày": "1 day, 16:00:00",
+        "Tổng công/ngày": 5.000000000000001
+    },
+    {
+        "Mã địa điểm": "137-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ/ca/ngày": "2 days, 18:30:00",
+        "Tổng công/ngày": 8.3125
+    },
+    {
+        "Mã địa điểm": "137-1",
+        "Lịch": "T2 đến T7",
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+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "3 days, 8:00:00",
+        "Tổng công/ngày": 10.000000000000002
+    },
+    {
+        "Mã địa điểm": "114-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "14:00:00",
+        "Kết thúc": "22:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
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+        "Tổng giờ/ca/ngày": "1 day, 8:00:00",
+        "Tổng công/ngày": 3.999999999999999
+    },
+    {
+        "Mã địa điểm": "127-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Part time",
+        "Bắt đầu": "16:00:00",
+        "Kết thúc": "20:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": 0,
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+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.5000000000000002
+    },
+    {
+        "Mã địa điểm": "140-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "2 days, 8:00:00",
+        "Tổng công/ngày": 6.999999999999999
+    },
+    {
+        "Mã địa điểm": "140-1",
+        "Lịch": "T2 đến CN",
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+        "Kết thúc": "21:00:00",
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+        "Tổng giờ/ca/ngày": "1 day, 8:00:00",
+        "Tổng công/ngày": 4.0
+    },
+    {
+        "Mã địa điểm": "140-1",
+        "Lịch": "Lễ, tết",
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+        "Kết thúc": "14:30:00",
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+        "Tổng giờ/ca/ngày": "1 day, 8:00:00",
+        "Tổng công/ngày": 4.0
+    },
+    {
+        "Mã địa điểm": "140-1",
+        "Lịch": "Lễ, tết",
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+        "Kết thúc": "21:00:00",
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+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "114-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca đêm",
+        "Bắt đầu": "2025-01-01 22:00:00",
+        "Kết thúc": "2025-01-02 06:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.000000000021828
+    },
+    {
+        "Mã địa điểm": "457-1",
+        "Lịch": "T2 đến CN",
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+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": "18:00:00",
+        "Nghỉ 1 Kết thúc": "18:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "0:30:00",
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+        "Tổng giờ/ca/ngày": "15:00:00",
+        "Tổng công/ngày": 1.875
+    },
+    {
+        "Mã địa điểm": "495-1",
+        "Lịch": 0,
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
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+        "Tổng giờ nghỉ": "2:00:00",
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+        "Tổng giờ/ca/ngày": "5 days, 8:00:00",
+        "Tổng công/ngày": 16.0
+    },
+    {
+        "Mã địa điểm": "495-1",
+        "Lịch": 0,
+        "Loại ca": "Ca đêm",
+        "Bắt đầu": "16:00:00",
+        "Kết thúc": "1900-01-01 00:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "150-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": 0,
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+        "Tổng giờ/ca/ngày": "2 days, 0:00:00",
+        "Tổng công/ngày": 6.0
+    },
+    {
+        "Mã địa điểm": "495-1",
+        "Lịch": 0,
+        "Loại ca": "Part time",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "10:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.5
+    },
+    {
+        "Mã địa điểm": "150-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Part time",
+        "Bắt đầu": "14:30:00",
+        "Kết thúc": "18:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": 0,
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+        "Tổng giờ/ca/ngày": "12:00:00",
+        "Tổng công/ngày": 1.5000000000000007
+    },
+    {
+        "Mã địa điểm": "108-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "15:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "12:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 7.0,
+        "Tổng giờ/ca/ngày": "2 days, 8:00:00",
+        "Tổng công/ngày": 6.999999999999999
+    },
+    {
+        "Mã địa điểm": "108-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:30:00",
+        "Kết thúc": "22:00:00",
+        "Nghỉ 1 Bắt đầu": "18:00:00",
+        "Nghỉ 1 Kết thúc": "18:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 4.0,
+        "Tổng giờ/ca/ngày": "1 day, 8:00:00",
+        "Tổng công/ngày": 3.999999999999999
+    },
+    {
+        "Mã địa điểm": "123-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "17:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "2:00:00",
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "142-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "08:00:00",
+        "Kết thúc": "17:00:00",
+        "Nghỉ 1 Bắt đầu": "11:45:00",
+        "Nghỉ 1 Kết thúc": "12:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:45:00",
+        "Tổng giờ nghỉ": "0:15:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:45:00",
+        "Tổng công/ngày": 1.09375
+    },
+    {
+        "Mã địa điểm": "106-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "12:00:00",
+        "Kết thúc": "20:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "142-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:45:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "7:45:00",
+        "Tổng giờ nghỉ": "1:15:00",
+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": "0:00:00",
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "129-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "05:00:00",
+        "Kết thúc": "13:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 1.9999999999999996
+    },
+    {
+        "Mã địa điểm": "106-2",
+        "Lịch": 0,
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "12:00:00",
+        "Kết thúc": "20:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
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+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "129-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 5.0,
+        "Tổng giờ/ca/ngày": "1 day, 16:00:00",
+        "Tổng công/ngày": 5.000000000000001
+    },
+    {
+        "Mã địa điểm": "129-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "14:00:00",
+        "Kết thúc": "22:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 7.0,
+        "Tổng giờ/ca/ngày": "2 days, 8:00:00",
+        "Tổng công/ngày": 6.999999999999999
+    },
+    {
+        "Mã địa điểm": "105-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "11:50:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:40:00",
+        "Tổng giờ nghỉ": "0:50:00",
+        "Số lượng": 14.0,
+        "Tổng giờ/ca/ngày": "4 days, 11:20:00",
+        "Tổng công/ngày": 13.416666666666664
+    },
+    {
+        "Mã địa điểm": "105-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:30:00",
+        "Kết thúc": "22:00:00",
+        "Nghỉ 1 Bắt đầu": "17:25:00",
+        "Nghỉ 1 Kết thúc": "18:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:55:00",
+        "Tổng giờ nghỉ": "0:35:00",
+        "Số lượng": 13.0,
+        "Tổng giờ/ca/ngày": "4 days, 6:55:00",
+        "Tổng công/ngày": 12.864583333333332
+    },
+    {
+        "Mã địa điểm": "105-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "16:00:00",
+        "Kết thúc": "22:00:00",
+        "Nghỉ 1 Bắt đầu": "18:00:00",
+        "Nghỉ 1 Kết thúc": "18:20:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "5:40:00",
+        "Tổng giờ nghỉ": "0:20:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "5:40:00",
+        "Tổng công/ngày": 0.7083333333333335
+    },
+    {
+        "Mã địa điểm": "105-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca đêm",
+        "Bắt đầu": "2025-01-01 22:00:00",
+        "Kết thúc": "2025-01-02 06:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:30:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 7.0,
+        "Tổng giờ/ca/ngày": "2 days, 11:30:00",
+        "Tổng công/ngày": 7.437500000101863
+    },
+    {
+        "Mã địa điểm": "129-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "15:00:00",
+        "Nghỉ 1 Bắt đầu": "12:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "7:30:00",
+        "Tổng công/ngày": 0.9375000000000002
+    },
+    {
+        "Mã địa điểm": "105-2",
+        "Lịch": 0,
+        "Loại ca": 0,
+        "Bắt đầu": 0,
+        "Kết thúc": 0,
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": 0,
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "120-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Part time",
+        "Bắt đầu": "16:00:00",
+        "Kết thúc": "20:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "4:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.5000000000000002
+    },
+    {
+        "Mã địa điểm": "570-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Hành chính",
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+        "Kết thúc": "15:00:00",
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+        "Tổng giờ nghỉ": "1:25:00",
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+        "Tổng giờ/ca/ngày": "22:45:00",
+        "Tổng công/ngày": 2.84375
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+        "Tổng giờ/ca/ngày": "6:00:00",
+        "Tổng công/ngày": 0.75
+    },
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+        "Tổng công/ngày": 2.84375
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+        "Kết thúc": "15:00:00",
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+        "Tổng công/ngày": 12.75
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+        "Kết thúc": "22:00:00",
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+        "Tổng công/ngày": 7.4375
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+        "Tổng giờ/ca/ngày": "3 days, 0:00:00",
+        "Tổng công/ngày": 9.000000000000002
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+    {
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+        "Tổng công/ngày": 2.0
+    },
+    {
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+        "Kết thúc": "2025-01-02 06:00:00",
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+        "Tổng công/ngày": 2.000000000014552
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+        "Tổng giờ/ca/ngày": "6 days, 16:00:00",
+        "Tổng công/ngày": 20.000000000000004
+    },
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+        "Kết thúc": "21:00:00",
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+        "Tổng giờ/ca/ngày": "1 day, 4:00:00",
+        "Tổng công/ngày": 3.500000000000001
+    },
+    {
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+        "Tổng giờ/ca/ngày": "1 day, 8:00:00",
+        "Tổng công/ngày": 4.0
+    },
+    {
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+        "Tổng công/ngày": 4.0
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+        "Tổng giờ/ca/ngày": "1 day, 16:00:00",
+        "Tổng công/ngày": 5.000000000000001
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+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.0
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+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
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+        "Tổng giờ/ca/ngày": "2 days, 18:30:00",
+        "Tổng công/ngày": 8.3125
+    },
+    {
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+        "Kết thúc": "13:30:00",
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+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.0
+    },
+    {
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+        "Kết thúc": "21:30:00",
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+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.0
+    },
+    {
+        "Mã địa điểm": "137-1",
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+        "Tổng giờ/ca/ngày": "3 days, 8:00:00",
+        "Tổng công/ngày": 10.000000000000002
+    },
+    {
+        "Mã địa điểm": "121-4",
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+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "121-4",
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+        "Tổng công/ngày": 3.0
+    },
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+        "Mã địa điểm": "127-1",
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+        "Kết thúc": "16:00:00",
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+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "144-1",
+        "Lịch": "T2 đến CN",
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+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "14:30:00",
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+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "144-1",
+        "Lịch": "T2 đến CN",
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+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "127-1",
+        "Lịch": "T2 đến CN",
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+        "Bắt đầu": "16:00:00",
+        "Kết thúc": "20:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": 0,
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+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.5000000000000002
+    },
+    {
+        "Mã địa điểm": "140-1",
+        "Lịch": "T2 đến CN",
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+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "14:30:00",
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+        "Tổng giờ/ca/ngày": "2 days, 8:00:00",
+        "Tổng công/ngày": 6.999999999999999
+    },
+    {
+        "Mã địa điểm": "140-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "1 day, 8:00:00",
+        "Tổng công/ngày": 4.0
+    },
+    {
+        "Mã địa điểm": "373-1",
+        "Lịch": 0,
+        "Loại ca": 0,
+        "Bắt đầu": 0,
+        "Kết thúc": 0,
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
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+        "Tổng công/ngày": 0.0
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+        "Tổng công/ngày": 2.0
+    },
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+        "Tổng công/ngày": 4.0
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+        "Tổng công/ngày": 0.0
+    },
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+        "Tổng công/ngày": 1.875
+    },
+    {
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+        "Tổng công/ngày": 0.24999999999999978
+    },
+    {
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+        "Tổng công/ngày": 2.125
+    },
+    {
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+        "Kết thúc": "20:30:00",
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+        "Tổng giờ/ca/ngày": "9:00:00",
+        "Tổng công/ngày": 1.125
+    },
+    {
+        "Mã địa điểm": "128-2",
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+        "Kết thúc": "17:00:00",
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "150-1",
+        "Lịch": "T2 đến T7",
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+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "14:30:00",
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+        "Tổng giờ/ca/ngày": "2 days, 0:00:00",
+        "Tổng công/ngày": 6.0
+    },
+    {
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+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
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+        "Tổng giờ/ca/ngày": "2 days, 8:00:00",
+        "Tổng công/ngày": 6.999999999999999
+    },
+    {
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+        "Tổng giờ/ca/ngày": "12:00:00",
+        "Tổng công/ngày": 1.5000000000000007
+    },
+    {
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+        "Kết thúc": "15:00:00",
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+        "Tổng giờ/ca/ngày": "2 days, 8:00:00",
+        "Tổng công/ngày": 6.999999999999999
+    },
+    {
+        "Mã địa điểm": "468-1",
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+        "Tổng giờ/ca/ngày": "2 days, 16:00:00",
+        "Tổng công/ngày": 8.0
+    },
+    {
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+        "Lịch": "T2 đến CN",
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+        "Kết thúc": "22:00:00",
+        "Nghỉ 1 Bắt đầu": "18:00:00",
+        "Nghỉ 1 Kết thúc": "18:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Số lượng": 4.0,
+        "Tổng giờ/ca/ngày": "1 day, 8:00:00",
+        "Tổng công/ngày": 3.999999999999999
+    },
+    {
+        "Mã địa điểm": "468-1",
+        "Lịch": "T2 đến CN",
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+        "Kết thúc": "21:00:00",
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+        "Tổng giờ nghỉ": 0,
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+        "Tổng giờ/ca/ngày": "3 days, 0:00:00",
+        "Tổng công/ngày": 9.000000000000002
+    },
+    {
+        "Mã địa điểm": "103-1",
+        "Lịch": "T2 đến T7",
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+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "17:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "136-1",
+        "Lịch": 0,
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "106-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "12:00:00",
+        "Kết thúc": "20:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "106-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "2:00:00",
+        "Số lượng": 6.0,
+        "Tổng giờ/ca/ngày": "1 day, 18:00:00",
+        "Tổng công/ngày": 5.25
+    },
+    {
+        "Mã địa điểm": "169-1",
+        "Lịch": "T2 đến T7",
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+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "12:00:00",
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "106-2",
+        "Lịch": 0,
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "1:00:00",
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+        "Tổng giờ/ca/ngày": "2 days, 0:00:00",
+        "Tổng công/ngày": 6.0
+    },
+    {
+        "Mã địa điểm": "106-2",
+        "Lịch": 0,
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+        "Kết thúc": "20:00:00",
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "165-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "12:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 0.9999999999999998
+    },
+    {
+        "Mã địa điểm": "409-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "2:00:00",
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+        "Tổng giờ/ca/ngày": "1 day, 11:00:00",
+        "Tổng công/ngày": 4.374999999999999
+    },
+    {
+        "Mã địa điểm": "165-2",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "14:00:00",
+        "Kết thúc": "22:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 0.9999999999999998
+    },
+    {
+        "Mã địa điểm": "472-1",
+        "Lịch": 0,
+        "Loại ca": 0,
+        "Bắt đầu": 0,
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+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": 0,
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+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "283-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
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+        "Tổng giờ/ca/ngày": "2 days, 16:00:00",
+        "Tổng công/ngày": 8.0
+    },
+    {
+        "Mã địa điểm": "283-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 5.0,
+        "Tổng giờ/ca/ngày": "1 day, 16:00:00",
+        "Tổng công/ngày": 5.000000000000001
+    },
+    {
+        "Mã địa điểm": "105-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "11:50:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:40:00",
+        "Tổng giờ nghỉ": "0:50:00",
+        "Số lượng": 14.0,
+        "Tổng giờ/ca/ngày": "4 days, 11:20:00",
+        "Tổng công/ngày": 13.416666666666664
+    },
+    {
+        "Mã địa điểm": "105-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:30:00",
+        "Kết thúc": "22:00:00",
+        "Nghỉ 1 Bắt đầu": "17:25:00",
+        "Nghỉ 1 Kết thúc": "18:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:55:00",
+        "Tổng giờ nghỉ": "0:35:00",
+        "Số lượng": 13.0,
+        "Tổng giờ/ca/ngày": "4 days, 6:55:00",
+        "Tổng công/ngày": 12.864583333333332
+    },
+    {
+        "Mã địa điểm": "153-1",
+        "Lịch": "Thứ 2,4,6",
+        "Loại ca": "Part time",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "09:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "2:30:00",
+        "Tổng công/ngày": 0.3124999999999999
+    },
+    {
+        "Mã địa điểm": "105-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "16:00:00",
+        "Kết thúc": "22:00:00",
+        "Nghỉ 1 Bắt đầu": "18:00:00",
+        "Nghỉ 1 Kết thúc": "18:20:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
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+        "Tổng giờ nghỉ": "0:20:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "5:40:00",
+        "Tổng công/ngày": 0.7083333333333335
+    },
+    {
+        "Mã địa điểm": "188-2",
+        "Lịch": 0,
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+        "Nghỉ 1 Kết thúc": 0,
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+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "105-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca đêm",
+        "Bắt đầu": "2025-01-01 22:00:00",
+        "Kết thúc": "2025-01-02 06:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 7.0,
+        "Tổng giờ/ca/ngày": "2 days, 11:30:00",
+        "Tổng công/ngày": 7.437500000101863
+    },
+    {
+        "Mã địa điểm": "280-2",
+        "Lịch": 0,
+        "Loại ca": 0,
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+        "Kết thúc": 0,
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": 0,
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+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "105-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "12:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 26.0,
+        "Tổng giờ/ca/ngày": "8 days, 16:00:00",
+        "Tổng công/ngày": 25.999999999999993
+    },
+    {
+        "Mã địa điểm": "105-2",
+        "Lịch": 0,
+        "Loại ca": 0,
+        "Bắt đầu": 0,
+        "Kết thúc": 0,
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": 0,
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+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "120-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Part time",
+        "Bắt đầu": "16:00:00",
+        "Kết thúc": "20:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "4:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.5000000000000002
+    },
+    {
+        "Mã địa điểm": "119-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "16:45:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "12:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:45:00",
+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 5.0,
+        "Tổng giờ/ca/ngày": "1 day, 19:45:00",
+        "Tổng công/ngày": 5.468749999999999
+    },
+    {
+        "Mã địa điểm": "166-2",
+        "Lịch": 0,
+        "Loại ca": 0,
+        "Bắt đầu": 0,
+        "Kết thúc": 0,
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": 0,
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "119-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "12:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:30:00",
+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:30:00",
+        "Tổng công/ngày": 1.0625
+    },
+    {
+        "Mã địa điểm": "119-1",
+        "Lịch": "CN",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": 0,
+        "Kết thúc": 0,
+        "Nghỉ 1 Bắt đầu": "13:00:00",
+        "Nghỉ 1 Kết thúc": "13:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": 0,
+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "122-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "15:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "11:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:30:00",
+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 12.0,
+        "Tổng giờ/ca/ngày": "4 days, 6:00:00",
+        "Tổng công/ngày": 12.75
+    },
+    {
+        "Mã địa điểm": "187-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Part time",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "10:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "4:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "122-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "22:00:00",
+        "Nghỉ 1 Bắt đầu": "17:30:00",
+        "Nghỉ 1 Kết thúc": "18:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:30:00",
+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 7.0,
+        "Tổng giờ/ca/ngày": "2 days, 11:30:00",
+        "Tổng công/ngày": 7.4375
+    },
+    {
+        "Mã địa điểm": "163-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Part time",
+        "Bắt đầu": "16:30:00",
+        "Kết thúc": "20:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "4:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 0.9999999999999998
+    },
+    {
+        "Mã địa điểm": "122-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "11:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:30:00",
+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:30:00",
+        "Tổng công/ngày": 1.0624999999999998
+    },
+    {
+        "Mã địa điểm": "102-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:30:00",
+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 4.0,
+        "Tổng giờ/ca/ngày": "1 day, 10:00:00",
+        "Tổng công/ngày": 4.25
+    },
+    {
+        "Mã địa điểm": "148-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "13:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:30:00",
+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 16.0,
+        "Tổng giờ/ca/ngày": "5 days, 16:00:00",
+        "Tổng công/ngày": 17.0
+    },
+    {
+        "Mã địa điểm": "148-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "17:00:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "14:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "2:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "148-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": "18:30:00",
+        "Nghỉ 1 Kết thúc": "19:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:00:00",
+        "Tổng giờ nghỉ": "1:00:00",
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+        "Tổng giờ/ca/ngày": "1 day, 4:00:00",
+        "Tổng công/ngày": 3.500000000000001
+    },
+    {
+        "Mã địa điểm": "121-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "05:30:00",
+        "Kết thúc": "13:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "1 day, 8:00:00",
+        "Tổng công/ngày": 4.0
+    },
+    {
+        "Mã địa điểm": "121-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:30:00",
+        "Kết thúc": "21:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Nghỉ 2 Kết thúc": 0,
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+        "Tổng giờ/ca/ngày": "1 day, 8:00:00",
+        "Tổng công/ngày": 4.0
+    },
+    {
+        "Mã địa điểm": "121-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "16:25:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "6:55:00",
+        "Tổng giờ nghỉ": "2:30:00",
+        "Số lượng": 11.0,
+        "Tổng giờ/ca/ngày": "3 days, 4:05:00",
+        "Tổng công/ngày": 9.510416666666664
+    },
+    {
+        "Mã địa điểm": "121-2",
+        "Lịch": 0,
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "05:30:00",
+        "Kết thúc": "13:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
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+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.0
+    },
+    {
+        "Mã địa điểm": "121-2",
+        "Lịch": 0,
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:30:00",
+        "Kết thúc": "21:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "121-2",
+        "Lịch": 0,
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "17:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "2:30:00",
+        "Số lượng": 7.0,
+        "Tổng giờ/ca/ngày": "2 days, 4:30:00",
+        "Tổng công/ngày": 6.5625
+    },
+    {
+        "Mã địa điểm": "121-3",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "05:30:00",
+        "Kết thúc": "13:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 3.0,
+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.0
+    },
+    {
+        "Mã địa điểm": "171-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Part time",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "10:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "4:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.5
+    },
+    {
+        "Mã địa điểm": "121-3",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:30:00",
+        "Kết thúc": "21:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
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+        "Tổng giờ nghỉ": 0,
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+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.0
+    },
+    {
+        "Mã địa điểm": "121-3",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "17:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "2:00:00",
+        "Số lượng": 13.0,
+        "Tổng giờ/ca/ngày": "4 days, 8:00:00",
+        "Tổng công/ngày": 13.000000000000002
+    },
+    {
+        "Mã địa điểm": "121-4",
+        "Lịch": 0,
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "05:30:00",
+        "Kết thúc": "13:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "121-4",
+        "Lịch": 0,
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:30:00",
+        "Kết thúc": "21:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
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+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.0
+    },
+    {
+        "Mã địa điểm": "489-1",
+        "Lịch": 0,
+        "Loại ca": 0,
+        "Bắt đầu": 0,
+        "Kết thúc": 0,
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": 0,
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "121-4",
+        "Lịch": 0,
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "17:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "2:30:00",
+        "Số lượng": 11.0,
+        "Tổng giờ/ca/ngày": "3 days, 10:30:00",
+        "Tổng công/ngày": 10.3125
+    },
+    {
+        "Mã địa điểm": "144-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "144-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "144-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "12:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 1.9999999999999996
+    },
+    {
+        "Mã địa điểm": "627-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Part time",
+        "Bắt đầu": "16:30:00",
+        "Kết thúc": "20:30:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "2:30:00",
+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "2:30:00",
+        "Tổng công/ngày": 0.31250000000000006
+    },
+    {
+        "Mã địa điểm": "615-1",
+        "Lịch": 0,
+        "Loại ca": 0,
+        "Bắt đầu": 0,
+        "Kết thúc": 0,
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": 0,
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "618-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "12:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:00:00",
+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 3.0,
+        "Tổng giờ/ca/ngày": "21:00:00",
+        "Tổng công/ngày": 2.6249999999999996
+    },
+    {
+        "Mã địa điểm": "618-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:30:00",
+        "Kết thúc": "21:30:00",
+        "Nghỉ 1 Bắt đầu": "17:30:00",
+        "Nghỉ 1 Kết thúc": "18:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 4.0,
+        "Tổng giờ/ca/ngày": "1 day, 6:00:00",
+        "Tổng công/ngày": 3.75
+    },
+    {
+        "Mã địa điểm": "107-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "2:30:00",
+        "Số lượng": 3.0,
+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.0
+    },
+    {
+        "Mã địa điểm": "421-1",
+        "Lịch": 0,
+        "Loại ca": 0,
+        "Bắt đầu": 0,
+        "Kết thúc": 0,
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": 0,
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+        "Tổng công/ngày": 3.999999999999999
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+        "Tổng công/ngày": 166.0
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+        "Tổng công/ngày": 1.7499999999999998
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+        "Tổng công/ngày": 4.6875
+    },
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+        "Tổng giờ/ca/ngày": "3 days, 0:00:00",
+        "Tổng công/ngày": 9.000000000000004
+    },
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+        "Tổng công/ngày": 5.000000000000001
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+        "Tổng công/ngày": 0.0
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+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
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+        "Tổng công/ngày": 8.0
+    },
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
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+        "Tổng giờ/ca/ngày": "1 day, 11:00:00",
+        "Tổng công/ngày": 4.374999999999999
+    },
+    {
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+        "Kết thúc": "14:00:00",
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+        "Nghỉ 1 Kết thúc": "12:00:00",
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+        "Tổng giờ/ca/ngày": "22:30:00",
+        "Tổng công/ngày": 2.812500000000001
+    },
+    {
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+        "Tổng công/ngày": 0.5000000000000002
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+        "Tổng công/ngày": 1.25
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+        "Tổng công/ngày": 0.24999999999999978
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+        "Tổng công/ngày": 2.625
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+        "Tổng giờ/ca/ngày": "2:00:00",
+        "Tổng công/ngày": 0.25
+    },
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+        "Tổng giờ/ca/ngày": "2 days, 8:00:00",
+        "Tổng công/ngày": 6.999999999999999
+    },
+    {
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+        "Tổng giờ/ca/ngày": "4 days, 16:00:00",
+        "Tổng công/ngày": 13.999999999999998
+    },
+    {
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+        "Tổng công/ngày": 12.0
+    },
+    {
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+        "Tổng giờ/ca/ngày": "9:00:00",
+        "Tổng công/ngày": 1.125
+    },
+    {
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+        "Tổng công/ngày": 0.0
+    },
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+        "Mã địa điểm": "620-1",
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+        "Bắt đầu": "07:15:00",
+        "Kết thúc": "17:00:00",
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+        "Tổng giờ/ca/ngày": "3 days, 4:00:00",
+        "Tổng công/ngày": 9.5
+    },
+    {
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+        "Tổng công/ngày": 0.0
+    },
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+        "Kết thúc": "16:30:00",
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+        "Tổng giờ/ca/ngày": "1 day, 8:00:00",
+        "Tổng công/ngày": 4.0
+    },
+    {
+        "Mã địa điểm": "329-1",
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+        "Kết thúc": "21:00:00",
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
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+        "Kết thúc": "16:30:00",
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+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.0
+    },
+    {
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+        "Kết thúc": "17:00:00",
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+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "56-1",
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+        "Kết thúc": "11:00:00",
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+        "Tổng giờ/ca/ngày": "3:00:00",
+        "Tổng công/ngày": 0.375
+    },
+    {
+        "Mã địa điểm": "191-1",
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+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:30:00",
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+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 3.0,
+        "Tổng giờ/ca/ngày": "1 day, 1:30:00",
+        "Tổng công/ngày": 3.187500000000001
+    },
+    {
+        "Mã địa điểm": "468-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "2 days, 16:00:00",
+        "Tổng công/ngày": 8.0
+    },
+    {
+        "Mã địa điểm": "468-1",
+        "Lịch": "T2 đến CN",
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+        "Kết thúc": "16:00:00",
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+        "Tổng giờ/ca/ngày": "2 days, 16:00:00",
+        "Tổng công/ngày": 8.0
+    },
+    {
+        "Mã địa điểm": "591-1",
+        "Lịch": "T2 đến CN",
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+        "Kết thúc": "14:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "591-1",
+        "Lịch": "T2 đến CN",
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+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "468-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 9.0,
+        "Tổng giờ/ca/ngày": "3 days, 0:00:00",
+        "Tổng công/ngày": 9.000000000000002
+    },
+    {
+        "Mã địa điểm": "315-1",
+        "Lịch": 0,
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ làm": "8:30:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": "0:00:00",
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "315-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:30:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "17:00:00",
+        "Tổng công/ngày": 2.1250000000000004
+    },
+    {
+        "Mã địa điểm": "279-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "08:30:00",
+        "Kết thúc": "17:00:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "7:30:00",
+        "Tổng công/ngày": 0.9375000000000002
+    },
+    {
+        "Mã địa điểm": "315-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca đêm",
+        "Bắt đầu": "2025-01-01 21:30:00",
+        "Kết thúc": "2025-01-02 06:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:30:00",
+        "Tổng giờ nghỉ": 0,
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+        "Tổng giờ/ca/ngày": "3 days, 21:30:00",
+        "Tổng công/ngày": 11.687499999919964
+    },
+    {
+        "Mã địa điểm": "315-2",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:30:00",
+        "Kết thúc": "22:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "17:00:00",
+        "Tổng công/ngày": 2.125
+    },
+    {
+        "Mã địa điểm": "315-2",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca đêm",
+        "Bắt đầu": "2025-01-01 22:00:00",
+        "Kết thúc": "2025-01-02 06:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "2 days, 8:00:00",
+        "Tổng công/ngày": 7.000000000050932
+    },
+    {
+        "Mã địa điểm": "169-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "17:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "315-3",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "12:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "1:00:00",
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+        "Tổng giờ/ca/ngày": "1 day, 4:00:00",
+        "Tổng công/ngày": 3.5
+    },
+    {
+        "Mã địa điểm": "169-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
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+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "169-1",
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+        "Kết thúc": "15:30:00",
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+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "315-3",
+        "Lịch": 0,
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "14:00:00",
+        "Kết thúc": "22:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
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+        "Tổng giờ nghỉ": 0,
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+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 2.999999999999999
+    },
+    {
+        "Mã địa điểm": "315-3",
+        "Lịch": 0,
+        "Loại ca": "Ca đêm",
+        "Bắt đầu": "2025-01-01 22:00:00",
+        "Kết thúc": "2025-01-02 06:00:00",
+        "Nghỉ 1 Bắt đầu": "2025-01-01 00:00:00",
+        "Nghỉ 1 Kết thúc": "2025-01-01 01:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
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+        "Tổng giờ nghỉ": "1:00:00",
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+        "Tổng giờ/ca/ngày": "2 days, 1:00:00",
+        "Tổng công/ngày": 6.125000000101863
+    },
+    {
+        "Mã địa điểm": "169-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "12:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "0:30:00",
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "276-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "17:00:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "13:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:30:00",
+        "Tổng công/ngày": 1.0625
+    },
+    {
+        "Mã địa điểm": "482-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Part time",
+        "Bắt đầu": "15:30:00",
+        "Kết thúc": "19:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "10:30:00",
+        "Tổng công/ngày": 1.3124999999999993
+    },
+    {
+        "Mã địa điểm": "482-1",
+        "Lịch": 0,
+        "Loại ca": 0,
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+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "165-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "86-1",
+        "Lịch": 0,
+        "Loại ca": "Part time",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "11:00:00",
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+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.4999999999999999
+    },
+    {
+        "Mã địa điểm": "165-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "12:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 0.9999999999999998
+    },
+    {
+        "Mã địa điểm": "165-2",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "08:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "12:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "7:30:00",
+        "Tổng công/ngày": 0.9374999999999998
+    },
+    {
+        "Mã địa điểm": "350-1",
+        "Lịch": 0,
+        "Loại ca": "Part time",
+        "Bắt đầu": "16:00:00",
+        "Kết thúc": "18:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "2:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "2:00:00",
+        "Tổng công/ngày": 0.2500000000000001
+    },
+    {
+        "Mã địa điểm": "165-2",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "14:00:00",
+        "Kết thúc": "22:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 0.9999999999999998
+    },
+    {
+        "Mã địa điểm": "320-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "15:00:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "320-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "10:15:00",
+        "Kết thúc": "18:45:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "7:30:00",
+        "Tổng công/ngày": 0.9375000000000002
+    },
+    {
+        "Mã địa điểm": "311-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "15:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "9:00:00",
+        "Tổng giờ nghỉ": 0,
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+        "Tổng giờ/ca/ngày": "1 day, 12:00:00",
+        "Tổng công/ngày": 4.5
+    },
+    {
+        "Mã địa điểm": "311-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "15:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "9:30:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 5.0,
+        "Tổng giờ/ca/ngày": "1 day, 23:30:00",
+        "Tổng công/ngày": 5.937500000000001
+    },
+    {
+        "Mã địa điểm": "311-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "15:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "9:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "18:00:00",
+        "Tổng công/ngày": 2.2500000000000004
+    },
+    {
+        "Mã địa điểm": "283-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:30:00",
+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:30:00",
+        "Tổng công/ngày": 1.0625
+    },
+    {
+        "Mã địa điểm": "311-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "14:00:00",
+        "Kết thúc": "20:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "6:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "12:00:00",
+        "Tổng công/ngày": 1.5
+    },
+    {
+        "Mã địa điểm": "335-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Part time",
+        "Bắt đầu": "16:00:00",
+        "Kết thúc": "18:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "2:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "2:00:00",
+        "Tổng công/ngày": 0.2500000000000001
+    },
+    {
+        "Mã địa điểm": "335-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Part time",
+        "Bắt đầu": "16:00:00",
+        "Kết thúc": "20:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "4:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.5000000000000002
+    },
+    {
+        "Mã địa điểm": "283-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "2 days, 16:00:00",
+        "Tổng công/ngày": 8.0
+    },
+    {
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+        "Lịch": "T2 đến T7",
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+        "Kết thúc": "21:00:00",
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+        "Tổng công/ngày": 2.0
+    },
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+        "Tổng công/ngày": 0.0
+    },
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+        "Tổng công/ngày": 0.2500000000000001
+    },
+    {
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+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 0.9999999999999998
+    },
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+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "283-1",
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+        "Kết thúc": "21:00:00",
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+        "Tổng giờ/ca/ngày": "1 day, 16:00:00",
+        "Tổng công/ngày": 5.000000000000001
+    },
+    {
+        "Mã địa điểm": "283-1",
+        "Lịch": "Thứ 7",
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+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "17:00:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
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+        "Tổng giờ/ca/ngày": "3 days, 18:00:00",
+        "Tổng công/ngày": 11.250000000000002
+    },
+    {
+        "Mã địa điểm": "283-1",
+        "Lịch": "CN",
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+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "17:00:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
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+        "Tổng giờ/ca/ngày": "2 days, 6:00:00",
+        "Tổng công/ngày": 6.750000000000002
+    },
+    {
+        "Mã địa điểm": "612-1",
+        "Lịch": 0,
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+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "153-1",
+        "Lịch": "Thứ 2,4,6",
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+        "Kết thúc": "09:30:00",
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+        "Tổng giờ/ca/ngày": "2:30:00",
+        "Tổng công/ngày": 0.3124999999999999
+    },
+    {
+        "Mã địa điểm": "188-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "369-1",
+        "Lịch": 0,
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+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "370-1",
+        "Lịch": 0,
+        "Loại ca": 0,
+        "Bắt đầu": 0,
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+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "188-2",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": 0,
+        "Kết thúc": 0,
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+        "Nghỉ 1 Kết thúc": 0,
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+        "Số lượng": 3.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "280-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "05:45:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:15:00",
+        "Tổng giờ nghỉ": "2:00:00",
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+        "Tổng giờ/ca/ngày": "4 days, 3:00:00",
+        "Tổng công/ngày": 12.374999999999996
+    },
+    {
+        "Mã địa điểm": "324-1",
+        "Lịch": 0,
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+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": "18:00:00",
+        "Nghỉ 1 Kết thúc": "18:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 3.0,
+        "Tổng giờ/ca/ngày": "22:30:00",
+        "Tổng công/ngày": 2.8125
+    },
+    {
+        "Mã địa điểm": "490-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Part time",
+        "Bắt đầu": "16:00:00",
+        "Kết thúc": "18:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ/ca/ngày": "2:00:00",
+        "Tổng công/ngày": 0.2500000000000001
+    },
+    {
+        "Mã địa điểm": "280-2",
+        "Lịch": 0,
+        "Loại ca": 0,
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+        "Nghỉ 1 Kết thúc": 0,
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+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": 0,
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+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "424-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "15:00:00",
+        "Nghỉ 1 Bắt đầu": "10:00:00",
+        "Nghỉ 1 Kết thúc": "12:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "6:30:00",
+        "Tổng giờ nghỉ": "2:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "6:30:00",
+        "Tổng công/ngày": 0.8125000000000001
+    },
+    {
+        "Mã địa điểm": "579-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "10:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": "14:00:00",
+        "Nghỉ 1 Kết thúc": "17:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "3:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "424-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "18:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "2:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "9:00:00",
+        "Tổng công/ngày": 1.125
+    },
+    {
+        "Mã địa điểm": "424-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "10:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": "0:00:00",
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "166-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "2:00:00",
+        "Số lượng": 3.0,
+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.0
+    },
+    {
+        "Mã địa điểm": "27-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 3.0,
+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.0
+    },
+    {
+        "Mã địa điểm": "27-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "166-2",
+        "Lịch": 0,
+        "Loại ca": 0,
+        "Bắt đầu": 0,
+        "Kết thúc": 0,
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": 0,
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "166-3",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "2:00:00",
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "27-1",
+        "Lịch": "Thứ 7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
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+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.0
+    },
+    {
+        "Mã địa điểm": "166-4",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "2:00:00",
+        "Số lượng": 4.0,
+        "Tổng giờ/ca/ngày": "1 day, 8:00:00",
+        "Tổng công/ngày": 4.0
+    },
+    {
+        "Mã địa điểm": "589-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 6.0,
+        "Tổng giờ/ca/ngày": "2 days, 0:00:00",
+        "Tổng công/ngày": 6.0
+    },
+    {
+        "Mã địa điểm": "589-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:30:00",
+        "Kết thúc": "21:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 5.0,
+        "Tổng giờ/ca/ngày": "1 day, 16:00:00",
+        "Tổng công/ngày": 5.000000000000001
+    },
+    {
+        "Mã địa điểm": "187-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "2:00:00",
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "187-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Part time",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "10:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "4:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "609-1",
+        "Lịch": 0,
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 27.0,
+        "Tổng giờ/ca/ngày": "9 days, 0:00:00",
+        "Tổng công/ngày": 27.000000000000007
+    },
+    {
+        "Mã địa điểm": "609-1",
+        "Lịch": 0,
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 20.0,
+        "Tổng giờ/ca/ngày": "6 days, 16:00:00",
+        "Tổng công/ngày": 20.000000000000004
+    },
+    {
+        "Mã địa điểm": "163-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 7.0,
+        "Tổng giờ/ca/ngày": "2 days, 8:00:00",
+        "Tổng công/ngày": 7.0
+    },
+    {
+        "Mã địa điểm": "163-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Part time",
+        "Bắt đầu": "16:30:00",
+        "Kết thúc": "20:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "4:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 0.9999999999999998
+    },
+    {
+        "Mã địa điểm": "74-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "10:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "2:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "74-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "17:00:00",
+        "Nghỉ 1 Bắt đầu": "10:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "3:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0000000000000002
+    },
+    {
+        "Mã địa điểm": "74-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "15:30:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0000000000000002
+    },
+    {
+        "Mã địa điểm": "33-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 9.0,
+        "Tổng giờ/ca/ngày": "3 days, 0:00:00",
+        "Tổng công/ngày": 9.0
+    },
+    {
+        "Mã địa điểm": "33-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 5.0,
+        "Tổng giờ/ca/ngày": "1 day, 16:00:00",
+        "Tổng công/ngày": 5.000000000000001
+    },
+    {
+        "Mã địa điểm": "422-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "18:00:00",
+        "Nghỉ 1 Bắt đầu": "10:30:00",
+        "Nghỉ 1 Kết thúc": "13:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:30:00",
+        "Tổng giờ nghỉ": "3:00:00",
+        "Số lượng": 16.0,
+        "Tổng giờ/ca/ngày": "5 days, 16:00:00",
+        "Tổng công/ngày": 17.0
+    },
+    {
+        "Mã địa điểm": "24-1",
+        "Lịch": 0,
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "12:00:00",
+        "Kết thúc": "20:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "422-1",
+        "Lịch": "Thứ 7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": 0,
+        "Kết thúc": 0,
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": 0,
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 16.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "422-1",
+        "Lịch": "CN",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": 0,
+        "Kết thúc": 0,
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": 0,
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 3.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "422-1",
+        "Lịch": "Lễ, tết",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "18:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "11:30:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 3.0,
+        "Tổng giờ/ca/ngày": "1 day, 10:30:00",
+        "Tổng công/ngày": 4.3125
+    },
+    {
+        "Mã địa điểm": "67-1",
+        "Lịch": "Thứ 7",
+        "Loại ca": "Part time",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "11:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "4:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.4999999999999999
+    },
+    {
+        "Mã địa điểm": "7-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "15:00:00",
+        "Nghỉ 1 Bắt đầu": "10:00:00",
+        "Nghỉ 1 Kết thúc": "11:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 5.0,
+        "Tổng giờ/ca/ngày": "1 day, 16:00:00",
+        "Tổng công/ngày": 5.000000000000001
+    },
+    {
+        "Mã địa điểm": "7-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "14:00:00",
+        "Kết thúc": "22:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 1.9999999999999996
+    },
+    {
+        "Mã địa điểm": "7-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca đêm",
+        "Bắt đầu": "2025-01-01 22:00:00",
+        "Kết thúc": "2025-01-02 06:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.000000000014552
+    },
+    {
+        "Mã địa điểm": "8-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "15:00:00",
+        "Nghỉ 1 Bắt đầu": "10:10:00",
+        "Nghỉ 1 Kết thúc": "11:10:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 4.0,
+        "Tổng giờ/ca/ngày": "1 day, 8:00:00",
+        "Tổng công/ngày": 4.0
+    },
+    {
+        "Mã địa điểm": "8-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "422-2",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "18:00:00",
+        "Nghỉ 1 Bắt đầu": "10:30:00",
+        "Nghỉ 1 Kết thúc": "13:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:30:00",
+        "Tổng giờ nghỉ": "3:00:00",
+        "Số lượng": 3.0,
+        "Tổng giờ/ca/ngày": "1 day, 1:30:00",
+        "Tổng công/ngày": 3.1875
+    },
+    {
+        "Mã địa điểm": "8-1",
+        "Lịch": 0,
+        "Loại ca": "Ca đêm",
+        "Bắt đầu": "2025-01-01 21:00:00",
+        "Kết thúc": "2025-01-02 06:00:00",
+        "Nghỉ 1 Bắt đầu": "22:00:00",
+        "Nghỉ 1 Kết thúc": "22:30:00",
+        "Nghỉ 2 Bắt đầu": "02:00:00",
+        "Nghỉ 2 Kết thúc": "03:00:00",
+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "15:00:00",
+        "Tổng công/ngày": 1.8749999999999996
+    },
+    {
+        "Mã địa điểm": "25-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:00:00",
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+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng công/ngày": 1.875
+    },
+    {
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+        "Tổng công/ngày": 0.4999999999999999
+    },
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+        "Tổng công/ngày": 8.4375
+    },
+    {
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+        "Tổng công/ngày": 7.5
+    },
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+        "Tổng công/ngày": 3.375
+    },
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+        "Tổng công/ngày": 1.25
+    },
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+        "Tổng công/ngày": 3.0
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+        "Tổng công/ngày": 0.0
+    },
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+        "Tổng công/ngày": 5.0
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+        "Tổng công/ngày": 0.0
+    },
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0000000000000002
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+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "627-1",
+        "Lịch": "T2 đến T7",
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+        "Kết thúc": "16:30:00",
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+        "Tổng giờ làm": "8:00:00",
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+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "627-1",
+        "Lịch": "T2 đến T7",
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+        "Bắt đầu": "16:30:00",
+        "Kết thúc": "20:30:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "2:30:00",
+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "2:30:00",
+        "Tổng công/ngày": 0.31250000000000006
+    },
+    {
+        "Mã địa điểm": "2-006",
+        "Lịch": "T2 đến T7",
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+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "08:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ nghỉ": 0,
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+        "Tổng giờ/ca/ngày": "2:00:00",
+        "Tổng công/ngày": 0.24999999999999994
+    },
+    {
+        "Mã địa điểm": "2-007",
+        "Lịch": 0,
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+        "Tổng công/ngày": 0.0
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+    {
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+        "Tổng công/ngày": 0.0
+    },
+    {
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+        "Kết thúc": "14:30:00",
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+        "Tổng công/ngày": 2.6249999999999996
+    },
+    {
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+        "Kết thúc": "21:30:00",
+        "Nghỉ 1 Bắt đầu": "17:30:00",
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+        "Tổng công/ngày": 3.75
+    },
+    {
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+        "Tổng công/ngày": 0.5
+    },
+    {
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+        "Tổng giờ/ca/ngày": "11:00:00",
+        "Tổng công/ngày": 1.375
+    },
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+        "Tổng công/ngày": 0.0
+    },
+    {
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+        "Tổng công/ngày": 0.5
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+        "Tổng công/ngày": 0.0
+    },
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+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "2-017",
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+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.5
+    },
+    {
+        "Mã địa điểm": "425-3",
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+        "Tổng giờ/ca/ngày": "2:00:00",
+        "Tổng công/ngày": 0.2500000000000001
+    },
+    {
+        "Mã địa điểm": "425-4",
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+        "Bắt đầu": "16:00:00",
+        "Kết thúc": "18:00:00",
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+        "Tổng giờ/ca/ngày": "2:00:00",
+        "Tổng công/ngày": 0.2500000000000001
+    },
+    {
+        "Mã địa điểm": "2-020",
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+        "Kết thúc": "10:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.5
+    },
+    {
+        "Mã địa điểm": "2-021",
+        "Lịch": "T2 đến T7",
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+        "Bắt đầu": "15:00:00",
+        "Kết thúc": "19:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.4999999999999999
+    },
+    {
+        "Mã địa điểm": "425-5",
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+        "Tổng giờ/ca/ngày": "2:00:00",
+        "Tổng công/ngày": 0.2500000000000001
+    },
+    {
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+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "553-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:30:00",
+        "Kết thúc": "17:30:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "12:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "1:00:00",
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+        "Tổng giờ/ca/ngày": "9:00:00",
+        "Tổng công/ngày": 1.1249999999999998
+    },
+    {
+        "Mã địa điểm": "559-1",
+        "Lịch": "T2 đến CN",
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+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:00:00",
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+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ/ca/ngày": "1 day, 8:00:00",
+        "Tổng công/ngày": 4.0
+    },
+    {
+        "Mã địa điểm": "2-026",
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+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "2-027",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "559-1",
+        "Lịch": "T2 đến CN",
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+        "Bắt đầu": "14:00:00",
+        "Kết thúc": "22:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "1 day, 8:00:00",
+        "Tổng công/ngày": 3.999999999999999
+    },
+    {
+        "Mã địa điểm": "2-028",
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+        "Loại ca": 0,
+        "Bắt đầu": 0,
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+        "Nghỉ 1 Kết thúc": 0,
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+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "559-1",
+        "Lịch": "T2 đến CN",
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+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ/ca/ngày": "1 day, 6:00:00",
+        "Tổng công/ngày": 3.75
+    },
+    {
+        "Mã địa điểm": "559-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Part time",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "10:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ làm": "4:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.5
+    },
+    {
+        "Mã địa điểm": "559-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Part time",
+        "Bắt đầu": "16:30:00",
+        "Kết thúc": "18:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "1:30:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "1:30:00",
+        "Tổng công/ngày": 0.1875
+    },
+    {
+        "Mã địa điểm": "624-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "15:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "12:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 39.0,
+        "Tổng giờ/ca/ngày": "13 days, 0:00:00",
+        "Tổng công/ngày": 39.0
+    },
+    {
+        "Mã địa điểm": "624-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Part time",
+        "Bắt đầu": "15:00:00",
+        "Kết thúc": "17:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "2:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 5.0,
+        "Tổng giờ/ca/ngày": "10:00:00",
+        "Tổng công/ngày": 1.2500000000000004
+    },
+    {
+        "Mã địa điểm": "2-033",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Part time",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "10:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "4:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.5
+    },
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+        "Tổng công/ngày": 40.5
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+        "Tổng công/ngày": 39.18750000000001
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+        "Tổng công/ngày": 16.000000000000004
+    },
+    {
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 0.9999999999999998
+    },
+    {
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+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca đêm",
+        "Bắt đầu": "2025-01-01 22:00:00",
+        "Kết thúc": "2025-01-02 06:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "17:00:00",
+        "Tổng công/ngày": 2.125000000029104
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+        "Tổng công/ngày": 6.75
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+        "Tổng công/ngày": 13.000000000094587
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+        "Tổng công/ngày": 0.0
+    },
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+        "Kết thúc": "21:00:00",
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+        "Tổng công/ngày": 2.625000000000001
+    },
+    {
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+        "Tổng công/ngày": 1.25
+    },
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+        "Tổng công/ngày": 10.3125
+    },
+    {
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+        "Tổng công/ngày": 16.875
+    },
+    {
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+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.4999999999999999
+    },
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+        "Tổng giờ/ca/ngày": "1 day, 13:30:00",
+        "Tổng công/ngày": 4.6875
+    },
+    {
+        "Mã địa điểm": "356-1",
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+        "Kết thúc": "17:00:00",
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+        "Tổng giờ/ca/ngày": "10:00:00",
+        "Tổng công/ngày": 1.25
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+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
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+        "Tổng công/ngày": 0.0
+    },
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+        "Tổng công/ngày": 0.4999999999999999
+    },
+    {
+        "Mã địa điểm": "308-1",
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 0.9999999999999998
+    },
+    {
+        "Mã địa điểm": "247-1",
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+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "245-1",
+        "Lịch": 0,
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
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+        "Tổng giờ nghỉ": "1:30:00",
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+        "Tổng giờ/ca/ngày": "2 days, 0:00:00",
+        "Tổng công/ngày": 6.0
+    },
+    {
+        "Mã địa điểm": "245-1",
+        "Lịch": 0,
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": "19:00:00",
+        "Nghỉ 1 Kết thúc": "19:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "1 day, 6:00:00",
+        "Tổng công/ngày": 3.75
+    },
+    {
+        "Mã địa điểm": "308-1",
+        "Lịch": "T2 đến T7",
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+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:00:00",
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+        "Tổng giờ/ca/ngày": "3:00:00",
+        "Tổng công/ngày": 0.375
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+        "Tổng công/ngày": 1.875
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+    {
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+        "Tổng công/ngày": 4.0
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+        "Tổng công/ngày": 7.499999999999998
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+        "Tổng công/ngày": 4.0
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+        "Tổng công/ngày": 0.0
+    },
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+        "Tổng giờ/ca/ngày": "5 days, 0:00:00",
+        "Tổng công/ngày": 15.000000000000004
+    },
+    {
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+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.4999999999999999
+    },
+    {
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 0.9999999999999998
+    },
+    {
+        "Mã địa điểm": "575-1",
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+        "Kết thúc": "15:30:00",
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+        "Tổng giờ/ca/ngày": "6 days, 0:00:00",
+        "Tổng công/ngày": 18.000000000000004
+    },
+    {
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+        "Lịch": 0,
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+        "Kết thúc": "21:00:00",
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+        "Tổng giờ/ca/ngày": "3 days, 3:00:00",
+        "Tổng công/ngày": 9.375000000000004
+    },
+    {
+        "Mã địa điểm": "467-1",
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+        "Tổng công/ngày": 0.0
+    },
+    {
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+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "353-1",
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+        "Tổng giờ/ca/ngày": "5 days, 16:00:00",
+        "Tổng công/ngày": 17.0
+    },
+    {
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+        "Tổng giờ/ca/ngày": "5:00:00",
+        "Tổng công/ngày": 0.6250000000000001
+    },
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+        "Kết thúc": "21:00:00",
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+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.4999999999999999
+    },
+    {
+        "Mã địa điểm": "209-1",
+        "Lịch": 0,
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+        "Bắt đầu": "15:00:00",
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+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.4999999999999999
+    },
+    {
+        "Mã địa điểm": "353-1",
+        "Lịch": "Thứ 7",
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+        "Kết thúc": "22:00:00",
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 0.9999999999999998
+    },
+    {
+        "Mã địa điểm": "452-1",
+        "Lịch": "Thứ 3,6",
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+        "Bắt đầu": "16:00:00",
+        "Kết thúc": "18:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "2:00:00",
+        "Tổng công/ngày": 0.2500000000000001
+    },
+    {
+        "Mã địa điểm": "218-1",
+        "Lịch": 0,
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+        "Kết thúc": "19:00:00",
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+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.4999999999999999
+    },
+    {
+        "Mã địa điểm": "218-1",
+        "Lịch": 0,
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+        "Bắt đầu": "14:00:00",
+        "Kết thúc": "20:00:00",
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+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 3.0,
+        "Tổng giờ/ca/ngày": "18:00:00",
+        "Tổng công/ngày": 2.25
+    },
+    {
+        "Mã địa điểm": "353-1",
+        "Lịch": "Thứ 7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "286-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
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+        "Tổng giờ/ca/ngày": "1 day, 16:00:00",
+        "Tổng công/ngày": 5.000000000000001
+    },
+    {
+        "Mã địa điểm": "286-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:00:00",
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+        "Tổng giờ/ca/ngày": "1 day, 16:00:00",
+        "Tổng công/ngày": 5.000000000000001
+    },
+    {
+        "Mã địa điểm": "286-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca gãy",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "11:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "4:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 5.0,
+        "Tổng giờ/ca/ngày": "20:00:00",
+        "Tổng công/ngày": 2.4999999999999996
+    },
+    {
+        "Mã địa điểm": "353-1",
+        "Lịch": "Thứ 7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 13.0,
+        "Tổng giờ/ca/ngày": "4 days, 8:00:00",
+        "Tổng công/ngày": 13.000000000000002
+    },
+    {
+        "Mã địa điểm": "353-1",
+        "Lịch": "CN",
+        "Loại ca": "Part time",
+        "Bắt đầu": "18:00:00",
+        "Kết thúc": "22:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "4:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 0.9999999999999998
+    },
+    {
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+        "Tổng công/ngày": 2.0
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+        "Tổng công/ngày": 2.0
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+        "Tổng công/ngày": 0.4999999999999999
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+        "Tổng công/ngày": 0.5000000000000002
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+        "Tổng công/ngày": 0.2500000000000001
+    },
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+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
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+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
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+        "Tổng giờ/ca/ngày": "15:00:00",
+        "Tổng công/ngày": 1.875
+    },
+    {
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+        "Tổng công/ngày": 6.5625
+    },
+    {
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+        "Tổng giờ/ca/ngày": "8 days, 8:00:00",
+        "Tổng công/ngày": 25.0
+    },
+    {
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+        "Tổng giờ/ca/ngày": "21:00:00",
+        "Tổng công/ngày": 2.625
+    },
+    {
+        "Mã địa điểm": "491-1",
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+        "Tổng giờ/ca/ngày": "2:00:00",
+        "Tổng công/ngày": 0.25
+    },
+    {
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
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+        "Lịch": "T2 đến T6",
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+        "Tổng giờ/ca/ngày": "4 days, 16:00:00",
+        "Tổng công/ngày": 13.999999999999998
+    },
+    {
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+        "Tổng giờ/ca/ngày": "4 days, 0:00:00",
+        "Tổng công/ngày": 12.0
+    },
+    {
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+        "Tổng công/ngày": 0.0
+    },
+    {
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+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
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+        "Lịch": "Lễ, tết",
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+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
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+        "Lịch": 0,
+        "Loại ca": 0,
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+        "Nghỉ 1 Kết thúc": 0,
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+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
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+        "Mã địa điểm": "262-1",
+        "Lịch": 0,
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+        "Tổng giờ/ca/ngày": "1:30:00",
+        "Tổng công/ngày": 0.1875
+    },
+    {
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+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "329-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "1:30:00",
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+        "Tổng giờ/ca/ngày": "2 days, 8:00:00",
+        "Tổng công/ngày": 7.0
+    },
+    {
+        "Mã địa điểm": "329-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Nghỉ 2 Kết thúc": 0,
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+        "Tổng giờ nghỉ": 0,
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "474-1",
+        "Lịch": 0,
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": "18:00:00",
+        "Nghỉ 1 Kết thúc": "18:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "7:30:00",
+        "Tổng công/ngày": 0.9375
+    },
+    {
+        "Mã địa điểm": "227-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "12:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 6.0,
+        "Tổng giờ/ca/ngày": "1 day, 21:00:00",
+        "Tổng công/ngày": 5.625
+    },
+    {
+        "Mã địa điểm": "227-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": "18:30:00",
+        "Nghỉ 1 Kết thúc": "19:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 3.0,
+        "Tổng giờ/ca/ngày": "22:30:00",
+        "Tổng công/ngày": 2.812500000000001
+    },
+    {
+        "Mã địa điểm": "555-1",
+        "Lịch": 0,
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+        "Bắt đầu": "05:00:00",
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+        "Tổng công/ngày": 6.375
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+        "Tổng công/ngày": 0.0
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+        "Tổng công/ngày": 0.0
+    },
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+        "Tổng công/ngày": 0.9375000000000002
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+        "Tổng giờ/ca/ngày": "15:00:00",
+        "Tổng công/ngày": 1.8750000000000007
+    },
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+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "328-1",
+        "Lịch": "T2 đến T7",
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+        "Kết thúc": "17:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "56-1",
+        "Lịch": "T2 đến T6",
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+        "Kết thúc": "11:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "3:00:00",
+        "Tổng công/ngày": 0.375
+    },
+    {
+        "Mã địa điểm": "362-1",
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+        "Tổng công/ngày": 1.0625000000000002
+    },
+    {
+        "Mã địa điểm": "605-1",
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+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.5000000000000002
+    },
+    {
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+        "Kết thúc": "16:00:00",
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
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+        "Kết thúc": "16:30:00",
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+        "Nghỉ 1 Kết thúc": "13:30:00",
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "591-1",
+        "Lịch": "T2 đến CN",
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+        "Kết thúc": "14:00:00",
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
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+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
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+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
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+        "Lịch": "T2 đến CN",
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+        "Kết thúc": "21:00:00",
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
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+        "Tổng giờ/ca/ngày": "9:30:00",
+        "Tổng công/ngày": 1.1875
+    },
+    {
+        "Mã địa điểm": "315-1",
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+        "Tổng giờ/ca/ngày": "0:00:00",
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "315-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ/ca/ngày": "17:00:00",
+        "Tổng công/ngày": 2.1250000000000004
+    },
+    {
+        "Mã địa điểm": "315-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "10:30:00",
+        "Nghỉ 1 Kết thúc": "11:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 29.0,
+        "Tổng giờ/ca/ngày": "10 days, 6:30:00",
+        "Tổng công/ngày": 30.812499999999996
+    },
+    {
+        "Mã địa điểm": "315-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca đêm",
+        "Bắt đầu": "2025-01-01 21:30:00",
+        "Kết thúc": "2025-01-02 06:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:30:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 11.0,
+        "Tổng giờ/ca/ngày": "3 days, 21:30:00",
+        "Tổng công/ngày": 11.687499999919964
+    },
+    {
+        "Mã địa điểm": "315-2",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:30:00",
+        "Kết thúc": "22:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:30:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "17:00:00",
+        "Tổng công/ngày": 2.125
+    },
+    {
+        "Mã địa điểm": "315-2",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca đêm",
+        "Bắt đầu": "2025-01-01 22:00:00",
+        "Kết thúc": "2025-01-02 06:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
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+        "Tổng giờ/ca/ngày": "2 days, 8:00:00",
+        "Tổng công/ngày": 7.000000000050932
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+        "Kết thúc": "16:30:00",
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+        "Tổng công/ngày": 17.999999999999993
+    },
+    {
+        "Mã địa điểm": "315-3",
+        "Lịch": "T2 đến T7",
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+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:00:00",
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+        "Tổng công/ngày": 3.5
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+        "Tổng công/ngày": 1.9999999999999996
+    },
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+        "Kết thúc": "16:30:00",
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+        "Tổng công/ngày": 15.0
+    },
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+        "Tổng công/ngày": 2.999999999999999
+    },
+    {
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+        "Lịch": "T2 đến T7",
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+        "Bắt đầu": "2025-01-01 22:00:00",
+        "Kết thúc": "2025-01-02 06:00:00",
+        "Nghỉ 1 Bắt đầu": "2025-01-01 00:00:00",
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+        "Tổng giờ/ca/ngày": "2 days, 1:00:00",
+        "Tổng công/ngày": 6.125000000101863
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+    {
+        "Mã địa điểm": "330-1",
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+        "Kết thúc": "16:30:00",
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+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "482-1",
+        "Lịch": "T2 đến T6",
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+        "Kết thúc": "15:30:00",
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+        "Tổng công/ngày": 4.000000000000001
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+        "Lịch": "T2 đến T6",
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+        "Kết thúc": "19:00:00",
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+        "Tổng giờ/ca/ngày": "10:30:00",
+        "Tổng công/ngày": 1.3124999999999993
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+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
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+        "Kết thúc": "17:00:00",
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0000000000000002
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+        "Tổng công/ngày": 0.4999999999999999
+    },
+    {
+        "Mã địa điểm": "322-1",
+        "Lịch": "T2 đến T7",
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+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
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+        "Tổng giờ/ca/ngày": "1 day, 8:00:00",
+        "Tổng công/ngày": 4.0
+    },
+    {
+        "Mã địa điểm": "350-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
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+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "350-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Part time",
+        "Bắt đầu": "16:00:00",
+        "Kết thúc": "18:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ/ca/ngày": "2:00:00",
+        "Tổng công/ngày": 0.2500000000000001
+    },
+    {
+        "Mã địa điểm": "303-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "2:00:00",
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+        "Tổng giờ/ca/ngày": "1 day, 16:00:00",
+        "Tổng công/ngày": 5.0
+    },
+    {
+        "Mã địa điểm": "320-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "15:00:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "320-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "10:15:00",
+        "Kết thúc": "18:45:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "1:00:00",
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+        "Tổng giờ/ca/ngày": "7:30:00",
+        "Tổng công/ngày": 0.9375000000000002
+    },
+    {
+        "Mã địa điểm": "311-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "15:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "9:00:00",
+        "Tổng giờ nghỉ": 0,
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+        "Tổng giờ/ca/ngày": "1 day, 12:00:00",
+        "Tổng công/ngày": 4.5
+    },
+    {
+        "Mã địa điểm": "311-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "15:30:00",
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+        "Tổng giờ/ca/ngày": "1 day, 23:30:00",
+        "Tổng công/ngày": 5.937500000000001
+    },
+    {
+        "Mã địa điểm": "311-1",
+        "Lịch": "T2 đến T7",
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+        "Bắt đầu": "06:30:00",
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+        "Tổng giờ/ca/ngày": "18:00:00",
+        "Tổng công/ngày": 2.2500000000000004
+    },
+    {
+        "Mã địa điểm": "311-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 3.0,
+        "Tổng giờ/ca/ngày": "22:30:00",
+        "Tổng công/ngày": 2.8125
+    },
+    {
+        "Mã địa điểm": "311-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "14:00:00",
+        "Kết thúc": "20:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "12:00:00",
+        "Tổng công/ngày": 1.5
+    },
+    {
+        "Mã địa điểm": "335-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Part time",
+        "Bắt đầu": "16:00:00",
+        "Kết thúc": "18:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "2:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "2:00:00",
+        "Tổng công/ngày": 0.2500000000000001
+    },
+    {
+        "Mã địa điểm": "335-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Part time",
+        "Bắt đầu": "16:00:00",
+        "Kết thúc": "20:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "4:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.5000000000000002
+    },
+    {
+        "Mã địa điểm": "335-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "2:00:00",
+        "Số lượng": 5.0,
+        "Tổng giờ/ca/ngày": "1 day, 16:00:00",
+        "Tổng công/ngày": 5.000000000000001
+    },
+    {
+        "Mã địa điểm": "335-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
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+        "Lịch": 0,
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+        "Tổng công/ngày": 0.0
+    },
+    {
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+        "Kết thúc": "16:30:00",
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+        "Tổng công/ngày": 0.2500000000000001
+    },
+    {
+        "Mã địa điểm": "347-1",
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+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "12:30:00",
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 0.9999999999999998
+    },
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+        "Tổng công/ngày": 0.0
+    },
+    {
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+        "Lịch": 0,
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "17:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:30:00",
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "310-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "3 days, 8:00:00",
+        "Tổng công/ngày": 10.000000000000002
+    },
+    {
+        "Mã địa điểm": "310-1",
+        "Lịch": "Thứ 7, CN, Lễ Tết",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "18:30:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "10:30:00",
+        "Tổng giờ nghỉ": "2:00:00",
+        "Số lượng": 5.0,
+        "Tổng giờ/ca/ngày": "2 days, 4:30:00",
+        "Tổng công/ngày": 6.562500000000002
+    },
+    {
+        "Mã địa điểm": "612-1",
+        "Lịch": 0,
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+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "352-1",
+        "Lịch": "T2 đến T7",
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+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "12:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 0.9999999999999998
+    },
+    {
+        "Mã địa điểm": "352-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "2:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "369-1",
+        "Lịch": 0,
+        "Loại ca": 0,
+        "Bắt đầu": 0,
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+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "370-1",
+        "Lịch": 0,
+        "Loại ca": 0,
+        "Bắt đầu": 0,
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+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": 0,
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+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "357-1",
+        "Lịch": "Thứ 7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "324-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 5.0,
+        "Tổng giờ/ca/ngày": "1 day, 16:00:00",
+        "Tổng công/ngày": 5.000000000000001
+    },
+    {
+        "Mã địa điểm": "324-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": "18:00:00",
+        "Nghỉ 1 Kết thúc": "18:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 3.0,
+        "Tổng giờ/ca/ngày": "22:30:00",
+        "Tổng công/ngày": 2.8125
+    },
+    {
+        "Mã địa điểm": "490-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Part time",
+        "Bắt đầu": "16:00:00",
+        "Kết thúc": "18:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "2:00:00",
+        "Tổng công/ngày": 0.2500000000000001
+    },
+    {
+        "Mã địa điểm": "490-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:00:00",
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+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "579-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "05:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "09:00:00",
+        "Nghỉ 1 Kết thúc": "14:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "6:00:00",
+        "Tổng giờ nghỉ": "5:00:00",
+        "Số lượng": 32.0,
+        "Tổng giờ/ca/ngày": "8 days, 0:00:00",
+        "Tổng công/ngày": 24.0
+    },
+    {
+        "Mã địa điểm": "579-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "10:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": "14:00:00",
+        "Nghỉ 1 Kết thúc": "17:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "3:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "94-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "17:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "2:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "6-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:30:00",
+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 6.0,
+        "Tổng giờ/ca/ngày": "2 days, 3:00:00",
+        "Tổng công/ngày": 6.375000000000002
+    },
+    {
+        "Mã địa điểm": "27-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 3.0,
+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.0
+    },
+    {
+        "Mã địa điểm": "27-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "27-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 13.0,
+        "Tổng giờ/ca/ngày": "4 days, 8:00:00",
+        "Tổng công/ngày": 13.000000000000002
+    },
+    {
+        "Mã địa điểm": "27-1",
+        "Lịch": "Thứ 7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "9:30:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 13.0,
+        "Tổng giờ/ca/ngày": "5 days, 3:30:00",
+        "Tổng công/ngày": 15.4375
+    },
+    {
+        "Mã địa điểm": "27-1",
+        "Lịch": "Thứ 7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 3.0,
+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.0
+    },
+    {
+        "Mã địa điểm": "27-1",
+        "Lịch": "CN",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "9:30:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "19:00:00",
+        "Tổng công/ngày": 2.375
+    },
+    {
+        "Mã địa điểm": "589-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 6.0,
+        "Tổng giờ/ca/ngày": "2 days, 0:00:00",
+        "Tổng công/ngày": 6.0
+    },
+    {
+        "Mã địa điểm": "589-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:30:00",
+        "Kết thúc": "21:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "1 day, 16:00:00",
+        "Tổng công/ngày": 5.000000000000001
+    },
+    {
+        "Mã địa điểm": "589-1",
+        "Lịch": "Thứ 7",
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+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "16:00:00",
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+        "Tổng giờ/ca/ngày": "2 days, 6:00:00",
+        "Tổng công/ngày": 6.749999999999998
+    },
+    {
+        "Mã địa điểm": "589-1",
+        "Lịch": "CN",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "16:00:00",
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+        "Tổng giờ/ca/ngày": "1 day, 21:00:00",
+        "Tổng công/ngày": 5.624999999999999
+    },
+    {
+        "Mã địa điểm": "609-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "9 days, 0:00:00",
+        "Tổng công/ngày": 27.000000000000007
+    },
+    {
+        "Mã địa điểm": "609-1",
+        "Lịch": "T2 đến T7",
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+        "Kết thúc": "21:00:00",
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+        "Tổng giờ/ca/ngày": "6 days, 16:00:00",
+        "Tổng công/ngày": 20.000000000000004
+    },
+    {
+        "Mã địa điểm": "609-1",
+        "Lịch": "T2 đến T8",
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+        "Kết thúc": "14:30:00",
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+        "Tổng giờ/ca/ngày": "2 days, 4:30:00",
+        "Tổng công/ngày": 6.562499999999998
+    },
+    {
+        "Mã địa điểm": "595-1",
+        "Lịch": "T2 đến T6",
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+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "15:00:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "1:00:00",
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+        "Tổng giờ/ca/ngày": "4 days, 0:00:00",
+        "Tổng công/ngày": 12.0
+    },
+    {
+        "Mã địa điểm": "33-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "3 days, 0:00:00",
+        "Tổng công/ngày": 9.0
+    },
+    {
+        "Mã địa điểm": "33-1",
+        "Lịch": "T2 đến T6",
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+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
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+        "Tổng giờ/ca/ngày": "1 day, 16:00:00",
+        "Tổng công/ngày": 5.000000000000001
+    },
+    {
+        "Mã địa điểm": "24-1",
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+        "Kết thúc": "20:00:00",
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "24-1",
+        "Lịch": 0,
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "18:00:00",
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+        "Nghỉ 1 Kết thúc": "15:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "24-1",
+        "Lịch": 0,
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+        "Kết thúc": "15:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "12:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "67-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "67-1",
+        "Lịch": "Thứ 7",
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+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "11:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.4999999999999999
+    },
+    {
+        "Mã địa điểm": "7-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "15:00:00",
+        "Nghỉ 1 Bắt đầu": "10:00:00",
+        "Nghỉ 1 Kết thúc": "11:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 5.0,
+        "Tổng giờ/ca/ngày": "1 day, 16:00:00",
+        "Tổng công/ngày": 5.000000000000001
+    },
+    {
+        "Mã địa điểm": "7-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "14:00:00",
+        "Kết thúc": "22:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 1.9999999999999996
+    },
+    {
+        "Mã địa điểm": "7-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca đêm",
+        "Bắt đầu": "2025-01-01 22:00:00",
+        "Kết thúc": "2025-01-02 06:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.000000000014552
+    },
+    {
+        "Mã địa điểm": "8-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "15:00:00",
+        "Nghỉ 1 Bắt đầu": "10:10:00",
+        "Nghỉ 1 Kết thúc": "11:10:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 4.0,
+        "Tổng giờ/ca/ngày": "1 day, 8:00:00",
+        "Tổng công/ngày": 4.0
+    },
+    {
+        "Mã địa điểm": "8-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "8-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:00:00",
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+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "8-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca đêm",
+        "Bắt đầu": "2025-01-01 21:00:00",
+        "Kết thúc": "2025-01-02 06:00:00",
+        "Nghỉ 1 Bắt đầu": "22:00:00",
+        "Nghỉ 1 Kết thúc": "22:30:00",
+        "Nghỉ 2 Bắt đầu": "02:00:00",
+        "Nghỉ 2 Kết thúc": "03:00:00",
+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "15:00:00",
+        "Tổng công/ngày": 1.8749999999999996
+    },
+    {
+        "Mã địa điểm": "25-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "25-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "14:00:00",
+        "Kết thúc": "22:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 1.9999999999999996
+    },
+    {
+        "Mã địa điểm": "25-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "08:00:00",
+        "Kết thúc": "16:45:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "12:45:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "0:45:00",
+        "Số lượng": 16.0,
+        "Tổng giờ/ca/ngày": "5 days, 8:00:00",
+        "Tổng công/ngày": 16.0
+    },
+    {
+        "Mã địa điểm": "25-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca đêm",
+        "Bắt đầu": "2025-01-01 22:00:00",
+        "Kết thúc": "2025-01-02 06:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.000000000014552
+    },
+    {
+        "Mã địa điểm": "274-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 5.0,
+        "Tổng giờ/ca/ngày": "1 day, 16:00:00",
+        "Tổng công/ngày": 5.000000000000001
+    },
+    {
+        "Mã địa điểm": "274-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "274-2",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Part time",
+        "Bắt đầu": "14:00:00",
+        "Kết thúc": "18:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "4:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.4999999999999999
+    },
+    {
+        "Mã địa điểm": "16-1",
+        "Lịch": 0,
+        "Loại ca": 0,
+        "Bắt đầu": 0,
+        "Kết thúc": 0,
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": 0,
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "28-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:30:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": "18:00:00",
+        "Nghỉ 1 Kết thúc": "18:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:00:00",
+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 4.0,
+        "Tổng giờ/ca/ngày": "1 day, 4:00:00",
+        "Tổng công/ngày": 3.4999999999999996
+    },
+    {
+        "Mã địa điểm": "28-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "12:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 7.0,
+        "Tổng giờ/ca/ngày": "2 days, 4:30:00",
+        "Tổng công/ngày": 6.562499999999998
+    },
+    {
+        "Mã địa điểm": "28-1",
+        "Lịch": "CN, Lễ Tết",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "9:30:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "19:00:00",
+        "Tổng công/ngày": 2.375
+    },
+    {
+        "Mã địa điểm": "28-2",
+        "Lịch": 0,
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "12:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "15:00:00",
+        "Tổng công/ngày": 1.875
+    },
+    {
+        "Mã địa điểm": "546-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "547-1",
+        "Lịch": 0,
+        "Loại ca": "Part time",
+        "Bắt đầu": "10:30:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "4:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.4999999999999999
+    },
+    {
+        "Mã địa điểm": "272-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": "10:30:00",
+        "Nghỉ 1 Kết thúc": "11:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 9.0,
+        "Tổng giờ/ca/ngày": "2 days, 19:30:00",
+        "Tổng công/ngày": 8.4375
+    },
+    {
+        "Mã địa điểm": "272-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:30:00",
+        "Kết thúc": "21:30:00",
+        "Nghỉ 1 Bắt đầu": "19:00:00",
+        "Nghỉ 1 Kết thúc": "19:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 8.0,
+        "Tổng giờ/ca/ngày": "2 days, 12:00:00",
+        "Tổng công/ngày": 7.5
+    },
+    {
+        "Mã địa điểm": "272-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "2:00:00",
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "272-1",
+        "Lịch": "T2 đến CN",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "05:00:00",
+        "Kết thúc": "15:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "2:00:00",
+        "Số lượng": 3.0,
+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.0
+    },
+    {
+        "Mã địa điểm": "623-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "05:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "10:30:00",
+        "Nghỉ 1 Kết thúc": "13:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "3:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "623-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "2:00:00",
+        "Số lượng": 3.0,
+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.0
+    },
+    {
+        "Mã địa điểm": "623-1",
+        "Lịch": "Thứ 7, CN, Lễ Tết",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "10:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "20:00:00",
+        "Tổng công/ngày": 2.5
+    },
+    {
+        "Mã địa điểm": "625-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "9:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "9:00:00",
+        "Tổng công/ngày": 1.125
+    },
+    {
+        "Mã địa điểm": "66-1",
+        "Lịch": 0,
+        "Loại ca": 0,
+        "Bắt đầu": 0,
+        "Kết thúc": 0,
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": 0,
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "2-001",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "05:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "12:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "9:30:00",
+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "9:30:00",
+        "Tổng công/ngày": 1.1875
+    },
+    {
+        "Mã địa điểm": "2-002",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "17:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "2:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "2-003",
+        "Lịch": 0,
+        "Loại ca": 0,
+        "Bắt đầu": 0,
+        "Kết thúc": 0,
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": 0,
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "2-004",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "2:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "2-005",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "2-006",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Part time",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "08:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "2:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "2:00:00",
+        "Tổng công/ngày": 0.24999999999999994
+    },
+    {
+        "Mã địa điểm": "2-007",
+        "Lịch": 0,
+        "Loại ca": 0,
+        "Bắt đầu": 0,
+        "Kết thúc": 0,
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": 0,
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "2-008",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "10:30:00",
+        "Nghỉ 1 Kết thúc": "12:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 3.0,
+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.0
+    },
+    {
+        "Mã địa điểm": "2-009",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "17:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "2:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "2-010",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "2-011",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Part time",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "18:30:00",
+        "Nghỉ 1 Bắt đầu": "08:00:00",
+        "Nghỉ 1 Kết thúc": "16:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "4:00:00",
+        "Tổng giờ nghỉ": "8:30:00",
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+        "Tổng giờ/ca/ngày": "4:00:00",
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+        "Tổng công/ngày": 3.9375
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+        "Tổng công/ngày": 0.0
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+        "Tổng công/ngày": 2.625000000000001
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+        "Tổng công/ngày": 10.3125
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+        "Tổng công/ngày": 16.875
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+        "Tổng công/ngày": 0.4999999999999999
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+        "Tổng công/ngày": 4.6875
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+        "Tổng giờ/ca/ngày": "7 days, 0:00:00",
+        "Tổng công/ngày": 21.000000000000004
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+        "Tổng công/ngày": 0.0
+    },
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+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
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+        "Tổng công/ngày": 3.0
+    },
+    {
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+        "Kết thúc": "17:00:00",
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+        "Tổng giờ/ca/ngày": "1 day, 20:00:00",
+        "Tổng công/ngày": 5.5
+    },
+    {
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+        "Lịch": "T2 đến CN",
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+        "Kết thúc": "21:00:00",
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+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
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+        "Kết thúc": "16:00:00",
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+        "Nghỉ 1 Kết thúc": "13:00:00",
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+        "Tổng giờ nghỉ": "1:30:00",
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+        "Tổng giờ/ca/ngày": "2 days, 0:00:00",
+        "Tổng công/ngày": 6.0
+    },
+    {
+        "Mã địa điểm": "245-1",
+        "Lịch": "T2 đến CN",
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+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": "19:00:00",
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+        "Tổng công/ngày": 3.75
+    },
+    {
+        "Mã địa điểm": "245-1",
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+        "Kết thúc": "17:00:00",
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+        "Tổng công/ngày": 0.0
+    },
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+        "Kết thúc": "16:00:00",
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+        "Tổng giờ/ca/ngày": "3:00:00",
+        "Tổng công/ngày": 0.375
+    },
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+        "Kết thúc": "21:00:00",
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+        "Tổng giờ/ca/ngày": "15:00:00",
+        "Tổng công/ngày": 1.875
+    },
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+        "Mã địa điểm": "246-1",
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+        "Kết thúc": "16:00:00",
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+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "1 day, 16:00:00",
+        "Tổng công/ngày": 5.000000000000001
+    },
+    {
+        "Mã địa điểm": "257-1",
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+        "Kết thúc": "16:30:00",
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+        "Tổng giờ/ca/ngày": "1 day, 8:00:00",
+        "Tổng công/ngày": 4.0
+    },
+    {
+        "Mã địa điểm": "213-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "14:30:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "12:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "2 days, 12:00:00",
+        "Tổng công/ngày": 7.499999999999998
+    },
+    {
+        "Mã địa điểm": "213-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Ca chiều",
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+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "1 day, 8:00:00",
+        "Tổng công/ngày": 4.0
+    },
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+        "Mã địa điểm": "263-1",
+        "Lịch": 0,
+        "Loại ca": 0,
+        "Bắt đầu": 0,
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+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "53-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
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+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "215-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "2:00:00",
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+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.0
+    },
+    {
+        "Mã địa điểm": "206-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "575-1",
+        "Lịch": 0,
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "15:30:00",
+        "Nghỉ 1 Bắt đầu": "10:30:00",
+        "Nghỉ 1 Kết thúc": "11:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
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+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 18.0,
+        "Tổng giờ/ca/ngày": "6 days, 0:00:00",
+        "Tổng công/ngày": 18.000000000000004
+    },
+    {
+        "Mã địa điểm": "575-1",
+        "Lịch": 0,
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+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": "18:30:00",
+        "Nghỉ 1 Kết thúc": "19:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 10.0,
+        "Tổng giờ/ca/ngày": "3 days, 3:00:00",
+        "Tổng công/ngày": 9.375000000000004
+    },
+    {
+        "Mã địa điểm": "467-1",
+        "Lịch": 0,
+        "Loại ca": 0,
+        "Bắt đầu": 0,
+        "Kết thúc": 0,
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": 0,
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+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "240-1",
+        "Lịch": 0,
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:30:00",
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+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "2:00:00",
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+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.0
+    },
+    {
+        "Mã địa điểm": "244-1",
+        "Lịch": 0,
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
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+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "224-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "209-1",
+        "Lịch": 0,
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "15:00:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "209-1",
+        "Lịch": 0,
+        "Loại ca": "Part time",
+        "Bắt đầu": "15:00:00",
+        "Kết thúc": "19:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
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+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.4999999999999999
+    },
+    {
+        "Mã địa điểm": "210-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "18:00:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "11:00:00",
+        "Tổng giờ nghỉ": "1:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "11:00:00",
+        "Tổng công/ngày": 1.375
+    },
+    {
+        "Mã địa điểm": "452-1",
+        "Lịch": "Thứ 3,6",
+        "Loại ca": "Part time",
+        "Bắt đầu": "16:00:00",
+        "Kết thúc": "18:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "2:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "2:00:00",
+        "Tổng công/ngày": 0.2500000000000001
+    },
+    {
+        "Mã địa điểm": "218-1",
+        "Lịch": 0,
+        "Loại ca": "Part time",
+        "Bắt đầu": "15:00:00",
+        "Kết thúc": "19:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "4:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.4999999999999999
+    },
+    {
+        "Mã địa điểm": "218-1",
+        "Lịch": 0,
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "14:00:00",
+        "Kết thúc": "20:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "6:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 3.0,
+        "Tổng giờ/ca/ngày": "18:00:00",
+        "Tổng công/ngày": 2.25
+    },
+    {
+        "Mã địa điểm": "218-1",
+        "Lịch": 0,
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:30:00",
+        "Kết thúc": "17:00:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0000000000000002
+    },
+    {
+        "Mã địa điểm": "286-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca sáng",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 5.0,
+        "Tổng giờ/ca/ngày": "1 day, 16:00:00",
+        "Tổng công/ngày": 5.000000000000001
+    },
+    {
+        "Mã địa điểm": "286-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca chiều",
+        "Bắt đầu": "13:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 5.0,
+        "Tổng giờ/ca/ngày": "1 day, 16:00:00",
+        "Tổng công/ngày": 5.000000000000001
+    },
+    {
+        "Mã địa điểm": "286-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Ca gãy",
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+        "Kết thúc": "11:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "20:00:00",
+        "Tổng công/ngày": 2.4999999999999996
+    },
+    {
+        "Mã địa điểm": "286-1",
+        "Lịch": "T2 đến T6",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "11:00:00",
+        "Kết thúc": "19:30:00",
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+        "Tổng công/ngày": 1.0
+    },
+    {
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+        "Kết thúc": "16:00:00",
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+        "Tổng công/ngày": 5.0
+    },
+    {
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+        "Tổng công/ngày": 1.0
+    },
+    {
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+        "Lịch": "CN",
+        "Loại ca": "Hành chính",
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+        "Kết thúc": "17:00:00",
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+        "Tổng công/ngày": 5.000000000000002
+    },
+    {
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+        "Lịch": "CN",
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+        "Tổng công/ngày": 0.5
+    },
+    {
+        "Mã địa điểm": "216-1",
+        "Lịch": "T2 đến T7",
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+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
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+        "Tổng công/ngày": 3.0
+    },
+    {
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+        "Tổng công/ngày": 0.9375
+    },
+    {
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+        "Kết thúc": "17:00:00",
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+        "Tổng giờ/ca/ngày": "17:00:00",
+        "Tổng công/ngày": 2.125
+    },
+    {
+        "Mã địa điểm": "54-1",
+        "Lịch": "T2 đến T7",
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+        "Số lượng": 2.0,
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+        "Tổng công/ngày": 2.0000000000000004
+    },
+    {
+        "Mã địa điểm": "269-1",
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+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "2:00:00",
+        "Tổng công/ngày": 0.2500000000000001
+    },
+    {
+        "Mã địa điểm": "284-1",
+        "Lịch": 0,
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+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "16:00:00",
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+        "Nghỉ 1 Kết thúc": "13:00:00",
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+        "Tổng giờ nghỉ": "1:00:00",
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "451-1",
+        "Lịch": "T2 đến T7",
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+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
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+        "Tổng giờ/ca/ngày": "17:00:00",
+        "Tổng công/ngày": 2.1250000000000004
+    },
+    {
+        "Mã địa điểm": "236-1",
+        "Lịch": 0,
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+        "Kết thúc": "11:00:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Tổng giờ/ca/ngày": "15:00:00",
+        "Tổng công/ngày": 1.875
+    },
+    {
+        "Mã địa điểm": "236-1",
+        "Lịch": 0,
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+        "Kết thúc": "21:00:00",
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+        "Nghỉ 1 Kết thúc": "18:30:00",
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+        "Tổng giờ/ca/ngày": "2 days, 4:30:00",
+        "Tổng công/ngày": 6.5625
+    },
+    {
+        "Mã địa điểm": "236-1",
+        "Lịch": 0,
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "16:00:00",
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+        "Nghỉ 1 Kết thúc": "13:00:00",
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+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "2:00:00",
+        "Số lượng": 10.0,
+        "Tổng giờ/ca/ngày": "3 days, 8:00:00",
+        "Tổng công/ngày": 10.0
+    },
+    {
+        "Mã địa điểm": "236-1",
+        "Lịch": 0,
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:00:00",
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+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.0
+    },
+    {
+        "Mã địa điểm": "236-1",
+        "Lịch": 0,
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
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+        "Nghỉ 1 Kết thúc": "13:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "2:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "236-1",
+        "Lịch": 0,
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "14:00:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "12:30:00",
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+        "Tổng giờ làm": "7:30:00",
+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "7:30:00",
+        "Tổng công/ngày": 0.9375
+    },
+    {
+        "Mã địa điểm": "236-1",
+        "Lịch": 0,
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:00:00",
+        "Kết thúc": "15:30:00",
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+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
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+        "Tổng giờ/ca/ngày": "1 day, 0:00:00",
+        "Tổng công/ngày": 3.000000000000001
+    },
+    {
+        "Mã địa điểm": "235-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
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+        "Tổng giờ nghỉ": "2:00:00",
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+        "Tổng giờ/ca/ngày": "1 day, 8:00:00",
+        "Tổng công/ngày": 4.0
+    },
+    {
+        "Mã địa điểm": "222-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ nghỉ": "1:30:00",
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+        "Tổng giờ/ca/ngày": "1 day, 1:30:00",
+        "Tổng công/ngày": 3.187500000000001
+    },
+    {
+        "Mã địa điểm": "222-2",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "16:30:00",
+        "Nghỉ 1 Bắt đầu": "11:30:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "1:30:00",
+        "Số lượng": 2.0,
+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
+        "Mã địa điểm": "248-1",
+        "Lịch": 0,
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "08:00:00",
+        "Kết thúc": "18:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "2:00:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "447-1",
+        "Lịch": 0,
+        "Loại ca": 0,
+        "Bắt đầu": 0,
+        "Kết thúc": 0,
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": 0,
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "262-1",
+        "Lịch": 0,
+        "Loại ca": "Part time",
+        "Bắt đầu": "18:00:00",
+        "Kết thúc": "19:30:00",
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "1:30:00",
+        "Tổng giờ nghỉ": 0,
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "1:30:00",
+        "Tổng công/ngày": 0.1875
+    },
+    {
+        "Mã địa điểm": "454-2",
+        "Lịch": 0,
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "06:30:00",
+        "Kết thúc": "15:00:00",
+        "Nghỉ 1 Bắt đầu": "12:00:00",
+        "Nghỉ 1 Kết thúc": "12:30:00",
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": "8:00:00",
+        "Tổng giờ nghỉ": "0:30:00",
+        "Số lượng": 1.0,
+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
+        "Mã địa điểm": "470-1",
+        "Lịch": "T2 đến T7",
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "09:00:00",
+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": "17:30:00",
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+        "Tổng công/ngày": 5.625
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+    },
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+        "Tổng công/ngày": 6.375
+    },
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+        "Tổng công/ngày": 0.9375
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+        "Tổng công/ngày": 0.0
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+        "Tổng công/ngày": 0.0
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+        "Tổng công/ngày": 0.0
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+        "Tổng công/ngày": 0.0
+    },
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+    },
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+        "Tổng công/ngày": 0.9375000000000002
+    },
+    {
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+        "Kết thúc": "21:00:00",
+        "Nghỉ 1 Bắt đầu": "18:30:00",
+        "Nghỉ 1 Kết thúc": "19:00:00",
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+        "Tổng giờ/ca/ngày": "15:00:00",
+        "Tổng công/ngày": 1.8750000000000007
+    },
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+        "Mã địa điểm": "613-1",
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+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "605-1",
+        "Lịch": 0,
+        "Loại ca": "Hành chính",
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+        "Kết thúc": "16:00:00",
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0
+    },
+    {
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+        "Lịch": 0,
+        "Loại ca": "Hành chính",
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+        "Tổng giờ/ca/ngày": "16:00:00",
+        "Tổng công/ngày": 2.0
+    },
+    {
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+        "Kết thúc": "17:30:00",
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+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng công/ngày": 4.75
+    },
+    {
+        "Mã địa điểm": "605-1",
+        "Lịch": 0,
+        "Loại ca": "Part time",
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+        "Tổng giờ/ca/ngày": "4:00:00",
+        "Tổng công/ngày": 0.5000000000000002
+    },
+    {
+        "Mã địa điểm": "605-1",
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+        "Bắt đầu": "04:00:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "08:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ/ca/ngày": "7:00:00",
+        "Tổng công/ngày": 0.875
+    },
+    {
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+        "Lịch": 0,
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "04:00:00",
+        "Kết thúc": "20:00:00",
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+        "Nghỉ 1 Kết thúc": "16:00:00",
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+        "Tổng giờ nghỉ": "8:00:00",
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+        "Tổng giờ/ca/ngày": "8:00:00",
+        "Tổng công/ngày": 1.0000000000000002
+    },
+    {
+        "Mã địa điểm": "605-1",
+        "Lịch": 0,
+        "Loại ca": "Hành chính",
+        "Bắt đầu": "07:00:00",
+        "Kết thúc": "16:00:00",
+        "Nghỉ 1 Bắt đầu": "11:00:00",
+        "Nghỉ 1 Kết thúc": "13:00:00",
+        "Nghỉ 2 Bắt đầu": 0,
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+        "Tổng giờ làm": "7:00:00",
+        "Tổng giờ nghỉ": "2:00:00",
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+        "Tổng giờ/ca/ngày": "7:00:00",
+        "Tổng công/ngày": 0.8749999999999999
+    },
+    {
+        "Mã địa điểm": "622-1",
+        "Lịch": 0,
+        "Loại ca": 0,
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+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
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+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": 0,
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+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    },
+    {
+        "Mã địa điểm": "622-2",
+        "Lịch": 0,
+        "Loại ca": 0,
+        "Bắt đầu": 0,
+        "Kết thúc": 0,
+        "Nghỉ 1 Bắt đầu": 0,
+        "Nghỉ 1 Kết thúc": 0,
+        "Nghỉ 2 Bắt đầu": 0,
+        "Nghỉ 2 Kết thúc": 0,
+        "Tổng giờ làm": 0,
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+        "Số lượng": 0.0,
+        "Tổng giờ/ca/ngày": 0,
+        "Tổng công/ngày": 0.0
+    }
+]
\ No newline at end of file
diff --git a/data_raw/Link LLV 2025_clean.json b/data_raw/Link LLV 2025_clean.json
new file mode 100644
index 0000000..a2f4eae
--- /dev/null
+++ b/data_raw/Link LLV 2025_clean.json	
@@ -0,0 +1,424176 @@
+[
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+        "Tên Tòa Tháp":"Mipec Tower",
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+        "Khu vực":"Khu Ngoại cảnh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Sân ngoài trời",
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+        "Mã địa điểm":"222-1",
+        "Tên từng địa điểm làm sạch":"CTY SẢN XUẤT VACXIN SABIN TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 1 (418 Vĩnh Hưng , Tòa nhà 3 tầng Hoàng Mai)",
+        "Tên Tòa Tháp":"CTY SẢN XUẤT VACXIN SABIN TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 1 (418 Vĩnh Hưng , Tòa nhà 3 tầng Hoàng Mai)",
+        "Tầng":"Ngoại cảnh",
+        "Khu vực":"Khu Để xe",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Bãi xe",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Cao (21–40 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
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+        "Thảm cỏ":0.0,
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+        "Cửa căn hộ":0.0,
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+        "Gạt tàn":0.0,
+        "Xô đựng đầu mẩu thuốc":0.0,
+        "Thùng rác.1":0.0,
+        "Biển tên Tòa nhà":0.0,
+        "Lư hóa vàng":0.0
+    },
+    {
+        "Loại hình":0.0,
+        "Tên GS":"Lê Thị Thủy",
+        "Mã địa điểm":"222-1",
+        "Tên từng địa điểm làm sạch":"CTY SẢN XUẤT VACXIN SABIN TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 1 (418 Vĩnh Hưng , Tòa nhà 3 tầng Hoàng Mai)",
+        "Tên Tòa Tháp":"Khu nhà văn phòng",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Hành lang",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Hành lang",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Cao (21–40 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
+        "Sàn Hành lang":195.0,
+        "Sàn WC":0.0,
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+        "Cửa thang máy":0.0,
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+        "Giá để ô":0.0,
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+        "Xô đựng đầu mẩu thuốc":0.0,
+        "Thùng rác.1":0.0,
+        "Biển tên Tòa nhà":0.0,
+        "Lư hóa vàng":0.0
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+    {
+        "Loại hình":0.0,
+        "Tên GS":"Lê Thị Thủy",
+        "Mã địa điểm":"222-1",
+        "Tên từng địa điểm làm sạch":"CTY SẢN XUẤT VACXIN SABIN TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 1 (418 Vĩnh Hưng , Tòa nhà 3 tầng Hoàng Mai)",
+        "Tên Tòa Tháp":"Khu nhà văn phòng",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Thang bộ",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":0,
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Trung bình (11–20 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
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+        "Sàn WC":0.0,
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+        "Xô đựng đầu mẩu thuốc":0.0,
+        "Thùng rác.1":0.0,
+        "Biển tên Tòa nhà":0.0,
+        "Lư hóa vàng":0.0
+    },
+    {
+        "Loại hình":0.0,
+        "Tên GS":"Lê Thị Thủy",
+        "Mã địa điểm":"222-1",
+        "Tên từng địa điểm làm sạch":"CTY SẢN XUẤT VACXIN SABIN TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 1 (418 Vĩnh Hưng , Tòa nhà 3 tầng Hoàng Mai)",
+        "Tên Tòa Tháp":"Khu nhà văn phòng",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Nhà ăn",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng sinh hoạt cộng đồng",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Trung bình (11–20 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
+        "Sàn Hành lang":0.0,
+        "Sàn WC":0.0,
+        "Sàn Phòng":70,
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+        "Thảm cỏ":0.0,
+        "Vỉa hè":0.0,
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+        "Ốp gỗ":0.0,
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+        "Giường bệnh":0.0,
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+        "Tủ để đồ cho bệnh nhân":0.0,
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+        "Chậu cây":0.0,
+        "Gạt tàn":0.0,
+        "Xô đựng đầu mẩu thuốc":0.0,
+        "Thùng rác.1":0.0,
+        "Biển tên Tòa nhà":0.0,
+        "Lư hóa vàng":0.0
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+    {
+        "Loại hình":0.0,
+        "Tên GS":"Lê Thị Thủy",
+        "Mã địa điểm":"222-1",
+        "Tên từng địa điểm làm sạch":"CTY SẢN XUẤT VACXIN SABIN TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 1 (418 Vĩnh Hưng , Tòa nhà 3 tầng Hoàng Mai)",
+        "Tên Tòa Tháp":"Khu nhà văn phòng",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Vệ sinh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Nam",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Trung bình (11–20 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
+        "Sàn Hành lang":0.0,
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+        "Sàn Phòng":0,
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+        "Thảm cỏ":0.0,
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+        "Thùng rác.1":0.0,
+        "Biển tên Tòa nhà":0.0,
+        "Lư hóa vàng":0.0
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+    {
+        "Loại hình":0.0,
+        "Tên GS":"Lê Thị Thủy",
+        "Mã địa điểm":"222-1",
+        "Tên từng địa điểm làm sạch":"CTY SẢN XUẤT VACXIN SABIN TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 1 (418 Vĩnh Hưng , Tòa nhà 3 tầng Hoàng Mai)",
+        "Tên Tòa Tháp":"Khu nhà văn phòng",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Vệ sinh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Nữ",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Trung bình (11–20 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
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+        "Xô đựng đầu mẩu thuốc":0.0,
+        "Thùng rác.1":0.0,
+        "Biển tên Tòa nhà":0.0,
+        "Lư hóa vàng":0.0
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+    {
+        "Loại hình":0.0,
+        "Tên GS":"Lê Thị Thủy",
+        "Mã địa điểm":"222-1",
+        "Tên từng địa điểm làm sạch":"CTY SẢN XUẤT VACXIN SABIN TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 1 (418 Vĩnh Hưng , Tòa nhà 3 tầng Hoàng Mai)",
+        "Tên Tòa Tháp":"Khu nhà văn phòng",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu Hành lang",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Hành lang",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Trung bình (11–20 người)",
+        "Sàn ngoại cảnh":0.0,
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+        "Xô đựng đầu mẩu thuốc":0.0,
+        "Thùng rác.1":0.0,
+        "Biển tên Tòa nhà":0.0,
+        "Lư hóa vàng":0.0
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+    {
+        "Loại hình":0.0,
+        "Tên GS":"Lê Thị Thủy",
+        "Mã địa điểm":"222-1",
+        "Tên từng địa điểm làm sạch":"CTY SẢN XUẤT VACXIN SABIN TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 1 (418 Vĩnh Hưng , Tòa nhà 3 tầng Hoàng Mai)",
+        "Tên Tòa Tháp":"Khu nhà văn phòng",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu Tiếp khách",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng họp",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Trung bình (11–20 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
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+        "Xô đựng đầu mẩu thuốc":0.0,
+        "Thùng rác.1":0.0,
+        "Biển tên Tòa nhà":0.0,
+        "Lư hóa vàng":0.0
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+    {
+        "Loại hình":0.0,
+        "Tên GS":"Lê Thị Thủy",
+        "Mã địa điểm":"222-1",
+        "Tên từng địa điểm làm sạch":"CTY SẢN XUẤT VACXIN SABIN TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 1 (418 Vĩnh Hưng , Tòa nhà 3 tầng Hoàng Mai)",
+        "Tên Tòa Tháp":"Khu nhà văn phòng",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu Thang bộ",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":0,
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Trung bình (11–20 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
+        "Sàn Hành lang":0.0,
+        "Sàn WC":0.0,
+        "Sàn Phòng":0,
+        "Thảm":0.0,
+        "Thảm cỏ":0.0,
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+        "Xô đựng đầu mẩu thuốc":0.0,
+        "Thùng rác.1":0.0,
+        "Biển tên Tòa nhà":0.0,
+        "Lư hóa vàng":0.0
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+    {
+        "Loại hình":0.0,
+        "Tên GS":"Lê Thị Thủy",
+        "Mã địa điểm":"222-1",
+        "Tên từng địa điểm làm sạch":"CTY SẢN XUẤT VACXIN SABIN TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 1 (418 Vĩnh Hưng , Tòa nhà 3 tầng Hoàng Mai)",
+        "Tên Tòa Tháp":"Khu nhà văn phòng",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu Vệ sinh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Nam",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Trung bình (11–20 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
+        "Sàn Hành lang":0.0,
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+        "Thảm cỏ":0.0,
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+        "Hộp đựng nước rửa tay":1.0,
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+        "Xô đựng đầu mẩu thuốc":0.0,
+        "Thùng rác.1":0.0,
+        "Biển tên Tòa nhà":0.0,
+        "Lư hóa vàng":0.0
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+    {
+        "Loại hình":0.0,
+        "Tên GS":"Lê Thị Thủy",
+        "Mã địa điểm":"222-1",
+        "Tên từng địa điểm làm sạch":"CTY SẢN XUẤT VACXIN SABIN TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 1 (418 Vĩnh Hưng , Tòa nhà 3 tầng Hoàng Mai)",
+        "Tên Tòa Tháp":"Khu nhà văn phòng",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu Vệ sinh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Nữ",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Trung bình (11–20 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
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+        "Thảm cỏ":0.0,
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+        "Xô đựng đầu mẩu thuốc":0.0,
+        "Thùng rác.1":0.0,
+        "Biển tên Tòa nhà":0.0,
+        "Lư hóa vàng":0.0
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+    {
+        "Loại hình":0.0,
+        "Tên GS":"Lê Thị Thủy",
+        "Mã địa điểm":"222-1",
+        "Tên từng địa điểm làm sạch":"CTY SẢN XUẤT VACXIN SABIN TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 1 (418 Vĩnh Hưng , Tòa nhà 3 tầng Hoàng Mai)",
+        "Tên Tòa Tháp":"Khu nhà văn phòng",
+        "Tầng":"Tầng 3",
+        "Khu vực":"Khu Hành lang",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Hành lang",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Trung bình (11–20 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
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+        "Cửa căn hộ":0.0,
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+        "Chậu cây":10.0,
+        "Gạt tàn":0.0,
+        "Xô đựng đầu mẩu thuốc":0.0,
+        "Thùng rác.1":0.0,
+        "Biển tên Tòa nhà":0.0,
+        "Lư hóa vàng":0.0
+    },
+    {
+        "Loại hình":0.0,
+        "Tên GS":"Lê Thị Thủy",
+        "Mã địa điểm":"222-1",
+        "Tên từng địa điểm làm sạch":"CTY SẢN XUẤT VACXIN SABIN TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 1 (418 Vĩnh Hưng , Tòa nhà 3 tầng Hoàng Mai)",
+        "Tên Tòa Tháp":"Khu nhà văn phòng",
+        "Tầng":"Tầng 3",
+        "Khu vực":"Khu Chức năng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng họp",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Trung bình (11–20 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
+        "Sàn Hành lang":0.0,
+        "Sàn WC":0.0,
+        "Sàn Phòng":150,
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+        "Thảm cỏ":0.0,
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+        "Cửa phòng chức năng":2.0,
+        "Cửa căn hộ":0.0,
+        "Cửa exit":0.0,
+        "Cửa thang máy":0.0,
+        "Vách ngăn":0.0,
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+        "Bộ bàn ghế":25.0,
+        "Ghế chờ":70.0,
+        "Bàn ghế chờ":0.0,
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+        "Gạt tàn":0.0,
+        "Xô đựng đầu mẩu thuốc":0.0,
+        "Thùng rác.1":0.0,
+        "Biển tên Tòa nhà":0.0,
+        "Lư hóa vàng":0.0
+    },
+    {
+        "Loại hình":0.0,
+        "Tên GS":"Lê Thị Thủy",
+        "Mã địa điểm":"222-1",
+        "Tên từng địa điểm làm sạch":"CTY SẢN XUẤT VACXIN SABIN TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 1 (418 Vĩnh Hưng , Tòa nhà 3 tầng Hoàng Mai)",
+        "Tên Tòa Tháp":"Khu nhà văn phòng",
+        "Tầng":"Tầng 3",
+        "Khu vực":"Khu Thang bộ",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":0,
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Trung bình (11–20 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
+        "Sàn Hành lang":0.0,
+        "Sàn WC":0.0,
+        "Sàn Phòng":0,
+        "Thảm":0.0,
+        "Thảm cỏ":0.0,
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+        "Dốc hầm":0.0,
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+        "Ốp gỗ":0.0,
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+        "Gạt tàn":0.0,
+        "Xô đựng đầu mẩu thuốc":0.0,
+        "Thùng rác.1":0.0,
+        "Biển tên Tòa nhà":0.0,
+        "Lư hóa vàng":0.0
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+    {
+        "Loại hình":0.0,
+        "Tên GS":"Lê Thị Thủy",
+        "Mã địa điểm":"222-1",
+        "Tên từng địa điểm làm sạch":"CTY SẢN XUẤT VACXIN SABIN TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 1 (418 Vĩnh Hưng , Tòa nhà 3 tầng Hoàng Mai)",
+        "Tên Tòa Tháp":"Khu nhà văn phòng",
+        "Tầng":"Tầng 3",
+        "Khu vực":"Khu Vệ sinh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Nam",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Trung bình (11–20 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
+        "Sàn Hành lang":0.0,
+        "Sàn WC":13.0,
+        "Sàn Phòng":0,
+        "Thảm":0.0,
+        "Thảm cỏ":0.0,
+        "Vỉa hè":0.0,
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+        "Ốp gỗ":0.0,
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+        "Bệ đá":0.0,
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+        "Lavabo rửa tay":1.0,
+        "Gương":0.0,
+        "Bồn rửa tay":0.0,
+        "Hộp đựng nước rửa tay":1.0,
+        "Bệt":1.0,
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+        "Mã địa điểm":"222-1",
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+        "Tên Tòa Tháp":"Khu nhà văn phòng",
+        "Tầng":"Tầng 3",
+        "Khu vực":"Khu Vệ sinh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Nữ",
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+        "Mã địa điểm":"222-1",
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+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Hành lang",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Hành lang",
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+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Thang máy",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Hành lang",
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+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Hành lang",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Hành lang",
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+        "Mã địa điểm":"222-1",
+        "Tên từng địa điểm làm sạch":"CTY SẢN XUẤT VACXIN SABIN TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 1 (418 Vĩnh Hưng , Tòa nhà 3 tầng Hoàng Mai)",
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+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Thang bộ",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":0,
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+        "Mã địa điểm":"222-1",
+        "Tên từng địa điểm làm sạch":"CTY SẢN XUẤT VACXIN SABIN TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 1 (418 Vĩnh Hưng , Tòa nhà 3 tầng Hoàng Mai)",
+        "Tên Tòa Tháp":"Khu nhà sản xuất Vacsin",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Vệ sinh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Nam",
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+        "Mã địa điểm":"222-1",
+        "Tên từng địa điểm làm sạch":"CTY SẢN XUẤT VACXIN SABIN TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 1 (418 Vĩnh Hưng , Tòa nhà 3 tầng Hoàng Mai)",
+        "Tên Tòa Tháp":"Khu nhà sản xuất Vacsin",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Vệ sinh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Nữ",
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+        "Tên GS":"Lê Thị Thủy",
+        "Mã địa điểm":"222-1",
+        "Tên từng địa điểm làm sạch":"CTY SẢN XUẤT VACXIN SABIN TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 1 (418 Vĩnh Hưng , Tòa nhà 3 tầng Hoàng Mai)",
+        "Tên Tòa Tháp":"Khu nhà sản xuất Vacsin",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu Hành lang",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Hành lang",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Trung bình (11–20 người)",
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+        "Loại hình":0.0,
+        "Tên GS":"Lê Thị Thủy",
+        "Mã địa điểm":"222-1",
+        "Tên từng địa điểm làm sạch":"CTY SẢN XUẤT VACXIN SABIN TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 1 (418 Vĩnh Hưng , Tòa nhà 3 tầng Hoàng Mai)",
+        "Tên Tòa Tháp":"Khu nhà sản xuất Vacsin",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu Thang máy",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Hành lang",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Trung bình (11–20 người)",
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+        "Xô đựng đầu mẩu thuốc":0.0,
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+        "Biển tên Tòa nhà":0.0,
+        "Lư hóa vàng":0.0
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+        "Tên GS":"Lê Thị Thủy",
+        "Mã địa điểm":"222-1",
+        "Tên từng địa điểm làm sạch":"CTY SẢN XUẤT VACXIN SABIN TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 1 (418 Vĩnh Hưng , Tòa nhà 3 tầng Hoàng Mai)",
+        "Tên Tòa Tháp":"Khu nhà sản xuất Vacsin",
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+        "Tên từng địa điểm làm sạch":"CTY SẢN XUẤT VACXIN SABIN TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 1 (418 Vĩnh Hưng , Tòa nhà 3 tầng Hoàng Mai)",
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+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Kỹ thuật",
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+        "Tên từng địa điểm làm sạch":"VACSIN SABIN LÒ ĐÚC TRUNG TÂM NGHIÊN CỨU SẢN XUẤT VACXIN VÀ SINH PHẨM Y TẾ - CƠ SỞ 2 (Tòa tòa nhà 5 tầng só 135 lò đúc)",
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+        "Tầng":"Sân trước",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":0,
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+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Hành lang",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":0,
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+        "Khu vực":"Khu Vệ sinh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Chung",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Trung bình (11–20 người)",
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+        "Mã địa điểm":"54-1",
+        "Tên từng địa điểm làm sạch":"VĂN PHÒNG CỤC CNTT- TC THUẾ",
+        "Tên Tòa Tháp":"VĂN PHÒNG CỤC CNTT- TC THUẾ",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu Văn phòng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":0,
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+        "Mã địa điểm":"54-1",
+        "Tên từng địa điểm làm sạch":"VĂN PHÒNG CỤC CNTT- TC THUẾ",
+        "Tên Tòa Tháp":"VĂN PHÒNG CỤC CNTT- TC THUẾ",
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+        "Khu vực":"Khu Hành lang",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Hành lang",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Cao (21–40 người)",
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+        "Mã địa điểm":"54-1",
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+        "Tên Tòa Tháp":"VĂN PHÒNG CỤC CNTT- TC THUẾ",
+        "Tầng":"Tầng 2 đầu a",
+        "Khu vực":"Khu Vệ sinh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Nam",
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+        "Mã địa điểm":"54-1",
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+        "Tên Tòa Tháp":"VĂN PHÒNG CỤC CNTT- TC THUẾ",
+        "Tầng":"Tầng 2 đầu a",
+        "Khu vực":"Khu Vệ sinh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Nữ",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Cao (21–40 người)",
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+        "Khu vực":"Khu Vệ sinh",
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+        "Mã địa điểm":"54-1",
+        "Tên từng địa điểm làm sạch":"VĂN PHÒNG CỤC CNTT- TC THUẾ",
+        "Tên Tòa Tháp":"VĂN PHÒNG CỤC CNTT- TC THUẾ",
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+        "Mã địa điểm":"54-1",
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+        "Tên Tòa Tháp":"VĂN PHÒNG CỤC CNTT- TC THUẾ",
+        "Tầng":"Tầng 2",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu pantry bếp nhỏ",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Nữ",
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+        "Mã địa điểm":"357-1",
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+        "Khu vực":"Quầy tiếp đón, văn thư",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Quầy tiếp đón, văn thư",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Tên GS":"Hoàng Minh Phương",
+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Tòa 5 tầng",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Sảnh,hành tiếp đón",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Sảnh,hành tiếp đón",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Tên GS":"Hoàng Minh Phương",
+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Ngoại cảnh",
+        "Tầng":"Ngoại cảnh",
+        "Khu vực":"Khu Ngoại cảnh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Sân, cổng, tầng hầm, nhà xe bệnh nhân",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
+        "Sàn ngoại cảnh":1700.0,
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+        "Tên GS":"Hoàng Minh Phương",
+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Ngoại cảnh",
+        "Tầng":"Ngoại cảnh",
+        "Khu vực":"Khu Ngoại cảnh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Bốt bảo vệ, kho tài liệu",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Cao (21–40 người)",
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+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Khu tiêm chủng",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Hành lang",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu Hành lang",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
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+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Khu tiêm chủng",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Khám",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng khám",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Khu tiêm chủng",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu tiêm chủng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu Vệ sinh",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Nhà 4 tầng",
+        "Tầng":"Nhà 4 tầng",
+        "Khu vực":"Khu Văn phòng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu Văn phòng",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Khu nhà vi sinh",
+        "Tầng":"Khu nhà vi sinh",
+        "Khu vực":"Khu nhà vi sinh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu nhà vi sinh",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Tòa 5 tầng",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Phòng cấp cứu",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng cấp cứu",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"101-1",
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+        "Tên Tòa Tháp":"Tòa 5 tầng",
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+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Tên GS":"Hoàng Minh Phương",
+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Tòa 5 tầng",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu Văn phòng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng giám đốc, các phòng ban",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Cao (21–40 người)",
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+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Tòa 5 tầng",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu Hành lang",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu Hành lang",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
+        "Sàn ngoại cảnh":0.0,
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+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Tòa 5 tầng",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Phòng tiêm,khoa dược",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng tiêm,khoa dược",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Tòa 5 tầng",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu Tâm linh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu Tâm linh",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Cao (21–40 người)",
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+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Tòa 5 tầng",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Hội trường",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Hội trường",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Tên GS":"Hoàng Minh Phương",
+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Tòa 5 tầng",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu phòng bệnh, phòng tinh trùng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu phòng bệnh, phòng tinh trùng",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Tòa 5 tầng",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu phòng nhân viên, phòng chờ",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu phòng nhân viên, phòng chờ",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Tòa 5 tầng",
+        "Tầng":"Tầng 3",
+        "Khu vực":"Phòng khám, phòng tinh trùng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng khám, phòng tinh trùng",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
+        "Sàn ngoại cảnh":0.0,
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+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Tòa 5 tầng",
+        "Tầng":"Tầng 3",
+        "Khu vực":"Khu Hành lang",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu Hành lang",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
+        "Sàn ngoại cảnh":0.0,
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+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Tòa 5 tầng",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng nhân viên, giao ban, tắm bé",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Tên GS":"Hoàng Minh Phương",
+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Tòa 5 tầng",
+        "Tầng":"Tầng 3",
+        "Khu vực":"Khu phòng đẻ, hậu phẫu",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu phòng đẻ, hậu phẫu",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Cao (21–40 người)",
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+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Tòa 5 tầng",
+        "Tầng":"Tầng 3",
+        "Khu vực":"Khu phòng bệnh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu phòng bệnh",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
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+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Tòa 5 tầng",
+        "Tầng":"Tầng 4",
+        "Khu vực":"Khu phòng bệnh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu phòng bệnh",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
+        "Sàn ngoại cảnh":0.0,
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+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Tòa 5 tầng",
+        "Tầng":"Tầng 4",
+        "Khu vực":"Khu Hành lang",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu Hành lang",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
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+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Tòa 5 tầng",
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+        "Khu vực":"Phòng bệnh, chức năng , nhân viên",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng bệnh, chức năng , nhân viên",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Tòa 5 tầng",
+        "Tầng":"Tầng 5",
+        "Khu vực":"Khu Hành lang",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu Hành lang",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Tòa 5 tầng",
+        "Tầng":"Tầng 5",
+        "Khu vực":"Hội trường",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Hội trường",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
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+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Tòa 5 tầng",
+        "Tầng":"Tầng 5",
+        "Khu vực":"Khu phòng bệnh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu phòng bệnh",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Tòa 5 tầng",
+        "Tầng":"Tầng 5,6",
+        "Khu vực":"Nhà ăn",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Nhà ăn",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
+        "Sàn ngoại cảnh":0.0,
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+        "Mã địa điểm":"101-1",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 1",
+        "Tên Tòa Tháp":"Tòa 5 tầng",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng bệnh, chức năng , nhân viên",
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+        "Tên Tòa Tháp":"BỆNH VIỆN BƯU ĐIỆN 2",
+        "Tầng":"Tầng 1",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng khám",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Tên Tòa Tháp":"BỆNH VIỆN BƯU ĐIỆN 2",
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+        "Tên Tòa Tháp":"BỆNH VIỆN BƯU ĐIỆN 2",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Hành lang",
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+        "Tên Tòa Tháp":"BỆNH VIỆN BƯU ĐIỆN 2",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Chung",
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+        "Tên Tòa Tháp":"BỆNH VIỆN BƯU ĐIỆN 2",
+        "Tầng":"Tầng 1",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu Thang máy",
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+        "Tên Tòa Tháp":"BỆNH VIỆN BƯU ĐIỆN 2",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Điều trị",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Buồng bệnh",
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+        "Tên Tòa Tháp":"BỆNH VIỆN BƯU ĐIỆN 2",
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+        "Mã địa điểm":"101-2",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 2",
+        "Tên Tòa Tháp":"BỆNH VIỆN BƯU ĐIỆN 2",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu nhân viên",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Trung bình (11–20 người)",
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+        "Mã địa điểm":"101-2",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 2",
+        "Tên Tòa Tháp":"BỆNH VIỆN BƯU ĐIỆN 2",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu Điều trị",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Buồng bệnh",
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+        "Mã địa điểm":"101-2",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 2",
+        "Tên Tòa Tháp":"BỆNH VIỆN BƯU ĐIỆN 2",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu Văn phòng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu nhân viên",
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+        "Mã địa điểm":"101-2",
+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 2",
+        "Tên Tòa Tháp":"BỆNH VIỆN BƯU ĐIỆN 2",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu Chức năng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng soi, đông y",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Trung bình (11–20 người)",
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+        "Mã địa điểm":"101-2",
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+        "Tên Tòa Tháp":"BỆNH VIỆN BƯU ĐIỆN 2",
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+        "Tên Tòa Tháp":"BỆNH VIỆN BƯU ĐIỆN 2",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng giám đốc",
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+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN BƯU ĐIỆN 2",
+        "Tên Tòa Tháp":"BỆNH VIỆN BƯU ĐIỆN 2",
+        "Tầng":"Tầng 2",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu Thang bộ",
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+        "Tên Tòa Tháp":"Nhà 5 tầng",
+        "Tầng":"Tầng 1",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Buồng bệnh",
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+        "Tên Tòa Tháp":"Nhà 5 tầng",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Hành lang",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Hành lang",
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+        "Mã địa điểm":"310-1",
+        "Tên từng địa điểm làm sạch":"VIỆN Y HỌC PHÒNG KHÔNG KHÔNG QUÂN",
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+        "Tầng":"Tầng 1 khoa Xq",
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+        "Mã địa điểm":"310-1",
+        "Tên từng địa điểm làm sạch":"VIỆN Y HỌC PHÒNG KHÔNG KHÔNG QUÂN",
+        "Tên Tòa Tháp":"Nhà 14 tầng",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu Vệ sinh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Chung",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"310-1",
+        "Tên từng địa điểm làm sạch":"VIỆN Y HỌC PHÒNG KHÔNG KHÔNG QUÂN",
+        "Tên Tòa Tháp":"Nhà 5 tầng",
+        "Tầng":"Tầng 3",
+        "Khu vực":"Khu Hành lang",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Hành lang",
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+        "Mã địa điểm":"310-1",
+        "Tên từng địa điểm làm sạch":"VIỆN Y HỌC PHÒNG KHÔNG KHÔNG QUÂN",
+        "Tên Tòa Tháp":"Nhà 5 tầng",
+        "Tầng":"Tầng 3",
+        "Khu vực":"Khu Vệ sinh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Chung",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"310-1",
+        "Tên từng địa điểm làm sạch":"VIỆN Y HỌC PHÒNG KHÔNG KHÔNG QUÂN",
+        "Tên Tòa Tháp":"Nhà 5 tầng",
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+        "Tên từng địa điểm làm sạch":"VIỆN Y HỌC PHÒNG KHÔNG KHÔNG QUÂN",
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+        "Tên từng địa điểm làm sạch":"VIỆN Y HỌC PHÒNG KHÔNG KHÔNG QUÂN",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Hành lang",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Chung",
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+        "Mã địa điểm":"310-1",
+        "Tên từng địa điểm làm sạch":"VIỆN Y HỌC PHÒNG KHÔNG KHÔNG QUÂN",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng giám đốc",
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+        "Tên Tòa Tháp":"Trung tâm nha khoa",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Chung",
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+        "Mã địa điểm":"310-1",
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+        "Tên Tòa Tháp":"Trung tâm nha khoa",
+        "Tầng":"Trung tâm nha khoa",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu Khám",
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+        "Mã địa điểm":"310-1",
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+        "Tên Tòa Tháp":"Trung tâm nha khoa",
+        "Tầng":"Trung tâm nha khoa",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Hành lang",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Tên Tòa Tháp":"Nhà 3 tầng",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Hành lang",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Nhà ăn",
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+        "Tên Tòa Tháp":"CTY TNHH DÂY ĐIỆN SUMI HÀ NAM",
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+        "Tên Tòa Tháp":"CTY TNHH DÂY ĐIỆN SUMI HÀ NAM",
+        "Tầng":"CTY TNHH DÂY ĐIỆN SUMI HÀ NAM",
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+        "Tên Tòa Tháp":"CTY TNHH DÂY ĐIỆN SUMI HÀ NAM",
+        "Tầng":"CTY TNHH DÂY ĐIỆN SUMI HÀ NAM",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"PHÒNG HÀNH CHÍNH TỔNG HỢP",
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+        "Tên Tòa Tháp":"CTY TNHH DÂY ĐIỆN SUMI HÀ NAM",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"khu vực VP",
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+        "Biển tên Tòa nhà":0.0,
+        "Lư hóa vàng":0.0
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+        "Tên GS":"Nguyễn Thị Huê",
+        "Mã địa điểm":"315-1",
+        "Tên từng địa điểm làm sạch":"SUMI NAM ĐỊNH",
+        "Tên Tòa Tháp":"SUMI NAM ĐỊNH",
+        "Tầng":"Tầng 1",
+        "Khu vực":"khu vực VP + y tế",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"phòng y tế",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Thấp (6–10 người)",
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+        "Thùng rác.1":0.0,
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+        "Lư hóa vàng":0.0
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+        "Tên GS":"Đỗ Thị Thúy Hằng",
+        "Mã địa điểm":"286-1",
+        "Tên từng địa điểm làm sạch":"Tòa nhà HCO Building",
+        "Tên Tòa Tháp":"Tòa nhà HCO Building",
+        "Tầng":"Ngoại cảnh",
+        "Khu vực":"Khu Ngoại cảnh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":0,
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
+        "Sàn ngoại cảnh":500.0,
+        "Sàn Sảnh":0.0,
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+        "Thùng rác.1":0.0,
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+        "Tên GS":"Đỗ Thị Thúy Hằng",
+        "Mã địa điểm":"286-1",
+        "Tên từng địa điểm làm sạch":"Tòa nhà HCO Building",
+        "Tên Tòa Tháp":"Tòa nhà HCO Building",
+        "Tầng":"Tầng G",
+        "Khu vực":"Khu Để xe",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Bãi xe",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Cao (21–40 người)",
+        "Sàn ngoại cảnh":0.0,
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+        "Thùng rác.1":1.0,
+        "Biển tên Tòa nhà":1.0,
+        "Lư hóa vàng":0.0
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+        "Loại hình":0.0,
+        "Tên GS":"Đỗ Thị Thúy Hằng",
+        "Mã địa điểm":"286-1",
+        "Tên từng địa điểm làm sạch":"Tòa nhà HCO Building",
+        "Tên Tòa Tháp":"Tòa nhà HCO Building",
+        "Tầng":"Tầng G",
+        "Khu vực":"Khu Kỹ thuật",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng kỹ thuật điện, nước",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Cao (21–40 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":150.0,
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+        "Mã địa điểm":"286-1",
+        "Tên từng địa điểm làm sạch":"Tòa nhà HCO Building",
+        "Tên Tòa Tháp":"Tòa nhà HCO Building",
+        "Tầng":"Tầng 1",
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+        "Tên Tòa Tháp":"Nhà D",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khoa Vi sinh, sinh hóa",
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+        "Tên Tòa Tháp":"Nhà D",
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+        "Khu vực":"Khu Chức năng",
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+        "Tên từng địa điểm làm sạch":"Bệnh viện Hữu nghị Việt Đức",
+        "Tên Tòa Tháp":"Nhà D",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu Chức năng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khoa huyết học",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Tên từng địa điểm làm sạch":"Bệnh viện Hữu nghị Việt Đức",
+        "Tên Tòa Tháp":"Nhà D",
+        "Tầng":"Tầng 3",
+        "Khu vực":"Khu Chức năng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khoa Thận lọc máu",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"81-1",
+        "Tên từng địa điểm làm sạch":"Bệnh viện Hữu nghị Việt Đức",
+        "Tên Tòa Tháp":"Nhà D",
+        "Tầng":"Tầng 3",
+        "Khu vực":"Khu Chức năng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khoa Phẫu thuật Hàm mặt- Tạo hình thẩm mỹ",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"81-1",
+        "Tên từng địa điểm làm sạch":"Bệnh viện Hữu nghị Việt Đức",
+        "Tên Tòa Tháp":"Nhà D",
+        "Tầng":"Tầng 3",
+        "Khu vực":"Khu Chức năng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Trung tâm nội soi",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"81-1",
+        "Tên từng địa điểm làm sạch":"Bệnh viện Hữu nghị Việt Đức",
+        "Tên Tòa Tháp":"Nhà D",
+        "Tầng":"Tầng 4",
+        "Khu vực":"Khu Chức năng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khoa Phẫu thuật cấp cứu tiêu hóa",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":120.0,
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+        "Tên Tòa Tháp":"Nhà D",
+        "Tầng":"Tầng 7",
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+        "Tên Tòa Tháp":"Nhà D",
+        "Tầng":"Tầng 7",
+        "Khu vực":"Khu Chức năng",
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+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu vực khoa Dược",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"81-1",
+        "Tên từng địa điểm làm sạch":"Bệnh viện Hữu nghị Việt Đức",
+        "Tên Tòa Tháp":"Nhà B1",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Chức năng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Trung tâm đào tạo chỉ đạo tuyến",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Tên GS":"Nguyễn Thị Minh Phượng",
+        "Mã địa điểm":"81-1",
+        "Tên từng địa điểm làm sạch":"Bệnh viện Hữu nghị Việt Đức",
+        "Tên Tòa Tháp":"Nhà B1",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Kỹ thuật",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khoa Chẩn đoán hỉnh ảnh",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
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+        "Mã địa điểm":"81-1",
+        "Tên từng địa điểm làm sạch":"Bệnh viện Hữu nghị Việt Đức",
+        "Tên Tòa Tháp":"Nhà B1",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Điều trị",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khoa Nội- Hồi sức Thần kinh",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Tên GS":"Nguyễn Thị Minh Phượng",
+        "Mã địa điểm":"81-1",
+        "Tên từng địa điểm làm sạch":"Bệnh viện Hữu nghị Việt Đức",
+        "Tên Tòa Tháp":"Nhà B1",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu Điều trị",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khoa phẫu thuật Thần kinh 2",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
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+        "Mã địa điểm":"81-1",
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+        "Tên Tòa Tháp":"Nhà B1",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khoa phẫu thuật Thần kinh 3",
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+        "Mã địa điểm":"81-1",
+        "Tên từng địa điểm làm sạch":"Bệnh viện Hữu nghị Việt Đức",
+        "Tên Tòa Tháp":"Nhà B1",
+        "Tầng":"Tầng 3",
+        "Khu vực":"Khu Điều trị",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khoa phẫu thuật Thần kinh 1",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"81-1",
+        "Tên từng địa điểm làm sạch":"Bệnh viện Hữu nghị Việt Đức",
+        "Tên Tòa Tháp":"Nhà B1",
+        "Tầng":"Tầng 4",
+        "Khu vực":"Khu Điều trị",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Trung tâm Nam học",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"81-1",
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+        "Tên Tòa Tháp":"Nhà B1",
+        "Tầng":"Tầng 4",
+        "Khu vực":"Khu Điều trị",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khoa phẫu thuật Tiết niệu",
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+        "Mã địa điểm":"81-1",
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+        "Tên Tòa Tháp":"Nhà B1",
+        "Tầng":"Tầng 5",
+        "Khu vực":"Khu Điều trị",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khoa phẫu thuật Tiết niệu",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"81-1",
+        "Tên từng địa điểm làm sạch":"Bệnh viện Hữu nghị Việt Đức",
+        "Tên Tòa Tháp":"Nhà B1",
+        "Tầng":"Tầng 6",
+        "Khu vực":"Khu Điều trị",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khoa Phẫu thuật Gan mật+ tầng mái",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"81-1",
+        "Tên từng địa điểm làm sạch":"Bệnh viện Hữu nghị Việt Đức",
+        "Tên Tòa Tháp":"Nhà B1",
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+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"81-1",
+        "Tên từng địa điểm làm sạch":"Bệnh viện Hữu nghị Việt Đức",
+        "Tên Tòa Tháp":"Nhà B3",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Chức năng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khoa Chấn thương chỉnh hình 3",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Tên Tòa Tháp":"Nhà B3",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu Chức năng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khoa Chấn thương chỉnh hình 2",
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+        "Tên Tòa Tháp":"Nhà B3",
+        "Tầng":"Tầng 3",
+        "Khu vực":"Khu Chức năng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khoa Chấn thương chỉnh hình 1",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Tên Tòa Tháp":"Nhà H",
+        "Tầng":"Tầng Hầm",
+        "Khu vực":"Khu Chức năng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng Kế hoạch Tổng hợp, Khu vực Bác sĩ cột 1",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
+        "Sàn Hành lang":50.0,
+        "Sàn WC":1.0,
+        "Sàn Phòng":170,
+        "Thảm":0.0,
+        "Thảm cỏ":0.0,
+        "Vỉa hè":0.0,
+        "Dốc hầm":0.0,
+        "Viền phản quang":0.0,
+        "Ốp tường":510.0,
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+        "Ốp chân tường":0.0,
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+        "Lavabo rửa tay":2.0,
+        "Gương":0.0,
+        "Bồn rửa tay":2.0,
+        "Hộp đựng nước rửa tay":2.0,
+        "Bệt":2.0,
+        "Tiểu nam":0.0,
+        "Hộp đựng giấy":2.0,
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+        "Thùng rác":5.0,
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+        "Quạt thông gió":1.0,
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+        "Biển báo":6.0,
+        "Bình cứu hỏa":6.0,
+        "Hộp cứu hỏa":1.0,
+        "Barie":0.0,
+        "Gương cầu lồi":0.0,
+        "Kính":0.0,
+        "Cửa":0.0,
+        "Cửa kính":6.0,
+        "Cửa phòng chức năng":0.0,
+        "Cửa căn hộ":0.0,
+        "Cửa exit":0.0,
+        "Cửa thang máy":0.0,
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+        "Bộ bàn ghế":0.0,
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+        "Bàn ghế chờ":0.0,
+        "Bàn giáo viên":0.0,
+        "Bàn ghế SV":0.0,
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+        "Gạt tàn":0.0,
+        "Xô đựng đầu mẩu thuốc":0.0,
+        "Thùng rác.1":0.0,
+        "Biển tên Tòa nhà":6.0,
+        "Lư hóa vàng":0.0
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+    {
+        "Loại hình":0.0,
+        "Tên GS":"Nguyễn Thị Minh Phượng",
+        "Mã địa điểm":"81-1",
+        "Tên từng địa điểm làm sạch":"Bệnh viện Hữu nghị Việt Đức",
+        "Tên Tòa Tháp":"Nhà H",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Khám",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu vực nhà vệ sinh công cộng, khu vực chờ lấy số, phiếu đang ký khám",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
+        "Sàn Hành lang":400.0,
+        "Sàn WC":60.0,
+        "Sàn Phòng":30,
+        "Thảm":0.0,
+        "Thảm cỏ":0.0,
+        "Vỉa hè":0.0,
+        "Dốc hầm":0.0,
+        "Viền phản quang":0.0,
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+        "Bậc tam cấp":4.0,
+        "Bàn đá":0.0,
+        "Lavabo rửa tay":6.0,
+        "Gương":4.0,
+        "Bồn rửa tay":6.0,
+        "Hộp đựng nước rửa tay":6.0,
+        "Bệt":8.0,
+        "Tiểu nam":6.0,
+        "Hộp đựng giấy":8.0,
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+        "Quạt thông gió":4.0,
+        "Đèn":40.0,
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+        "Barie":0.0,
+        "Gương cầu lồi":0.0,
+        "Kính":60.0,
+        "Cửa":5.0,
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+        "Cửa căn hộ":0.0,
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+        "Cửa thang máy":5.0,
+        "Vách ngăn":0.0,
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+        "Ghế chờ":51.0,
+        "Bàn ghế chờ":0.0,
+        "Bàn giáo viên":0.0,
+        "Bàn ghế SV":0.0,
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+        "Chậu cây":0.0,
+        "Gạt tàn":0.0,
+        "Xô đựng đầu mẩu thuốc":0.0,
+        "Thùng rác.1":9.0,
+        "Biển tên Tòa nhà":4.0,
+        "Lư hóa vàng":0.0
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+        "Loại hình":0.0,
+        "Tên GS":"Nguyễn Thị Minh Phượng",
+        "Mã địa điểm":"81-1",
+        "Tên từng địa điểm làm sạch":"Bệnh viện Hữu nghị Việt Đức",
+        "Tên Tòa Tháp":"Nhà H",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Chức năng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khoa chấn đoán hình ảnh",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
+        "Sàn Hành lang":50.0,
+        "Sàn WC":10.0,
+        "Sàn Phòng":120,
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+        "Thảm cỏ":0.0,
+        "Vỉa hè":0.0,
+        "Dốc hầm":0.0,
+        "Viền phản quang":0.0,
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+        "Bàn đá":0.0,
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+        "Gương":1.0,
+        "Bồn rửa tay":1.0,
+        "Hộp đựng nước rửa tay":1.0,
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+        "Tiểu nam":2.0,
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+        "Kính":0.0,
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+        "Vách ngăn":0.0,
+        "Quầy lễ tân":0.0,
+        "Bộ bàn ghế":0.0,
+        "Ghế chờ":3.0,
+        "Bàn ghế chờ":0.0,
+        "Bàn giáo viên":0.0,
+        "Bàn ghế SV":0.0,
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+        "Gạt tàn":0.0,
+        "Xô đựng đầu mẩu thuốc":0.0,
+        "Thùng rác.1":0.0,
+        "Biển tên Tòa nhà":28.0,
+        "Lư hóa vàng":0.0
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+    {
+        "Loại hình":0.0,
+        "Tên GS":"Nguyễn Thị Minh Phượng",
+        "Mã địa điểm":"81-1",
+        "Tên từng địa điểm làm sạch":"Bệnh viện Hữu nghị Việt Đức",
+        "Tên Tòa Tháp":"Nhà H",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Chức năng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu vực Viện phí (Nhà C5+ Khu viện phí mới)",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
+        "Sàn Hành lang":60.0,
+        "Sàn WC":10.0,
+        "Sàn Phòng":120,
+        "Thảm":0.0,
+        "Thảm cỏ":0.0,
+        "Vỉa hè":0.0,
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+        "Viền phản quang":0.0,
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+        "Bàn đá":0.0,
+        "Lavabo rửa tay":1.0,
+        "Gương":1.0,
+        "Bồn rửa tay":0.0,
+        "Hộp đựng nước rửa tay":1.0,
+        "Bệt":2.0,
+        "Tiểu nam":0.0,
+        "Hộp đựng giấy":1.0,
+        "Cài giấy":0.0,
+        "Vòi xịt":1.0,
+        "Thùng rác":6.0,
+        "Đường ống hầm":0.0,
+        "Rãnh thoát nước":0.0,
+        "Công tắc ổ điện":40.0,
+        "Quạt thông gió":2.0,
+        "Đèn":24.0,
+        "Đèn trang trí":0.0,
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+        "Hộp cứu hỏa":2.0,
+        "Barie":0.0,
+        "Gương cầu lồi":0.0,
+        "Kính":100.0,
+        "Cửa":2.0,
+        "Cửa kính":0.0,
+        "Cửa phòng chức năng":0.0,
+        "Cửa căn hộ":0.0,
+        "Cửa exit":0.0,
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+        "Vách ngăn":0.0,
+        "Quầy lễ tân":0.0,
+        "Bộ bàn ghế":0.0,
+        "Ghế chờ":20.0,
+        "Bàn ghế chờ":0.0,
+        "Bàn giáo viên":0.0,
+        "Bàn ghế SV":0.0,
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+        "Tủ tài liệu":0.0,
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+        "Phòng để đồ ship":0.0,
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+        "Gạt tàn":0.0,
+        "Xô đựng đầu mẩu thuốc":0.0,
+        "Thùng rác.1":0.0,
+        "Biển tên Tòa nhà":0.0,
+        "Lư hóa vàng":0.0
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+    {
+        "Loại hình":0.0,
+        "Tên GS":"Nguyễn Thị Minh Phượng",
+        "Mã địa điểm":"81-1",
+        "Tên từng địa điểm làm sạch":"Bệnh viện Hữu nghị Việt Đức",
+        "Tên Tòa Tháp":"Nhà H",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu Khám",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu khám chuyên khoa, phòng khám thường",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
+        "Sàn Hành lang":0.0,
+        "Sàn WC":0.0,
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+        "Thảm":0.0,
+        "Thảm cỏ":0.0,
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+        "Gương":5.0,
+        "Bồn rửa tay":27.0,
+        "Hộp đựng nước rửa tay":27.0,
+        "Bệt":6.0,
+        "Tiểu nam":3.0,
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+        "Vòi xịt":6.0,
+        "Thùng rác":37.0,
+        "Đường ống hầm":0.0,
+        "Rãnh thoát nước":0.0,
+        "Công tắc ổ điện":150.0,
+        "Quạt thông gió":6.0,
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+        "Bàn giáo viên":0.0,
+        "Bàn ghế SV":0.0,
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+        "Gạt tàn":0.0,
+        "Xô đựng đầu mẩu thuốc":0.0,
+        "Thùng rác.1":37.0,
+        "Biển tên Tòa nhà":28.0,
+        "Lư hóa vàng":0.0
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+        "Mã địa điểm":"81-1",
+        "Tên từng địa điểm làm sạch":"Bệnh viện Hữu nghị Việt Đức",
+        "Tên Tòa Tháp":"Nhà H",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu Khám",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khám xương và điều trị ngoại trú",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Tên từng địa điểm làm sạch":"Bệnh viện Hữu nghị Việt Đức",
+        "Tên Tòa Tháp":"Nhà H",
+        "Tầng":"Tầng 3",
+        "Khu vực":"Khu Khám",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Trung tâm Truyền máu, Xét nghiệm huyết học, Dinh dưỡng, Trung tâm điều phối ghép tạng, Quản lý chất lượng, Công tác xã hội",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"81-1",
+        "Tên từng địa điểm làm sạch":"Bệnh viện Hữu nghị Việt Đức",
+        "Tên Tòa Tháp":"Nhà H",
+        "Tầng":"Tầng 4",
+        "Khu vực":"Khu Khám",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu khám bệnh theo yêu cầu",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"81-1",
+        "Tên từng địa điểm làm sạch":"Bệnh viện Hữu nghị Việt Đức",
+        "Tên Tòa Tháp":"Nhà H",
+        "Tầng":"Tầng 5",
+        "Khu vực":"Khu Điều trị",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khoa Phục hồi chức năng",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Mã địa điểm":"81-1",
+        "Tên từng địa điểm làm sạch":"Bệnh viện Hữu nghị Việt Đức",
+        "Tên Tòa Tháp":"Nhà H",
+        "Tầng":"Tầng 5",
+        "Khu vực":"Khu Điều trị",
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+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Tầng":"Ngoại cảnh",
+        "Khu vực":"Khu Ngoại cảnh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Đường nội bộ",
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+        "Tên GS":"Trần Thị Lý",
+        "Mã địa điểm":"55-1",
+        "Tên từng địa điểm làm sạch":"BIDV TOWER",
+        "Tên Tòa Tháp":"BIDV TOWER",
+        "Tầng":"Hầm B1",
+        "Khu vực":"Khu Để xe",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Bãi xe",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Cao (21–40 người)",
+        "Sàn ngoại cảnh":2000.0,
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+        "Mã địa điểm":"55-1",
+        "Tên từng địa điểm làm sạch":"BIDV TOWER",
+        "Tên Tòa Tháp":"BIDV TOWER",
+        "Tầng":"Hầm B2",
+        "Khu vực":"Khu Để xe",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Bãi xe",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Cao (21–40 người)",
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+        "Mã địa điểm":"55-1",
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+        "Tên Tòa Tháp":"BIDV TOWER",
+        "Tầng":"Tầng G",
+        "Khu vực":"Khu Công cộng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Sảnh & lễ tân",
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+        "Mã địa điểm":"55-1",
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+        "Tên Tòa Tháp":"BIDV TOWER",
+        "Tầng":"Tầng 1M",
+        "Khu vực":"Khu Công cộng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":0,
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+        "Tên Tòa Tháp":"BIDV TOWER",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu Công cộng",
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+        "Tầng":"Tầng 1",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Sảnh & lễ tân",
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+        "Tên Tòa Tháp":"D1+D2",
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+        "Tên Tòa Tháp":"D1+D2",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu Trung tâm thương mại",
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+        "Tên Tòa Tháp":"D1+D2",
+        "Tầng":"Tầng 3",
+        "Khu vực":"Khu Trung tâm thương mại",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Nữ",
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+        "Tên Tòa Tháp":"D1+D2",
+        "Tầng":"Tầng 4",
+        "Khu vực":"Khu Trung tâm thương mại",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Nam",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu Hành lang",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu ngoại cảnh ,bãi xe.khu sân tenis",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Nữ",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Nam",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Ga hành khách",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng nghỉ nhân viên vệ sinh bảo vệ",
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+        "Tầng":"Tầng 1",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Hành lang",
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+        "Tầng":"Tầng 2",
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+        "Tầng":"Tầng 2",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng kỹ thuật điện, nước",
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+        "Tầng":"Tầng 2",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Hành lang",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Trung bình (11–20 người)",
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+        "Mã địa điểm":"43-030",
+        "Tên từng địa điểm làm sạch":"SEABANK PGD NGUYỄN KHÁNH TOÀN",
+        "Tên Tòa Tháp":"SEABANK PGD NGUYỄN KHÁNH TOÀN",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Vệ sinh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Nam",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Thấp (6–10 người)",
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+        "Mã địa điểm":"43-030",
+        "Tên từng địa điểm làm sạch":"SEABANK PGD NGUYỄN KHÁNH TOÀN",
+        "Tên Tòa Tháp":"SEABANK PGD NGUYỄN KHÁNH TOÀN",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Vệ sinh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Nữ",
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+        "Mã địa điểm":"43-031",
+        "Tên từng địa điểm làm sạch":"SEABANK PGD 51 LLQ",
+        "Tên Tòa Tháp":"SEABANK PGD 51 LLQ",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Văn phòng",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Ga hành khách",
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+        "Tên GS":"Phan Thị Thu Hòa",
+        "Mã địa điểm":"43-031",
+        "Tên từng địa điểm làm sạch":"SEABANK PGD 51 LLQ",
+        "Tên Tòa Tháp":"SEABANK PGD 51 LLQ",
+        "Tầng":"Tầng 2",
+        "Khu vực":"Khu Vệ sinh",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Chung",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":0,
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+        "Tên GS":"Phan Thị Thu Hòa",
+        "Mã địa điểm":"43-032",
+        "Tên từng địa điểm làm sạch":"SEABANK PGD THĂNG LONG",
+        "Tên Tòa Tháp":"SEABANK PGD THĂNG LONG",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Văn phòng",
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+        "Tầng":"Tầng 1",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Phòng WC Chung",
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+        "Tên Tòa Tháp":"KHOA NTK",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Điều trị",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Buồng bệnh",
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+        "Khu vực":"Khu Điều trị",
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+        "Tên Tòa Tháp":"KHOA CNK",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Kỹ thuật",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Kho vật tư",
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+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN ĐK TUYÊN QUANG",
+        "Tên Tòa Tháp":"KHOA TTK",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Điều trị",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Buồng bệnh",
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+        "Tên từng địa điểm làm sạch":"BỆNH VIỆN ĐK TUYÊN QUANG",
+        "Tên Tòa Tháp":"KHU LỌC THẬN",
+        "Tầng":"Tầng 1",
+        "Khu vực":"Khu Điều trị",
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Buồng bệnh",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Cao (21–40 người)",
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+        "Tên Tòa Tháp":"BỆNH VIỆN ĐK TUYÊN QUANG",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Cảnh quan",
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":"Rất cao (Trên 40 người)",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Khu pantry bếp nhỏ",
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+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":"Sân ngoài trời",
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+        "Cột ốp":0.0,
+        "Ốp chân tường":0.0,
+        "Bệ đá":0.0,
+        "Bậc tam cấp":0.0,
+        "Bàn đá":0.0,
+        "Lavabo rửa tay":0.0,
+        "Gương":0.0,
+        "Bồn rửa tay":0.0,
+        "Hộp đựng nước rửa tay":0.0,
+        "Bệt":0.0,
+        "Tiểu nam":0.0,
+        "Hộp đựng giấy":0.0,
+        "Cài giấy":0.0,
+        "Vòi xịt":0.0,
+        "Thùng rác":0.0,
+        "Đường ống hầm":0.0,
+        "Rãnh thoát nước":0.0,
+        "Công tắc ổ điện":0.0,
+        "Quạt thông gió":0.0,
+        "Đèn":0.0,
+        "Đèn trang trí":0.0,
+        "Biển báo":0.0,
+        "Bình cứu hỏa":0.0,
+        "Hộp cứu hỏa":0.0,
+        "Barie":0.0,
+        "Gương cầu lồi":0.0,
+        "Kính":0.0,
+        "Cửa":0.0,
+        "Cửa kính":0.0,
+        "Cửa phòng chức năng":0.0,
+        "Cửa căn hộ":0.0,
+        "Cửa exit":0.0,
+        "Cửa thang máy":0.0,
+        "Vách ngăn":0.0,
+        "Quầy lễ tân":0.0,
+        "Bộ bàn ghế":0.0,
+        "Ghế chờ":0.0,
+        "Bàn ghế chờ":0.0,
+        "Bàn giáo viên":0.0,
+        "Bàn ghế SV":0.0,
+        "Rèm":0.0,
+        "Cột line":0.0,
+        "Khung tranh":0.0,
+        "Giá kệ sách":0.0,
+        "Máy tính":0.0,
+        "Máy chiếu":0.0,
+        "Bảng":0.0,
+        "Tủ tài liệu":0.0,
+        "Tủ thư":0.0,
+        "Phòng để đồ ship":0.0,
+        "Tủ để đồ":0.0,
+        "Tủ lạnh":0.0,
+        "Cân":0.0,
+        "Giường bệnh":0.0,
+        "Thanh oxy":0.0,
+        "Tủ để đồ cho bệnh nhân":0.0,
+        "Cây nước":0.0,
+        "Cốc chén":0.0,
+        "Giá để dép":0.0,
+        "Giá để ô":0.0,
+        "Chậu cây":0.0,
+        "Gạt tàn":0.0,
+        "Xô đựng đầu mẩu thuốc":0.0,
+        "Thùng rác.1":0.0,
+        "Biển tên Tòa nhà":0.0,
+        "Lư hóa vàng":0.0
+    },
+    {
+        "Loại hình":0.0,
+        "Tên GS":0,
+        "Mã địa điểm":0,
+        "Tên từng địa điểm làm sạch":0,
+        "Tên Tòa Tháp":0,
+        "Tầng":0,
+        "Khu vực":0,
+        "Phạm vi Khoa Phòng Chức năng,... (Đặc thù riêng của từng địa điểm làm sạch)":0,
+        "Mức độ Lưu lượng KH hoạt động trên mặt sàn giờ":0,
+        "Sàn ngoại cảnh":0.0,
+        "Sàn Sảnh":0.0,
+        "Sàn Hành lang":0.0,
+        "Sàn WC":0.0,
+        "Sàn Phòng":0,
+        "Thảm":0.0,
+        "Thảm cỏ":0.0,
+        "Vỉa hè":0.0,
+        "Dốc hầm":0.0,
+        "Viền phản quang":0.0,
+        "Ốp tường":0.0,
+        "Ốp gỗ":0.0,
+        "Cột ốp":0.0,
+        "Ốp chân tường":0.0,
+        "Bệ đá":0.0,
+        "Bậc tam cấp":0.0,
+        "Bàn đá":0.0,
+        "Lavabo rửa tay":0.0,
+        "Gương":0.0,
+        "Bồn rửa tay":0.0,
+        "Hộp đựng nước rửa tay":0.0,
+        "Bệt":0.0,
+        "Tiểu nam":0.0,
+        "Hộp đựng giấy":0.0,
+        "Cài giấy":0.0,
+        "Vòi xịt":0.0,
+        "Thùng rác":0.0,
+        "Đường ống hầm":0.0,
+        "Rãnh thoát nước":0.0,
+        "Công tắc ổ điện":0.0,
+        "Quạt thông gió":0.0,
+        "Đèn":0.0,
+        "Đèn trang trí":0.0,
+        "Biển báo":0.0,
+        "Bình cứu hỏa":0.0,
+        "Hộp cứu hỏa":0.0,
+        "Barie":0.0,
+        "Gương cầu lồi":0.0,
+        "Kính":0.0,
+        "Cửa":0.0,
+        "Cửa kính":0.0,
+        "Cửa phòng chức năng":0.0,
+        "Cửa căn hộ":0.0,
+        "Cửa exit":0.0,
+        "Cửa thang máy":0.0,
+        "Vách ngăn":0.0,
+        "Quầy lễ tân":0.0,
+        "Bộ bàn ghế":0.0,
+        "Ghế chờ":0.0,
+        "Bàn ghế chờ":0.0,
+        "Bàn giáo viên":0.0,
+        "Bàn ghế SV":0.0,
+        "Rèm":0.0,
+        "Cột line":0.0,
+        "Khung tranh":0.0,
+        "Giá kệ sách":0.0,
+        "Máy tính":0.0,
+        "Máy chiếu":0.0,
+        "Bảng":0.0,
+        "Tủ tài liệu":0.0,
+        "Tủ thư":0.0,
+        "Phòng để đồ ship":0.0,
+        "Tủ để đồ":0.0,
+        "Tủ lạnh":0.0,
+        "Cân":0.0,
+        "Giường bệnh":0.0,
+        "Thanh oxy":0.0,
+        "Tủ để đồ cho bệnh nhân":0.0,
+        "Cây nước":0.0,
+        "Cốc chén":0.0,
+        "Giá để dép":0.0,
+        "Giá để ô":0.0,
+        "Chậu cây":0.0,
+        "Gạt tàn":0.0,
+        "Xô đựng đầu mẩu thuốc":0.0,
+        "Thùng rác.1":0.0,
+        "Biển tên Tòa nhà":0.0,
+        "Lư hóa vàng":0.0
+    }
+]
\ No newline at end of file
diff --git a/example_text_features.py b/example_text_features.py
deleted file mode 100644
index 7228576..0000000
--- a/example_text_features.py
+++ /dev/null
@@ -1,367 +0,0 @@
-"""
-Example: Using TextFeatureExtractor for Staff Prediction
-=========================================================
-
-This script demonstrates how to use extract_text_features.py
-to extract TF-IDF+SVD features and train a prediction model.
-
-Run this script to see a complete example workflow.
-"""
-
-import pandas as pd
-import numpy as np
-from extract_text_features import TextFeatureExtractor, extract_features_from_dataframe
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import StandardScaler
-from sklearn.tree import DecisionTreeRegressor
-from sklearn.metrics import r2_score, mean_absolute_error
-import pickle
-import os
-
-
-def example_1_basic_usage():
-    """Example 1: Basic text feature extraction"""
-    print("=" * 80)
-    print("EXAMPLE 1: BASIC USAGE")
-    print("=" * 80)
-    
-    # Sample Vietnamese task texts
-    texts = [
-        "Kiểm tra hệ thống điện tòa nhà A định kỳ",
-        "Bảo trì thang máy tầng 5 và kiểm tra an toàn",
-        "Sửa chữa điều hòa phòng họp B tầng 3",
-        "Vệ sinh kính và kiểm tra hệ thống chiếu sáng",
-        "Bảo trì máy phát điện dự phòng"
-    ]
-    
-    print(f"\nInput: {len(texts)} task descriptions")
-    print(f"Sample: '{texts[0]}'")
-    
-    # Initialize extractor
-    extractor = TextFeatureExtractor(
-        max_features=20,  # Small for demo
-        n_components=5    # Small for demo
-    )
-    
-    # Fit and transform
-    features = extractor.fit_transform(texts)
-    
-    print(f"\nOutput shape: {features.shape}")
-    print(f"Feature names: {extractor.get_feature_names()}")
-    print(f"\nFirst sample features:")
-    print(features[0])
-    
-    # Show summary
-    print(f"\nExtractor summary:")
-    for key, value in extractor.get_summary().items():
-        print(f"  {key}: {value}")
-    
-    print("\n✅ Example 1 complete!\n")
-
-
-def example_2_dataframe_extraction():
-    """Example 2: Extract features from DataFrame"""
-    print("=" * 80)
-    print("EXAMPLE 2: DATAFRAME EXTRACTION")
-    print("=" * 80)
-    
-    # Create sample DataFrame
-    df = pd.DataFrame({
-        'ma_dia_diem': ['A01', 'A02', 'A03', 'A04', 'A05'],
-        'all_task_normal': [
-            'Kiểm tra điện',
-            'Bảo trì thang máy',
-            'Sửa điều hòa',
-            'Vệ sinh kính',
-            'Bảo trì máy phát'
-        ],
-        'all_task_dinhky': [
-            'Định kỳ hàng tháng',
-            'Định kỳ hàng tuần',
-            'Khẩn cấp',
-            'Hàng ngày',
-            'Định kỳ quý'
-        ],
-        'so_luong': [5, 3, 2, 8, 4]
-    })
-    
-    print(f"\nInput DataFrame shape: {df.shape}")
-    print(df)
-    
-    # Extract features
-    text_features_df, extractor = extract_features_from_dataframe(
-        df,
-        text_columns=['all_task_normal', 'all_task_dinhky'],
-        fit=True
-    )
-    
-    print(f"\nExtracted features shape: {text_features_df.shape}")
-    print(f"\nSample features:")
-    print(text_features_df.head())
-    
-    print("\n✅ Example 2 complete!\n")
-
-
-def example_3_save_and_load():
-    """Example 3: Save and load extractor"""
-    print("=" * 80)
-    print("EXAMPLE 3: SAVE AND LOAD")
-    print("=" * 80)
-    
-    # Training data
-    train_texts = [
-        "Kiểm tra hệ thống điện",
-        "Bảo trì thang máy",
-        "Sửa chữa điều hòa"
-    ]
-    
-    # Fit extractor
-    print("\n1. Training extractor...")
-    extractor = TextFeatureExtractor(max_features=10, n_components=3)
-    train_features = extractor.fit_transform(train_texts)
-    print(f"   Train features shape: {train_features.shape}")
-    
-    # Save
-    save_path = 'example_extractor.pkl'
-    extractor.save(save_path)
-    print(f"   Saved to: {save_path}")
-    
-    # Load
-    print("\n2. Loading extractor...")
-    loaded_extractor = TextFeatureExtractor.load(save_path)
-    
-    # Use loaded extractor on new data
-    print("\n3. Using loaded extractor on new data...")
-    new_texts = ["Vệ sinh kính tầng 5", "Kiểm tra máy phát điện"]
-    new_features = loaded_extractor.transform(new_texts)
-    print(f"   New features shape: {new_features.shape}")
-    print(f"   Features:\n{new_features}")
-    
-    # Cleanup
-    if os.path.exists(save_path):
-        os.remove(save_path)
-        print(f"\n   Cleaned up: {save_path}")
-    
-    print("\n✅ Example 3 complete!\n")
-
-
-def example_4_full_pipeline():
-    """Example 4: Complete ML pipeline with text features"""
-    print("=" * 80)
-    print("EXAMPLE 4: FULL ML PIPELINE")
-    print("=" * 80)
-    
-    # Create sample dataset
-    np.random.seed(42)
-    n_samples = 100
-    
-    tasks_pool = [
-        "Kiểm tra hệ thống điện",
-        "Bảo trì thang máy",
-        "Sửa chữa điều hòa",
-        "Vệ sinh kính",
-        "Bảo trì máy phát điện",
-        "Kiểm tra an toàn",
-        "Sửa chữa ống nước",
-        "Bảo trì hệ thống PCCC"
-    ]
-    
-    df = pd.DataFrame({
-        'all_task_normal': [np.random.choice(tasks_pool) for _ in range(n_samples)],
-        'all_task_dinhky': [np.random.choice(['Hàng ngày', 'Hàng tuần', 'Hàng tháng', 'Quý']) 
-                            for _ in range(n_samples)],
-        'dien_tich': np.random.uniform(100, 500, n_samples),
-        'so_tang': np.random.randint(5, 30, n_samples),
-        'so_luong': np.random.randint(1, 10, n_samples)
-    })
-    
-    print(f"\n📊 Dataset: {df.shape}")
-    print(f"   Target (so_luong): mean={df['so_luong'].mean():.2f}, std={df['so_luong'].std():.2f}")
-    
-    # === TRAINING PHASE ===
-    print("\n1️⃣ TRAINING PHASE")
-    print("-" * 80)
-    
-    # Split data
-    train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)
-    print(f"\n   Train: {len(train_df)}, Test: {len(test_df)}")
-    
-    # Extract text features
-    print("\n   Extracting text features...")
-    text_features_train, extractor = extract_features_from_dataframe(
-        train_df,
-        text_columns=['all_task_normal', 'all_task_dinhky'],
-        fit=True
-    )
-    
-    # Prepare numeric features
-    numeric_cols = ['dien_tich', 'so_tang']
-    X_numeric_train = train_df[numeric_cols].reset_index(drop=True)
-    
-    # Combine features
-    X_train = pd.concat([X_numeric_train, text_features_train], axis=1)
-    y_train = train_df['so_luong'].values
-    
-    print(f"\n   Combined features: {X_train.shape}")
-    print(f"     - Numeric: {len(numeric_cols)}")
-    print(f"     - Text SVD: {text_features_train.shape[1]}")
-    
-    # Scale features
-    scaler = StandardScaler()
-    X_train_scaled = scaler.fit_transform(X_train)
-    
-    # Train model
-    print("\n   Training model...")
-    model = DecisionTreeRegressor(max_depth=5, random_state=42)
-    model.fit(X_train_scaled, y_train)
-    
-    # Evaluate on training set
-    y_train_pred = model.predict(X_train_scaled)
-    train_r2 = r2_score(y_train, y_train_pred)
-    train_mae = mean_absolute_error(y_train, y_train_pred)
-    
-    print(f"\n   Training metrics:")
-    print(f"     R²:  {train_r2:.4f}")
-    print(f"     MAE: {train_mae:.4f}")
-    
-    # === INFERENCE PHASE ===
-    print("\n2️⃣ INFERENCE PHASE")
-    print("-" * 80)
-    
-    # Extract text features (transform only, no fitting!)
-    print("\n   Extracting text features (transform only)...")
-    text_features_test, _ = extract_features_from_dataframe(
-        test_df,
-        text_columns=['all_task_normal', 'all_task_dinhky'],
-        extractor=extractor,
-        fit=False  # Important!
-    )
-    
-    # Prepare numeric features
-    X_numeric_test = test_df[numeric_cols].reset_index(drop=True)
-    
-    # Combine features
-    X_test = pd.concat([X_numeric_test, text_features_test], axis=1)
-    y_test = test_df['so_luong'].values
-    
-    # Scale features
-    X_test_scaled = scaler.transform(X_test)
-    
-    # Predict
-    print("\n   Making predictions...")
-    y_test_pred = model.predict(X_test_scaled)
-    
-    # Evaluate
-    test_r2 = r2_score(y_test, y_test_pred)
-    test_mae = mean_absolute_error(y_test, y_test_pred)
-    
-    print(f"\n   Test metrics:")
-    print(f"     R²:  {test_r2:.4f}")
-    print(f"     MAE: {test_mae:.4f}")
-    
-    # Show sample predictions
-    print(f"\n   Sample predictions:")
-    results_df = pd.DataFrame({
-        'actual': y_test[:5],
-        'predicted': y_test_pred[:5],
-        'error': y_test[:5] - y_test_pred[:5]
-    })
-    print(results_df.to_string(index=False))
-    
-    # === FEATURE IMPORTANCE ===
-    print("\n3️⃣ FEATURE IMPORTANCE")
-    print("-" * 80)
-    
-    importances = model.feature_importances_
-    feature_names = X_train.columns.tolist()
-    
-    importance_df = pd.DataFrame({
-        'feature': feature_names,
-        'importance': importances
-    }).sort_values('importance', ascending=False)
-    
-    print("\n   Top 10 important features:")
-    print(importance_df.head(10).to_string(index=False))
-    
-    # Aggregate by feature type
-    n_numeric = len(numeric_cols)
-    text_importance = importances[n_numeric:].sum()
-    numeric_importance = importances[:n_numeric].sum()
-    
-    print(f"\n   Feature type contribution:")
-    print(f"     Numeric features: {numeric_importance:.4f} ({numeric_importance/(text_importance+numeric_importance)*100:.1f}%)")
-    print(f"     Text features:    {text_importance:.4f} ({text_importance/(text_importance+numeric_importance)*100:.1f}%)")
-    
-    print("\n✅ Example 4 complete!\n")
-
-
-def example_5_top_tfidf_terms():
-    """Example 5: Analyze top TF-IDF terms"""
-    print("=" * 80)
-    print("EXAMPLE 5: TOP TF-IDF TERMS ANALYSIS")
-    print("=" * 80)
-    
-    # Sample task texts
-    texts = [
-        "Kiểm tra hệ thống điện tòa nhà",
-        "Bảo trì thang máy và kiểm tra an toàn",
-        "Sửa chữa hệ thống điều hòa không khí",
-        "Kiểm tra và vệ sinh kính tòa nhà",
-        "Bảo trì máy phát điện dự phòng",
-        "Kiểm tra hệ thống PCCC định kỳ",
-        "Sửa chữa ống nước và hệ thống cấp thoát",
-        "Bảo trì hệ thống thang máy tòa nhà"
-    ]
-    
-    print(f"\nInput: {len(texts)} task descriptions")
-    
-    # Fit extractor
-    extractor = TextFeatureExtractor(max_features=50, n_components=10)
-    extractor.fit(texts)
-    
-    # Get top TF-IDF features
-    print("\n📋 Top 20 TF-IDF terms (by document frequency):")
-    top_features = extractor.get_top_tfidf_features(top_n=20)
-    print(top_features.to_string(index=False))
-    
-    # Get summary
-    summary = extractor.get_summary()
-    print(f"\n📊 Summary:")
-    print(f"   Actual TF-IDF features: {summary['actual_tfidf_features']}")
-    print(f"   SVD components: {summary['n_components']}")
-    print(f"   Explained variance: {summary['explained_variance']*100:.2f}%")
-    
-    print("\n✅ Example 5 complete!\n")
-
-
-def main():
-    """Run all examples"""
-    print("\n" + "=" * 80)
-    print("TEXT FEATURE EXTRACTION - EXAMPLES")
-    print("=" * 80 + "\n")
-    
-    try:
-        example_1_basic_usage()
-        example_2_dataframe_extraction()
-        example_3_save_and_load()
-        example_4_full_pipeline()
-        example_5_top_tfidf_terms()
-        
-        print("\n" + "=" * 80)
-        print("✅ ALL EXAMPLES COMPLETED SUCCESSFULLY!")
-        print("=" * 80 + "\n")
-        
-        print("Next steps:")
-        print("  1. Try with your own dataset: FINAL_DATASET_WITH_TEXT_BACKUP_20260105_213507.xlsx")
-        print("  2. Adjust hyperparameters: max_features, n_components")
-        print("  3. Integrate into your ML pipeline")
-        print("  4. Save extractor for production use")
-        
-    except Exception as e:
-        print(f"\n❌ Error: {e}")
-        import traceback
-        traceback.print_exc()
-
-
-if __name__ == '__main__':
-    main()
diff --git a/extract_25_features.py b/extract_25_features.py
deleted file mode 100644
index 2703b0a..0000000
--- a/extract_25_features.py
+++ /dev/null
@@ -1,439 +0,0 @@
-"""
-EXTRACTION FUNCTION: 25 KEYWORD FEATURES TỪ TASKS TEXT
-Dựa trên phân tích 30,917 công việc từ 302 tòa nhà
-Updated: January 5, 2026
-"""
-
-import pandas as pd
-import re
-from typing import Dict, List
-
-def extract_25_keyword_features(tasks_text: str) -> Dict[str, float]:
-    """
-    Trích xuất 25 keyword-based features từ tasks text
-    
-    Args:
-        tasks_text: Chuỗi text chứa tất cả công việc (all_task_normal + all_task_dinhky)
-        
-    Returns:
-        Dict với 25 features
-    """
-    
-    # Xử lý missing values
-    if pd.isna(tasks_text) or str(tasks_text).strip() == '':
-        return _get_empty_features()
-    
-    # Chuyển về lowercase và tách tasks
-    tasks_text = str(tasks_text).lower()
-    
-    # Tách tasks bằng các delimiter: ; | hoặc xuống dòng
-    tasks = re.split(r'[;|\n]+', tasks_text)
-    tasks = [t.strip() for t in tasks if t.strip()]
-    
-    # =================================================================
-    # NHÓM 1: TASK COUNTS THEO LOẠI CÔNG VIỆC (9 features)
-    # =================================================================
-    
-    # 1. Tổng số công việc
-    num_tasks = len(tasks)
-    
-    # 2. Vệ sinh thường ngày (55.9% dữ liệu)
-    cleaning_keywords = [
-        'vệ sinh', 'tvs', 'tổng vệ sinh', 'lau', 'chùi', 'quét', 'hút', 
-        'đẩy khô', 'lau ẩm', 'đẩy ẩm', 'làm sạch', 'lau bụi', 'quét bụi',
-        'lau kính', 'lau sàn', 'quét sàn', 'hút bụi', 'lau nền'
-    ]
-    num_cleaning_tasks = _count_tasks_with_keywords(tasks, cleaning_keywords)
-    
-    # 3. Thu gom/thay rác (7.9% dữ liệu) - MỚI ⭐
-    trash_keywords = [
-        'thu gom rác', 'thay rác', 'vận chuyển rác', 'tua rác', 'đổ rác',
-        'thu rác', 'gom rác', 'chuyển rác', 'bỏ rác', 'đẩy rác',
-        'quét rác nổi', 'trực rác', 'rác nổi'
-    ]
-    num_trash_collection_tasks = _count_tasks_with_keywords(tasks, trash_keywords)
-    
-    # 4. Trực/kiểm tra phát sinh (16.1% dữ liệu) - MỚI ⭐
-    monitoring_keywords = [
-        'trực', 'trực phát sinh', 'trực lại', 'trực ps', 'trực tua',
-        'kiểm tra', 'check', 'giám sát', 'theo dõi', 'tuần tra'
-    ]
-    num_monitoring_tasks = _count_tasks_with_keywords(tasks, monitoring_keywords)
-    
-    # 5. Dọn phòng Y TẾ (0.4% nhưng đặc trưng) - MỚI ⭐
-    room_cleaning_keywords = [
-        'dọn mổ', 'dọn đẻ', 'dọn can thiệp', 'ra viện', 'dọn phòng',
-        'bệnh nhân ra viện', 'dọn khi bệnh nhân', 'dọn phòng bệnh'
-    ]
-    num_room_cleaning_tasks = _count_tasks_with_keywords(tasks, room_cleaning_keywords)
-    
-    # 6. Vệ sinh chuyên sâu (4.5% dữ liệu) - MỚI ⭐
-    deep_cleaning_keywords = [
-        'cọ rửa', 'cọ bồn cầu', 'cọ', 'gạt kính', 'gạt', 'đánh sàn', 
-        'đánh chân tường', 'đánh cọ', 'đánh vết bẩn', 'chà tường',
-        'đánh dép', 'cọ gương', 'cọ lavabo', 'cọ thùng rác'
-    ]
-    num_deep_cleaning_tasks = _count_tasks_with_keywords(tasks, deep_cleaning_keywords)
-    
-    # 7. Bảo trì/sửa chữa (0.6% dữ liệu)
-    maintenance_keywords = [
-        'bảo dưỡng', 'sửa chữa', 'bảo trì', 'thay thế', 'sửa', 
-        'thay', 'kiểm định', 'bảo dưỡng máy'
-    ]
-    num_maintenance_tasks = _count_tasks_with_keywords(tasks, maintenance_keywords)
-    
-    # 8. Hỗ trợ (5.8% dữ liệu) - MỚI ⭐
-    support_keywords = [
-        'giao ca', 'bàn giao', 'bàn giao ca', 'chụp ảnh', 'nhận ca',
-        'vsdc', 'vệ sinh dụng cụ', 'chuẩn bị dụng cụ', 'vệ sinh xe đồ',
-        'chuẩn bị nước', 'chuẩn bị', 'giao ban'
-    ]
-    num_support_tasks = _count_tasks_with_keywords(tasks, support_keywords)
-    
-    # 9. Công việc khác (không thuộc các loại trên)
-    # Tạm tính = tổng - (các loại đã đếm, nhưng lưu ý có overlap)
-    # Để tính chính xác, cần đếm tasks không match bất kỳ keyword nào
-    all_keywords = (cleaning_keywords + trash_keywords + monitoring_keywords + 
-                   room_cleaning_keywords + deep_cleaning_keywords + 
-                   maintenance_keywords + support_keywords)
-    num_other_tasks = _count_tasks_without_keywords(tasks, all_keywords)
-    
-    # =================================================================
-    # NHÓM 2: AREA COVERAGE - KHU VỰC (10 features)
-    # =================================================================
-    
-    # 10. WC/Nhà vệ sinh (20.4% dữ liệu)
-    wc_keywords = [
-        'wc', 'toilet', 'nhà vệ sinh', 'restroom', 'phòng vệ sinh',
-        'bồn cầu', 'lavabo', 'tiểu nam', 'bồn tiểu', 'wc công cộng',
-        'wc nhân viên', 'nhà wc'
-    ]
-    num_wc_tasks = _count_tasks_with_keywords(tasks, wc_keywords)
-    
-    # 11. Hành lang (13.7% dữ liệu)
-    hallway_keywords = [
-        'hành lang', 'corridor', 'lối đi', 'hall', 'hành lang tầng',
-        'hl', 'hanh lang'
-    ]
-    num_hallway_tasks = _count_tasks_with_keywords(tasks, hallway_keywords)
-    
-    # 12. Sảnh (7.6% dữ liệu)
-    lobby_keywords = [
-        'sảnh', 'lobby', 'tiền sảnh', 'sảnh đỏ', 'sảnh chính',
-        'tiền sảnh', 'sảnh tầng', 'sanh'
-    ]
-    num_lobby_tasks = _count_tasks_with_keywords(tasks, lobby_keywords)
-    
-    # 13. Phòng bệnh Y TẾ (1.5% dữ liệu) - MỚI ⭐
-    patient_room_keywords = [
-        'phòng bệnh', 'giường bệnh', 'phòng víp', 'phòng vip',
-        'phòng bệnh nhân', 'pb', 'phòng bv'
-    ]
-    num_patient_room_tasks = _count_tasks_with_keywords(tasks, patient_room_keywords)
-    
-    # 14. Phòng khám Y TẾ (0.3% dữ liệu) - MỚI ⭐
-    clinic_room_keywords = [
-        'phòng khám', 'khoa khám', 'phòng nội', 'phòng sản',
-        'phòng khám bệnh', 'khu khám', 'pk'
-    ]
-    num_clinic_room_tasks = _count_tasks_with_keywords(tasks, clinic_room_keywords)
-    
-    # 15. Phòng mổ Y TẾ (0.4% dữ liệu) - MỚI ⭐
-    surgery_room_keywords = [
-        'phòng mổ', 'hậu phẫu', 'phòng phẫu thuật', 'khu mổ',
-        'phòng pt', 'ngoài phòng mổ', 'trong phòng mổ'
-    ]
-    num_surgery_room_tasks = _count_tasks_with_keywords(tasks, surgery_room_keywords)
-    
-    # 16. Ngoại cảnh (4.3% dữ liệu)
-    outdoor_keywords = [
-        'ngoại cảnh', 'sân', 'vỉa hè', 'khuôn viên', 'cổng',
-        'outdoor', 'bãi xe', 'tầng hầm', 'sân sau', 'sân trước'
-    ]
-    num_outdoor_tasks = _count_tasks_with_keywords(tasks, outdoor_keywords)
-    
-    # 17. Thang máy/Cầu thang (10.6% dữ liệu)
-    elevator_keywords = [
-        'thang máy', 'elevator', 'lift', 'cầu thang', 'bậc tam cấp',
-        'thang bộ', 'cầu thang bộ', 'tay vịn', 'tam cấp'
-    ]
-    num_elevator_tasks = _count_tasks_with_keywords(tasks, elevator_keywords)
-    
-    # 18. Phòng nhân viên/hành chính (4.4% dữ liệu) - MỚI ⭐
-    office_keywords = [
-        'phòng nhân viên', 'phòng giám đốc', 'phòng họp', 'phòng hành chính',
-        'văn phòng', 'phòng gd', 'phòng pgd', 'phòng ban', 'phòng giao ban',
-        'phòng bác sĩ', 'phòng trưởng khoa', 'hội trường', 'phòng kế toán'
-    ]
-    num_office_tasks = _count_tasks_with_keywords(tasks, office_keywords)
-    
-    # 19. Phòng kỹ thuật Y TẾ (0.2% dữ liệu) - MỚI ⭐
-    technical_room_keywords = [
-        'phòng xét nghiệm', 'phòng chụp', 'xq', 'siêu âm', 'kho dược',
-        'phòng xn', 'labo', 'phòng thí nghiệm', 'phòng kỹ thuật',
-        'phòng điện tim', 'phòng nội soi', 'phòng cấp cứu', 'phòng hồi sức'
-    ]
-    num_technical_room_tasks = _count_tasks_with_keywords(tasks, technical_room_keywords)
-    
-    # =================================================================
-    # NHÓM 3: RATIOS & COMPLEXITY (6 features)
-    # =================================================================
-    
-    # 20. Tỷ lệ vệ sinh thường ngày
-    cleaning_ratio = num_cleaning_tasks / num_tasks if num_tasks > 0 else 0.0
-    
-    # 21. Tỷ lệ thu gom rác - MỚI ⭐
-    trash_collection_ratio = num_trash_collection_tasks / num_tasks if num_tasks > 0 else 0.0
-    
-    # 22. Tỷ lệ trực/kiểm tra - MỚI ⭐
-    monitoring_ratio = num_monitoring_tasks / num_tasks if num_tasks > 0 else 0.0
-    
-    # 23. Tỷ lệ dọn phòng Y TẾ - MỚI ⭐
-    room_cleaning_ratio = num_room_cleaning_tasks / num_tasks if num_tasks > 0 else 0.0
-    
-    # 24. Độ đa dạng khu vực (0-10)
-    area_counts = [
-        num_wc_tasks, num_hallway_tasks, num_lobby_tasks,
-        num_patient_room_tasks, num_clinic_room_tasks, num_surgery_room_tasks,
-        num_outdoor_tasks, num_elevator_tasks, num_office_tasks, num_technical_room_tasks
-    ]
-    area_diversity = sum(1 for count in area_counts if count > 0)
-    
-    # 25. Điểm phức tạp (0.0 - 10.0) - MỚI ⭐
-    # Dựa vào:
-    # - Độ dài text (càng dài càng phức tạp)
-    # - Số lượng công việc (càng nhiều càng phức tạp)
-    # - Từ khóa kỹ thuật (Y TẾ, máy móc...)
-    
-    technical_keywords = [
-        'bms', 'hvac', 'camera', 'access control', 'máy phát', 'máy móc',
-        'hệ thống', 'thiết bị', 'bảo dưỡng máy', 'sửa máy', 'kiểm tra máy',
-        'xét nghiệm', 'chụp chiếu', 'điện tim', 'nội soi', 'phẫu thuật'
-    ]
-    num_technical_keywords = _count_tasks_with_keywords(tasks, technical_keywords)
-    
-    # Công thức tính task_complexity_score:
-    # - Text length: 0-3 điểm (0-1000 chars = 0, 1000-5000 = 1-2, 5000+ = 3)
-    # - Num tasks: 0-3 điểm (0-10 = 0-1, 10-50 = 1-2, 50+ = 2-3)
-    # - Technical keywords: 0-2 điểm (0 = 0, 1-3 = 1, 4+ = 2)
-    # - Area diversity: 0-2 điểm (0-3 = 0-1, 4-7 = 1-1.5, 8-10 = 1.5-2)
-    
-    text_length = len(tasks_text)
-    length_score = min(3.0, text_length / 2000)  # Max 3.0
-    
-    tasks_score = min(3.0, num_tasks / 20)  # Max 3.0
-    
-    technical_score = min(2.0, num_technical_keywords / 2)  # Max 2.0
-    
-    diversity_score = min(2.0, area_diversity / 5)  # Max 2.0
-    
-    task_complexity_score = round(length_score + tasks_score + technical_score + diversity_score, 2)
-    
-    # =================================================================
-    # TRẢ VỀ DICT VỚI 25 FEATURES
-    # =================================================================
-    
-    return {
-        # NHÓM 1: Task Counts (9 features)
-        'num_tasks': num_tasks,
-        'num_cleaning_tasks': num_cleaning_tasks,
-        'num_trash_collection_tasks': num_trash_collection_tasks,
-        'num_monitoring_tasks': num_monitoring_tasks,
-        'num_room_cleaning_tasks': num_room_cleaning_tasks,
-        'num_deep_cleaning_tasks': num_deep_cleaning_tasks,
-        'num_maintenance_tasks': num_maintenance_tasks,
-        'num_support_tasks': num_support_tasks,
-        'num_other_tasks': num_other_tasks,
-        
-        # NHÓM 2: Area Coverage (10 features)
-        'num_wc_tasks': num_wc_tasks,
-        'num_hallway_tasks': num_hallway_tasks,
-        'num_lobby_tasks': num_lobby_tasks,
-        'num_patient_room_tasks': num_patient_room_tasks,
-        'num_clinic_room_tasks': num_clinic_room_tasks,
-        'num_surgery_room_tasks': num_surgery_room_tasks,
-        'num_outdoor_tasks': num_outdoor_tasks,
-        'num_elevator_tasks': num_elevator_tasks,
-        'num_office_tasks': num_office_tasks,
-        'num_technical_room_tasks': num_technical_room_tasks,
-        
-        # NHÓM 3: Ratios & Complexity (6 features)
-        'cleaning_ratio': round(cleaning_ratio, 4),
-        'trash_collection_ratio': round(trash_collection_ratio, 4),
-        'monitoring_ratio': round(monitoring_ratio, 4),
-        'room_cleaning_ratio': round(room_cleaning_ratio, 4),
-        'area_diversity': area_diversity,
-        'task_complexity_score': task_complexity_score
-    }
-
-
-def _count_tasks_with_keywords(tasks: List[str], keywords: List[str]) -> int:
-    """Đếm số tasks chứa ít nhất 1 keyword"""
-    count = 0
-    for task in tasks:
-        task_lower = task.lower()
-        if any(keyword in task_lower for keyword in keywords):
-            count += 1
-    return count
-
-
-def _count_tasks_without_keywords(tasks: List[str], all_keywords: List[str]) -> int:
-    """Đếm số tasks KHÔNG chứa bất kỳ keyword nào"""
-    count = 0
-    for task in tasks:
-        task_lower = task.lower()
-        if not any(keyword in task_lower for keyword in all_keywords):
-            count += 1
-    return count
-
-
-def _get_empty_features() -> Dict[str, float]:
-    """Trả về dict với tất cả features = 0 (cho missing data)"""
-    return {
-        # NHÓM 1
-        'num_tasks': 0,
-        'num_cleaning_tasks': 0,
-        'num_trash_collection_tasks': 0,
-        'num_monitoring_tasks': 0,
-        'num_room_cleaning_tasks': 0,
-        'num_deep_cleaning_tasks': 0,
-        'num_maintenance_tasks': 0,
-        'num_support_tasks': 0,
-        'num_other_tasks': 0,
-        
-        # NHÓM 2
-        'num_wc_tasks': 0,
-        'num_hallway_tasks': 0,
-        'num_lobby_tasks': 0,
-        'num_patient_room_tasks': 0,
-        'num_clinic_room_tasks': 0,
-        'num_surgery_room_tasks': 0,
-        'num_outdoor_tasks': 0,
-        'num_elevator_tasks': 0,
-        'num_office_tasks': 0,
-        'num_technical_room_tasks': 0,
-        
-        # NHÓM 3
-        'cleaning_ratio': 0.0,
-        'trash_collection_ratio': 0.0,
-        'monitoring_ratio': 0.0,
-        'room_cleaning_ratio': 0.0,
-        'area_diversity': 0,
-        'task_complexity_score': 0.0
-    }
-
-
-# =================================================================
-# MAIN: ÁP DỤNG CHO TẤT CẢ TÒA NHÀ
-# =================================================================
-
-if __name__ == '__main__':
-    print("=" * 100)
-    print("TRÍCH XUẤT 25 KEYWORD FEATURES TỪ TASKS TEXT")
-    print("=" * 100)
-    
-    # Đọc file
-    print("\n📂 Đọc file ket_qua_cong_viec_full.xlsx...")
-    df = pd.read_excel('ket_qua_cong_viec_full.xlsx')
-    print(f"   ✅ Đọc thành công {len(df)} tòa nhà")
-    
-    # Gộp all_task_normal và all_task_dinhky
-    print("\n🔗 Gộp all_task_normal + all_task_dinhky...")
-    df['all_tasks_combined'] = (
-        df['all_task_normal'].fillna('') + ' ; ' + df['all_task_dinhky'].fillna('')
-    )
-    
-    # Áp dụng extraction function cho tất cả tòa
-    print("\n⚙️  Trích xuất 25 features cho từng tòa...")
-    features_list = []
-    
-    for idx, row in df.iterrows():
-        if (idx + 1) % 50 == 0:
-            print(f"   Đang xử lý... {idx + 1}/{len(df)} tòa")
-        
-        features = extract_25_keyword_features(row['all_tasks_combined'])
-        features['ma_dia_diem'] = row['ma_dia_diem']
-        features_list.append(features)
-    
-    print(f"   ✅ Hoàn thành {len(df)} tòa")
-    
-    # Tạo DataFrame
-    print("\n📊 Tạo DataFrame với 25 features...")
-    df_features = pd.DataFrame(features_list)
-    
-    # Sắp xếp lại cột: ma_dia_diem đầu tiên
-    cols = ['ma_dia_diem'] + [col for col in df_features.columns if col != 'ma_dia_diem']
-    df_features = df_features[cols]
-    
-    # Hiển thị thống kê
-    print("\n" + "=" * 100)
-    print("📈 THỐNG KÊ 25 FEATURES")
-    print("=" * 100)
-    
-    print("\n🔹 NHÓM 1: TASK COUNTS (9 features)")
-    group1_cols = [
-        'num_tasks', 'num_cleaning_tasks', 'num_trash_collection_tasks',
-        'num_monitoring_tasks', 'num_room_cleaning_tasks', 'num_deep_cleaning_tasks',
-        'num_maintenance_tasks', 'num_support_tasks', 'num_other_tasks'
-    ]
-    print(df_features[group1_cols].describe().round(2))
-    
-    print("\n🔹 NHÓM 2: AREA COVERAGE (10 features)")
-    group2_cols = [
-        'num_wc_tasks', 'num_hallway_tasks', 'num_lobby_tasks',
-        'num_patient_room_tasks', 'num_clinic_room_tasks', 'num_surgery_room_tasks',
-        'num_outdoor_tasks', 'num_elevator_tasks', 'num_office_tasks', 'num_technical_room_tasks'
-    ]
-    print(df_features[group2_cols].describe().round(2))
-    
-    print("\n🔹 NHÓM 3: RATIOS & COMPLEXITY (6 features)")
-    group3_cols = [
-        'cleaning_ratio', 'trash_collection_ratio', 'monitoring_ratio',
-        'room_cleaning_ratio', 'area_diversity', 'task_complexity_score'
-    ]
-    print(df_features[group3_cols].describe().round(4))
-    
-    # Top 10 tòa có nhiều công việc nhất
-    print("\n" + "=" * 100)
-    print("🏆 TOP 10 TÒA CÓ NHIỀU CÔNG VIỆC NHẤT")
-    print("=" * 100)
-    top10 = df_features.nlargest(10, 'num_tasks')[
-        ['ma_dia_diem', 'num_tasks', 'cleaning_ratio', 'area_diversity', 'task_complexity_score']
-    ]
-    print(top10.to_string(index=False))
-    
-    # Top 10 tòa phức tạp nhất
-    print("\n" + "=" * 100)
-    print("🎯 TOP 10 TÒA PHỨC TẠP NHẤT (task_complexity_score)")
-    print("=" * 100)
-    top10_complex = df_features.nlargest(10, 'task_complexity_score')[
-        ['ma_dia_diem', 'num_tasks', 'task_complexity_score', 'area_diversity']
-    ]
-    print(top10_complex.to_string(index=False))
-    
-    # Lưu file
-    output_file = 'features_25_keywords.csv'
-    print(f"\n💾 Lưu features vào file: {output_file}")
-    df_features.to_csv(output_file, index=False, encoding='utf-8-sig')
-    print(f"   ✅ Đã lưu {len(df_features)} tòa với 25 features")
-    
-    # Hiển thị 5 tòa đầu tiên
-    print("\n" + "=" * 100)
-    print("📋 MẪU DỮ LIỆU (5 tòa đầu tiên)")
-    print("=" * 100)
-    print(df_features.head().to_string(index=False))
-    
-    print("\n" + "=" * 100)
-    print("✅ HOÀN THÀNH!")
-    print("=" * 100)
-    print(f"\n📊 Tổng kết:")
-    print(f"   - Số tòa nhà: {len(df_features)}")
-    print(f"   - Số features: {len(df_features.columns) - 1}")  # Trừ cột ma_dia_diem
-    print(f"   - File output: {output_file}")
-    print(f"\n🎯 Bước tiếp theo:")
-    print(f"   1. Kiểm tra file {output_file}")
-    print(f"   2. Phân tích correlation giữa các features")
-    print(f"   3. Visualize distribution")
-    print(f"   4. Tiếp tục với TF-IDF features (10 features)")
-    print(f"   5. Join với building features (18 features)")
-    print(f"   → TỔNG: 25 + 10 + 18 = 53 features")
diff --git a/extract_25_features_new.py b/extract_25_features_new.py
new file mode 100644
index 0000000..c8208c9
--- /dev/null
+++ b/extract_25_features_new.py
@@ -0,0 +1,250 @@
+"""
+EXTRACTION FUNCTION: REDUCED KEYWORD FEATURES TỪ TASKS TEXT
+Dựa trên phân tích 30,917 công việc từ 302 tòa nhà
+Updated: January 2026 (Reduced version for small dataset)
+"""
+
+import pandas as pd
+import re
+from typing import Dict, List
+
+
+# =========================================================
+# 1) HELPERS
+# =========================================================
+def _split_tasks(tasks_text: str) -> List[str]:
+    """Tách tasks bằng delimiter: ; | hoặc xuống dòng"""
+    tasks_text = str(tasks_text).lower()
+    tasks = re.split(r"[;|\n]+", tasks_text)
+    return [t.strip() for t in tasks if t.strip()]
+
+
+def _count_tasks_with_keywords(tasks: List[str], keywords: List[str]) -> int:
+    """Đếm số tasks chứa ít nhất 1 keyword"""
+    count = 0
+    for task in tasks:
+        if any(k in task for k in keywords):
+            count += 1
+    return count
+
+
+def _count_tasks_without_keywords(tasks: List[str], all_keywords: List[str]) -> int:
+    """Đếm số tasks KHÔNG chứa bất kỳ keyword nào"""
+    count = 0
+    for task in tasks:
+        if not any(k in task for k in all_keywords):
+            count += 1
+    return count
+
+
+def _get_empty_features() -> Dict[str, float]:
+    """Trả về dict với tất cả features = 0 (cho missing data)"""
+    return {
+        # TASK COUNTS (7)
+        "num_tasks": 0,
+        "num_cleaning_tasks": 0,
+        "num_trash_collection_tasks": 0,
+        "num_monitoring_tasks": 0,
+        "num_deep_cleaning_tasks": 0,
+        "num_support_tasks": 0,
+        "num_other_tasks": 0,
+
+        # AREA (reduced + aggregated) (7)
+        "num_wc_tasks": 0,
+        "num_hallway_tasks": 0,
+        "num_lobby_tasks": 0,
+        "num_outdoor_tasks": 0,
+        "num_elevator_tasks": 0,
+        "num_medical_tasks_total": 0,
+        "num_indoor_room_tasks": 0,
+
+        # RATIOS & DIVERSITY (4)
+        "cleaning_ratio": 0.0,
+        "trash_collection_ratio": 0.0,
+        "monitoring_ratio": 0.0,
+        "area_diversity": 0,
+    }
+
+
+# =========================================================
+# 2) MAIN EXTRACTION (NEW FEATURES)
+# =========================================================
+def extract_keyword_features_reduced(tasks_text: str) -> Dict[str, float]:
+    """
+    Trích xuất bộ keyword-based features (Reduced + Aggregated)
+
+    Args:
+        tasks_text: Chuỗi text chứa tất cả công việc (all_task_normal + all_task_dinhky)
+
+    Returns:
+        Dict với features mới (reduced)
+    """
+
+    if pd.isna(tasks_text) or str(tasks_text).strip() == "":
+        return _get_empty_features()
+
+    tasks = _split_tasks(tasks_text)
+    num_tasks = len(tasks)
+
+    # -----------------------------
+    # GROUP 1: TASK COUNTS (reduced)
+    # -----------------------------
+    cleaning_keywords = [
+        "vệ sinh", "lau", "chùi", "quét", "hút",
+        "đẩy khô", "lau ẩm", "làm sạch", "lau bụi", "lau kính", "lau sàn", "hút bụi"
+    ]
+    trash_keywords = [
+        "thu gom rác", "thay rác", "vận chuyển rác", "tua rác", "đổ rác",
+        "thu rác", "gom rác", "quét rác nổi", "trực rác", "rác nổi"
+    ]
+    monitoring_keywords = [
+        "trực", "trực phát sinh", "trực ps", "kiểm tra", "check",
+        "giám sát", "theo dõi", "tuần tra"
+    ]
+    deep_cleaning_keywords = [
+        "cọ rửa", "cọ bồn cầu", "cọ", "gạt kính", "đánh sàn",
+        "đánh chân tường", "chà tường", "cọ gương", "cọ lavabo"
+    ]
+    support_keywords = [
+        "giao ca", "bàn giao", "bàn giao ca", "chụp ảnh", "nhận ca",
+        "vsdc", "vệ sinh dụng cụ", "chuẩn bị dụng cụ", "chuẩn bị nước", "chuẩn bị", "giao ban"
+    ]
+
+    num_cleaning_tasks = _count_tasks_with_keywords(tasks, cleaning_keywords)
+    num_trash_collection_tasks = _count_tasks_with_keywords(tasks, trash_keywords)
+    num_monitoring_tasks = _count_tasks_with_keywords(tasks, monitoring_keywords)
+    num_deep_cleaning_tasks = _count_tasks_with_keywords(tasks, deep_cleaning_keywords)
+    num_support_tasks = _count_tasks_with_keywords(tasks, support_keywords)
+
+    all_keywords_for_other = (
+        cleaning_keywords + trash_keywords + monitoring_keywords + deep_cleaning_keywords + support_keywords
+    )
+    num_other_tasks = _count_tasks_without_keywords(tasks, all_keywords_for_other)
+
+    # -----------------------------
+    # GROUP 2: AREA COVERAGE (reduced + aggregated)
+    # -----------------------------
+    wc_keywords = [
+        "wc", "toilet", "nhà vệ sinh", "restroom", "phòng vệ sinh",
+        "bồn cầu", "lavabo", "tiểu nam", "bồn tiểu"
+    ]
+    hallway_keywords = ["hành lang", "corridor", "lối đi", "hall", "hl", "hanh lang"]
+    lobby_keywords = ["sảnh", "lobby", "tiền sảnh", "sảnh chính", "sanh"]
+    outdoor_keywords = ["ngoại cảnh", "sân", "vỉa hè", "khuôn viên", "cổng", "bãi xe", "tầng hầm"]
+    elevator_keywords = ["thang máy", "elevator", "lift", "cầu thang", "thang bộ", "tay vịn", "tam cấp"]
+
+    # medical detail keywords (we count but only output total)
+    patient_room_keywords = ["phòng bệnh", "giường bệnh", "phòng vip", "phòng bệnh nhân", "pb", "phòng bv"]
+    clinic_room_keywords = ["phòng khám", "khoa khám", "phòng khám bệnh", "khu khám", "pk"]
+    surgery_room_keywords = ["phòng mổ", "hậu phẫu", "phòng phẫu thuật", "khu mổ", "phòng pt"]
+    technical_room_keywords = [
+        "phòng xét nghiệm", "phòng chụp", "xq", "siêu âm", "kho dược",
+        "phòng xn", "labo", "phòng thí nghiệm", "nội soi", "cấp cứu", "hồi sức"
+    ]
+
+    office_keywords = [
+        "phòng nhân viên", "phòng giám đốc", "phòng họp", "phòng hành chính",
+        "văn phòng", "phòng ban", "phòng giao ban", "hội trường", "phòng kế toán"
+    ]
+
+    num_wc_tasks = _count_tasks_with_keywords(tasks, wc_keywords)
+    num_hallway_tasks = _count_tasks_with_keywords(tasks, hallway_keywords)
+    num_lobby_tasks = _count_tasks_with_keywords(tasks, lobby_keywords)
+    num_outdoor_tasks = _count_tasks_with_keywords(tasks, outdoor_keywords)
+    num_elevator_tasks = _count_tasks_with_keywords(tasks, elevator_keywords)
+
+    num_patient_room_tasks = _count_tasks_with_keywords(tasks, patient_room_keywords)
+    num_clinic_room_tasks = _count_tasks_with_keywords(tasks, clinic_room_keywords)
+    num_surgery_room_tasks = _count_tasks_with_keywords(tasks, surgery_room_keywords)
+    num_technical_room_tasks = _count_tasks_with_keywords(tasks, technical_room_keywords)
+
+    num_medical_tasks_total = (
+        num_patient_room_tasks + num_clinic_room_tasks + num_surgery_room_tasks + num_technical_room_tasks
+    )
+
+    num_indoor_room_tasks = _count_tasks_with_keywords(tasks, office_keywords)
+
+    # -----------------------------
+    # GROUP 3: RATIOS & DIVERSITY (reduced)
+    # -----------------------------
+    cleaning_ratio = num_cleaning_tasks / num_tasks if num_tasks > 0 else 0.0
+    trash_collection_ratio = num_trash_collection_tasks / num_tasks if num_tasks > 0 else 0.0
+    monitoring_ratio = num_monitoring_tasks / num_tasks if num_tasks > 0 else 0.0
+
+    area_counts = [
+        num_wc_tasks, num_hallway_tasks, num_lobby_tasks, num_outdoor_tasks, num_elevator_tasks,
+        num_medical_tasks_total, num_indoor_room_tasks
+    ]
+    area_diversity = sum(1 for c in area_counts if c > 0)
+
+    return {
+        # TASK COUNTS (7)
+        "num_tasks": num_tasks,
+        "num_cleaning_tasks": num_cleaning_tasks,
+        "num_trash_collection_tasks": num_trash_collection_tasks,
+        "num_monitoring_tasks": num_monitoring_tasks,
+        "num_deep_cleaning_tasks": num_deep_cleaning_tasks,
+        "num_support_tasks": num_support_tasks,
+        "num_other_tasks": num_other_tasks,
+
+        # AREA (reduced + aggregated) (7)
+        "num_wc_tasks": num_wc_tasks,
+        "num_hallway_tasks": num_hallway_tasks,
+        "num_lobby_tasks": num_lobby_tasks,
+        "num_outdoor_tasks": num_outdoor_tasks,
+        "num_elevator_tasks": num_elevator_tasks,
+        "num_medical_tasks_total": num_medical_tasks_total,
+        "num_indoor_room_tasks": num_indoor_room_tasks,
+
+        # RATIOS & DIVERSITY (4)
+        "cleaning_ratio": round(cleaning_ratio, 4),
+        "trash_collection_ratio": round(trash_collection_ratio, 4),
+        "monitoring_ratio": round(monitoring_ratio, 4),
+        "area_diversity": area_diversity,
+    }
+
+
+# =========================================================
+# 3) MAIN (APPLY TO EXCEL)
+# =========================================================
+if __name__ == "__main__":
+    print("=" * 100)
+    print("TRÍCH XUẤT REDUCED KEYWORD FEATURES TỪ TASKS TEXT")
+    print("=" * 100)
+
+    input_file = "ket_qua_cong_viec_full.xlsx"
+    output_csv = "features_keywords_reduced.csv"
+    output_xlsx = "features_keywords_reduced.xlsx"
+
+    print(f"\n📂 Đọc file {input_file} ...")
+    df = pd.read_excel(input_file)
+    print(f"✅ Đọc thành công {len(df)} rows")
+
+    print("\n🔗 Gộp all_task_normal + all_task_dinhky ...")
+    df["all_tasks_combined"] = df["all_task_normal"].fillna("") + " ; " + df["all_task_dinhky"].fillna("")
+
+    print("\n⚙️  Trích xuất features ...")
+    features_list = []
+    for idx, row in df.iterrows():
+        if (idx + 1) % 50 == 0:
+            print(f"   Đang xử lý... {idx + 1}/{len(df)}")
+        feats = extract_keyword_features_reduced(row["all_tasks_combined"])
+        feats["ma_dia_diem"] = row.get("ma_dia_diem", None)
+        features_list.append(feats)
+
+    df_features = pd.DataFrame(features_list)
+
+    # ma_dia_diem lên đầu
+    cols = ["ma_dia_diem"] + [c for c in df_features.columns if c != "ma_dia_diem"]
+    df_features = df_features[cols]
+
+    print("\n✅ DONE. Shape:", df_features.shape)
+
+    print(f"\n💾 Save CSV: {output_csv}")
+    df_features.to_csv(output_csv, index=False, encoding="utf-8-sig")
+
+    print(f"💾 Save XLSX: {output_xlsx}")
+    df_features.to_excel(output_xlsx, index=False, engine="openpyxl")
+
+    print("\n📋 Sample:")
+    print(df_features.head(5).to_string(index=False))
diff --git a/extract_shift_features.py b/extract_shift_features.py
deleted file mode 100644
index f3324f7..0000000
--- a/extract_shift_features.py
+++ /dev/null
@@ -1,220 +0,0 @@
-"""
-EXTRACTION: SHIFT FEATURES FROM JSON FOR STAFF PREDICTION
-Trích xuất features ca làm việc để predict số nhân sự
-Created: January 5, 2026
-"""
-
-import pandas as pd
-import json
-from datetime import datetime
-from typing import Dict, List
-
-def parse_time_string(time_str):
-    """Parse time string to extract hours"""
-    if pd.isna(time_str) or time_str == 0:
-        return 0.0
-    
-    time_str = str(time_str)
-    
-    # Handle datetime format: "2025-01-01 22:00:00"
-    if '2025' in time_str or '2024' in time_str:
-        try:
-            dt = pd.to_datetime(time_str)
-            return dt.hour + dt.minute/60.0
-        except:
-            pass
-    
-    # Handle time format: "HH:MM:SS"
-    try:
-        parts = time_str.split(':')
-        if len(parts) >= 2:
-            hours = float(parts[0])
-            minutes = float(parts[1])
-            return hours + minutes/60.0
-    except:
-        pass
-    
-    return 0.0
-
-def extract_shift_features(json_file_path: str, output_excel_path: str):
-    """
-    Trích xuất shift features từ JSON và tạo Excel file
-    
-    Features:
-    - ma_dia_diem: Mã tòa nhà
-    - loai_ca: Loại ca làm việc
-    - bat_dau: Giờ bắt đầu ca
-    - ket_thuc: Giờ kết thúc ca
-    - tong_gio_lam: Tổng số giờ làm việc
-    - so_ca_cua_toa: Số lượng ca của tòa nhà xuất hiện trong file
-    - so_luong: Target variable - Số nhân sự trong ca (để predict)
-    
-    Args:
-        json_file_path: Path đến file JSON
-        output_excel_path: Path output file Excel
-    """
-    
-    print("=" * 80)
-    print("🚀 TRÍCH XUẤT SHIFT FEATURES TỪ JSON")
-    print("=" * 80)
-    
-    # 1. Đọc JSON file
-    print(f"\n📂 Đọc file: {json_file_path}")
-    with open(json_file_path, 'r', encoding='utf-8') as f:
-        data = json.load(f)
-    
-    print(f"✅ Đọc thành công: {len(data)} records")
-    
-    # 2. Chuyển sang DataFrame
-    df = pd.DataFrame(data)
-    
-    print(f"\n📊 Cấu trúc dữ liệu gốc:")
-    print(f"   - Số dòng: {len(df)}")
-    print(f"   - Số cột: {len(df.columns)}")
-    print(f"   - Các cột: {list(df.columns)}")
-    
-    # 3. Đếm số ca của mỗi tòa nhà
-    print(f"\n🔢 Đếm số ca của mỗi tòa nhà...")
-    shift_counts = df['Mã địa điểm'].value_counts().to_dict()
-    
-    # Thống kê số ca
-    unique_buildings = len(shift_counts)
-    total_shifts = sum(shift_counts.values())
-    avg_shifts = total_shifts / unique_buildings if unique_buildings > 0 else 0
-    
-    print(f"   - Số tòa nhà unique: {unique_buildings}")
-    print(f"   - Tổng số ca: {total_shifts}")
-    print(f"   - Trung bình ca/tòa: {avg_shifts:.2f}")
-    
-    # 4. Tạo features DataFrame
-    print(f"\n🔧 Trích xuất features...")
-    
-    features_data = []
-    
-    for idx, row in df.iterrows():
-        ma_dia_diem = row['Mã địa điểm']
-        
-        # Feature: Số ca của tòa nhà
-        so_ca_cua_toa = shift_counts.get(ma_dia_diem, 0)
-        
-        # Parse time strings
-        bat_dau_str = str(row['Bắt đầu'])
-        ket_thuc_str = str(row['Kết thúc'])
-        tong_gio_lam_str = str(row['Tổng giờ làm'])
-        
-        # Parse tổng giờ làm
-        try:
-            if 'day' in tong_gio_lam_str:
-                # Format: "7 days, 12:00:00"
-                parts = tong_gio_lam_str.split(',')
-                days = int(parts[0].split()[0])
-                time_parts = parts[1].strip().split(':')
-                hours = days * 24 + int(time_parts[0])
-                minutes = int(time_parts[1])
-                tong_gio_lam = hours + minutes/60.0
-            else:
-                # Format: "8:00:00"
-                time_parts = tong_gio_lam_str.split(':')
-                tong_gio_lam = float(time_parts[0]) + float(time_parts[1])/60.0
-        except:
-            tong_gio_lam = 0.0
-        
-        # Target variable
-        so_luong = row['Số lượng']
-        
-        # Thêm vào list
-        features_data.append({
-            'ma_dia_diem': ma_dia_diem,
-            'loai_ca': row['Loại ca'],
-            'bat_dau': bat_dau_str,
-            'ket_thuc': ket_thuc_str,
-            'tong_gio_lam': round(tong_gio_lam, 2),
-            'so_ca_cua_toa': so_ca_cua_toa,
-            'so_luong': so_luong
-        })
-    
-    # 5. Tạo DataFrame
-    features_df = pd.DataFrame(features_data)
-    
-    print(f"✅ Trích xuất thành công {len(features_df)} shifts")
-    
-    # 6. Thống kê
-    print(f"\n📈 THỐNG KÊ FEATURES:")
-    print(f"\n   🏢 Tòa nhà:")
-    print(f"      - Số tòa unique: {features_df['ma_dia_diem'].nunique()}")
-    
-    print(f"\n   🕐 Loại ca:")
-    for loai_ca, count in features_df['loai_ca'].value_counts().items():
-        print(f"      - {loai_ca}: {count} ca ({count/len(features_df)*100:.1f}%)")
-    
-    print(f"\n   ⏱️ Tổng giờ làm:")
-    print(f"      - Min: {features_df['tong_gio_lam'].min():.2f} giờ")
-    print(f"      - Mean: {features_df['tong_gio_lam'].mean():.2f} giờ")
-    print(f"      - Max: {features_df['tong_gio_lam'].max():.2f} giờ")
-    
-    print(f"\n   📊 Số ca của tòa:")
-    print(f"      - Min: {features_df['so_ca_cua_toa'].min()} ca")
-    print(f"      - Mean: {features_df['so_ca_cua_toa'].mean():.2f} ca")
-    print(f"      - Max: {features_df['so_ca_cua_toa'].max()} ca")
-    
-    print(f"\n   👥 Số lượng nhân sự (TARGET):")
-    print(f"      - Min: {features_df['so_luong'].min()} người")
-    print(f"      - Mean: {features_df['so_luong'].mean():.2f} người")
-    print(f"      - Median: {features_df['so_luong'].median():.0f} người")
-    print(f"      - Max: {features_df['so_luong'].max()} người")
-    
-    # Top 5 tòa có nhiều nhân sự nhất
-    print(f"\n   🏆 TOP 5 CA CÓ NHIỀU NHÂN SỰ NHẤT:")
-    top5 = features_df.nlargest(5, 'so_luong')[['ma_dia_diem', 'loai_ca', 'tong_gio_lam', 'so_luong']]
-    for idx, row in top5.iterrows():
-        print(f"      - {row['ma_dia_diem']}: {row['loai_ca']} ({row['tong_gio_lam']:.1f}h) → {row['so_luong']} người")
-    
-    # 7. Export sang Excel
-    print(f"\n💾 Xuất file Excel...")
-    features_df.to_excel(output_excel_path, index=False, engine='openpyxl')
-    
-    print(f"✅ Đã tạo file: {output_excel_path}")
-    print(f"   - Số dòng: {len(features_df)}")
-    print(f"   - Số cột: {len(features_df.columns)}")
-    
-    # 8. Tạo thêm CSV backup
-    csv_path = output_excel_path.replace('.xlsx', '.csv')
-    features_df.to_csv(csv_path, index=False, encoding='utf-8-sig')
-    print(f"✅ Đã tạo CSV backup: {csv_path}")
-    
-    # 9. Summary statistics
-    print(f"\n📊 SUMMARY STATISTICS:")
-    print(features_df.describe())
-    
-    print(f"\n" + "=" * 80)
-    print("✅ HOÀN THÀNH!")
-    print("=" * 80)
-    
-    return features_df
-
-
-if __name__ == "__main__":
-    # Paths
-    json_file = "Link LLV 2025.json"
-    output_excel = "shift_features_for_prediction.xlsx"
-    
-    # Run extraction
-    df = extract_shift_features(json_file, output_excel)
-    
-    print(f"\n📋 SAMPLE DATA (10 dòng đầu):")
-    print(df.head(10).to_string())
-    
-    print(f"\n🎯 CÁC FILE ĐÃ TẠO:")
-    print(f"   1. {output_excel} - File Excel chính (để mở và xem)")
-    print(f"   2. shift_features_for_prediction.csv - File CSV backup")
-    
-    print(f"\n🎯 CẤU TRÚC FILE:")
-    print(f"   - ma_dia_diem: Mã tòa nhà")
-    print(f"   - loai_ca: Loại ca (Hành chính, Ca sáng, Ca chiều, ...)")
-    print(f"   - bat_dau: Giờ bắt đầu ca")
-    print(f"   - ket_thuc: Giờ kết thúc ca")
-    print(f"   - tong_gio_lam: Tổng số giờ làm việc")
-    print(f"   - so_ca_cua_toa: Số ca của tòa nhà (feature)")
-    print(f"   - so_luong: Số nhân sự (TARGET VARIABLE)")
-    
-    print(f"\n✨ READY FOR MODELING!")
diff --git a/extract_text_features.py b/extract_text_features.py
deleted file mode 100644
index 5572301..0000000
--- a/extract_text_features.py
+++ /dev/null
@@ -1,500 +0,0 @@
-"""
-Text Feature Extraction Pipeline for Staff Prediction Model
-=============================================================
-
-This script extracts TF-IDF and SVD features from Vietnamese task descriptions.
-Can be used for both training (fit_transform) and inference (transform).
-
-Usage:
-------
-Training mode:
-    python extract_text_features.py --mode train --input data.xlsx --output features.csv
-
-Inference mode:
-    python extract_text_features.py --mode predict --input new_data.xlsx --output predictions.csv
-
-As a module:
-    from extract_text_features import TextFeatureExtractor
-    extractor = TextFeatureExtractor()
-    features = extractor.fit_transform(texts)
-
-Author: ML Team
-Date: 2026-01-06
-"""
-
-import pandas as pd
-import numpy as np
-import re
-import pickle
-import argparse
-import os
-from typing import List, Tuple, Optional, Union
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.decomposition import TruncatedSVD
-
-
-class TextFeatureExtractor:
-    """
-    Extract TF-IDF and SVD features from Vietnamese text.
-    
-    Attributes:
-        max_features (int): Maximum number of TF-IDF features
-        n_components (int): Number of SVD components
-        ngram_range (tuple): N-gram range for TF-IDF
-        min_df (int): Minimum document frequency
-        max_df (float): Maximum document frequency
-        tfidf (TfidfVectorizer): Fitted TF-IDF vectorizer
-        svd (TruncatedSVD): Fitted SVD model
-        is_fitted (bool): Whether the extractor has been fitted
-    """
-    
-    def __init__(
-        self,
-        max_features: int = 200,
-        n_components: int = 50,
-        ngram_range: Tuple[int, int] = (1, 2),
-        min_df: int = 2,
-        max_df: float = 0.95,
-        random_state: int = 42
-    ):
-        """
-        Initialize the TextFeatureExtractor.
-        
-        Args:
-            max_features: Maximum number of TF-IDF features (default: 200)
-            n_components: Number of SVD components (default: 50)
-            ngram_range: N-gram range for TF-IDF (default: (1, 2))
-            min_df: Minimum document frequency (default: 2)
-            max_df: Maximum document frequency (default: 0.95)
-            random_state: Random seed for reproducibility (default: 42)
-        """
-        self.max_features = max_features
-        self.n_components = n_components
-        self.ngram_range = ngram_range
-        self.min_df = min_df
-        self.max_df = max_df
-        self.random_state = random_state
-        
-        # Initialize models
-        self.tfidf = TfidfVectorizer(
-            max_features=max_features,
-            ngram_range=ngram_range,
-            min_df=min_df,
-            max_df=max_df,
-            sublinear_tf=True,
-            strip_accents=None
-        )
-        
-        self.svd = TruncatedSVD(
-            n_components=n_components,
-            random_state=random_state
-        )
-        
-        self.is_fitted = False
-        
-    @staticmethod
-    def preprocess_text(text: Union[str, float, None]) -> str:
-        """
-        Preprocess Vietnamese text.
-        
-        Args:
-            text: Input text (can be str, float, or None)
-            
-        Returns:
-            Cleaned text string
-        """
-        if pd.isna(text) or text is None or str(text).strip() == '':
-            return ''
-        
-        text = str(text).lower()
-        
-        # Keep Vietnamese characters, numbers, spaces
-        text = re.sub(
-            r'[^a-zàáạảãâầấậẩẫăằắặẳẵèéẹẻẽêềếệểễìíịỉĩòóọỏõôồốộổỗơờớợởỡùúụủũưừứựửữỳýỵỷỹđ0-9\s]',
-            ' ',
-            text
-        )
-        
-        # Remove multiple spaces
-        text = re.sub(r'\s+', ' ', text).strip()
-        
-        return text
-    
-    def preprocess_texts(self, texts: List[str]) -> List[str]:
-        """
-        Preprocess a list of texts.
-        
-        Args:
-            texts: List of text strings
-            
-        Returns:
-            List of cleaned text strings
-        """
-        return [self.preprocess_text(text) for text in texts]
-    
-    def combine_task_columns(
-        self,
-        task_normal: pd.Series,
-        task_dinhky: pd.Series
-    ) -> List[str]:
-        """
-        Combine two task columns into one.
-        
-        Args:
-            task_normal: Series of normal task descriptions
-            task_dinhky: Series of scheduled task descriptions
-            
-        Returns:
-            List of combined and cleaned texts
-        """
-        # Preprocess both columns
-        normal_clean = task_normal.apply(self.preprocess_text)
-        dinhky_clean = task_dinhky.apply(self.preprocess_text)
-        
-        # Combine
-        combined = (normal_clean + ' ' + dinhky_clean).str.strip()
-        
-        return combined.tolist()
-    
-    def fit(self, texts: List[str]) -> 'TextFeatureExtractor':
-        """
-        Fit the TF-IDF and SVD models on training texts.
-        
-        Args:
-            texts: List of text strings
-            
-        Returns:
-            self
-        """
-        print(f"Fitting TF-IDF on {len(texts)} documents...")
-        
-        # Preprocess
-        texts_clean = self.preprocess_texts(texts)
-        
-        # Fit TF-IDF
-        tfidf_matrix = self.tfidf.fit_transform(texts_clean)
-        
-        print(f"  TF-IDF shape: {tfidf_matrix.shape}")
-        print(f"  Sparsity: {(1.0 - tfidf_matrix.nnz / (tfidf_matrix.shape[0] * tfidf_matrix.shape[1])) * 100:.2f}%")
-        
-        # Fit SVD
-        print(f"\nFitting SVD ({self.n_components} components)...")
-        self.svd.fit(tfidf_matrix)
-        
-        print(f"  Explained variance: {self.svd.explained_variance_ratio_.sum()*100:.2f}%")
-        
-        self.is_fitted = True
-        print("\n✅ Fitting complete!")
-        
-        return self
-    
-    def transform(self, texts: List[str]) -> np.ndarray:
-        """
-        Transform texts to SVD features.
-        
-        Args:
-            texts: List of text strings
-            
-        Returns:
-            Array of SVD features (n_samples, n_components)
-        """
-        if not self.is_fitted:
-            raise ValueError("Extractor must be fitted before transform. Call fit() first.")
-        
-        # Preprocess
-        texts_clean = self.preprocess_texts(texts)
-        
-        # TF-IDF transform
-        tfidf_matrix = self.tfidf.transform(texts_clean)
-        
-        # SVD transform
-        svd_features = self.svd.transform(tfidf_matrix)
-        
-        return svd_features
-    
-    def fit_transform(self, texts: List[str]) -> np.ndarray:
-        """
-        Fit and transform in one step.
-        
-        Args:
-            texts: List of text strings
-            
-        Returns:
-            Array of SVD features (n_samples, n_components)
-        """
-        self.fit(texts)
-        return self.transform(texts)
-    
-    def get_feature_names(self) -> List[str]:
-        """
-        Get feature names for the SVD components.
-        
-        Returns:
-            List of feature names
-        """
-        return [f'text_svd_{i+1}' for i in range(self.n_components)]
-    
-    def get_top_tfidf_features(self, top_n: int = 20) -> pd.DataFrame:
-        """
-        Get top TF-IDF features by document frequency.
-        
-        Args:
-            top_n: Number of top features to return
-            
-        Returns:
-            DataFrame with feature names and document frequencies
-        """
-        if not self.is_fitted:
-            raise ValueError("Extractor must be fitted first.")
-        
-        feature_names = self.tfidf.get_feature_names_out()
-        doc_freq = np.asarray(self.tfidf.transform(self.tfidf.get_feature_names_out()).sum(axis=0)).ravel()
-        
-        top_features = pd.DataFrame({
-            'feature': feature_names,
-            'doc_frequency': doc_freq
-        }).sort_values('doc_frequency', ascending=False).head(top_n)
-        
-        return top_features
-    
-    def save(self, filepath: str):
-        """
-        Save the fitted extractor to disk.
-        
-        Args:
-            filepath: Path to save the extractor (should end with .pkl)
-        """
-        if not self.is_fitted:
-            raise ValueError("Extractor must be fitted before saving.")
-        
-        with open(filepath, 'wb') as f:
-            pickle.dump(self, f)
-        
-        print(f"✅ Saved extractor to: {filepath}")
-    
-    @staticmethod
-    def load(filepath: str) -> 'TextFeatureExtractor':
-        """
-        Load a fitted extractor from disk.
-        
-        Args:
-            filepath: Path to the saved extractor
-            
-        Returns:
-            Loaded TextFeatureExtractor
-        """
-        with open(filepath, 'rb') as f:
-            extractor = pickle.load(f)
-        
-        print(f"✅ Loaded extractor from: {filepath}")
-        return extractor
-    
-    def get_summary(self) -> dict:
-        """
-        Get summary statistics of the extractor.
-        
-        Returns:
-            Dictionary with summary information
-        """
-        if not self.is_fitted:
-            return {'status': 'not_fitted'}
-        
-        return {
-            'status': 'fitted',
-            'max_features': self.max_features,
-            'n_components': self.n_components,
-            'ngram_range': self.ngram_range,
-            'min_df': self.min_df,
-            'max_df': self.max_df,
-            'actual_tfidf_features': len(self.tfidf.get_feature_names_out()),
-            'explained_variance': float(self.svd.explained_variance_ratio_.sum()),
-            'random_state': self.random_state
-        }
-
-
-def extract_features_from_dataframe(
-    df: pd.DataFrame,
-    text_columns: List[str] = ['all_task_normal', 'all_task_dinhky'],
-    extractor: Optional[TextFeatureExtractor] = None,
-    fit: bool = True
-) -> Tuple[pd.DataFrame, TextFeatureExtractor]:
-    """
-    Extract text features from a DataFrame.
-    
-    Args:
-        df: Input DataFrame
-        text_columns: List of text column names to combine
-        extractor: Pre-fitted extractor (optional, for inference)
-        fit: Whether to fit the extractor (True for training, False for inference)
-        
-    Returns:
-        Tuple of (features_df, extractor)
-    """
-    print("=" * 80)
-    print("TEXT FEATURE EXTRACTION")
-    print("=" * 80)
-    
-    # Combine text columns
-    if len(text_columns) == 1:
-        texts = df[text_columns[0]].tolist()
-    else:
-        print(f"\nCombining {len(text_columns)} text columns...")
-        texts = []
-        for _, row in df.iterrows():
-            combined = ' '.join([str(row[col]) if pd.notna(row[col]) else '' for col in text_columns])
-            texts.append(combined)
-    
-    print(f"Total documents: {len(texts)}")
-    
-    # Initialize or use existing extractor
-    if extractor is None:
-        print("\nInitializing new TextFeatureExtractor...")
-        extractor = TextFeatureExtractor()
-    
-    # Extract features
-    if fit:
-        print("\nMode: TRAINING (fit_transform)")
-        features = extractor.fit_transform(texts)
-    else:
-        print("\nMode: INFERENCE (transform)")
-        features = extractor.transform(texts)
-    
-    # Create DataFrame
-    feature_names = extractor.get_feature_names()
-    features_df = pd.DataFrame(features, columns=feature_names)
-    
-    print(f"\n✅ Extraction complete!")
-    print(f"   Output shape: {features_df.shape}")
-    print(f"   Feature names: {feature_names[:5]}... (showing first 5)")
-    
-    # Summary
-    summary = extractor.get_summary()
-    print(f"\n📊 Extractor Summary:")
-    for key, value in summary.items():
-        print(f"   {key}: {value}")
-    
-    return features_df, extractor
-
-
-def main():
-    """Command-line interface for text feature extraction."""
-    parser = argparse.ArgumentParser(
-        description='Extract TF-IDF and SVD features from Vietnamese task descriptions'
-    )
-    
-    parser.add_argument(
-        '--mode',
-        type=str,
-        choices=['train', 'predict'],
-        required=True,
-        help='Mode: train (fit and save) or predict (load and transform)'
-    )
-    
-    parser.add_argument(
-        '--input',
-        type=str,
-        required=True,
-        help='Input file path (Excel or CSV)'
-    )
-    
-    parser.add_argument(
-        '--output',
-        type=str,
-        required=True,
-        help='Output file path for features (CSV)'
-    )
-    
-    parser.add_argument(
-        '--text-columns',
-        type=str,
-        nargs='+',
-        default=['all_task_normal', 'all_task_dinhky'],
-        help='Text column names to combine (default: all_task_normal all_task_dinhky)'
-    )
-    
-    parser.add_argument(
-        '--extractor-path',
-        type=str,
-        default='text_feature_extractor.pkl',
-        help='Path to save/load the extractor (default: text_feature_extractor.pkl)'
-    )
-    
-    parser.add_argument(
-        '--max-features',
-        type=int,
-        default=200,
-        help='Maximum TF-IDF features (default: 200)'
-    )
-    
-    parser.add_argument(
-        '--n-components',
-        type=int,
-        default=50,
-        help='Number of SVD components (default: 50)'
-    )
-    
-    args = parser.parse_args()
-    
-    # Load data
-    print(f"\n📂 Loading data from: {args.input}")
-    if args.input.endswith('.xlsx'):
-        df = pd.read_excel(args.input)
-    elif args.input.endswith('.csv'):
-        df = pd.read_csv(args.input)
-    else:
-        raise ValueError("Input file must be .xlsx or .csv")
-    
-    print(f"   Shape: {df.shape}")
-    
-    # Check columns
-    missing_cols = [col for col in args.text_columns if col not in df.columns]
-    if missing_cols:
-        raise ValueError(f"Missing columns in input data: {missing_cols}")
-    
-    # Extract features
-    if args.mode == 'train':
-        # Training mode: fit and save
-        features_df, extractor = extract_features_from_dataframe(
-            df,
-            text_columns=args.text_columns,
-            extractor=TextFeatureExtractor(
-                max_features=args.max_features,
-                n_components=args.n_components
-            ),
-            fit=True
-        )
-        
-        # Save extractor
-        extractor.save(args.extractor_path)
-        
-    else:  # predict mode
-        # Inference mode: load and transform
-        if not os.path.exists(args.extractor_path):
-            raise FileNotFoundError(f"Extractor not found: {args.extractor_path}")
-        
-        extractor = TextFeatureExtractor.load(args.extractor_path)
-        
-        features_df, _ = extract_features_from_dataframe(
-            df,
-            text_columns=args.text_columns,
-            extractor=extractor,
-            fit=False
-        )
-    
-    # Save features
-    features_df.to_csv(args.output, index=False)
-    print(f"\n✅ Saved features to: {args.output}")
-    
-    # Show top TF-IDF features (training mode only)
-    if args.mode == 'train':
-        print("\n📋 Top 20 TF-IDF features:")
-        top_features = extractor.get_top_tfidf_features(top_n=20)
-        print(top_features.to_string(index=False))
-    
-    print("\n" + "=" * 80)
-    print("✅ COMPLETE!")
-    print("=" * 80)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/final.ipynb b/final.ipynb
index 6ac33a7..a55e2c2 100644
--- a/final.ipynb
+++ b/final.ipynb
@@ -2,48 +2,3671 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "76aa1b75",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Loaded: final_2.xlsx | sheet: final\n",
+      "Shape (raw): (401, 42)\n",
+      "\n",
+      "=== TARGET SUMMARY (so_luong) ===\n",
+      "count    401.000000\n",
+      "mean       4.660848\n",
+      "std        6.799242\n",
+      "min        0.000000\n",
+      "25%        1.000000\n",
+      "50%        2.000000\n",
+      "75%        5.000000\n",
+      "max       64.000000\n",
+      "Name: so_luong, dtype: float64\n",
+      "Missing target: 0\n",
+      "Negative target: 0\n",
+      "Zero target: 3\n",
+      "\n",
+      "Duplicate full rows: 7\n",
+      "Shape (dedup): (394, 42)\n",
+      "\n",
+      "Columns: 42\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ma_dia_diem</th>\n",
+       "      <th>all_task_normal</th>\n",
+       "      <th>all_task_dinhky</th>\n",
+       "      <th>loai_ca</th>\n",
+       "      <th>bat_dau</th>\n",
+       "      <th>ket_thuc</th>\n",
+       "      <th>tong_gio_lam</th>\n",
+       "      <th>so_ca_cua_toa</th>\n",
+       "      <th>so_luong</th>\n",
+       "      <th>num_tasks</th>\n",
+       "      <th>...</th>\n",
+       "      <th>dien_tich_tham</th>\n",
+       "      <th>doc_ham</th>\n",
+       "      <th>vien_phan_quang</th>\n",
+       "      <th>op_tuong</th>\n",
+       "      <th>op_chan_tuong</th>\n",
+       "      <th>ranh_thoat_nuoc</th>\n",
+       "      <th>dien_tich_kinh</th>\n",
+       "      <th>num_medical_tasks_total</th>\n",
+       "      <th>num_indoor_room_tasks</th>\n",
+       "      <th>is_tasks_text_missing</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>115-2</td>\n",
+       "      <td>Làm sạch toàn bộ phòng giao dịch tầng 1 (kể cả...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Part time</td>\n",
+       "      <td>06:30:00</td>\n",
+       "      <td>10:30:00</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>101-1</td>\n",
+       "      <td>Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...</td>\n",
+       "      <td>Lau bảng biển, bình cứu hỏa , cây nước hành la...</td>\n",
+       "      <td>Hành chính</td>\n",
+       "      <td>06:30:00</td>\n",
+       "      <td>16:00:00</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>24</td>\n",
+       "      <td>441</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9176.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>894.0</td>\n",
+       "      <td>112</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>101-1</td>\n",
+       "      <td>Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...</td>\n",
+       "      <td>Lau bảng biển, bình cứu hỏa , cây nước hành la...</td>\n",
+       "      <td>Ca sáng</td>\n",
+       "      <td>06:00:00</td>\n",
+       "      <td>14:00:00</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>441</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9176.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>894.0</td>\n",
+       "      <td>112</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 42 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  ma_dia_diem                                    all_task_normal  \\\n",
+       "0       115-2  Làm sạch toàn bộ phòng giao dịch tầng 1 (kể cả...   \n",
+       "1       101-1  Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...   \n",
+       "2       101-1  Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...   \n",
+       "\n",
+       "                                     all_task_dinhky     loai_ca   bat_dau  \\\n",
+       "0                                                NaN   Part time  06:30:00   \n",
+       "1  Lau bảng biển, bình cứu hỏa , cây nước hành la...  Hành chính  06:30:00   \n",
+       "2  Lau bảng biển, bình cứu hỏa , cây nước hành la...     Ca sáng  06:00:00   \n",
+       "\n",
+       "   ket_thuc  tong_gio_lam  so_ca_cua_toa  so_luong  num_tasks  ...  \\\n",
+       "0  10:30:00           4.0              1         1          7  ...   \n",
+       "1  16:00:00           7.5              6        24        441  ...   \n",
+       "2  14:00:00           8.0              6         3        441  ...   \n",
+       "\n",
+       "   dien_tich_tham  doc_ham  vien_phan_quang  op_tuong  op_chan_tuong  \\\n",
+       "0             0.0        0                0       0.0            0.0   \n",
+       "1             0.0       70                0    9176.0           89.0   \n",
+       "2             0.0       70                0    9176.0           89.0   \n",
+       "\n",
+       "   ranh_thoat_nuoc  dien_tich_kinh  num_medical_tasks_total  \\\n",
+       "0                0            20.0                        0   \n",
+       "1               25           894.0                      112   \n",
+       "2               25           894.0                      112   \n",
+       "\n",
+       "   num_indoor_room_tasks  is_tasks_text_missing  \n",
+       "0                      1                      0  \n",
+       "1                     39                      0  \n",
+       "2                     39                      0  \n",
+       "\n",
+       "[3 rows x 42 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "DATA_PATH = \"final_2.xlsx\"\n",
+    "SHEET = \"final\"\n",
+    "TARGET = \"so_luong\"\n",
+    "\n",
+    "# 1) Load\n",
+    "df = pd.read_excel(DATA_PATH, sheet_name=SHEET)\n",
+    "print(\"✅ Loaded:\", DATA_PATH, \"| sheet:\", SHEET)\n",
+    "print(\"Shape (raw):\", df.shape)\n",
+    "\n",
+    "# 2) Target sanity\n",
+    "assert TARGET in df.columns, f\"❌ Missing target column: {TARGET}\"\n",
+    "df[TARGET] = pd.to_numeric(df[TARGET], errors=\"coerce\")\n",
+    "\n",
+    "print(\"\\n=== TARGET SUMMARY (so_luong) ===\")\n",
+    "print(df[TARGET].describe())\n",
+    "print(\"Missing target:\", df[TARGET].isna().sum())\n",
+    "print(\"Negative target:\", (df[TARGET] < 0).sum())\n",
+    "print(\"Zero target:\", (df[TARGET] == 0).sum())\n",
+    "\n",
+    "# 3) Deduplicate full rows\n",
+    "dup = df.duplicated().sum()\n",
+    "print(\"\\nDuplicate full rows:\", dup)\n",
+    "if dup > 0:\n",
+    "    df = df.drop_duplicates().reset_index(drop=True)\n",
+    "print(\"Shape (dedup):\", df.shape)\n",
+    "\n",
+    "# 4) Quick preview\n",
+    "print(\"\\nColumns:\", len(df.columns))\n",
+    "display(df.head(3))\n"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "50877c2b",
+   "execution_count": 13,
+   "id": "421b7556",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['ma_dia_diem', 'all_task_normal', 'all_task_dinhky', 'loai_ca',\n",
+       "       'bat_dau', 'ket_thuc', 'tong_gio_lam', 'so_ca_cua_toa', 'so_luong',\n",
+       "       'num_tasks', 'num_cleaning_tasks', 'num_trash_collection_tasks',\n",
+       "       'num_monitoring_tasks', 'num_deep_cleaning_tasks', 'num_support_tasks',\n",
+       "       'num_other_tasks', 'num_wc_tasks', 'num_hallway_tasks',\n",
+       "       'num_lobby_tasks', 'num_outdoor_tasks', 'num_elevator_tasks',\n",
+       "       'cleaning_ratio', 'trash_collection_ratio', 'monitoring_ratio',\n",
+       "       'area_diversity', 'so_tang', 'so_cua_thang_may', 'dien_tich_ngoai_canh',\n",
+       "       'dien_tich_sanh', 'dien_tich_hanh_lang', 'dien_tich_wc',\n",
+       "       'dien_tich_phong', 'dien_tich_tham', 'doc_ham', 'vien_phan_quang',\n",
+       "       'op_tuong', 'op_chan_tuong', 'ranh_thoat_nuoc', 'dien_tich_kinh',\n",
+       "       'num_medical_tasks_total', 'num_indoor_room_tasks',\n",
+       "       'is_tasks_text_missing'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "daf5a333",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ X shape: (394, 48) | y shape: (394,) | #buildings: 192\n",
+      "Columns sample: ['tong_gio_lam', 'so_ca_cua_toa', 'num_tasks', 'num_cleaning_tasks', 'num_trash_collection_tasks', 'num_monitoring_tasks', 'num_deep_cleaning_tasks', 'num_support_tasks', 'num_other_tasks', 'num_wc_tasks', 'num_hallway_tasks', 'num_lobby_tasks', 'num_outdoor_tasks', 'num_elevator_tasks', 'cleaning_ratio']\n",
+      "Any NaN left?: False\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tong_gio_lam</th>\n",
+       "      <th>so_ca_cua_toa</th>\n",
+       "      <th>num_tasks</th>\n",
+       "      <th>num_cleaning_tasks</th>\n",
+       "      <th>num_trash_collection_tasks</th>\n",
+       "      <th>num_monitoring_tasks</th>\n",
+       "      <th>num_deep_cleaning_tasks</th>\n",
+       "      <th>num_support_tasks</th>\n",
+       "      <th>num_other_tasks</th>\n",
+       "      <th>num_wc_tasks</th>\n",
+       "      <th>...</th>\n",
+       "      <th>is_night_shift</th>\n",
+       "      <th>is_morning_shift</th>\n",
+       "      <th>is_afternoon_shift</th>\n",
+       "      <th>is_evening_shift</th>\n",
+       "      <th>loai_ca_Ca chiều</th>\n",
+       "      <th>loai_ca_Ca gãy</th>\n",
+       "      <th>loai_ca_Ca sáng</th>\n",
+       "      <th>loai_ca_Ca đêm</th>\n",
+       "      <th>loai_ca_Hành chính</th>\n",
+       "      <th>loai_ca_Part time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>441</td>\n",
+       "      <td>258</td>\n",
+       "      <td>145</td>\n",
+       "      <td>134</td>\n",
+       "      <td>75</td>\n",
+       "      <td>57</td>\n",
+       "      <td>45</td>\n",
+       "      <td>89</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>441</td>\n",
+       "      <td>258</td>\n",
+       "      <td>145</td>\n",
+       "      <td>134</td>\n",
+       "      <td>75</td>\n",
+       "      <td>57</td>\n",
+       "      <td>45</td>\n",
+       "      <td>89</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>441</td>\n",
+       "      <td>258</td>\n",
+       "      <td>145</td>\n",
+       "      <td>134</td>\n",
+       "      <td>75</td>\n",
+       "      <td>57</td>\n",
+       "      <td>45</td>\n",
+       "      <td>89</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>441</td>\n",
+       "      <td>258</td>\n",
+       "      <td>145</td>\n",
+       "      <td>134</td>\n",
+       "      <td>75</td>\n",
+       "      <td>57</td>\n",
+       "      <td>45</td>\n",
+       "      <td>89</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>7.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>441</td>\n",
+       "      <td>258</td>\n",
+       "      <td>145</td>\n",
+       "      <td>134</td>\n",
+       "      <td>75</td>\n",
+       "      <td>57</td>\n",
+       "      <td>45</td>\n",
+       "      <td>89</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>9.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>441</td>\n",
+       "      <td>258</td>\n",
+       "      <td>145</td>\n",
+       "      <td>134</td>\n",
+       "      <td>75</td>\n",
+       "      <td>57</td>\n",
+       "      <td>45</td>\n",
+       "      <td>89</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>9.5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>135</td>\n",
+       "      <td>81</td>\n",
+       "      <td>35</td>\n",
+       "      <td>38</td>\n",
+       "      <td>10</td>\n",
+       "      <td>20</td>\n",
+       "      <td>21</td>\n",
+       "      <td>25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>7.5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>135</td>\n",
+       "      <td>81</td>\n",
+       "      <td>35</td>\n",
+       "      <td>38</td>\n",
+       "      <td>10</td>\n",
+       "      <td>20</td>\n",
+       "      <td>21</td>\n",
+       "      <td>25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>7.5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>135</td>\n",
+       "      <td>81</td>\n",
+       "      <td>35</td>\n",
+       "      <td>38</td>\n",
+       "      <td>10</td>\n",
+       "      <td>20</td>\n",
+       "      <td>21</td>\n",
+       "      <td>25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 48 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   tong_gio_lam  so_ca_cua_toa  num_tasks  num_cleaning_tasks  \\\n",
+       "0           4.0              1          7                   7   \n",
+       "1           7.5              6        441                 258   \n",
+       "2           8.0              6        441                 258   \n",
+       "3           8.0              6        441                 258   \n",
+       "4           8.0              6        441                 258   \n",
+       "5           7.5              6        441                 258   \n",
+       "6           9.5              6        441                 258   \n",
+       "7           9.5              3        135                  81   \n",
+       "8           7.5              3        135                  81   \n",
+       "9           7.5              3        135                  81   \n",
+       "\n",
+       "   num_trash_collection_tasks  num_monitoring_tasks  num_deep_cleaning_tasks  \\\n",
+       "0                           1                     2                        1   \n",
+       "1                         145                   134                       75   \n",
+       "2                         145                   134                       75   \n",
+       "3                         145                   134                       75   \n",
+       "4                         145                   134                       75   \n",
+       "5                         145                   134                       75   \n",
+       "6                         145                   134                       75   \n",
+       "7                          35                    38                       10   \n",
+       "8                          35                    38                       10   \n",
+       "9                          35                    38                       10   \n",
+       "\n",
+       "   num_support_tasks  num_other_tasks  num_wc_tasks  ...  is_night_shift  \\\n",
+       "0                  0                0             4  ...               0   \n",
+       "1                 57               45            89  ...               0   \n",
+       "2                 57               45            89  ...               0   \n",
+       "3                 57               45            89  ...               0   \n",
+       "4                 57               45            89  ...               1   \n",
+       "5                 57               45            89  ...               0   \n",
+       "6                 57               45            89  ...               0   \n",
+       "7                 20               21            25  ...               0   \n",
+       "8                 20               21            25  ...               0   \n",
+       "9                 20               21            25  ...               1   \n",
+       "\n",
+       "   is_morning_shift  is_afternoon_shift  is_evening_shift  loai_ca_Ca chiều  \\\n",
+       "0                 1                   0                 0             False   \n",
+       "1                 1                   0                 0             False   \n",
+       "2                 1                   0                 0             False   \n",
+       "3                 0                   1                 0              True   \n",
+       "4                 0                   0                 0             False   \n",
+       "5                 0                   1                 0             False   \n",
+       "6                 1                   0                 0             False   \n",
+       "7                 1                   0                 0             False   \n",
+       "8                 1                   0                 0             False   \n",
+       "9                 0                   0                 0             False   \n",
+       "\n",
+       "   loai_ca_Ca gãy  loai_ca_Ca sáng  loai_ca_Ca đêm  loai_ca_Hành chính  \\\n",
+       "0           False            False           False               False   \n",
+       "1           False            False           False                True   \n",
+       "2           False             True           False               False   \n",
+       "3           False            False           False               False   \n",
+       "4           False            False            True               False   \n",
+       "5            True            False           False               False   \n",
+       "6           False            False           False                True   \n",
+       "7           False            False           False                True   \n",
+       "8            True            False           False               False   \n",
+       "9           False            False            True               False   \n",
+       "\n",
+       "   loai_ca_Part time  \n",
+       "0               True  \n",
+       "1              False  \n",
+       "2              False  \n",
+       "3              False  \n",
+       "4              False  \n",
+       "5              False  \n",
+       "6              False  \n",
+       "7              False  \n",
+       "8              False  \n",
+       "9              False  \n",
+       "\n",
+       "[10 rows x 48 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "TARGET = \"so_luong\"\n",
+    "GROUP_COL = \"ma_dia_diem\"\n",
+    "\n",
+    "# chỉ bỏ 4 cột bạn yêu cầu\n",
+    "DROP_COLS = [\"ma_dia_diem\", \"all_task_normal\", \"all_task_dinhky\", \"is_tasks_text_missing\"]\n",
+    "\n",
+    "# ---------- helpers ----------\n",
+    "def parse_hour(t):\n",
+    "    \"\"\"Convert 'HH:MM:SS' (or datetime-like) -> float hour in [0,24).\"\"\"\n",
+    "    if pd.isna(t):\n",
+    "        return np.nan\n",
+    "    # pandas Timestamp / datetime\n",
+    "    if hasattr(t, \"hour\"):\n",
+    "        return float(t.hour) + float(getattr(t, \"minute\", 0))/60.0\n",
+    "    s = str(t).strip()\n",
+    "    # handle '06:30:00'\n",
+    "    if \":\" in s:\n",
+    "        parts = s.split(\":\")\n",
+    "        try:\n",
+    "            hh = int(float(parts[0]))\n",
+    "            mm = int(float(parts[1])) if len(parts) > 1 else 0\n",
+    "            return hh + mm/60.0\n",
+    "        except:\n",
+    "            return np.nan\n",
+    "    # handle '6.5' etc\n",
+    "    try:\n",
+    "        return float(s)\n",
+    "    except:\n",
+    "        return np.nan\n",
+    "\n",
+    "# ---------- 1) y + groups ----------\n",
+    "y = df[TARGET].astype(float).copy()\n",
+    "groups = df[GROUP_COL].astype(str).copy()   # để split theo tòa ở cell sau\n",
+    "\n",
+    "# ---------- 2) time features (bat_dau/ket_thuc) ----------\n",
+    "hour_start = df[\"bat_dau\"].apply(parse_hour)\n",
+    "hour_end   = df[\"ket_thuc\"].apply(parse_hour)\n",
+    "\n",
+    "# cross-day (vd 22 -> 6)\n",
+    "is_cross_day = ((hour_end < hour_start) & hour_start.notna() & hour_end.notna()).astype(int)\n",
+    "\n",
+    "# shift length in hours, safe modulo 24\n",
+    "shift_length = ((hour_end - hour_start) % 24).fillna(0)\n",
+    "\n",
+    "# flags\n",
+    "is_night_shift = ((hour_start >= 22) | (hour_start < 6)).fillna(False).astype(int)\n",
+    "is_morning_shift = ((hour_start >= 6) & (hour_start < 12)).fillna(False).astype(int)\n",
+    "is_afternoon_shift = ((hour_start >= 12) & (hour_start < 18)).fillna(False).astype(int)\n",
+    "is_evening_shift = ((hour_start >= 18) & (hour_start < 22)).fillna(False).astype(int)\n",
+    "\n",
+    "# ---------- 3) Build X from df: drop forbidden + drop raw time cols + add time features ----------\n",
+    "X = df.drop(columns=[c for c in DROP_COLS if c in df.columns] + [TARGET], errors=\"ignore\").copy()\n",
+    "\n",
+    "# Drop raw time columns (không one-hot time)\n",
+    "for c in [\"bat_dau\", \"ket_thuc\"]:\n",
+    "    if c in X.columns:\n",
+    "        X = X.drop(columns=[c])\n",
+    "\n",
+    "# Add engineered time features\n",
+    "X[\"hour_start\"] = hour_start.fillna(0)\n",
+    "X[\"hour_end\"] = hour_end.fillna(0)\n",
+    "X[\"shift_length\"] = shift_length\n",
+    "X[\"is_cross_day\"] = is_cross_day\n",
+    "X[\"is_night_shift\"] = is_night_shift\n",
+    "X[\"is_morning_shift\"] = is_morning_shift\n",
+    "X[\"is_afternoon_shift\"] = is_afternoon_shift\n",
+    "X[\"is_evening_shift\"] = is_evening_shift\n",
+    "\n",
+    "# ---------- 4) Fill NA numeric + one-hot ONLY loai_ca ----------\n",
+    "# numeric fill\n",
+    "num_cols = X.select_dtypes(include=[np.number]).columns\n",
+    "X[num_cols] = X[num_cols].fillna(0)\n",
+    "\n",
+    "# One-hot loai_ca (nếu có)\n",
+    "if \"loai_ca\" in X.columns:\n",
+    "    X[\"loai_ca\"] = X[\"loai_ca\"].fillna(\"UNKNOWN\").astype(str)\n",
+    "    X = pd.get_dummies(X, columns=[\"loai_ca\"], drop_first=True)\n",
+    "\n",
+    "print(\"✅ X shape:\", X.shape, \"| y shape:\", y.shape, \"| #buildings:\", groups.nunique())\n",
+    "print(\"Columns sample:\", list(X.columns[:15]))\n",
+    "print(\"Any NaN left?:\", X.isna().any().any())\n",
+    "\n",
+    "X.head(10)\n"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "483c1818",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "0ad8de9d",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Final X shape: (394, 45) | y shape: (394,) | #buildings: 192\n",
+      "Any NaN left in X?: False\n",
+      "Time one-hot columns (should be 0): 0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tong_gio_lam</th>\n",
+       "      <th>so_ca_cua_toa</th>\n",
+       "      <th>num_tasks</th>\n",
+       "      <th>num_cleaning_tasks</th>\n",
+       "      <th>num_trash_collection_tasks</th>\n",
+       "      <th>num_monitoring_tasks</th>\n",
+       "      <th>num_deep_cleaning_tasks</th>\n",
+       "      <th>num_support_tasks</th>\n",
+       "      <th>num_other_tasks</th>\n",
+       "      <th>num_wc_tasks</th>\n",
+       "      <th>...</th>\n",
+       "      <th>hour_end</th>\n",
+       "      <th>shift_length</th>\n",
+       "      <th>is_cross_day</th>\n",
+       "      <th>is_night_shift</th>\n",
+       "      <th>loai_ca_Ca chiều</th>\n",
+       "      <th>loai_ca_Ca gãy</th>\n",
+       "      <th>loai_ca_Ca sáng</th>\n",
+       "      <th>loai_ca_Ca đêm</th>\n",
+       "      <th>loai_ca_Hành chính</th>\n",
+       "      <th>loai_ca_Part time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>441</td>\n",
+       "      <td>258</td>\n",
+       "      <td>145</td>\n",
+       "      <td>134</td>\n",
+       "      <td>75</td>\n",
+       "      <td>57</td>\n",
+       "      <td>45</td>\n",
+       "      <td>89</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>441</td>\n",
+       "      <td>258</td>\n",
+       "      <td>145</td>\n",
+       "      <td>134</td>\n",
+       "      <td>75</td>\n",
+       "      <td>57</td>\n",
+       "      <td>45</td>\n",
+       "      <td>89</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 45 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   tong_gio_lam  so_ca_cua_toa  num_tasks  num_cleaning_tasks  \\\n",
+       "0           4.0              1          7                   7   \n",
+       "1           7.5              6        441                 258   \n",
+       "2           8.0              6        441                 258   \n",
+       "\n",
+       "   num_trash_collection_tasks  num_monitoring_tasks  num_deep_cleaning_tasks  \\\n",
+       "0                           1                     2                        1   \n",
+       "1                         145                   134                       75   \n",
+       "2                         145                   134                       75   \n",
+       "\n",
+       "   num_support_tasks  num_other_tasks  num_wc_tasks  ...  hour_end  \\\n",
+       "0                  0                0             4  ...      10.5   \n",
+       "1                 57               45            89  ...      16.0   \n",
+       "2                 57               45            89  ...      14.0   \n",
+       "\n",
+       "   shift_length  is_cross_day  is_night_shift  loai_ca_Ca chiều  \\\n",
+       "0           4.0             0               0             False   \n",
+       "1           9.5             0               0             False   \n",
+       "2           8.0             0               0             False   \n",
+       "\n",
+       "   loai_ca_Ca gãy  loai_ca_Ca sáng  loai_ca_Ca đêm  loai_ca_Hành chính  \\\n",
+       "0           False            False           False               False   \n",
+       "1           False            False           False                True   \n",
+       "2           False             True           False               False   \n",
+       "\n",
+       "   loai_ca_Part time  \n",
+       "0               True  \n",
+       "1              False  \n",
+       "2              False  \n",
+       "\n",
+       "[3 rows x 45 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "TARGET = \"so_luong\"\n",
+    "GROUP_COL = \"ma_dia_diem\"\n",
+    "DROP_COLS = [\"ma_dia_diem\", \"all_task_normal\", \"all_task_dinhky\", \"is_tasks_text_missing\"]\n",
+    "\n",
+    "def parse_hour(t):\n",
+    "    \"\"\"Convert 'HH:MM:SS' or datetime-like -> float hour.\"\"\"\n",
+    "    if pd.isna(t):\n",
+    "        return np.nan\n",
+    "    if hasattr(t, \"hour\"):\n",
+    "        return float(t.hour) + float(getattr(t, \"minute\", 0))/60.0\n",
+    "    s = str(t).strip()\n",
+    "    if \":\" in s:\n",
+    "        parts = s.split(\":\")\n",
+    "        try:\n",
+    "            hh = int(float(parts[0]))\n",
+    "            mm = int(float(parts[1])) if len(parts) > 1 else 0\n",
+    "            return hh + mm/60.0\n",
+    "        except:\n",
+    "            return np.nan\n",
+    "    try:\n",
+    "        return float(s)\n",
+    "    except:\n",
+    "        return np.nan\n",
+    "\n",
+    "# 1) y + groups (groups dùng ở cell 3 để split theo tòa)\n",
+    "y = df[TARGET].astype(float).copy()\n",
+    "groups = df[GROUP_COL].astype(str).copy()\n",
+    "\n",
+    "# 2) Time features từ bat_dau/ket_thuc (KHÔNG one-hot)\n",
+    "hour_start = df[\"bat_dau\"].apply(parse_hour)\n",
+    "hour_end   = df[\"ket_thuc\"].apply(parse_hour)\n",
+    "\n",
+    "is_cross_day = ((hour_end < hour_start) & hour_start.notna() & hour_end.notna()).astype(int)\n",
+    "shift_length = ((hour_end - hour_start) % 24).fillna(0)\n",
+    "is_night_shift = ((hour_start >= 22) | (hour_start < 6)).fillna(False).astype(int)\n",
+    "\n",
+    "# 3) Build X: drop 4 cols + drop target + drop raw time cols\n",
+    "X = df.drop(columns=[c for c in DROP_COLS if c in df.columns] + [TARGET], errors=\"ignore\").copy()\n",
+    "X = X.drop(columns=[c for c in [\"bat_dau\", \"ket_thuc\"] if c in X.columns], errors=\"ignore\")\n",
+    "\n",
+    "# Add engineered time cols\n",
+    "X[\"hour_start\"] = hour_start.fillna(0)\n",
+    "X[\"hour_end\"] = hour_end.fillna(0)\n",
+    "X[\"shift_length\"] = shift_length\n",
+    "X[\"is_cross_day\"] = is_cross_day\n",
+    "X[\"is_night_shift\"] = is_night_shift\n",
+    "\n",
+    "# 4) Fill NA numeric\n",
+    "num_cols = X.select_dtypes(include=[np.number]).columns\n",
+    "X[num_cols] = X[num_cols].fillna(0)\n",
+    "\n",
+    "# 5) One-hot ONLY loai_ca (nếu có)\n",
+    "if \"loai_ca\" in X.columns:\n",
+    "    X[\"loai_ca\"] = X[\"loai_ca\"].fillna(\"UNKNOWN\").astype(str)\n",
+    "    X = pd.get_dummies(X, columns=[\"loai_ca\"], drop_first=True)\n",
+    "\n",
+    "print(\"✅ Final X shape:\", X.shape, \"| y shape:\", y.shape, \"| #buildings:\", groups.nunique())\n",
+    "print(\"Any NaN left in X?:\", X.isna().any().any())\n",
+    "\n",
+    "# sanity: confirm no ket_thuc_* or bat_dau_* one-hot columns\n",
+    "bad_cols = [c for c in X.columns if str(c).startswith(\"bat_dau_\") or str(c).startswith(\"ket_thuc_\")]\n",
+    "print(\"Time one-hot columns (should be 0):\", len(bad_cols))\n",
+    "\n",
+    "X.head(3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "2df3b609",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes:\n",
+      "Train: (283, 45) (283,) | buildings: 134\n",
+      "Val:   (58, 45) (58,) | buildings: 29\n",
+      "Test:  (53, 45) (53,) | buildings: 29\n",
+      "\n",
+      "Leakage check (should all be 0):\n",
+      "Train ∩ Val : 0\n",
+      "Train ∩ Test: 0\n",
+      "Val   ∩ Test: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import GroupShuffleSplit\n",
+    "\n",
+    "# ----- 1) Split: (train+val) vs test = 85% / 15% -----\n",
+    "gss1 = GroupShuffleSplit(n_splits=1, test_size=0.15, random_state=42)\n",
+    "trainval_idx, test_idx = next(gss1.split(X, y, groups=groups))\n",
+    "\n",
+    "X_trainval, X_test = X.iloc[trainval_idx].reset_index(drop=True), X.iloc[test_idx].reset_index(drop=True)\n",
+    "y_trainval, y_test = y.iloc[trainval_idx].reset_index(drop=True), y.iloc[test_idx].reset_index(drop=True)\n",
+    "groups_trainval = groups.iloc[trainval_idx].reset_index(drop=True)\n",
+    "groups_test = groups.iloc[test_idx].reset_index(drop=True)\n",
+    "\n",
+    "# ----- 2) Split: train vs val inside trainval = 82.35% / 17.65%  -> overall 70% / 15% -----\n",
+    "gss2 = GroupShuffleSplit(n_splits=1, test_size=0.1765, random_state=42)\n",
+    "train_idx, val_idx = next(gss2.split(X_trainval, y_trainval, groups=groups_trainval))\n",
+    "\n",
+    "X_train, X_val = X_trainval.iloc[train_idx].reset_index(drop=True), X_trainval.iloc[val_idx].reset_index(drop=True)\n",
+    "y_train, y_val = y_trainval.iloc[train_idx].reset_index(drop=True), y_trainval.iloc[val_idx].reset_index(drop=True)\n",
+    "groups_train = groups_trainval.iloc[train_idx].reset_index(drop=True)\n",
+    "groups_val = groups_trainval.iloc[val_idx].reset_index(drop=True)\n",
+    "\n",
+    "# ----- 3) Report -----\n",
+    "print(\"Shapes:\")\n",
+    "print(\"Train:\", X_train.shape, y_train.shape, \"| buildings:\", groups_train.nunique())\n",
+    "print(\"Val:  \", X_val.shape,   y_val.shape,   \"| buildings:\", groups_val.nunique())\n",
+    "print(\"Test: \", X_test.shape,  y_test.shape,  \"| buildings:\", groups_test.nunique())\n",
+    "\n",
+    "# Leakage check: ensure no building appears in multiple splits\n",
+    "train_b = set(groups_train.unique())\n",
+    "val_b   = set(groups_val.unique())\n",
+    "test_b  = set(groups_test.unique())\n",
+    "print(\"\\nLeakage check (should all be 0):\")\n",
+    "print(\"Train ∩ Val :\", len(train_b & val_b))\n",
+    "print(\"Train ∩ Test:\", len(train_b & test_b))\n",
+    "print(\"Val   ∩ Test:\", len(val_b & test_b))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "8cc64019",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Model: Ridge_log\n",
+      "Train | MAE=2.995 | RMSE=5.484 | R2=0.484\n",
+      "Val   | MAE=1.398 | RMSE=2.015 | R2=0.037\n",
+      "Test  | MAE=2.744 | RMSE=4.416 | R2=0.155\n",
+      "\n",
+      "Model: GBR_log\n",
+      "Train | MAE=1.201 | RMSE=2.466 | R2=0.896\n",
+      "Val   | MAE=1.213 | RMSE=1.832 | R2=0.203\n",
+      "Test  | MAE=2.979 | RMSE=4.810 | R2=-0.002\n",
+      "\n",
+      "✅ Saved: test_predictions_gbr.xlsx\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>y_true</th>\n",
+       "      <th>y_pred</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>29.0</td>\n",
+       "      <td>17.014321</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.206821</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.930329</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.853829</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.561030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.867534</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.601529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.436945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>3.495240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.596085</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   y_true     y_pred\n",
+       "0    29.0  17.014321\n",
+       "1     4.0   5.206821\n",
+       "2     4.0   2.930329\n",
+       "3     3.0   1.853829\n",
+       "4     2.0   1.561030\n",
+       "5     1.0   0.867534\n",
+       "6     3.0   1.601529\n",
+       "7     3.0   2.436945\n",
+       "8    13.0   3.495240\n",
+       "9     2.0   1.596085"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+    "from sklearn.linear_model import Ridge\n",
+    "from sklearn.ensemble import GradientBoostingRegressor\n",
+    "\n",
+    "def rmse(y, yhat):\n",
+    "    return float(np.sqrt(mean_squared_error(y, yhat)))\n",
+    "\n",
+    "def eval_model(name, model, X_tr, y_tr, X_va, y_va, X_te, y_te, log_target=False):\n",
+    "    # fit\n",
+    "    model.fit(X_tr, np.log1p(y_tr) if log_target else y_tr)\n",
+    "\n",
+    "    # predict helper\n",
+    "    def pred(m, X):\n",
+    "        p = m.predict(X)\n",
+    "        return np.expm1(p) if log_target else p\n",
+    "\n",
+    "    yhat_tr = pred(model, X_tr)\n",
+    "    yhat_va = pred(model, X_va)\n",
+    "    yhat_te = pred(model, X_te)\n",
+    "\n",
+    "    def metrics(y, yhat):\n",
+    "        return {\n",
+    "            \"MAE\": float(mean_absolute_error(y, yhat)),\n",
+    "            \"RMSE\": rmse(y, yhat),\n",
+    "            \"R2\": float(r2_score(y, yhat)),\n",
+    "        }\n",
+    "\n",
+    "    res = {\n",
+    "        \"model\": name,\n",
+    "        \"Train\": metrics(y_tr, yhat_tr),\n",
+    "        \"Val\":   metrics(y_va, yhat_va),\n",
+    "        \"Test\":  metrics(y_te, yhat_te),\n",
+    "    }\n",
+    "    return res, yhat_te\n",
+    "\n",
+    "\n",
+    "results = []\n",
+    "\n",
+    "# 1) Ridge (log target)\n",
+    "ridge = Ridge(alpha=1.0, random_state=42)\n",
+    "res_ridge, ridge_test_pred = eval_model(\n",
+    "    \"Ridge_log\", ridge,\n",
+    "    X_train, y_train,\n",
+    "    X_val, y_val,\n",
+    "    X_test, y_test,\n",
+    "    log_target=True\n",
+    ")\n",
+    "results.append(res_ridge)\n",
+    "\n",
+    "# 2) Gradient Boosting (log target)\n",
+    "gbr = GradientBoostingRegressor(\n",
+    "    n_estimators=300,\n",
+    "    learning_rate=0.05,\n",
+    "    max_depth=3,\n",
+    "    random_state=42\n",
+    ")\n",
+    "res_gbr, gbr_test_pred = eval_model(\n",
+    "    \"GBR_log\", gbr,\n",
+    "    X_train, y_train,\n",
+    "    X_val, y_val,\n",
+    "    X_test, y_test,\n",
+    "    log_target=True\n",
+    ")\n",
+    "results.append(res_gbr)\n",
+    "\n",
+    "# Print results\n",
+    "for r in results:\n",
+    "    print(\"\\nModel:\", r[\"model\"])\n",
+    "    for split in [\"Train\", \"Val\", \"Test\"]:\n",
+    "        m = r[split]\n",
+    "        print(f\"{split:5s} | MAE={m['MAE']:.3f} | RMSE={m['RMSE']:.3f} | R2={m['R2']:.3f}\")\n",
+    "\n",
+    "# Save TEST predictions (GBR)\n",
+    "out = pd.DataFrame({\n",
+    "    \"y_true\": y_test.values,\n",
+    "    \"y_pred\": np.maximum(0, gbr_test_pred)  # clamp negative\n",
+    "})\n",
+    "out.to_excel(\"test_predictions_gbr.xlsx\", index=False)\n",
+    "print(\"\\n✅ Saved: test_predictions_gbr.xlsx\")\n",
+    "out.head(10)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e238b641",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== ML only (GBR_reg, log target) ===\n",
+      "Train | MAE=2.145 | RMSE=4.539 | R2=0.647\n",
+      "Val   | MAE=1.074 | RMSE=1.722 | R2=0.296\n",
+      "Test  | MAE=2.669 | RMSE=4.565 | R2=0.097\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.ensemble import GradientBoostingRegressor\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+    "\n",
+    "def rmse(y, yhat):\n",
+    "    return float(np.sqrt(mean_squared_error(y, yhat)))\n",
+    "\n",
+    "def report(name, y_true, y_pred):\n",
+    "    print(f\"{name:5s} | MAE={mean_absolute_error(y_true, y_pred):.3f} \"\n",
+    "          f\"| RMSE={rmse(y_true, y_pred):.3f} | R2={r2_score(y_true, y_pred):.3f}\")\n",
+    "\n",
+    "# GBR \"hiền\" hơn để giảm overfit\n",
+    "gbr_reg = GradientBoostingRegressor(\n",
+    "    n_estimators=600,\n",
+    "    learning_rate=0.03,\n",
+    "    max_depth=2,\n",
+    "    min_samples_leaf=10,\n",
+    "    min_samples_split=20,\n",
+    "    random_state=42\n",
+    ")\n",
+    "\n",
+    "# Train với log1p(target)\n",
+    "gbr_reg.fit(X_train, np.log1p(y_train))\n",
+    "\n",
+    "def predict_original_scale(model, X):\n",
+    "    return np.maximum(0, np.expm1(model.predict(X)))\n",
+    "\n",
+    "pred_train = predict_original_scale(gbr_reg, X_train)\n",
+    "pred_val   = predict_original_scale(gbr_reg, X_val)\n",
+    "pred_test  = predict_original_scale(gbr_reg, X_test)\n",
+    "\n",
+    "print(\"=== ML only (GBR_reg, log target) ===\")\n",
+    "report(\"Train\", y_train, pred_train)\n",
+    "report(\"Val\",   y_val,   pred_val)\n",
+    "report(\"Test\",  y_test,  pred_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "635bf672",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best K on VAL: {'K': 150, 'mae_val': 1.0616528195190562}\n",
+      "\n",
+      "=== After Rule: y_pred_final = max(ML_pred, ceil(num_tasks/K)) ===\n",
+      "Rule used: min_staff = ceil(num_tasks / 150)\n",
+      "Train | MAE=2.259 | RMSE=4.601 | R2=0.637\n",
+      "Val   | MAE=1.062 | RMSE=1.721 | R2=0.297\n",
+      "Test  | MAE=2.602 | RMSE=4.527 | R2=0.112\n",
+      "\n",
+      "TEST big cases (y_true >= 10): 6\n",
+      "ML    | MAE=8.512 | RMSE=9.050 | R2=-0.959\n",
+      "Rule  | MAE=8.320 | RMSE=8.909 | R2=-0.899\n",
+      "\n",
+      "✅ Saved: test_predictions_ml_plus_rule.xlsx\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>y_true</th>\n",
+       "      <th>y_pred_ml</th>\n",
+       "      <th>y_pred_final</th>\n",
+       "      <th>num_tasks</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>29.0</td>\n",
+       "      <td>15.005548</td>\n",
+       "      <td>15.005548</td>\n",
+       "      <td>593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.369563</td>\n",
+       "      <td>4.369563</td>\n",
+       "      <td>593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.902404</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.591762</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.089570</td>\n",
+       "      <td>2.089570</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.946909</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.167053</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.154357</td>\n",
+       "      <td>2.154357</td>\n",
+       "      <td>182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>3.034284</td>\n",
+       "      <td>3.034284</td>\n",
+       "      <td>182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.674387</td>\n",
+       "      <td>1.674387</td>\n",
+       "      <td>124</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   y_true  y_pred_ml  y_pred_final  num_tasks\n",
+       "0    29.0  15.005548     15.005548        593\n",
+       "1     4.0   4.369563      4.369563        593\n",
+       "2     4.0   2.902404      4.000000        593\n",
+       "3     3.0   2.591762      4.000000        593\n",
+       "4     2.0   2.089570      2.089570         33\n",
+       "5     1.0   0.946909      1.000000         33\n",
+       "6     3.0   1.167053      2.000000        182\n",
+       "7     3.0   2.154357      2.154357        182\n",
+       "8    13.0   3.034284      3.034284        182\n",
+       "9     2.0   1.674387      1.674387        124"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+    "\n",
+    "def rmse(y, yhat):\n",
+    "    return float(np.sqrt(mean_squared_error(y, yhat)))\n",
+    "\n",
+    "def report(name, y_true, y_pred):\n",
+    "    print(f\"{name:5s} | MAE={mean_absolute_error(y_true, y_pred):.3f} \"\n",
+    "          f\"| RMSE={rmse(y_true, y_pred):.3f} | R2={r2_score(y_true, y_pred):.3f}\")\n",
+    "\n",
+    "# num_tasks phải tồn tại trong X_train/X_val/X_test\n",
+    "assert \"num_tasks\" in X_train.columns, \"❌ Missing num_tasks in X_train\"\n",
+    "\n",
+    "nt_train = X_train[\"num_tasks\"].values\n",
+    "nt_val   = X_val[\"num_tasks\"].values\n",
+    "nt_test  = X_test[\"num_tasks\"].values\n",
+    "\n",
+    "# ---- tune K on VAL (KHÔNG đụng test khi tune) ----\n",
+    "Ks = [30, 40, 50, 60, 70, 80, 100, 120, 150]\n",
+    "best = None\n",
+    "\n",
+    "for K in Ks:\n",
+    "    min_val = np.ceil(nt_val / K)\n",
+    "    pred_val_rule = np.maximum(pred_val, min_val)\n",
+    "    mae = mean_absolute_error(y_val, pred_val_rule)\n",
+    "    if (best is None) or (mae < best[\"mae_val\"]):\n",
+    "        best = {\"K\": K, \"mae_val\": mae}\n",
+    "\n",
+    "print(\"Best K on VAL:\", best)\n",
+    "\n",
+    "K_best = best[\"K\"]\n",
+    "\n",
+    "def apply_rule(pred, num_tasks, K):\n",
+    "    min_staff = np.ceil(num_tasks / K)\n",
+    "    return np.maximum(pred, min_staff)\n",
+    "\n",
+    "pred_train_rule = apply_rule(pred_train, nt_train, K_best)\n",
+    "pred_val_rule   = apply_rule(pred_val,   nt_val,   K_best)\n",
+    "pred_test_rule  = apply_rule(pred_test,  nt_test,  K_best)\n",
+    "\n",
+    "print(\"\\n=== After Rule: y_pred_final = max(ML_pred, ceil(num_tasks/K)) ===\")\n",
+    "print(\"Rule used: min_staff = ceil(num_tasks / %d)\" % K_best)\n",
+    "\n",
+    "report(\"Train\", y_train, pred_train_rule)\n",
+    "report(\"Val\",   y_val,   pred_val_rule)\n",
+    "report(\"Test\",  y_test,  pred_test_rule)\n",
+    "\n",
+    "# ---- big cases analysis on TEST ----\n",
+    "mask_big = (y_test.values >= 10)\n",
+    "print(\"\\nTEST big cases (y_true >= 10):\", int(mask_big.sum()))\n",
+    "if mask_big.sum() > 0:\n",
+    "    report(\"ML\",   y_test.values[mask_big], pred_test[mask_big])\n",
+    "    report(\"Rule\", y_test.values[mask_big], pred_test_rule[mask_big])\n",
+    "\n",
+    "# save test predictions (after rule)\n",
+    "out_rule = pd.DataFrame({\n",
+    "    \"y_true\": y_test.values,\n",
+    "    \"y_pred_ml\": pred_test,\n",
+    "    \"y_pred_final\": pred_test_rule,\n",
+    "    \"num_tasks\": nt_test\n",
+    "})\n",
+    "out_rule.to_excel(\"test_predictions_ml_plus_rule.xlsx\", index=False)\n",
+    "print(\"\\n✅ Saved: test_predictions_ml_plus_rule.xlsx\")\n",
+    "out_rule.head(10)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "70493591",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best (a,b) on VAL: {'a': 0, 'b': 0.1, 'mae_val': 1.0737151241156944}\n",
+      "\n",
+      "=== After Smooth Rule: y_pred_final = max(ML_pred, ceil(a + b*sqrt(num_tasks))) ===\n",
+      "Rule used: ceil(0 + 0.1*sqrt(num_tasks))\n",
+      "Train | MAE=2.185 | RMSE=4.551 | R2=0.645\n",
+      "Val   | MAE=1.074 | RMSE=1.701 | R2=0.314\n",
+      "Test  | MAE=2.599 | RMSE=4.556 | R2=0.101\n",
+      "\n",
+      "TEST big cases (y_true >= 10): 6\n",
+      "ML    | MAE=8.512 | RMSE=9.050 | R2=-0.959\n",
+      "Smooth | MAE=8.512 | RMSE=9.050 | R2=-0.959\n",
+      "\n",
+      "✅ Saved: test_predictions_ml_plus_smooth_rule.xlsx\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>y_true</th>\n",
+       "      <th>y_pred_ml</th>\n",
+       "      <th>y_pred_final</th>\n",
+       "      <th>num_tasks</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>29.0</td>\n",
+       "      <td>15.005548</td>\n",
+       "      <td>15.005548</td>\n",
+       "      <td>593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.369563</td>\n",
+       "      <td>4.369563</td>\n",
+       "      <td>593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.902404</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.591762</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.089570</td>\n",
+       "      <td>2.089570</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.946909</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.167053</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.154357</td>\n",
+       "      <td>2.154357</td>\n",
+       "      <td>182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>3.034284</td>\n",
+       "      <td>3.034284</td>\n",
+       "      <td>182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.674387</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>124</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   y_true  y_pred_ml  y_pred_final  num_tasks\n",
+       "0    29.0  15.005548     15.005548        593\n",
+       "1     4.0   4.369563      4.369563        593\n",
+       "2     4.0   2.902404      3.000000        593\n",
+       "3     3.0   2.591762      3.000000        593\n",
+       "4     2.0   2.089570      2.089570         33\n",
+       "5     1.0   0.946909      1.000000         33\n",
+       "6     3.0   1.167053      2.000000        182\n",
+       "7     3.0   2.154357      2.154357        182\n",
+       "8    13.0   3.034284      3.034284        182\n",
+       "9     2.0   1.674387      2.000000        124"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+    "\n",
+    "def rmse(y, yhat):\n",
+    "    return float(np.sqrt(mean_squared_error(y, yhat)))\n",
+    "\n",
+    "def report(name, y_true, y_pred):\n",
+    "    print(f\"{name:5s} | MAE={mean_absolute_error(y_true, y_pred):.3f} \"\n",
+    "          f\"| RMSE={rmse(y_true, y_pred):.3f} | R2={r2_score(y_true, y_pred):.3f}\")\n",
+    "\n",
+    "nt_train = X_train[\"num_tasks\"].values\n",
+    "nt_val   = X_val[\"num_tasks\"].values\n",
+    "nt_test  = X_test[\"num_tasks\"].values\n",
+    "\n",
+    "# search grid (nhỏ thôi để bạn dễ đọc)\n",
+    "a_list = [0, 1, 2]\n",
+    "b_list = [0.10, 0.15, 0.20, 0.25, 0.30]\n",
+    "\n",
+    "best = None\n",
+    "for a in a_list:\n",
+    "    for b in b_list:\n",
+    "        min_val = np.ceil(a + b * np.sqrt(nt_val))\n",
+    "        pred_val_rule = np.maximum(pred_val, min_val)\n",
+    "        mae = mean_absolute_error(y_val, pred_val_rule)\n",
+    "        if (best is None) or (mae < best[\"mae_val\"]):\n",
+    "            best = {\"a\": a, \"b\": b, \"mae_val\": mae}\n",
+    "\n",
+    "print(\"Best (a,b) on VAL:\", best)\n",
+    "\n",
+    "a_best, b_best = best[\"a\"], best[\"b\"]\n",
+    "\n",
+    "def apply_smooth_rule(pred, num_tasks, a, b):\n",
+    "    min_staff = np.ceil(a + b * np.sqrt(num_tasks))\n",
+    "    return np.maximum(pred, min_staff)\n",
+    "\n",
+    "pred_train_rule2 = apply_smooth_rule(pred_train, nt_train, a_best, b_best)\n",
+    "pred_val_rule2   = apply_smooth_rule(pred_val,   nt_val,   a_best, b_best)\n",
+    "pred_test_rule2  = apply_smooth_rule(pred_test,  nt_test,  a_best, b_best)\n",
+    "\n",
+    "print(\"\\n=== After Smooth Rule: y_pred_final = max(ML_pred, ceil(a + b*sqrt(num_tasks))) ===\")\n",
+    "print(f\"Rule used: ceil({a_best} + {b_best}*sqrt(num_tasks))\")\n",
+    "\n",
+    "report(\"Train\", y_train, pred_train_rule2)\n",
+    "report(\"Val\",   y_val,   pred_val_rule2)\n",
+    "report(\"Test\",  y_test,  pred_test_rule2)\n",
+    "\n",
+    "mask_big = (y_test.values >= 10)\n",
+    "print(\"\\nTEST big cases (y_true >= 10):\", int(mask_big.sum()))\n",
+    "if mask_big.sum() > 0:\n",
+    "    report(\"ML\",    y_test.values[mask_big], pred_test[mask_big])\n",
+    "    report(\"Smooth\",y_test.values[mask_big], pred_test_rule2[mask_big])\n",
+    "\n",
+    "# save\n",
+    "out_rule2 = pd.DataFrame({\n",
+    "    \"y_true\": y_test.values,\n",
+    "    \"y_pred_ml\": pred_test,\n",
+    "    \"y_pred_final\": pred_test_rule2,\n",
+    "    \"num_tasks\": nt_test\n",
+    "})\n",
+    "out_rule2.to_excel(\"test_predictions_ml_plus_smooth_rule.xlsx\", index=False)\n",
+    "print(\"\\n✅ Saved: test_predictions_ml_plus_smooth_rule.xlsx\")\n",
+    "out_rule2.head(10)\n"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2df3b609",
+   "id": "e71605c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Big-rate (positive class=1):\n",
+      "Train: 0.14840989399293286 | count: 42 / 283\n",
+      "Val:   0.017241379310344827 | count: 1 / 58\n",
+      "Test:  0.11320754716981132 | count: 6 / 53\n",
+      "\n",
+      "================================================================================\n",
+      "MODEL: LogReg_balanced\n",
+      "\n",
+      "[VAL] classification_report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0      0.981     0.930     0.955        57\n",
+      "           1      0.000     0.000     0.000         1\n",
+      "\n",
+      "    accuracy                          0.914        58\n",
+      "   macro avg      0.491     0.465     0.477        58\n",
+      "weighted avg      0.965     0.914     0.938        58\n",
+      "\n",
+      "VAL confusion_matrix:\n",
+      " [[53  4]\n",
+      " [ 1  0]]\n",
+      "\n",
+      "[TEST] classification_report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0      0.881     0.787     0.831        47\n",
+      "           1      0.091     0.167     0.118         6\n",
+      "\n",
+      "    accuracy                          0.717        53\n",
+      "   macro avg      0.486     0.477     0.475        53\n",
+      "weighted avg      0.792     0.717     0.751        53\n",
+      "\n",
+      "TEST confusion_matrix:\n",
+      " [[37 10]\n",
+      " [ 5  1]]\n",
+      "\n",
+      "================================================================================\n",
+      "MODEL: GBC_depth2\n",
+      "\n",
+      "[VAL] classification_report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0      0.983     1.000     0.991        57\n",
+      "           1      0.000     0.000     0.000         1\n",
+      "\n",
+      "    accuracy                          0.983        58\n",
+      "   macro avg      0.491     0.500     0.496        58\n",
+      "weighted avg      0.966     0.983     0.974        58\n",
+      "\n",
+      "VAL confusion_matrix:\n",
+      " [[57  0]\n",
+      " [ 1  0]]\n",
+      "\n",
+      "[TEST] classification_report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0      0.907     0.830     0.867        47\n",
+      "           1      0.200     0.333     0.250         6\n",
+      "\n",
+      "    accuracy                          0.774        53\n",
+      "   macro avg      0.553     0.582     0.558        53\n",
+      "weighted avg      0.827     0.774     0.797        53\n",
+      "\n",
+      "TEST confusion_matrix:\n",
+      " [[39  8]\n",
+      " [ 4  2]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\SLG PC\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "c:\\Users\\SLG PC\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "c:\\Users\\SLG PC\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Liệt kê các biến model đang tồn tại\n",
+    "[name for name in globals().keys() if \"gbr\" in name.lower()]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "ce971deb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['gbr', 'res_gbr', 'gbr_test_pred', 'gbr_reg']"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Liệt kê các biến model đang tồn tại\n",
+    "[name for name in globals().keys() if \"gbr\" in name.lower()]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "9ad22a15",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "=== TEST RESULTS ===\n",
+      "ML only              | MAE=2.669 | RMSE=4.565 | R2=0.097\n",
+      "ML + Business Rules  | MAE=2.663 | RMSE=4.510 | R2=0.119\n",
+      "✅ Saved: test_predictions_ml_plus_business_rules.xlsx\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>y_true</th>\n",
+       "      <th>y_pred_ml</th>\n",
+       "      <th>min_staff_rule</th>\n",
+       "      <th>y_pred_final</th>\n",
+       "      <th>num_tasks</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>29.0</td>\n",
+       "      <td>15.006</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>15.006</td>\n",
+       "      <td>593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.370</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.370</td>\n",
+       "      <td>593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.902</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.000</td>\n",
+       "      <td>593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.592</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.000</td>\n",
+       "      <td>593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.090</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.090</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.947</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.167</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.000</td>\n",
+       "      <td>182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.154</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.154</td>\n",
+       "      <td>182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>3.034</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.034</td>\n",
+       "      <td>182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.674</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.000</td>\n",
+       "      <td>124</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   y_true  y_pred_ml  min_staff_rule  y_pred_final  num_tasks\n",
+       "0    29.0     15.006             4.0        15.006        593\n",
+       "1     4.0      4.370             4.0         4.370        593\n",
+       "2     4.0      2.902             4.0         4.000        593\n",
+       "3     3.0      2.592             6.0         6.000        593\n",
+       "4     2.0      2.090             1.0         2.090         33\n",
+       "5     1.0      0.947             1.0         1.000         33\n",
+       "6     3.0      1.167             3.0         3.000        182\n",
+       "7     3.0      2.154             2.0         2.154        182\n",
+       "8    13.0      3.034             2.0         3.034        182\n",
+       "9     2.0      1.674             3.0         3.000        124"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+    "\n",
+    "def eval_reg(y_true, y_pred, name):\n",
+    "    rmse = float(np.sqrt(mean_squared_error(y_true, y_pred)))\n",
+    "    print(f\"{name:20s} | MAE={mean_absolute_error(y_true, y_pred):.3f} | RMSE={rmse:.3f} | R2={r2_score(y_true, y_pred):.3f}\")\n",
+    "\n",
+    "# -----------------------\n",
+    "# 1) ML prediction (log target -> original)\n",
+    "# -----------------------\n",
+    "y_pred_ml = np.maximum(0, np.expm1(gbr_reg.predict(X_test)))\n",
+    "\n",
+    "# -----------------------\n",
+    "# 2) Business rules: minimum staffing\n",
+    "# (các cột có thể có/không có nên check)\n",
+    "# -----------------------\n",
+    "min_staff = np.ceil(X_test[\"num_tasks\"] / 150)\n",
+    "\n",
+    "if \"num_wc_tasks\" in X_test.columns:\n",
+    "    min_staff = np.maximum(min_staff, np.ceil(X_test[\"num_wc_tasks\"] / 40))\n",
+    "\n",
+    "if \"num_outdoor_tasks\" in X_test.columns:\n",
+    "    min_staff = np.maximum(min_staff, np.ceil(X_test[\"num_outdoor_tasks\"] / 60))\n",
+    "\n",
+    "# Nếu bạn có cột shift (đêm / qua ngày) thì cộng thêm\n",
+    "for col in [\"is_night_shift\", \"is_cross_day\"]:\n",
+    "    if col in X_test.columns:\n",
+    "        min_staff = min_staff + X_test[col].astype(int)\n",
+    "\n",
+    "# -----------------------\n",
+    "# 3) Final prediction\n",
+    "# -----------------------\n",
+    "y_pred_final = np.maximum(y_pred_ml, min_staff)\n",
+    "\n",
+    "# -----------------------\n",
+    "# 4) Evaluate\n",
+    "# -----------------------\n",
+    "print(\"\\n=== TEST RESULTS ===\")\n",
+    "eval_reg(y_test, y_pred_ml,   \"ML only\")\n",
+    "eval_reg(y_test, y_pred_final,\"ML + Business Rules\")\n",
+    "\n",
+    "# -----------------------\n",
+    "# 5) Save file\n",
+    "# -----------------------\n",
+    "out = pd.DataFrame({\n",
+    "    \"y_true\": y_test.values,\n",
+    "    \"y_pred_ml\": np.round(y_pred_ml, 3),\n",
+    "    \"min_staff_rule\": min_staff.astype(float).values,\n",
+    "    \"y_pred_final\": np.round(y_pred_final, 3),\n",
+    "    \"num_tasks\": X_test[\"num_tasks\"].values\n",
+    "})\n",
+    "\n",
+    "out.to_excel(\"test_predictions_ml_plus_business_rules.xlsx\", index=False)\n",
+    "print(\"✅ Saved: test_predictions_ml_plus_business_rules.xlsx\")\n",
+    "\n",
+    "out.head(10)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "79387bd4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Best rule on VAL: {'k_tasks': 150, 'k_wc': 30, 'k_out': 40, 'mae_val': 1.0984917361586377}\n",
+      "\n",
+      "=== TEST EVAL ===\n",
+      "ML only                   | MAE=2.669 | RMSE=4.565 | R2=0.097\n",
+      "ML + tuned business rules | MAE=2.600 | RMSE=4.440 | R2=0.146\n",
+      "✅ Saved: test_predictions_ml_plus_tuned_rules.xlsx\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>y_true</th>\n",
+       "      <th>y_pred_ml</th>\n",
+       "      <th>min_staff_rule</th>\n",
+       "      <th>y_pred_final</th>\n",
+       "      <th>num_tasks</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>29.0</td>\n",
+       "      <td>15.006</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>15.006</td>\n",
+       "      <td>593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.370</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.370</td>\n",
+       "      <td>593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.902</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.000</td>\n",
+       "      <td>593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.592</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.000</td>\n",
+       "      <td>593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.090</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.090</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.947</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.167</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.000</td>\n",
+       "      <td>182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.154</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.154</td>\n",
+       "      <td>182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>3.034</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.034</td>\n",
+       "      <td>182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.674</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.000</td>\n",
+       "      <td>124</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   y_true  y_pred_ml  min_staff_rule  y_pred_final  num_tasks\n",
+       "0    29.0     15.006             4.0        15.006        593\n",
+       "1     4.0      4.370             4.0         4.370        593\n",
+       "2     4.0      2.902             4.0         4.000        593\n",
+       "3     3.0      2.592             6.0         6.000        593\n",
+       "4     2.0      2.090             1.0         2.090         33\n",
+       "5     1.0      0.947             1.0         1.000         33\n",
+       "6     3.0      1.167             3.0         3.000        182\n",
+       "7     3.0      2.154             2.0         2.154        182\n",
+       "8    13.0      3.034             2.0         3.034        182\n",
+       "9     2.0      1.674             3.0         3.000        124"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+    "\n",
+    "def rmse(y, yhat):\n",
+    "    return float(np.sqrt(mean_squared_error(y, yhat)))\n",
+    "\n",
+    "def eval_reg(y_true, y_pred, name):\n",
+    "    print(f\"{name:25s} | MAE={mean_absolute_error(y_true, y_pred):.3f} | RMSE={rmse(y_true, y_pred):.3f} | R2={r2_score(y_true, y_pred):.3f}\")\n",
+    "\n",
+    "# ML preds (đã train gbr_reg)\n",
+    "pred_train = np.maximum(0, np.expm1(gbr_reg.predict(X_train)))\n",
+    "pred_val   = np.maximum(0, np.expm1(gbr_reg.predict(X_val)))\n",
+    "pred_test  = np.maximum(0, np.expm1(gbr_reg.predict(X_test)))\n",
+    "\n",
+    "def compute_min_staff(X, k_tasks, k_wc=None, k_out=None):\n",
+    "    ms = np.ceil(X[\"num_tasks\"] / k_tasks)\n",
+    "\n",
+    "    if (k_wc is not None) and (\"num_wc_tasks\" in X.columns):\n",
+    "        ms = np.maximum(ms, np.ceil(X[\"num_wc_tasks\"] / k_wc))\n",
+    "\n",
+    "    if (k_out is not None) and (\"num_outdoor_tasks\" in X.columns):\n",
+    "        ms = np.maximum(ms, np.ceil(X[\"num_outdoor_tasks\"] / k_out))\n",
+    "\n",
+    "    for col in [\"is_night_shift\", \"is_cross_day\"]:\n",
+    "        if col in X.columns:\n",
+    "            ms = ms + X[col].astype(int)\n",
+    "    return ms\n",
+    "\n",
+    "# Grid search trên VAL\n",
+    "k_tasks_list = [100, 120, 150, 180, 200]\n",
+    "k_wc_list    = [25, 30, 40, 50, None]      # None = bỏ rule wc\n",
+    "k_out_list   = [40, 60, 80, None]          # None = bỏ rule outdoor\n",
+    "\n",
+    "best = None\n",
+    "\n",
+    "for kt in k_tasks_list:\n",
+    "    for kw in k_wc_list:\n",
+    "        for ko in k_out_list:\n",
+    "            ms_val = compute_min_staff(X_val, kt, kw, ko)\n",
+    "            pred_val_final = np.maximum(pred_val, ms_val)\n",
+    "            mae_val = mean_absolute_error(y_val, pred_val_final)\n",
+    "\n",
+    "            if (best is None) or (mae_val < best[\"mae_val\"]):\n",
+    "                best = {\"k_tasks\": kt, \"k_wc\": kw, \"k_out\": ko, \"mae_val\": mae_val}\n",
+    "\n",
+    "print(\"✅ Best rule on VAL:\", best)\n",
+    "\n",
+    "# Apply best rule to TEST\n",
+    "ms_test = compute_min_staff(X_test, best[\"k_tasks\"], best[\"k_wc\"], best[\"k_out\"])\n",
+    "pred_test_final = np.maximum(pred_test, ms_test)\n",
+    "\n",
+    "print(\"\\n=== TEST EVAL ===\")\n",
+    "eval_reg(y_test, pred_test, \"ML only\")\n",
+    "eval_reg(y_test, pred_test_final, \"ML + tuned business rules\")\n",
+    "\n",
+    "# Save predictions\n",
+    "out = pd.DataFrame({\n",
+    "    \"y_true\": y_test.values,\n",
+    "    \"y_pred_ml\": np.round(pred_test, 3),\n",
+    "    \"min_staff_rule\": ms_test.astype(float).values,\n",
+    "    \"y_pred_final\": np.round(pred_test_final, 3),\n",
+    "    \"num_tasks\": X_test[\"num_tasks\"].values\n",
+    "})\n",
+    "out.to_excel(\"test_predictions_ml_plus_tuned_rules.xlsx\", index=False)\n",
+    "print(\"✅ Saved: test_predictions_ml_plus_tuned_rules.xlsx\")\n",
+    "out.head(10)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "6e9841d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Class distribution:\n",
+      "Train:\n",
+      "so_luong\n",
+      "0    207\n",
+      "1     39\n",
+      "2     37\n",
+      "Name: count, dtype: int64\n",
+      "Val:\n",
+      "so_luong\n",
+      "0    53\n",
+      "1     4\n",
+      "2     1\n",
+      "Name: count, dtype: int64\n",
+      "Test:\n",
+      "so_luong\n",
+      "0    43\n",
+      "1     5\n",
+      "2     5\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ===== CELL 9: Create staff size class =====\n",
+    "\n",
+    "def staff_class(y):\n",
+    "    if y <= 5:\n",
+    "        return 0\n",
+    "    elif y <= 10:\n",
+    "        return 1\n",
+    "    else:\n",
+    "        return 2\n",
+    "\n",
+    "y_train_cls = y_train.apply(staff_class)\n",
+    "y_val_cls   = y_val.apply(staff_class)\n",
+    "y_test_cls  = y_test.apply(staff_class)\n",
+    "\n",
+    "print(\"Class distribution:\")\n",
+    "for name, y in [(\"Train\", y_train_cls), (\"Val\", y_val_cls), (\"Test\", y_test_cls)]:\n",
+    "    print(f\"{name}:\")\n",
+    "    print(y.value_counts().sort_index())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "daf4acc7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\SLG PC\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\SLG PC\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "=== VAL ===\n",
+      "\n",
+      "================================================================================\n",
+      "MODEL: LR_balanced (VAL)\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0      0.956     0.811     0.878        53\n",
+      "           1      0.111     0.250     0.154         4\n",
+      "           2      0.000     0.000     0.000         1\n",
+      "\n",
+      "    accuracy                          0.759        58\n",
+      "   macro avg      0.356     0.354     0.344        58\n",
+      "weighted avg      0.881     0.759     0.813        58\n",
+      "\n",
+      "Confusion matrix:\n",
+      " [[43  7  3]\n",
+      " [ 2  1  1]\n",
+      " [ 0  1  0]]\n",
+      "\n",
+      "================================================================================\n",
+      "MODEL: HGB (VAL)\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0      0.946     1.000     0.972        53\n",
+      "           1      0.500     0.250     0.333         4\n",
+      "           2      0.000     0.000     0.000         1\n",
+      "\n",
+      "    accuracy                          0.931        58\n",
+      "   macro avg      0.482     0.417     0.435        58\n",
+      "weighted avg      0.899     0.931     0.912        58\n",
+      "\n",
+      "Confusion matrix:\n",
+      " [[53  0  0]\n",
+      " [ 3  1  0]\n",
+      " [ 0  1  0]]\n",
+      "\n",
+      "=== TEST ===\n",
+      "\n",
+      "================================================================================\n",
+      "MODEL: LR_balanced (TEST)\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0      0.914     0.744     0.821        43\n",
+      "           1      0.231     0.600     0.333         5\n",
+      "           2      0.000     0.000     0.000         5\n",
+      "\n",
+      "    accuracy                          0.660        53\n",
+      "   macro avg      0.382     0.448     0.385        53\n",
+      "weighted avg      0.764     0.660     0.697        53\n",
+      "\n",
+      "Confusion matrix:\n",
+      " [[32  7  4]\n",
+      " [ 1  3  1]\n",
+      " [ 2  3  0]]\n",
+      "\n",
+      "================================================================================\n",
+      "MODEL: HGB (TEST)\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0      0.925     0.860     0.892        43\n",
+      "           1      0.200     0.200     0.200         5\n",
+      "           2      0.125     0.200     0.154         5\n",
+      "\n",
+      "    accuracy                          0.736        53\n",
+      "   macro avg      0.417     0.420     0.415        53\n",
+      "weighted avg      0.781     0.736     0.757        53\n",
+      "\n",
+      "Confusion matrix:\n",
+      " [[37  2  4]\n",
+      " [ 1  1  3]\n",
+      " [ 2  2  1]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\SLG PC\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "c:\\Users\\SLG PC\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "c:\\Users\\SLG PC\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.ensemble import HistGradientBoostingClassifier\n",
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "\n",
+    "# ---- 1) Models ----\n",
+    "clf_lr = LogisticRegression(\n",
+    "    max_iter=5000,\n",
+    "    class_weight=\"balanced\",\n",
+    "    solver=\"lbfgs\",\n",
+    "    multi_class=\"auto\",\n",
+    "    random_state=42\n",
+    ")\n",
+    "\n",
+    "clf_hgb = HistGradientBoostingClassifier(\n",
+    "    max_depth=3,\n",
+    "    learning_rate=0.05,\n",
+    "    max_iter=500,\n",
+    "    random_state=42\n",
+    ")\n",
+    "\n",
+    "# ---- 2) Fit ----\n",
+    "clf_lr.fit(X_train, y_train_cls)\n",
+    "clf_hgb.fit(X_train, y_train_cls)\n",
+    "\n",
+    "# ---- 3) Predict (proba) ----\n",
+    "proba_val_lr  = clf_lr.predict_proba(X_val)\n",
+    "proba_test_lr = clf_lr.predict_proba(X_test)\n",
+    "\n",
+    "proba_val_hgb  = clf_hgb.predict_proba(X_val)\n",
+    "proba_test_hgb = clf_hgb.predict_proba(X_test)\n",
+    "\n",
+    "pred_val_lr  = np.argmax(proba_val_lr, axis=1)\n",
+    "pred_test_lr = np.argmax(proba_test_lr, axis=1)\n",
+    "\n",
+    "pred_val_hgb  = np.argmax(proba_val_hgb, axis=1)\n",
+    "pred_test_hgb = np.argmax(proba_test_hgb, axis=1)\n",
+    "\n",
+    "def show_clf(name, y_true, y_pred):\n",
+    "    print(\"\\n\" + \"=\"*80)\n",
+    "    print(\"MODEL:\", name)\n",
+    "    print(classification_report(y_true, y_pred, digits=3))\n",
+    "    print(\"Confusion matrix:\\n\", confusion_matrix(y_true, y_pred))\n",
+    "\n",
+    "print(\"\\n=== VAL ===\")\n",
+    "show_clf(\"LR_balanced (VAL)\",  y_val_cls, pred_val_lr)\n",
+    "show_clf(\"HGB (VAL)\",          y_val_cls, pred_val_hgb)\n",
+    "\n",
+    "print(\"\\n=== TEST ===\")\n",
+    "show_clf(\"LR_balanced (TEST)\", y_test_cls, pred_test_lr)\n",
+    "show_clf(\"HGB (TEST)\",         y_test_cls, pred_test_hgb)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "38c8d00e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Best (min_hc, min_2424) on VAL: {'min_hc': 0, 'min_2424': 0, 'mae_val': 1.0984917361586377}\n",
+      "\n",
+      "=== TEST EVAL ===\n",
+      "ML only (GBR log)            | MAE=2.669 | RMSE=4.565 | R2=0.097\n",
+      "ML + tuned base rules        | MAE=2.600 | RMSE=4.440 | R2=0.146\n",
+      "ML + base + loai_ca rules    | MAE=2.600 | RMSE=4.440 | R2=0.146\n",
+      "✅ Saved: test_predictions_ml_plus_rules_plus_ca.xlsx\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>y_true</th>\n",
+       "      <th>y_pred_ml</th>\n",
+       "      <th>min_staff_base</th>\n",
+       "      <th>min_staff_ca</th>\n",
+       "      <th>min_staff_final</th>\n",
+       "      <th>y_pred_final</th>\n",
+       "      <th>num_tasks</th>\n",
+       "      <th>loai_ca</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>29.0</td>\n",
+       "      <td>15.006</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>15.006</td>\n",
+       "      <td>593</td>\n",
+       "      <td>Hành chính</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.370</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.370</td>\n",
+       "      <td>593</td>\n",
+       "      <td>Ca sáng</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.902</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.000</td>\n",
+       "      <td>593</td>\n",
+       "      <td>Ca chiều</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.592</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.000</td>\n",
+       "      <td>593</td>\n",
+       "      <td>Ca đêm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.090</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.090</td>\n",
+       "      <td>33</td>\n",
+       "      <td>Hành chính</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.947</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>33</td>\n",
+       "      <td>Part time</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.167</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.000</td>\n",
+       "      <td>182</td>\n",
+       "      <td>Ca sáng</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.154</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.154</td>\n",
+       "      <td>182</td>\n",
+       "      <td>Ca chiều</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>3.034</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.034</td>\n",
+       "      <td>182</td>\n",
+       "      <td>Hành chính</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.674</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.000</td>\n",
+       "      <td>124</td>\n",
+       "      <td>Ca sáng</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   y_true  y_pred_ml  min_staff_base  min_staff_ca  min_staff_final  \\\n",
+       "0    29.0     15.006             4.0           0.0              4.0   \n",
+       "1     4.0      4.370             4.0           0.0              4.0   \n",
+       "2     4.0      2.902             4.0           0.0              4.0   \n",
+       "3     3.0      2.592             6.0           0.0              6.0   \n",
+       "4     2.0      2.090             1.0           0.0              1.0   \n",
+       "5     1.0      0.947             1.0           0.0              1.0   \n",
+       "6     3.0      1.167             3.0           0.0              3.0   \n",
+       "7     3.0      2.154             2.0           0.0              2.0   \n",
+       "8    13.0      3.034             2.0           0.0              2.0   \n",
+       "9     2.0      1.674             3.0           0.0              3.0   \n",
+       "\n",
+       "   y_pred_final  num_tasks     loai_ca  \n",
+       "0        15.006        593  Hành chính  \n",
+       "1         4.370        593     Ca sáng  \n",
+       "2         4.000        593    Ca chiều  \n",
+       "3         6.000        593      Ca đêm  \n",
+       "4         2.090         33  Hành chính  \n",
+       "5         1.000         33   Part time  \n",
+       "6         3.000        182     Ca sáng  \n",
+       "7         2.154        182    Ca chiều  \n",
+       "8         3.034        182  Hành chính  \n",
+       "9         3.000        124     Ca sáng  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+    "\n",
+    "def rmse(y, yhat):\n",
+    "    return float(np.sqrt(mean_squared_error(y, yhat)))\n",
+    "\n",
+    "def eval_reg(y_true, y_pred, name):\n",
+    "    print(f\"{name:28s} | MAE={mean_absolute_error(y_true, y_pred):.3f} | RMSE={rmse(y_true, y_pred):.3f} | R2={r2_score(y_true, y_pred):.3f}\")\n",
+    "\n",
+    "# ========= 1) ML predictions (GBR log-target) =========\n",
+    "pred_train = np.maximum(0, np.expm1(gbr_reg.predict(X_train)))\n",
+    "pred_val   = np.maximum(0, np.expm1(gbr_reg.predict(X_val)))\n",
+    "pred_test  = np.maximum(0, np.expm1(gbr_reg.predict(X_test)))\n",
+    "\n",
+    "# ========= 2) Base min-staff rule (tuned earlier) =========\n",
+    "# dùng lại best bạn đã tìm: k_tasks=150, k_wc=30, k_out=40\n",
+    "def compute_min_staff_base(X, k_tasks=150, k_wc=30, k_out=40):\n",
+    "    ms = np.ceil(X[\"num_tasks\"] / k_tasks)\n",
+    "\n",
+    "    if \"num_wc_tasks\" in X.columns:\n",
+    "        ms = np.maximum(ms, np.ceil(X[\"num_wc_tasks\"] / k_wc))\n",
+    "\n",
+    "    if \"num_outdoor_tasks\" in X.columns:\n",
+    "        ms = np.maximum(ms, np.ceil(X[\"num_outdoor_tasks\"] / k_out))\n",
+    "\n",
+    "    # bonus theo ca đêm / cross-day nếu có\n",
+    "    for col in [\"is_night_shift\", \"is_cross_day\"]:\n",
+    "        if col in X.columns:\n",
+    "            ms = ms + X[col].astype(int)\n",
+    "\n",
+    "    return ms\n",
+    "\n",
+    "# ========= 3) Tune MIN staff theo loai_ca trên VAL =========\n",
+    "# Ta tune 2 tham số: min_HC (Hành chính), min_2424 (24/24)\n",
+    "# Nếu loai_ca đang one-hot, ta sẽ suy ra label từ cột gốc nếu có\n",
+    "def get_loai_ca_series(X):\n",
+    "    # ưu tiên nếu còn cột gốc 'loai_ca' (string)\n",
+    "    if \"loai_ca\" in X.columns and X[\"loai_ca\"].dtype == \"object\":\n",
+    "        return X[\"loai_ca\"].astype(str)\n",
+    "    # nếu đã one-hot: tìm các cột bắt đầu bằng \"loai_ca_\"\n",
+    "    onehot_cols = [c for c in X.columns if c.startswith(\"loai_ca_\")]\n",
+    "    if onehot_cols:\n",
+    "        # lấy tên category có value True/1\n",
+    "        def decode_row(row):\n",
+    "            for c in onehot_cols:\n",
+    "                if row[c] == 1 or row[c] is True:\n",
+    "                    return c.replace(\"loai_ca_\", \"\")\n",
+    "            return \"UNKNOWN\"\n",
+    "        return X[onehot_cols].apply(decode_row, axis=1)\n",
+    "    return pd.Series([\"UNKNOWN\"] * len(X), index=X.index)\n",
+    "\n",
+    "loai_ca_val = get_loai_ca_series(X_val)\n",
+    "loai_ca_test = get_loai_ca_series(X_test)\n",
+    "loai_ca_train = get_loai_ca_series(X_train)\n",
+    "\n",
+    "def apply_ca_rule(loai_ca_series, min_hc, min_2424):\n",
+    "    # map tên ca -> min staff\n",
+    "    # bạn có thể thêm biến thể viết khác nếu dữ liệu có\n",
+    "    lc = loai_ca_series.str.lower()\n",
+    "    min_by_ca = np.zeros(len(lc), dtype=float)\n",
+    "\n",
+    "    # Hành chính\n",
+    "    mask_hc = lc.str.contains(\"hành chính\") | lc.str.contains(\"hanh chinh\")\n",
+    "    min_by_ca[mask_hc.values] = min_hc\n",
+    "\n",
+    "    # 24/24 hoặc 24-24\n",
+    "    mask_2424 = lc.str.contains(\"24/24\") | lc.str.contains(\"24-24\") | lc.str.contains(\"24 24\")\n",
+    "    min_by_ca[mask_2424.values] = np.maximum(min_by_ca[mask_2424.values], min_2424)\n",
+    "\n",
+    "    return min_by_ca\n",
+    "\n",
+    "best = None\n",
+    "min_hc_list = [0, 4, 6, 8, 10]\n",
+    "min_2424_list = [0, 6, 8, 10, 12]\n",
+    "\n",
+    "base_val = compute_min_staff_base(X_val, 150, 30, 40)\n",
+    "\n",
+    "for mhc in min_hc_list:\n",
+    "    for m24 in min_2424_list:\n",
+    "        min_ca = apply_ca_rule(loai_ca_val, mhc, m24)\n",
+    "        min_staff = np.maximum(base_val, min_ca)\n",
+    "        pred_final = np.maximum(pred_val, min_staff)\n",
+    "\n",
+    "        mae_val = mean_absolute_error(y_val, pred_final)\n",
+    "        if (best is None) or (mae_val < best[\"mae_val\"]):\n",
+    "            best = {\"min_hc\": mhc, \"min_2424\": m24, \"mae_val\": mae_val}\n",
+    "\n",
+    "print(\"✅ Best (min_hc, min_2424) on VAL:\", best)\n",
+    "\n",
+    "# ========= 4) Apply best rule to TEST =========\n",
+    "base_test = compute_min_staff_base(X_test, 150, 30, 40)\n",
+    "min_ca_test = apply_ca_rule(loai_ca_test, best[\"min_hc\"], best[\"min_2424\"])\n",
+    "min_staff_test = np.maximum(base_test, min_ca_test)\n",
+    "\n",
+    "pred_test_final = np.maximum(pred_test, min_staff_test)\n",
+    "\n",
+    "print(\"\\n=== TEST EVAL ===\")\n",
+    "eval_reg(y_test, pred_test, \"ML only (GBR log)\")\n",
+    "eval_reg(y_test, np.maximum(pred_test, base_test), \"ML + tuned base rules\")\n",
+    "eval_reg(y_test, pred_test_final, \"ML + base + loai_ca rules\")\n",
+    "\n",
+    "# ========= 5) Save excel =========\n",
+    "out = pd.DataFrame({\n",
+    "    \"y_true\": y_test.values,\n",
+    "    \"y_pred_ml\": np.round(pred_test, 3),\n",
+    "    \"min_staff_base\": base_test.astype(float),\n",
+    "    \"min_staff_ca\": min_ca_test.astype(float),\n",
+    "    \"min_staff_final\": min_staff_test.astype(float),\n",
+    "    \"y_pred_final\": np.round(pred_test_final, 3),\n",
+    "    \"num_tasks\": X_test[\"num_tasks\"].values if \"num_tasks\" in X_test.columns else np.nan,\n",
+    "    \"loai_ca\": loai_ca_test.values\n",
+    "})\n",
+    "out.to_excel(\"test_predictions_ml_plus_rules_plus_ca.xlsx\", index=False)\n",
+    "print(\"✅ Saved: test_predictions_ml_plus_rules_plus_ca.xlsx\")\n",
+    "\n",
+    "out.head(10)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "6dc15922",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded: final_2.xlsx | sheet: final\n",
+      "Shape (raw): (401, 42)\n",
+      "Shape (dedup): (394, 42)\n",
+      "\n",
+      "=== Target summary (so_luong) ===\n",
+      "count    394.000000\n",
+      "mean       4.710660\n",
+      "std        6.848602\n",
+      "min        0.000000\n",
+      "25%        1.000000\n",
+      "50%        2.000000\n",
+      "75%        5.000000\n",
+      "max       64.000000\n",
+      "Name: so_luong, dtype: float64\n",
+      "\n",
+      "=== staff_band distribution ===\n",
+      "staff_band\n",
+      "0    216\n",
+      "1     87\n",
+      "2     48\n",
+      "3     27\n",
+      "4     16\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Sample rows:\n",
+      "  ma_dia_diem     loai_ca  tong_gio_lam  num_tasks  so_luong  staff_band\n",
+      "0       115-2   Part time           4.0          7         1           0\n",
+      "1       101-1  Hành chính           7.5        441        24           4\n",
+      "2       101-1     Ca sáng           8.0        441         3           1\n",
+      "3       101-1    Ca chiều           8.0        441         5           1\n",
+      "4       101-1      Ca đêm           8.0        441         1           0\n",
+      "5       101-1      Ca gãy           7.5        441         1           0\n",
+      "6       101-1  Hành chính           9.5        441        22           4\n",
+      "7       101-2  Hành chính           9.5        135         8           2\n",
+      "8       101-2      Ca gãy           7.5        135         1           0\n",
+      "9       101-2      Ca đêm           7.5        135         1           0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "DATA_PATH = \"final_2.xlsx\"\n",
+    "SHEET_NAME = \"final\"\n",
+    "\n",
+    "df = pd.read_excel(DATA_PATH, sheet_name=SHEET_NAME)\n",
+    "print(\"Loaded:\", DATA_PATH, \"| sheet:\", SHEET_NAME)\n",
+    "print(\"Shape (raw):\", df.shape)\n",
+    "\n",
+    "# drop duplicates\n",
+    "df = df.drop_duplicates()\n",
+    "print(\"Shape (dedup):\", df.shape)\n",
+    "\n",
+    "# target\n",
+    "assert \"so_luong\" in df.columns, \"Missing target so_luong\"\n",
+    "\n",
+    "# ---- Define ordinal bins (bậc nhân sự) ----\n",
+    "# 0: 0-2  (rất nhỏ)\n",
+    "# 1: 3-5  (nhỏ)\n",
+    "# 2: 6-10 (trung bình)\n",
+    "# 3: 11-20 (lớn)\n",
+    "# 4: >20 (rất lớn)\n",
+    "bins = [-0.1, 2, 5, 10, 20, 10**9]\n",
+    "labels = [0, 1, 2, 3, 4]\n",
+    "\n",
+    "df[\"staff_band\"] = pd.cut(df[\"so_luong\"], bins=bins, labels=labels).astype(int)\n",
+    "\n",
+    "print(\"\\n=== Target summary (so_luong) ===\")\n",
+    "print(df[\"so_luong\"].describe())\n",
+    "\n",
+    "print(\"\\n=== staff_band distribution ===\")\n",
+    "print(df[\"staff_band\"].value_counts().sort_index())\n",
+    "\n",
+    "print(\"\\nSample rows:\")\n",
+    "print(df[[\"ma_dia_diem\",\"loai_ca\",\"tong_gio_lam\",\"num_tasks\",\"so_luong\",\"staff_band\"]].head(10))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "666be810",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== SPLIT SUMMARY (by ma_dia_diem) ===\n",
+      "Buildings: 192\n",
+      "Train buildings: 138 | rows: 282\n",
+      "Val   buildings: 29 | rows: 56\n",
+      "Test  buildings: 25 | rows: 56\n",
+      "\n",
+      "Leakage check (should be 0):\n",
+      "Train ∩ Val : 0\n",
+      "Train ∩ Test: 0\n",
+      "Val   ∩ Test: 0\n",
+      "\n",
+      "=== staff_band distribution ===\n",
+      "\n",
+      "Train:\n",
+      "staff_band\n",
+      "0    155\n",
+      "1     55\n",
+      "2     38\n",
+      "3     22\n",
+      "4     12\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Val:\n",
+      "staff_band\n",
+      "0    28\n",
+      "1    20\n",
+      "2     5\n",
+      "3     3\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Test:\n",
+      "staff_band\n",
+      "0    33\n",
+      "1    12\n",
+      "2     5\n",
+      "3     2\n",
+      "4     4\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "SEED = 42\n",
+    "TRAIN_RATIO = 0.72\n",
+    "VAL_RATIO   = 0.15\n",
+    "TEST_RATIO  = 0.13\n",
+    "\n",
+    "assert abs(TRAIN_RATIO + VAL_RATIO + TEST_RATIO - 1.0) < 1e-9\n",
+    "\n",
+    "# 1) lấy danh sách tòa nhà (group)\n",
+    "buildings = df[\"ma_dia_diem\"].astype(str).unique()\n",
+    "rng = np.random.RandomState(SEED)\n",
+    "rng.shuffle(buildings)\n",
+    "\n",
+    "n = len(buildings)\n",
+    "n_train = int(round(n * TRAIN_RATIO))\n",
+    "n_val   = int(round(n * VAL_RATIO))\n",
+    "# phần còn lại là test\n",
+    "train_b = set(buildings[:n_train])\n",
+    "val_b   = set(buildings[n_train:n_train+n_val])\n",
+    "test_b  = set(buildings[n_train+n_val:])\n",
+    "\n",
+    "# 2) tạo mask theo group\n",
+    "train_mask = df[\"ma_dia_diem\"].astype(str).isin(train_b)\n",
+    "val_mask   = df[\"ma_dia_diem\"].astype(str).isin(val_b)\n",
+    "test_mask  = df[\"ma_dia_diem\"].astype(str).isin(test_b)\n",
+    "\n",
+    "df_train = df[train_mask].copy()\n",
+    "df_val   = df[val_mask].copy()\n",
+    "df_test  = df[test_mask].copy()\n",
+    "\n",
+    "print(\"=== SPLIT SUMMARY (by ma_dia_diem) ===\")\n",
+    "print(\"Buildings:\", n)\n",
+    "print(\"Train buildings:\", len(train_b), \"| rows:\", df_train.shape[0])\n",
+    "print(\"Val   buildings:\", len(val_b),   \"| rows:\", df_val.shape[0])\n",
+    "print(\"Test  buildings:\", len(test_b),  \"| rows:\", df_test.shape[0])\n",
+    "\n",
+    "# 3) leakage check (phải = 0)\n",
+    "train_set = set(df_train[\"ma_dia_diem\"].astype(str).unique())\n",
+    "val_set   = set(df_val[\"ma_dia_diem\"].astype(str).unique())\n",
+    "test_set  = set(df_test[\"ma_dia_diem\"].astype(str).unique())\n",
+    "\n",
+    "print(\"\\nLeakage check (should be 0):\")\n",
+    "print(\"Train ∩ Val :\", len(train_set & val_set))\n",
+    "print(\"Train ∩ Test:\", len(train_set & test_set))\n",
+    "print(\"Val   ∩ Test:\", len(val_set & test_set))\n",
+    "\n",
+    "# 4) distribution staff_band theo từng tập\n",
+    "print(\"\\n=== staff_band distribution ===\")\n",
+    "for name, d in [(\"Train\", df_train), (\"Val\", df_val), (\"Test\", df_test)]:\n",
+    "    vc = d[\"staff_band\"].value_counts().sort_index()\n",
+    "    print(f\"\\n{name}:\")\n",
+    "    print(vc)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "d898cfe6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Categorical cols: ['loai_ca', 'bat_dau', 'ket_thuc']\n",
+      "\n",
+      "Shapes:\n",
+      "Train: (282, 115) (282,)\n",
+      "Val  : (56, 115) (56,)\n",
+      "Test : (56, 115) (56,)\n",
+      "\n",
+      "Sample feature columns (first 25):\n",
+      "['tong_gio_lam', 'so_ca_cua_toa', 'num_tasks', 'num_cleaning_tasks', 'num_trash_collection_tasks', 'num_monitoring_tasks', 'num_deep_cleaning_tasks', 'num_support_tasks', 'num_other_tasks', 'num_wc_tasks', 'num_hallway_tasks', 'num_lobby_tasks', 'num_outdoor_tasks', 'num_elevator_tasks', 'cleaning_ratio', 'trash_collection_ratio', 'monitoring_ratio', 'area_diversity', 'so_tang', 'so_cua_thang_may', 'dien_tich_ngoai_canh', 'dien_tich_sanh', 'dien_tich_hanh_lang', 'dien_tich_wc', 'dien_tich_phong']\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# ========= 1) Helper: parse time to hour float =========\n",
+    "def time_to_hour(x):\n",
+    "    if pd.isna(x):\n",
+    "        return np.nan\n",
+    "    # nếu là kiểu datetime/time của pandas\n",
+    "    if hasattr(x, \"hour\"):\n",
+    "        return float(x.hour) + float(getattr(x, \"minute\", 0))/60.0\n",
+    "    s = str(x).strip()\n",
+    "    # format \"HH:MM:SS\" hoặc \"HH:MM\"\n",
+    "    if \":\" in s:\n",
+    "        parts = s.split(\":\")\n",
+    "        try:\n",
+    "            h = float(parts[0])\n",
+    "            m = float(parts[1]) if len(parts) > 1 else 0.0\n",
+    "            return h + m/60.0\n",
+    "        except:\n",
+    "            return np.nan\n",
+    "    # fallback\n",
+    "    try:\n",
+    "        return float(s)\n",
+    "    except:\n",
+    "        return np.nan\n",
+    "\n",
+    "def add_time_features(d):\n",
+    "    d = d.copy()\n",
+    "    d[\"hour_start\"] = d[\"bat_dau\"].apply(time_to_hour)\n",
+    "    d[\"hour_end\"]   = d[\"ket_thuc\"].apply(time_to_hour)\n",
+    "\n",
+    "    # shift length (handle cross-day)\n",
+    "    # nếu end < start -> qua ngày: +24\n",
+    "    end_adj = d[\"hour_end\"].copy()\n",
+    "    mask_cross = (d[\"hour_end\"].notna()) & (d[\"hour_start\"].notna()) & (d[\"hour_end\"] < d[\"hour_start\"])\n",
+    "    end_adj[mask_cross] = end_adj[mask_cross] + 24.0\n",
+    "\n",
+    "    d[\"shift_length\"] = (end_adj - d[\"hour_start\"]).clip(lower=0)\n",
+    "    d[\"is_cross_day\"] = mask_cross.astype(int)\n",
+    "\n",
+    "    # buckets theo giờ bắt đầu\n",
+    "    hs = d[\"hour_start\"].fillna(-1)\n",
+    "    d[\"is_morning_shift\"]   = ((hs >= 6)  & (hs < 12)).astype(int)\n",
+    "    d[\"is_afternoon_shift\"] = ((hs >= 12) & (hs < 18)).astype(int)\n",
+    "    d[\"is_evening_shift\"]   = ((hs >= 18) & (hs < 24)).astype(int)\n",
+    "    d[\"is_night_shift\"]     = ((hs >= 0)  & (hs < 6)).astype(int)\n",
+    "\n",
+    "    return d\n",
+    "\n",
+    "# ========= 2) Apply time features =========\n",
+    "df_train_fe = add_time_features(df_train)\n",
+    "df_val_fe   = add_time_features(df_val)\n",
+    "df_test_fe  = add_time_features(df_test)\n",
+    "\n",
+    "# ========= 3) Drop columns (the ones you requested) + leakage columns =========\n",
+    "DROP_COLS = [\"ma_dia_diem\", \"all_task_normal\", \"all_task_dinhky\", \"is_tasks_text_missing\"]\n",
+    "LEAK_COLS = [\"so_luong\"]   # label thật -> tuyệt đối không dùng feature\n",
+    "\n",
+    "# giữ lại staff_band làm y\n",
+    "y_train = df_train_fe[\"staff_band\"].astype(int)\n",
+    "y_val   = df_val_fe[\"staff_band\"].astype(int)\n",
+    "y_test  = df_test_fe[\"staff_band\"].astype(int)\n",
+    "\n",
+    "X_train = df_train_fe.drop(columns=[c for c in (DROP_COLS + LEAK_COLS + [\"staff_band\"]) if c in df_train_fe.columns])\n",
+    "X_val   = df_val_fe.drop(columns=[c for c in (DROP_COLS + LEAK_COLS + [\"staff_band\"]) if c in df_val_fe.columns])\n",
+    "X_test  = df_test_fe.drop(columns=[c for c in (DROP_COLS + LEAK_COLS + [\"staff_band\"]) if c in df_test_fe.columns])\n",
+    "\n",
+    "# ========= 4) One-hot only categorical columns =========\n",
+    "cat_cols = [c for c in X_train.columns if X_train[c].dtype == \"object\"]\n",
+    "print(\"Categorical cols:\", cat_cols)\n",
+    "\n",
+    "X_train = pd.get_dummies(X_train, columns=cat_cols, dummy_na=True)\n",
+    "X_val   = pd.get_dummies(X_val,   columns=cat_cols, dummy_na=True)\n",
+    "X_test  = pd.get_dummies(X_test,  columns=cat_cols, dummy_na=True)\n",
+    "\n",
+    "# align columns across splits\n",
+    "X_train, X_val = X_train.align(X_val, join=\"left\", axis=1, fill_value=0)\n",
+    "X_train, X_test = X_train.align(X_test, join=\"left\", axis=1, fill_value=0)\n",
+    "\n",
+    "# fill NaN numeric\n",
+    "X_train = X_train.fillna(0)\n",
+    "X_val   = X_val.fillna(0)\n",
+    "X_test  = X_test.fillna(0)\n",
+    "\n",
+    "print(\"\\nShapes:\")\n",
+    "print(\"Train:\", X_train.shape, y_train.shape)\n",
+    "print(\"Val  :\", X_val.shape, y_val.shape)\n",
+    "print(\"Test :\", X_test.shape, y_test.shape)\n",
+    "\n",
+    "print(\"\\nSample feature columns (first 25):\")\n",
+    "print(list(X_train.columns[:25]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "253b34f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\SLG PC\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "c:\\Users\\SLG PC\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "c:\\Users\\SLG PC\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "c:\\Users\\SLG PC\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "======================================================================\n",
+      "MODEL: LR_balanced\n",
+      "Train | Acc=0.624 | MacroF1=0.552\n",
+      "Val   | Acc=0.429 | MacroF1=0.277\n",
+      "Test  | Acc=0.518 | MacroF1=0.402\n",
+      "\n",
+      "[VAL] Confusion matrix:\n",
+      "[[18  5  0  5]\n",
+      " [ 6  4  0 10]\n",
+      " [ 2  2  0  1]\n",
+      " [ 0  1  0  2]]\n",
+      "\n",
+      "[VAL] Classification report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0      0.692     0.643     0.667        28\n",
+      "           1      0.333     0.200     0.250        20\n",
+      "           2      0.000     0.000     0.000         5\n",
+      "           3      0.111     0.667     0.190         3\n",
+      "\n",
+      "    accuracy                          0.429        56\n",
+      "   macro avg      0.284     0.377     0.277        56\n",
+      "weighted avg      0.471     0.429     0.433        56\n",
+      "\n",
+      "\n",
+      "[TEST] Confusion matrix:\n",
+      "[[20  8  1  2  2]\n",
+      " [ 3  4  2  1  2]\n",
+      " [ 0  1  1  3  0]\n",
+      " [ 0  0  0  2  0]\n",
+      " [ 0  1  0  1  2]]\n",
+      "\n",
+      "[TEST] Classification report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0      0.870     0.606     0.714        33\n",
+      "           1      0.286     0.333     0.308        12\n",
+      "           2      0.250     0.200     0.222         5\n",
+      "           3      0.222     1.000     0.364         2\n",
+      "           4      0.333     0.500     0.400         4\n",
+      "\n",
+      "    accuracy                          0.518        56\n",
+      "   macro avg      0.392     0.528     0.402        56\n",
+      "weighted avg      0.628     0.518     0.548        56\n",
+      "\n",
+      "\n",
+      "======================================================================\n",
+      "MODEL: RF_balanced\n",
+      "Train | Acc=0.894 | MacroF1=0.875\n",
+      "Val   | Acc=0.607 | MacroF1=0.424\n",
+      "Test  | Acc=0.625 | MacroF1=0.453\n",
+      "\n",
+      "[VAL] Confusion matrix:\n",
+      "[[19  8  1  0]\n",
+      " [ 6 13  1  0]\n",
+      " [ 0  2  2  1]\n",
+      " [ 0  2  1  0]]\n",
+      "\n",
+      "[VAL] Classification report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0      0.760     0.679     0.717        28\n",
+      "           1      0.520     0.650     0.578        20\n",
+      "           2      0.400     0.400     0.400         5\n",
+      "           3      0.000     0.000     0.000         3\n",
+      "\n",
+      "    accuracy                          0.607        56\n",
+      "   macro avg      0.420     0.432     0.424        56\n",
+      "weighted avg      0.601     0.607     0.601        56\n",
+      "\n",
+      "\n",
+      "[TEST] Confusion matrix:\n",
+      "[[23  2  8  0  0]\n",
+      " [ 3  7  2  0  0]\n",
+      " [ 0  0  4  1  0]\n",
+      " [ 0  1  0  1  0]\n",
+      " [ 0  0  4  0  0]]\n",
+      "\n",
+      "[TEST] Classification report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0      0.885     0.697     0.780        33\n",
+      "           1      0.700     0.583     0.636        12\n",
+      "           2      0.222     0.800     0.348         5\n",
+      "           3      0.500     0.500     0.500         2\n",
+      "           4      0.000     0.000     0.000         4\n",
+      "\n",
+      "    accuracy                          0.625        56\n",
+      "   macro avg      0.461     0.516     0.453        56\n",
+      "weighted avg      0.709     0.625     0.645        56\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\SLG PC\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "c:\\Users\\SLG PC\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "c:\\Users\\SLG PC\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "======================================================================\n",
+      "MODEL: HGB\n",
+      "Train | Acc=0.989 | MacroF1=0.989\n",
+      "Val   | Acc=0.589 | MacroF1=0.405\n",
+      "Test  | Acc=0.518 | MacroF1=0.326\n",
+      "\n",
+      "[VAL] Confusion matrix:\n",
+      "[[22  5  1  0]\n",
+      " [ 8  9  2  1]\n",
+      " [ 1  1  2  1]\n",
+      " [ 1  2  0  0]]\n",
+      "\n",
+      "[VAL] Classification report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0      0.688     0.786     0.733        28\n",
+      "           1      0.529     0.450     0.486        20\n",
+      "           2      0.400     0.400     0.400         5\n",
+      "           3      0.000     0.000     0.000         3\n",
+      "\n",
+      "    accuracy                          0.589        56\n",
+      "   macro avg      0.404     0.409     0.405        56\n",
+      "weighted avg      0.569     0.589     0.576        56\n",
+      "\n",
+      "\n",
+      "[TEST] Confusion matrix:\n",
+      "[[22  6  4  1  0]\n",
+      " [ 4  5  3  0  0]\n",
+      " [ 1  2  1  1  0]\n",
+      " [ 0  1  1  0  0]\n",
+      " [ 0  1  2  0  1]]\n",
+      "\n",
+      "[TEST] Classification report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0      0.815     0.667     0.733        33\n",
+      "           1      0.333     0.417     0.370        12\n",
+      "           2      0.091     0.200     0.125         5\n",
+      "           3      0.000     0.000     0.000         2\n",
+      "           4      1.000     0.250     0.400         4\n",
+      "\n",
+      "    accuracy                          0.518        56\n",
+      "   macro avg      0.448     0.307     0.326        56\n",
+      "weighted avg      0.631     0.518     0.551        56\n",
+      "\n",
+      "\n",
+      "======================================================================\n",
+      "SUMMARY (sorted by Val MacroF1):\n",
+      "RF_balanced  | Val MacroF1=0.424 | Test MacroF1=0.453\n",
+      "HGB          | Val MacroF1=0.405 | Test MacroF1=0.326\n",
+      "LR_balanced  | Val MacroF1=0.277 | Test MacroF1=0.402\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, f1_score\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.ensemble import HistGradientBoostingClassifier, RandomForestClassifier\n",
+    "\n",
+    "def eval_cls(name, model, Xtr, ytr, Xva, yva, Xte, yte):\n",
+    "    model.fit(Xtr, ytr)\n",
+    "\n",
+    "    def _metrics(split_name, X, y):\n",
+    "        pred = model.predict(X)\n",
+    "        acc = accuracy_score(y, pred)\n",
+    "        f1m = f1_score(y, pred, average=\"macro\")\n",
+    "        return pred, acc, f1m\n",
+    "\n",
+    "    pred_tr, acc_tr, f1_tr = _metrics(\"Train\", Xtr, ytr)\n",
+    "    pred_va, acc_va, f1_va = _metrics(\"Val\",   Xva, yva)\n",
+    "    pred_te, acc_te, f1_te = _metrics(\"Test\",  Xte, yte)\n",
+    "\n",
+    "    print(\"\\n\" + \"=\"*70)\n",
+    "    print(f\"MODEL: {name}\")\n",
+    "    print(f\"Train | Acc={acc_tr:.3f} | MacroF1={f1_tr:.3f}\")\n",
+    "    print(f\"Val   | Acc={acc_va:.3f} | MacroF1={f1_va:.3f}\")\n",
+    "    print(f\"Test  | Acc={acc_te:.3f} | MacroF1={f1_te:.3f}\")\n",
+    "\n",
+    "    print(\"\\n[VAL] Confusion matrix:\")\n",
+    "    print(confusion_matrix(yva, pred_va))\n",
+    "    print(\"\\n[VAL] Classification report:\")\n",
+    "    print(classification_report(yva, pred_va, digits=3))\n",
+    "\n",
+    "    print(\"\\n[TEST] Confusion matrix:\")\n",
+    "    print(confusion_matrix(yte, pred_te))\n",
+    "    print(\"\\n[TEST] Classification report:\")\n",
+    "    print(classification_report(yte, pred_te, digits=3))\n",
+    "\n",
+    "    return {\n",
+    "        \"name\": name,\n",
+    "        \"model\": model,\n",
+    "        \"val_macro_f1\": f1_va,\n",
+    "        \"test_macro_f1\": f1_te\n",
+    "    }\n",
+    "\n",
+    "# 1) Logistic Regression (balanced) - baseline mạnh cho tabular\n",
+    "lr = LogisticRegression(\n",
+    "    max_iter=5000,\n",
+    "    class_weight=\"balanced\",\n",
+    "    n_jobs=None\n",
+    ")\n",
+    "\n",
+    "# 2) RandomForest (balanced_subsample)\n",
+    "rf = RandomForestClassifier(\n",
+    "    n_estimators=600,\n",
+    "    max_depth=None,\n",
+    "    min_samples_leaf=2,\n",
+    "    random_state=42,\n",
+    "    class_weight=\"balanced_subsample\",\n",
+    "    n_jobs=-1\n",
+    ")\n",
+    "\n",
+    "# 3) HistGradientBoosting (mạnh cho tabular, chạy nhanh)\n",
+    "hgb = HistGradientBoostingClassifier(\n",
+    "    learning_rate=0.06,\n",
+    "    max_depth=6,\n",
+    "    max_iter=600,\n",
+    "    random_state=42\n",
+    ")\n",
+    "\n",
+    "results = []\n",
+    "results.append(eval_cls(\"LR_balanced\", lr, X_train, y_train, X_val, y_val, X_test, y_test))\n",
+    "results.append(eval_cls(\"RF_balanced\", rf, X_train, y_train, X_val, y_val, X_test, y_test))\n",
+    "results.append(eval_cls(\"HGB\", hgb, X_train, y_train, X_val, y_val, X_test, y_test))\n",
+    "\n",
+    "# Summary\n",
+    "print(\"\\n\" + \"=\"*70)\n",
+    "print(\"SUMMARY (sorted by Val MacroF1):\")\n",
+    "for r in sorted(results, key=lambda x: x[\"val_macro_f1\"], reverse=True):\n",
+    "    print(f\"{r['name']:12s} | Val MacroF1={r['val_macro_f1']:.3f} | Test MacroF1={r['test_macro_f1']:.3f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "e1851e78",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded: final_2.xlsx | sheet: final\n",
+      "Shape (raw): (401, 42)\n",
+      "Shape (after dedup): (394, 42)\n",
+      "\n",
+      "=== TARGET SUMMARY (so_luong) ===\n",
+      "count    394.000000\n",
+      "mean       4.710660\n",
+      "std        6.848602\n",
+      "min        0.000000\n",
+      "25%        1.000000\n",
+      "50%        2.000000\n",
+      "75%        5.000000\n",
+      "max       64.000000\n",
+      "Name: so_luong, dtype: float64\n",
+      "Missing target: 0\n",
+      "Negative target: 0\n",
+      "Zero target: 3\n",
+      "\n",
+      "Sample rows:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ma_dia_diem</th>\n",
+       "      <th>all_task_normal</th>\n",
+       "      <th>all_task_dinhky</th>\n",
+       "      <th>loai_ca</th>\n",
+       "      <th>bat_dau</th>\n",
+       "      <th>ket_thuc</th>\n",
+       "      <th>tong_gio_lam</th>\n",
+       "      <th>so_ca_cua_toa</th>\n",
+       "      <th>so_luong</th>\n",
+       "      <th>num_tasks</th>\n",
+       "      <th>...</th>\n",
+       "      <th>dien_tich_tham</th>\n",
+       "      <th>doc_ham</th>\n",
+       "      <th>vien_phan_quang</th>\n",
+       "      <th>op_tuong</th>\n",
+       "      <th>op_chan_tuong</th>\n",
+       "      <th>ranh_thoat_nuoc</th>\n",
+       "      <th>dien_tich_kinh</th>\n",
+       "      <th>num_medical_tasks_total</th>\n",
+       "      <th>num_indoor_room_tasks</th>\n",
+       "      <th>is_tasks_text_missing</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>115-2</td>\n",
+       "      <td>Làm sạch toàn bộ phòng giao dịch tầng 1 (kể cả...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Part time</td>\n",
+       "      <td>06:30:00</td>\n",
+       "      <td>10:30:00</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>101-1</td>\n",
+       "      <td>Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...</td>\n",
+       "      <td>Lau bảng biển, bình cứu hỏa , cây nước hành la...</td>\n",
+       "      <td>Hành chính</td>\n",
+       "      <td>06:30:00</td>\n",
+       "      <td>16:00:00</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>24</td>\n",
+       "      <td>441</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9176.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>894.0</td>\n",
+       "      <td>112</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>101-1</td>\n",
+       "      <td>Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...</td>\n",
+       "      <td>Lau bảng biển, bình cứu hỏa , cây nước hành la...</td>\n",
+       "      <td>Ca sáng</td>\n",
+       "      <td>06:00:00</td>\n",
+       "      <td>14:00:00</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>441</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9176.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>894.0</td>\n",
+       "      <td>112</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 42 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  ma_dia_diem                                    all_task_normal  \\\n",
+       "0       115-2  Làm sạch toàn bộ phòng giao dịch tầng 1 (kể cả...   \n",
+       "1       101-1  Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...   \n",
+       "2       101-1  Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...   \n",
+       "\n",
+       "                                     all_task_dinhky     loai_ca   bat_dau  \\\n",
+       "0                                                NaN   Part time  06:30:00   \n",
+       "1  Lau bảng biển, bình cứu hỏa , cây nước hành la...  Hành chính  06:30:00   \n",
+       "2  Lau bảng biển, bình cứu hỏa , cây nước hành la...     Ca sáng  06:00:00   \n",
+       "\n",
+       "   ket_thuc  tong_gio_lam  so_ca_cua_toa  so_luong  num_tasks  ...  \\\n",
+       "0  10:30:00           4.0              1         1          7  ...   \n",
+       "1  16:00:00           7.5              6        24        441  ...   \n",
+       "2  14:00:00           8.0              6         3        441  ...   \n",
+       "\n",
+       "   dien_tich_tham  doc_ham  vien_phan_quang  op_tuong  op_chan_tuong  \\\n",
+       "0             0.0        0                0       0.0            0.0   \n",
+       "1             0.0       70                0    9176.0           89.0   \n",
+       "2             0.0       70                0    9176.0           89.0   \n",
+       "\n",
+       "   ranh_thoat_nuoc  dien_tich_kinh  num_medical_tasks_total  \\\n",
+       "0                0            20.0                        0   \n",
+       "1               25           894.0                      112   \n",
+       "2               25           894.0                      112   \n",
+       "\n",
+       "   num_indoor_room_tasks  is_tasks_text_missing  \n",
+       "0                      1                      0  \n",
+       "1                     39                      0  \n",
+       "2                     39                      0  \n",
+       "\n",
+       "[3 rows x 42 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CELL 1 — LOAD DATA & BASIC CLEAN\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "DATA_PATH = \"final_2.xlsx\"\n",
+    "SHEET_NAME = \"final\"\n",
+    "\n",
+    "# 1. Load\n",
+    "df = pd.read_excel(DATA_PATH, sheet_name=SHEET_NAME)\n",
+    "print(f\"Loaded: {DATA_PATH} | sheet: {SHEET_NAME}\")\n",
+    "print(\"Shape (raw):\", df.shape)\n",
+    "\n",
+    "# 2. Drop duplicate full rows\n",
+    "df = df.drop_duplicates().reset_index(drop=True)\n",
+    "print(\"Shape (after dedup):\", df.shape)\n",
+    "\n",
+    "# 3. Check target\n",
+    "assert \"so_luong\" in df.columns, \"❌ Missing target so_luong\"\n",
+    "\n",
+    "print(\"\\n=== TARGET SUMMARY (so_luong) ===\")\n",
+    "print(df[\"so_luong\"].describe())\n",
+    "print(\"Missing target:\", df[\"so_luong\"].isna().sum())\n",
+    "print(\"Negative target:\", (df[\"so_luong\"] < 0).sum())\n",
+    "print(\"Zero target:\", (df[\"so_luong\"] == 0).sum())\n",
+    "\n",
+    "# 4. Peek data\n",
+    "print(\"\\nSample rows:\")\n",
+    "display(df.head(3))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8c9268c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# CELL 2 — FEATURE SELECTION (STRICT)\n",
+    "\n",
+    "# 1. Xem toàn bộ cột\n",
+    "print(\"All columns:\")\n",
+    "for i, c in enumerate(df.columns):\n",
+    "    print(f\"{i:2d}: {c}\")\n",
+    "\n",
+    "# 2. Xác định cột cần loại bỏ (THEO THỎA THUẬN)\n",
+    "DROP_COLS = [\n",
+    "    df.columns[0],   # ma_dia_diem\n",
+    "    df.columns[1],   # all_task_normal\n",
+    "    df.columns[2],   # all_task_dinhky\n",
+    "    df.columns[-1],  # is_tasks_text_missing\n",
+    "]\n",
+    "\n",
+    "print(\"\\nDropped columns:\")\n",
+    "for c in DROP_COLS:\n",
+    "    print(\" -\", c)\n",
+    "\n",
+    "# 3. Tạo X, y\n",
+    "X = df.drop(columns=DROP_COLS + [\"so_luong\"])\n",
+    "y = df[\"so_luong\"].astype(float)\n",
+    "\n",
+    "print(\"\\nShapes:\")\n",
+    "print(\"X:\", X.shape)\n",
+    "print(\"y:\", y.shape)\n",
+    "\n",
+    "# 4. Kiểm tra kiểu dữ liệu\n",
+    "print(\"\\nFeature dtypes:\")\n",
+    "display(X.dtypes.value_counts())\n",
+    "\n",
+    "# 5. Kiểm tra missing\n",
+    "print(\"\\nMissing values in X:\")\n",
+    "display(X.isna().sum().sort_values(ascending=False).head(10))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b975f6cf",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -51,15 +3674,37 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8cc64019",
+   "id": "1a595fe8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b2fb9c84",
    "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,
diff --git a/input.json b/input.json
new file mode 100644
index 0000000..f9a40d5
--- /dev/null
+++ b/input.json
@@ -0,0 +1,31 @@
+{
+  "ma_dia_diem": "TD-001",
+  "loai_ca": "Hành chính",
+
+  "bat_dau": "07:00:00", 
+  "ket_thuc": "15:00:00",
+  "tong_gio_lam": 8.0,
+
+  "so_ca_cua_toa": 3,
+
+  "all_task_normal": "Lau sàn hành lang; Thu gom rác WC; Vệ sinh sảnh chính; Lau kính thang máy",
+  "all_task_dinhky": "Cọ bồn cầu WC tầng 2; Đánh sàn lobby; Trực phát sinh",
+
+  "so_tang": 12,
+  "so_cua_thang_may": 4,
+
+  "dien_tich_ngoai_canh": 350.0,
+  "dien_tich_sanh": 220.0,
+  "dien_tich_hanh_lang": 1800.0,
+  "dien_tich_wc": 420.0,
+  "dien_tich_phong": 2600.0,
+  "dien_tich_tham": 800.0,
+  "dien_tich_kinh": 560.0,
+
+  "doc_ham": 1,
+  "vien_phan_quang": 0,
+  "op_tuong": 1,
+  "op_chan_tuong": 1,
+  "ranh_thoat_nuoc": 1
+
+}
diff --git a/input_explained.jsonc b/input_explained.jsonc
new file mode 100644
index 0000000..29ba7e3
--- /dev/null
+++ b/input_explained.jsonc
@@ -0,0 +1,72 @@
+{
+  // ========================================
+  // THÔNG TIN TÒA NHÀ & CA LÀM VIỆC
+  // ========================================
+  
+  "ma_dia_diem": "TD-001",              // Mã định danh tòa nhà
+  "loai_ca": "Hành chính",              // Loại ca: Hành chính / Ca sáng / Ca chiều / Ca tối / Ca đêm
+
+  // Thời gian ca làm việc
+  "bat_dau": "07:00:00",                // Giờ bắt đầu ca (HH:MM:SS)
+  "ket_thuc": "15:00:00",               // Giờ kết thúc ca (HH:MM:SS)
+  "tong_gio_lam": 8.0,                  // Tổng số giờ làm việc trong ca
+
+  "so_ca_cua_toa": 3,                   // Tổng số ca làm việc trong ngày của toà nhà
+
+  // ========================================
+  // CÔNG VIỆC (TEXT FEATURES INPUT)
+  // ========================================
+  
+  // ⭐ all_task_normal: Công việc HẰNG NGÀY
+  // - Các task được thực hiện MỖI NGÀY trong ca này
+  // - Ví dụ: Lau sàn, Thu rác, Vệ sinh WC (mỗi ngày đều làm)
+  // - Format: Các task cách nhau bởi dấu chấm phẩy (;)
+  "all_task_normal": "Lau sàn hành lang; Thu gom rác WC; Vệ sinh sảnh chính; Lau kính thang máy",
+  
+  // ⭐ all_task_dinhky: Công việc ĐỊNH KỲ (TUẦN / THÁNG)
+  // - Các task được thực hiện THEO TUẦN hoặc THEO THÁNG
+  // - Ví dụ: Cọ bồn cầu (1 tuần/lần), Đánh sàn (1 tháng/lần), Trực phát sinh
+  // - Format: Các task cách nhau bởi dấu chấm phẩy (;)
+  "all_task_dinhky": "Cọ bồn cầu WC tầng 2; Đánh sàn lobby; Trực phát sinh",
+
+  // ========================================
+  // ĐẶC ĐIỂM VẬT LÝ TÒA NHÀ
+  // ========================================
+  
+  "so_tang": 12,                        // Tổng số tầng của tòa nhà
+  "so_cua_thang_may": 4,                // Số lượng cửa thang máy (tổng tất cả các tầng)
+
+  // ========================================
+  // DIỆN TÍCH (m²) - TỔNG CỦA TẤT CẢ CÁC TẦNG
+  // ========================================
+  // ⚠️⚠️⚠️ LƯU Ý QUAN TRỌNG ⚠️⚠️⚠️
+  // Tất cả diện tích bên dưới là TỔNG diện tích của TẤT CẢ các tầng, KHÔNG PHẢI diện tích 1 tầng!
+  // 
+  // Ví dụ: 
+  //   - Tòa nhà có 12 tầng
+  //   - Mỗi tầng có 150m² hành lang
+  //   - → dien_tich_hanh_lang = 12 × 150 = 1800m² (KHÔNG PHẢI 150m²!)
+  // 
+  // Công thức: dien_tich_X = so_tang × dien_tich_X_moi_tang
+  
+  "dien_tich_ngoai_canh": 350.0,       // Tổng diện tích khu vực ngoại cảnh (sân, vỉa hè, khuôn viên, bãi xe)
+  "dien_tich_sanh": 220.0,              // Tổng diện tích sảnh, lobby (tất cả các tầng)
+  "dien_tich_hanh_lang": 1800.0,       // Tổng diện tích hành lang, lối đi (tất cả các tầng)
+  "dien_tich_wc": 420.0,                // Tổng diện tích WC, toilet, nhà vệ sinh (tất cả các tầng)
+  "dien_tich_phong": 2600.0,            // Tổng diện tích các phòng (văn phòng, phòng họp, phòng bệnh, v.v.)
+  "dien_tich_tham": 800.0,              // Tổng diện tích sàn có thảm (cần hút bụi thay vì lau)
+  "dien_tich_kinh": 560.0,              // Tổng diện tích kính cần lau (cửa kính, vách kính, mặt dựng)
+
+  // ========================================
+  // ĐẶC ĐIỂM BỀ MẶT & KẾT CẤU
+  // ========================================
+  // Giá trị: 1 = Có, 0 = Không
+  // Các đặc điểm này ảnh hưởng đến khối lượng công việc và thời gian vệ sinh
+  
+  "doc_ham": 1,                         // Có độc hầm (tầng hầm, basement) hay không - Cần thêm công việc vệ sinh
+  "vien_phan_quang": 0,                 // Có viền phản quang hay không - Cần làm sạch cẩn thận hơn
+  "op_tuong": 1,                        // Có ốp tường hay không (gạch, tấm ốp) - Dễ vệ sinh hơn tường sơn
+  "op_chan_tuong": 1,                   // Có ốp chân tường hay không - Cần chùi thêm phần chân tường
+  "ranh_thoat_nuoc": 1                  // Có rãnh thoát nước hay không - Cần vệ sinh rãnh
+
+}
diff --git a/merge_all_features.py b/merge_all_features.py
deleted file mode 100644
index acf0cc1..0000000
--- a/merge_all_features.py
+++ /dev/null
@@ -1,270 +0,0 @@
-"""
-MERGE 3 FILES: SHIFT + TASK + BUILDING FEATURES
-Gộp 3 file Excel thành 1 file tổng để predict số nhân sự
-Created: January 5, 2026
-"""
-
-import pandas as pd
-import numpy as np
-
-def merge_three_datasets(
-    shift_file: str,
-    task_file: str, 
-    building_file: str,
-    output_excel: str,
-    output_csv: str
-):
-    """
-    Merge 3 datasets:
-    1. shift_features_for_prediction.xlsx (942 rows × 7 cols)
-    2. ket_qua_cong_viec_full_WITH_FEATURES.xlsx (302 rows × 28 cols)
-    3. Du_Lieu_Toa_Nha_Aggregate.xlsx (233 rows × 18 cols)
-    
-    Join key: ma_dia_diem (Mã địa điểm)
-    Join type: LEFT JOIN (keep all shifts)
-    
-    Expected output: 942 rows × (7 + 25 + 17) = 49 cols
-    """
-    
-    print("=" * 80)
-    print("🚀 MERGE 3 DATASETS: SHIFT + TASK + BUILDING FEATURES")
-    print("=" * 80)
-    
-    # =====================================================================
-    # 1. ĐỌC FILE 1: SHIFT FEATURES (base dataset)
-    # =====================================================================
-    print(f"\n📂 [1/3] Đọc file SHIFT features: {shift_file}")
-    df_shift = pd.read_excel(shift_file)
-    
-    print(f"   ✅ Shape: {df_shift.shape}")
-    print(f"   ✅ Columns: {list(df_shift.columns)}")
-    print(f"   ✅ Unique buildings: {df_shift['ma_dia_diem'].nunique()}")
-    
-    # =====================================================================
-    # 2. ĐỌC FILE 2: TASK FEATURES
-    # =====================================================================
-    print(f"\n📂 [2/3] Đọc file TASK features: {task_file}")
-    df_task = pd.read_excel(task_file)
-    
-    print(f"   ✅ Shape: {df_task.shape}")
-    print(f"   ✅ Unique buildings: {df_task['ma_dia_diem'].nunique()}")
-    
-    # Loại bỏ cột text gốc (không cần cho modeling)
-    cols_to_drop = ['all_task_normal', 'all_task_dinhky']
-    df_task = df_task.drop(columns=[c for c in cols_to_drop if c in df_task.columns])
-    
-    print(f"   ✅ Đã loại bỏ cột text, còn lại: {df_task.shape[1]} cột")
-    print(f"   ✅ Task feature columns: {list(df_task.columns)}")
-    
-    # =====================================================================
-    # 3. ĐỌC FILE 3: BUILDING FEATURES
-    # =====================================================================
-    print(f"\n📂 [3/3] Đọc file BUILDING features: {building_file}")
-    df_building = pd.read_excel(building_file)
-    
-    print(f"   ✅ Shape: {df_building.shape}")
-    print(f"   ✅ Unique buildings: {df_building['Mã địa điểm'].nunique()}")
-    
-    # Rename column để match với các file khác
-    df_building = df_building.rename(columns={'Mã địa điểm': 'ma_dia_diem'})
-    
-    # Rename các cột để dễ dùng (bỏ dấu, khoảng trắng)
-    column_mapping = {
-        'Loại hình': 'loai_hinh',
-        'Tên Tòa Tháp': 'ten_toa_thap',
-        'Mức độ Lưu lượng KH': 'muc_do_luu_luong',
-        'Số tầng': 'so_tang',
-        'Tổng số cửa thang máy': 'so_cua_thang_may',
-        'Diện tích ngoại cảnh Tòa tháp (m2)': 'dien_tich_ngoai_canh',
-        'Sàn Sảnh (m2)': 'dien_tich_sanh',
-        'Sàn Hành lang (m2)': 'dien_tich_hanh_lang',
-        'Sàn WC (m2)': 'dien_tich_wc',
-        'Sàn Phòng (m2)': 'dien_tich_phong',
-        'Thảm (m2)': 'dien_tich_tham',
-        'Dốc hầm (m)': 'doc_ham',
-        'Viền phản quang (m)': 'vien_phan_quang',
-        'Ốp tường (m2)': 'op_tuong',
-        'Ốp chân tường (m2)': 'op_chan_tuong',
-        'Rãnh thoát nước (m)': 'ranh_thoat_nuoc',
-        'Kính (m2)': 'dien_tich_kinh'
-    }
-    df_building = df_building.rename(columns=column_mapping)
-    
-    print(f"   ✅ Đã rename columns: {list(df_building.columns)}")
-    
-    # =====================================================================
-    # 4. MERGE DATASET 1 (SHIFT) + DATASET 2 (TASK)
-    # =====================================================================
-    print(f"\n🔗 [MERGE 1/2] Merge SHIFT + TASK features...")
-    print(f"   Join key: ma_dia_diem")
-    print(f"   Join type: LEFT (keep all shifts)")
-    
-    df_merged = df_shift.merge(
-        df_task,
-        on='ma_dia_diem',
-        how='left',
-        suffixes=('', '_task')
-    )
-    
-    print(f"   ✅ Result shape: {df_merged.shape}")
-    print(f"   ✅ Số shift giữ nguyên: {len(df_merged)} (expected: {len(df_shift)})")
-    
-    # Check missing values sau merge
-    task_features = [col for col in df_task.columns if col != 'ma_dia_diem']
-    missing_task_features = df_merged[task_features].isna().sum().sum()
-    print(f"   ⚠️  Missing values trong task features: {missing_task_features}")
-    
-    # =====================================================================
-    # 5. MERGE RESULT + DATASET 3 (BUILDING)
-    # =====================================================================
-    print(f"\n🔗 [MERGE 2/2] Merge (SHIFT+TASK) + BUILDING features...")
-    print(f"   Join key: ma_dia_diem")
-    print(f"   Join type: LEFT (keep all shifts)")
-    
-    df_final = df_merged.merge(
-        df_building,
-        on='ma_dia_diem',
-        how='left',
-        suffixes=('', '_building')
-    )
-    
-    print(f"   ✅ Result shape: {df_final.shape}")
-    print(f"   ✅ Số shift giữ nguyên: {len(df_final)} (expected: {len(df_shift)})")
-    
-    # Check missing values sau merge
-    building_features = [col for col in df_building.columns if col != 'ma_dia_diem']
-    missing_building_features = df_final[building_features].isna().sum().sum()
-    print(f"   ⚠️  Missing values trong building features: {missing_building_features}")
-    
-    # =====================================================================
-    # 6. FINAL STATISTICS
-    # =====================================================================
-    print(f"\n📊 FINAL DATASET STATISTICS:")
-    print(f"   📐 Shape: {df_final.shape}")
-    print(f"   🏢 Unique buildings: {df_final['ma_dia_diem'].nunique()}")
-    print(f"   📋 Total columns: {len(df_final.columns)}")
-    
-    print(f"\n   📋 COLUMN BREAKDOWN:")
-    print(f"      - Shift features: 7 cols")
-    print(f"      - Task features: {len(task_features)} cols")
-    print(f"      - Building features: {len(building_features)} cols")
-    print(f"      - Total: {7 + len(task_features) + len(building_features)} cols")
-    
-    # Check missing values tổng thể
-    print(f"\n   ⚠️  MISSING VALUES BY COLUMN:")
-    missing_summary = df_final.isna().sum()
-    missing_summary = missing_summary[missing_summary > 0].sort_values(ascending=False)
-    
-    if len(missing_summary) > 0:
-        print(f"      Found {len(missing_summary)} columns with missing values:")
-        for col, count in missing_summary.head(20).items():
-            pct = count / len(df_final) * 100
-            print(f"      - {col}: {count} ({pct:.1f}%)")
-    else:
-        print(f"      ✅ No missing values!")
-    
-    # =====================================================================
-    # 7. DATA VALIDATION
-    # =====================================================================
-    print(f"\n✅ DATA VALIDATION:")
-    
-    # Check target variable
-    print(f"   🎯 Target variable (so_luong):")
-    print(f"      - Count: {df_final['so_luong'].notna().sum()}")
-    print(f"      - Missing: {df_final['so_luong'].isna().sum()}")
-    print(f"      - Min: {df_final['so_luong'].min()}")
-    print(f"      - Mean: {df_final['so_luong'].mean():.2f}")
-    print(f"      - Median: {df_final['so_luong'].median():.0f}")
-    print(f"      - Max: {df_final['so_luong'].max()}")
-    
-    # Check feature coverage
-    print(f"\n   📊 Feature coverage:")
-    shift_buildings = set(df_shift['ma_dia_diem'].unique())
-    task_buildings = set(df_task['ma_dia_diem'].unique())
-    building_buildings = set(df_building['ma_dia_diem'].unique())
-    
-    print(f"      - Shifts: {len(shift_buildings)} buildings")
-    print(f"      - Tasks: {len(task_buildings)} buildings")
-    print(f"      - Building info: {len(building_buildings)} buildings")
-    
-    # Overlap analysis
-    shift_with_task = shift_buildings.intersection(task_buildings)
-    shift_with_building = shift_buildings.intersection(building_buildings)
-    all_three = shift_buildings.intersection(task_buildings).intersection(building_buildings)
-    
-    print(f"\n   🔗 Overlap analysis:")
-    print(f"      - Shifts ∩ Tasks: {len(shift_with_task)} buildings")
-    print(f"      - Shifts ∩ Building: {len(shift_with_building)} buildings")
-    print(f"      - All three: {len(all_three)} buildings")
-    
-    shift_only = shift_buildings - task_buildings - building_buildings
-    if len(shift_only) > 0:
-        print(f"\n   ⚠️  Buildings with shift only (no task/building data): {len(shift_only)}")
-        print(f"      Examples: {list(shift_only)[:10]}")
-    
-    # =====================================================================
-    # 8. EXPORT FILES
-    # =====================================================================
-    print(f"\n💾 EXPORTING FILES...")
-    
-    # Excel
-    print(f"   [1/2] Exporting Excel: {output_excel}")
-    df_final.to_excel(output_excel, index=False, engine='openpyxl')
-    print(f"   ✅ Done!")
-    
-    # CSV
-    print(f"   [2/2] Exporting CSV: {output_csv}")
-    df_final.to_csv(output_csv, index=False, encoding='utf-8-sig')
-    print(f"   ✅ Done!")
-    
-    # =====================================================================
-    # 9. SUMMARY
-    # =====================================================================
-    print(f"\n" + "=" * 80)
-    print("✅ MERGE COMPLETED!")
-    print("=" * 80)
-    
-    print(f"\n📁 FILES CREATED:")
-    print(f"   1. {output_excel} ({df_final.shape[0]} rows × {df_final.shape[1]} columns)")
-    print(f"   2. {output_csv} (CSV backup)")
-    
-    print(f"\n📋 COLUMN STRUCTURE:")
-    print(f"   - ma_dia_diem (identifier)")
-    print(f"   - Shift features (6): loai_ca, bat_dau, ket_thuc, tong_gio_lam, so_ca_cua_toa")
-    print(f"   - Task features ({len(task_features)}): num_tasks, cleaning_ratio, ...")
-    print(f"   - Building features ({len(building_features)}): so_tang, dien_tich_*, ...")
-    print(f"   - so_luong (TARGET)")
-    
-    print(f"\n🎯 READY FOR MACHINE LEARNING!")
-    print(f"   - Total samples: {len(df_final)}")
-    print(f"   - Total features: {df_final.shape[1] - 2}  (excluding ma_dia_diem & target)")
-    print(f"   - Target variable: so_luong")
-    
-    return df_final
-
-
-if __name__ == "__main__":
-    # File paths
-    shift_file = "shift_features_for_prediction.xlsx"
-    task_file = "ket_qua_cong_viec_full_WITH_FEATURES.xlsx"
-    building_file = "Du_Lieu_Toa_Nha_Aggregate.xlsx"
-    
-    output_excel = "COMPLETE_DATASET_FOR_PREDICTION.xlsx"
-    output_csv = "COMPLETE_DATASET_FOR_PREDICTION.csv"
-    
-    # Run merge
-    df_final = merge_three_datasets(
-        shift_file,
-        task_file,
-        building_file,
-        output_excel,
-        output_csv
-    )
-    
-    # Display sample
-    print(f"\n📊 SAMPLE DATA (first 5 rows, first 15 columns):")
-    print(df_final.iloc[:5, :15].to_string())
-    
-    print(f"\n📊 COLUMN LIST:")
-    for i, col in enumerate(df_final.columns, 1):
-        print(f"   {i:2d}. {col}")
diff --git a/outputs/val_predictions_extratrees.csv b/outputs/val_predictions_extratrees.csv
new file mode 100644
index 0000000..eace59d
--- /dev/null
+++ b/outputs/val_predictions_extratrees.csv
@@ -0,0 +1,69 @@
+ma_dia_diem,so_luong_thuc_te,so_luong_du_doan_raw,so_luong_du_doan_round,abs_error
+579-1,32.0,16.876830318931813,17,15.123169681068187
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+43-079,1.0,1.0013520071353357,1,0.0013520071353356755
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+425-5,1.0,0.9999999999999947,1,5.329070518200751e-15
+43-002,1.0,0.9999999999999947,1,5.329070518200751e-15
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+2-014,1.0,0.9999999999999947,1,5.329070518200751e-15
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+443-3,1.0,0.9999999999999947,1,5.329070518200751e-15
diff --git a/predict.py b/predict.py
new file mode 100644
index 0000000..34e103a
--- /dev/null
+++ b/predict.py
@@ -0,0 +1,207 @@
+import re
+from typing import Dict, List, Optional
+
+import pandas as pd
+
+
+# =========================================================
+# 1) HELPERS
+# =========================================================
+_TASK_SPLIT_RE = re.compile(r"[;|\n]+")
+
+def _split_tasks(tasks_text: str) -> List[str]:
+    """Tách tasks bằng delimiter: ; | hoặc xuống dòng"""
+    tasks_text = str(tasks_text).lower()
+    tasks = _TASK_SPLIT_RE.split(tasks_text)
+    return [t.strip() for t in tasks if t.strip()]
+
+def _count_tasks_with_keywords(tasks: List[str], keywords: List[str]) -> int:
+    """Đếm số tasks chứa ít nhất 1 keyword"""
+    count = 0
+    for task in tasks:
+        if any(k in task for k in keywords):
+            count += 1
+    return count
+
+def _count_tasks_without_keywords(tasks: List[str], all_keywords: List[str]) -> int:
+    """Đếm số tasks KHÔNG chứa bất kỳ keyword nào"""
+    count = 0
+    for task in tasks:
+        if not any(k in task for k in all_keywords):
+            count += 1
+    return count
+
+def _get_empty_features() -> Dict[str, float]:
+    """Trả về dict với tất cả features = 0 (cho missing data)"""
+    return {
+        # TASK COUNTS (7)
+        "num_tasks": 0,
+        "num_cleaning_tasks": 0,
+        "num_trash_collection_tasks": 0,
+        "num_monitoring_tasks": 0,
+        "num_deep_cleaning_tasks": 0,
+        "num_support_tasks": 0,
+        "num_other_tasks": 0,
+
+        # AREA (reduced + aggregated) (7)
+        "num_wc_tasks": 0,
+        "num_hallway_tasks": 0,
+        "num_lobby_tasks": 0,
+        "num_outdoor_tasks": 0,
+        "num_elevator_tasks": 0,
+        "num_medical_tasks_total": 0,
+        "num_indoor_room_tasks": 0,
+
+        # RATIOS & DIVERSITY (4)
+        "cleaning_ratio": 0.0,
+        "trash_collection_ratio": 0.0,
+        "monitoring_ratio": 0.0,
+        "area_diversity": 0,
+    }
+
+
+# =========================================================
+# 2) MAIN: 2 TEXT INPUTS -> FEATURES
+# =========================================================
+def extract_keyword_features_reduced_from_two_texts(
+    task_normal: Optional[str],
+    task_dinhky: Optional[str],
+) -> Dict[str, float]:
+    """
+    Input:
+        task_normal: text công việc thường
+        task_dinhky: text công việc định kỳ
+    Output:
+        Dict keyword-features reduced (schema y như bạn định nghĩa)
+
+    Logic gộp giống bản gốc:
+        combined = task_normal + " ; " + task_dinhky
+    """
+
+    tn = "" if task_normal is None or (isinstance(task_normal, float) and pd.isna(task_normal)) else str(task_normal)
+    td = "" if task_dinhky is None or (isinstance(task_dinhky, float) and pd.isna(task_dinhky)) else str(task_dinhky)
+
+    combined = (tn.strip() + " ; " + td.strip()).strip()
+    if combined == "":
+        return _get_empty_features()
+
+    tasks = _split_tasks(combined)
+    num_tasks = len(tasks)
+    if num_tasks == 0:
+        return _get_empty_features()
+
+    # -----------------------------
+    # GROUP 1: TASK COUNTS (reduced)
+    # -----------------------------
+    cleaning_keywords = [
+        "vệ sinh", "lau", "chùi", "quét", "hút",
+        "đẩy khô", "lau ẩm", "làm sạch", "lau bụi", "lau kính", "lau sàn", "hút bụi"
+    ]
+    trash_keywords = [
+        "thu gom rác", "thay rác", "vận chuyển rác", "tua rác", "đổ rác",
+        "thu rác", "gom rác", "quét rác nổi", "trực rác", "rác nổi"
+    ]
+    monitoring_keywords = [
+        "trực", "trực phát sinh", "trực ps", "kiểm tra", "check",
+        "giám sát", "theo dõi", "tuần tra"
+    ]
+    deep_cleaning_keywords = [
+        "cọ rửa", "cọ bồn cầu", "cọ", "gạt kính", "đánh sàn",
+        "đánh chân tường", "chà tường", "cọ gương", "cọ lavabo"
+    ]
+    support_keywords = [
+        "giao ca", "bàn giao", "bàn giao ca", "chụp ảnh", "nhận ca",
+        "vsdc", "vệ sinh dụng cụ", "chuẩn bị dụng cụ", "chuẩn bị nước", "chuẩn bị", "giao ban"
+    ]
+
+    num_cleaning_tasks = _count_tasks_with_keywords(tasks, cleaning_keywords)
+    num_trash_collection_tasks = _count_tasks_with_keywords(tasks, trash_keywords)
+    num_monitoring_tasks = _count_tasks_with_keywords(tasks, monitoring_keywords)
+    num_deep_cleaning_tasks = _count_tasks_with_keywords(tasks, deep_cleaning_keywords)
+    num_support_tasks = _count_tasks_with_keywords(tasks, support_keywords)
+
+    all_keywords_for_other = (
+        cleaning_keywords + trash_keywords + monitoring_keywords + deep_cleaning_keywords + support_keywords
+    )
+    num_other_tasks = _count_tasks_without_keywords(tasks, all_keywords_for_other)
+
+    # -----------------------------
+    # GROUP 2: AREA COVERAGE (reduced + aggregated)
+    # -----------------------------
+    wc_keywords = [
+        "wc", "toilet", "nhà vệ sinh", "restroom", "phòng vệ sinh",
+        "bồn cầu", "lavabo", "tiểu nam", "bồn tiểu"
+    ]
+    hallway_keywords = ["hành lang", "corridor", "lối đi", "hall", "hl", "hanh lang"]
+    lobby_keywords = ["sảnh", "lobby", "tiền sảnh", "sảnh chính", "sanh"]
+    outdoor_keywords = ["ngoại cảnh", "sân", "vỉa hè", "khuôn viên", "cổng", "bãi xe", "tầng hầm"]
+    elevator_keywords = ["thang máy", "elevator", "lift", "cầu thang", "thang bộ", "tay vịn", "tam cấp"]
+
+    patient_room_keywords = ["phòng bệnh", "giường bệnh", "phòng vip", "phòng bệnh nhân", "pb", "phòng bv"]
+    clinic_room_keywords = ["phòng khám", "khoa khám", "phòng khám bệnh", "khu khám", "pk"]
+    surgery_room_keywords = ["phòng mổ", "hậu phẫu", "phòng phẫu thuật", "khu mổ", "phòng pt"]
+    technical_room_keywords = [
+        "phòng xét nghiệm", "phòng chụp", "xq", "siêu âm", "kho dược",
+        "phòng xn", "labo", "phòng thí nghiệm", "nội soi", "cấp cứu", "hồi sức"
+    ]
+
+    office_keywords = [
+        "phòng nhân viên", "phòng giám đốc", "phòng họp", "phòng hành chính",
+        "văn phòng", "phòng ban", "phòng giao ban", "hội trường", "phòng kế toán"
+    ]
+
+    num_wc_tasks = _count_tasks_with_keywords(tasks, wc_keywords)
+    num_hallway_tasks = _count_tasks_with_keywords(tasks, hallway_keywords)
+    num_lobby_tasks = _count_tasks_with_keywords(tasks, lobby_keywords)
+    num_outdoor_tasks = _count_tasks_with_keywords(tasks, outdoor_keywords)
+    num_elevator_tasks = _count_tasks_with_keywords(tasks, elevator_keywords)
+
+    num_patient_room_tasks = _count_tasks_with_keywords(tasks, patient_room_keywords)
+    num_clinic_room_tasks = _count_tasks_with_keywords(tasks, clinic_room_keywords)
+    num_surgery_room_tasks = _count_tasks_with_keywords(tasks, surgery_room_keywords)
+    num_technical_room_tasks = _count_tasks_with_keywords(tasks, technical_room_keywords)
+
+    num_medical_tasks_total = (
+        num_patient_room_tasks + num_clinic_room_tasks + num_surgery_room_tasks + num_technical_room_tasks
+    )
+
+    num_indoor_room_tasks = _count_tasks_with_keywords(tasks, office_keywords)
+
+    # -----------------------------
+    # GROUP 3: RATIOS & DIVERSITY (reduced)
+    # -----------------------------
+    cleaning_ratio = num_cleaning_tasks / num_tasks if num_tasks > 0 else 0.0
+    trash_collection_ratio = num_trash_collection_tasks / num_tasks if num_tasks > 0 else 0.0
+    monitoring_ratio = num_monitoring_tasks / num_tasks if num_tasks > 0 else 0.0
+
+    area_counts = [
+        num_wc_tasks, num_hallway_tasks, num_lobby_tasks, num_outdoor_tasks, num_elevator_tasks,
+        num_medical_tasks_total, num_indoor_room_tasks
+    ]
+    area_diversity = sum(1 for c in area_counts if c > 0)
+
+    return {
+        # TASK COUNTS (7)
+        "num_tasks": num_tasks,
+        "num_cleaning_tasks": num_cleaning_tasks,
+        "num_trash_collection_tasks": num_trash_collection_tasks,
+        "num_monitoring_tasks": num_monitoring_tasks,
+        "num_deep_cleaning_tasks": num_deep_cleaning_tasks,
+        "num_support_tasks": num_support_tasks,
+        "num_other_tasks": num_other_tasks,
+
+        # AREA (reduced + aggregated) (7)
+        "num_wc_tasks": num_wc_tasks,
+        "num_hallway_tasks": num_hallway_tasks,
+        "num_lobby_tasks": num_lobby_tasks,
+        "num_outdoor_tasks": num_outdoor_tasks,
+        "num_elevator_tasks": num_elevator_tasks,
+        "num_medical_tasks_total": num_medical_tasks_total,
+        "num_indoor_room_tasks": num_indoor_room_tasks,
+
+        # RATIOS & DIVERSITY (4)
+        "cleaning_ratio": round(cleaning_ratio, 4),
+        "trash_collection_ratio": round(trash_collection_ratio, 4),
+        "monitoring_ratio": round(monitoring_ratio, 4),
+        "area_diversity": area_diversity,
+    }
diff --git a/test3.ipynb b/test3.ipynb
deleted file mode 100644
index e69de29..0000000
diff --git a/train.ipynb b/train.ipynb
new file mode 100644
index 0000000..34ef06b
--- /dev/null
+++ b/train.ipynb
@@ -0,0 +1,2157 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e1667110",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded: final_2.xlsx | sheet: final\n",
+      "Shape (raw): (401, 42)\n",
+      "Shape (after dedup): (394, 42)\n",
+      "\n",
+      "=== TARGET SUMMARY (so_luong) ===\n",
+      "count    394.000000\n",
+      "mean       4.710660\n",
+      "std        6.848602\n",
+      "min        0.000000\n",
+      "25%        1.000000\n",
+      "50%        2.000000\n",
+      "75%        5.000000\n",
+      "max       64.000000\n",
+      "Name: so_luong, dtype: float64\n",
+      "Missing target: 0\n",
+      "Negative target: 0\n",
+      "Zero target: 3\n",
+      "\n",
+      "Sample rows:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ma_dia_diem</th>\n",
+       "      <th>all_task_normal</th>\n",
+       "      <th>all_task_dinhky</th>\n",
+       "      <th>loai_ca</th>\n",
+       "      <th>bat_dau</th>\n",
+       "      <th>ket_thuc</th>\n",
+       "      <th>tong_gio_lam</th>\n",
+       "      <th>so_ca_cua_toa</th>\n",
+       "      <th>so_luong</th>\n",
+       "      <th>num_tasks</th>\n",
+       "      <th>...</th>\n",
+       "      <th>dien_tich_tham</th>\n",
+       "      <th>doc_ham</th>\n",
+       "      <th>vien_phan_quang</th>\n",
+       "      <th>op_tuong</th>\n",
+       "      <th>op_chan_tuong</th>\n",
+       "      <th>ranh_thoat_nuoc</th>\n",
+       "      <th>dien_tich_kinh</th>\n",
+       "      <th>num_medical_tasks_total</th>\n",
+       "      <th>num_indoor_room_tasks</th>\n",
+       "      <th>is_tasks_text_missing</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>115-2</td>\n",
+       "      <td>Làm sạch toàn bộ phòng giao dịch tầng 1 (kể cả...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Part time</td>\n",
+       "      <td>06:30:00</td>\n",
+       "      <td>10:30:00</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>101-1</td>\n",
+       "      <td>Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...</td>\n",
+       "      <td>Lau bảng biển, bình cứu hỏa , cây nước hành la...</td>\n",
+       "      <td>Hành chính</td>\n",
+       "      <td>06:30:00</td>\n",
+       "      <td>16:00:00</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>24</td>\n",
+       "      <td>441</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9176.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>894.0</td>\n",
+       "      <td>112</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>101-1</td>\n",
+       "      <td>Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...</td>\n",
+       "      <td>Lau bảng biển, bình cứu hỏa , cây nước hành la...</td>\n",
+       "      <td>Ca sáng</td>\n",
+       "      <td>06:00:00</td>\n",
+       "      <td>14:00:00</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>441</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9176.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>894.0</td>\n",
+       "      <td>112</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 42 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  ma_dia_diem                                    all_task_normal  \\\n",
+       "0       115-2  Làm sạch toàn bộ phòng giao dịch tầng 1 (kể cả...   \n",
+       "1       101-1  Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...   \n",
+       "2       101-1  Kiểm tra nhân sự các vị trí Thay rác, đẩy khô,...   \n",
+       "\n",
+       "                                     all_task_dinhky     loai_ca   bat_dau  \\\n",
+       "0                                                NaN   Part time  06:30:00   \n",
+       "1  Lau bảng biển, bình cứu hỏa , cây nước hành la...  Hành chính  06:30:00   \n",
+       "2  Lau bảng biển, bình cứu hỏa , cây nước hành la...     Ca sáng  06:00:00   \n",
+       "\n",
+       "   ket_thuc  tong_gio_lam  so_ca_cua_toa  so_luong  num_tasks  ...  \\\n",
+       "0  10:30:00           4.0              1         1          7  ...   \n",
+       "1  16:00:00           7.5              6        24        441  ...   \n",
+       "2  14:00:00           8.0              6         3        441  ...   \n",
+       "\n",
+       "   dien_tich_tham  doc_ham  vien_phan_quang  op_tuong  op_chan_tuong  \\\n",
+       "0             0.0        0                0       0.0            0.0   \n",
+       "1             0.0       70                0    9176.0           89.0   \n",
+       "2             0.0       70                0    9176.0           89.0   \n",
+       "\n",
+       "   ranh_thoat_nuoc  dien_tich_kinh  num_medical_tasks_total  \\\n",
+       "0                0            20.0                        0   \n",
+       "1               25           894.0                      112   \n",
+       "2               25           894.0                      112   \n",
+       "\n",
+       "   num_indoor_room_tasks  is_tasks_text_missing  \n",
+       "0                      1                      0  \n",
+       "1                     39                      0  \n",
+       "2                     39                      0  \n",
+       "\n",
+       "[3 rows x 42 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CELL 1 — LOAD DATA & BASIC CLEAN\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "DATA_PATH = \"final_2.xlsx\"\n",
+    "SHEET_NAME = \"final\"\n",
+    "\n",
+    "# 1. Load\n",
+    "df = pd.read_excel(DATA_PATH, sheet_name=SHEET_NAME)\n",
+    "print(f\"Loaded: {DATA_PATH} | sheet: {SHEET_NAME}\")\n",
+    "print(\"Shape (raw):\", df.shape)\n",
+    "\n",
+    "# 2. Drop duplicate full rows\n",
+    "df = df.drop_duplicates().reset_index(drop=True)\n",
+    "print(\"Shape (after dedup):\", df.shape)\n",
+    "\n",
+    "# 3. Check target\n",
+    "assert \"so_luong\" in df.columns, \"❌ Missing target so_luong\"\n",
+    "\n",
+    "print(\"\\n=== TARGET SUMMARY (so_luong) ===\")\n",
+    "print(df[\"so_luong\"].describe())\n",
+    "print(\"Missing target:\", df[\"so_luong\"].isna().sum())\n",
+    "print(\"Negative target:\", (df[\"so_luong\"] < 0).sum())\n",
+    "print(\"Zero target:\", (df[\"so_luong\"] == 0).sum())\n",
+    "\n",
+    "# 4. Peek data\n",
+    "print(\"\\nSample rows:\")\n",
+    "display(df.head(3))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "5601efad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All columns:\n",
+      " 0: ma_dia_diem\n",
+      " 1: all_task_normal\n",
+      " 2: all_task_dinhky\n",
+      " 3: loai_ca\n",
+      " 4: bat_dau\n",
+      " 5: ket_thuc\n",
+      " 6: tong_gio_lam\n",
+      " 7: so_ca_cua_toa\n",
+      " 8: so_luong\n",
+      " 9: num_tasks\n",
+      "10: num_cleaning_tasks\n",
+      "11: num_trash_collection_tasks\n",
+      "12: num_monitoring_tasks\n",
+      "13: num_deep_cleaning_tasks\n",
+      "14: num_support_tasks\n",
+      "15: num_other_tasks\n",
+      "16: num_wc_tasks\n",
+      "17: num_hallway_tasks\n",
+      "18: num_lobby_tasks\n",
+      "19: num_outdoor_tasks\n",
+      "20: num_elevator_tasks\n",
+      "21: cleaning_ratio\n",
+      "22: trash_collection_ratio\n",
+      "23: monitoring_ratio\n",
+      "24: area_diversity\n",
+      "25: so_tang\n",
+      "26: so_cua_thang_may\n",
+      "27: dien_tich_ngoai_canh\n",
+      "28: dien_tich_sanh\n",
+      "29: dien_tich_hanh_lang\n",
+      "30: dien_tich_wc\n",
+      "31: dien_tich_phong\n",
+      "32: dien_tich_tham\n",
+      "33: doc_ham\n",
+      "34: vien_phan_quang\n",
+      "35: op_tuong\n",
+      "36: op_chan_tuong\n",
+      "37: ranh_thoat_nuoc\n",
+      "38: dien_tich_kinh\n",
+      "39: num_medical_tasks_total\n",
+      "40: num_indoor_room_tasks\n",
+      "41: is_tasks_text_missing\n",
+      "\n",
+      "Dropped columns:\n",
+      " - ma_dia_diem\n",
+      " - all_task_normal\n",
+      " - all_task_dinhky\n",
+      " - is_tasks_text_missing\n",
+      "\n",
+      "Shapes:\n",
+      "X: (394, 37)\n",
+      "y: (394,)\n",
+      "\n",
+      "Feature dtypes:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "int64      21\n",
+       "float64    13\n",
+       "object      3\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Missing values in X:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "loai_ca                       0\n",
+       "bat_dau                       0\n",
+       "ket_thuc                      0\n",
+       "tong_gio_lam                  0\n",
+       "so_ca_cua_toa                 0\n",
+       "num_tasks                     0\n",
+       "num_cleaning_tasks            0\n",
+       "num_trash_collection_tasks    0\n",
+       "num_monitoring_tasks          0\n",
+       "num_deep_cleaning_tasks       0\n",
+       "dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CELL 2 — FEATURE SELECTION (STRICT)\n",
+    "\n",
+    "# 1. Xem toàn bộ cột\n",
+    "print(\"All columns:\")\n",
+    "for i, c in enumerate(df.columns):\n",
+    "    print(f\"{i:2d}: {c}\")\n",
+    "\n",
+    "# 2. Xác định cột cần loại bỏ (THEO THỎA THUẬN)\n",
+    "DROP_COLS = [\n",
+    "    df.columns[0],   # ma_dia_diem\n",
+    "    df.columns[1],   # all_task_normal\n",
+    "    df.columns[2],   # all_task_dinhky\n",
+    "    df.columns[-1],  # is_tasks_text_missing\n",
+    "]\n",
+    "\n",
+    "print(\"\\nDropped columns:\")\n",
+    "for c in DROP_COLS:\n",
+    "    print(\" -\", c)\n",
+    "\n",
+    "# 3. Tạo X, y\n",
+    "X = df.drop(columns=DROP_COLS + [\"so_luong\"])\n",
+    "y = df[\"so_luong\"].astype(float)\n",
+    "\n",
+    "print(\"\\nShapes:\")\n",
+    "print(\"X:\", X.shape)\n",
+    "print(\"y:\", y.shape)\n",
+    "\n",
+    "# 4. Kiểm tra kiểu dữ liệu\n",
+    "print(\"\\nFeature dtypes:\")\n",
+    "display(X.dtypes.value_counts())\n",
+    "\n",
+    "# 5. Kiểm tra missing\n",
+    "print(\"\\nMissing values in X:\")\n",
+    "display(X.isna().sum().sort_values(ascending=False).head(10))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "bb467e4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Categorical columns: ['loai_ca']\n",
+      "\n",
+      "After preprocess:\n",
+      "X_proc shape: (394, 46)\n",
+      "Any non-numeric dtypes? False\n",
+      "\n",
+      "Sample columns (first 30):\n",
+      "['tong_gio_lam', 'so_ca_cua_toa', 'num_tasks', 'num_cleaning_tasks', 'num_trash_collection_tasks', 'num_monitoring_tasks', 'num_deep_cleaning_tasks', 'num_support_tasks', 'num_other_tasks', 'num_wc_tasks', 'num_hallway_tasks', 'num_lobby_tasks', 'num_outdoor_tasks', 'num_elevator_tasks', 'cleaning_ratio', 'trash_collection_ratio', 'monitoring_ratio', 'area_diversity', 'so_tang', 'so_cua_thang_may', 'dien_tich_ngoai_canh', 'dien_tich_sanh', 'dien_tich_hanh_lang', 'dien_tich_wc', 'dien_tich_phong', 'dien_tich_tham', 'doc_ham', 'vien_phan_quang', 'op_tuong', 'op_chan_tuong']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# CELL 3 — PREPROCESS (TIME + CATEGORICAL) WITHOUT JUNK\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "# ---------- 1) Time parsing ----------\n",
+    "def time_to_hour(x):\n",
+    "    if pd.isna(x):\n",
+    "        return np.nan\n",
+    "\n",
+    "    # datetime/time object\n",
+    "    if hasattr(x, \"hour\"):\n",
+    "        return float(x.hour) + float(getattr(x, \"minute\", 0))/60.0\n",
+    "\n",
+    "    s = str(x).strip()\n",
+    "    # \"YYYY-MM-DD HH:MM:SS\"\n",
+    "    if \" \" in s and \":\" in s:\n",
+    "        s = s.split(\" \", 1)[1].strip()\n",
+    "\n",
+    "    # \"HH:MM\" or \"HH:MM:SS\"\n",
+    "    if \":\" in s:\n",
+    "        parts = s.split(\":\")\n",
+    "        try:\n",
+    "            h = float(parts[0])\n",
+    "            m = float(parts[1]) if len(parts) > 1 else 0.0\n",
+    "            return h + m/60.0\n",
+    "        except:\n",
+    "            return np.nan\n",
+    "\n",
+    "    # numeric fallback\n",
+    "    try:\n",
+    "        return float(s)\n",
+    "    except:\n",
+    "        return np.nan\n",
+    "\n",
+    "# Create new numeric time features (do NOT one-hot time)\n",
+    "X_proc = X.copy()\n",
+    "\n",
+    "if \"bat_dau\" in X_proc.columns:\n",
+    "    X_proc[\"hour_start\"] = X_proc[\"bat_dau\"].apply(time_to_hour)\n",
+    "if \"ket_thuc\" in X_proc.columns:\n",
+    "    X_proc[\"hour_end\"] = X_proc[\"ket_thuc\"].apply(time_to_hour)\n",
+    "\n",
+    "# shift_length + cross day\n",
+    "if (\"hour_start\" in X_proc.columns) and (\"hour_end\" in X_proc.columns):\n",
+    "    end_adj = X_proc[\"hour_end\"].copy()\n",
+    "    cross = (X_proc[\"hour_start\"].notna()) & (X_proc[\"hour_end\"].notna()) & (X_proc[\"hour_end\"] < X_proc[\"hour_start\"])\n",
+    "    end_adj[cross] = end_adj[cross] + 24.0\n",
+    "\n",
+    "    X_proc[\"shift_length\"] = (end_adj - X_proc[\"hour_start\"]).clip(lower=0)\n",
+    "    X_proc[\"is_cross_day\"] = cross.astype(int)\n",
+    "\n",
+    "# Drop raw time cols to avoid junk\n",
+    "for c in [\"bat_dau\", \"ket_thuc\"]:\n",
+    "    if c in X_proc.columns:\n",
+    "        X_proc = X_proc.drop(columns=[c])\n",
+    "\n",
+    "# ---------- 2) One-hot categorical ----------\n",
+    "cat_cols = [c for c in X_proc.columns if X_proc[c].dtype == \"object\"]\n",
+    "print(\"Categorical columns:\", cat_cols)\n",
+    "\n",
+    "X_proc = pd.get_dummies(X_proc, columns=cat_cols, dummy_na=True)\n",
+    "\n",
+    "# ---------- 3) Fill missing ----------\n",
+    "X_proc = X_proc.replace([np.inf, -np.inf], np.nan).fillna(0)\n",
+    "\n",
+    "print(\"\\nAfter preprocess:\")\n",
+    "print(\"X_proc shape:\", X_proc.shape)\n",
+    "print(\"Any non-numeric dtypes?\", any(dt == \"object\" for dt in X_proc.dtypes))\n",
+    "\n",
+    "print(\"\\nSample columns (first 30):\")\n",
+    "print(list(X_proc.columns[:30]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "3cd119b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes:\n",
+      "Train: (326, 46) (326,)\n",
+      "Val  : (68, 46) (68,)\n",
+      "\n",
+      "ElasticNet(alpha=0.01, l1_ratio=0.5)\n",
+      " Train | MAE=2.754 | RMSE=5.200 | R2=0.458\n",
+      " Val   | MAE=2.420 | RMSE=4.792 | R2=0.259\n",
+      "\n",
+      "DecisionTree(max_depth=8, min_samples_leaf=5)\n",
+      " Train | MAE=2.187 | RMSE=5.313 | R2=0.434\n",
+      " Val   | MAE=2.312 | RMSE=4.149 | R2=0.445\n",
+      "\n",
+      "RandomForest(n_estimators=600, min_samples_leaf=3)\n",
+      " Train | MAE=1.894 | RMSE=4.820 | R2=0.535\n",
+      " Val   | MAE=2.402 | RMSE=4.467 | R2=0.356\n",
+      "\n",
+      "ExtraTrees(n_estimators=800, min_samples_leaf=2)\n",
+      " Train | MAE=1.095 | RMSE=3.320 | R2=0.779\n",
+      " Val   | MAE=1.968 | RMSE=3.461 | R2=0.614\n",
+      "\n",
+      "HistGradientBoosting(learning_rate=0.05, min_samples_leaf=20)\n",
+      " Train | MAE=1.990 | RMSE=4.871 | R2=0.525\n",
+      " Val   | MAE=2.406 | RMSE=4.599 | R2=0.318\n",
+      "\n",
+      "=== HOLDOUT VAL MAE summary (lower is better) ===\n",
+      "ExtraTrees  : Val MAE = 1.968\n",
+      "DecisionTree: Val MAE = 2.312\n",
+      "RandomForest: Val MAE = 2.402\n",
+      "HGBR        : Val MAE = 2.406\n",
+      "ElasticNet  : Val MAE = 2.420\n"
+     ]
+    }
+   ],
+   "source": [
+    "# CELL 4 (EXTENDED) — ADD TREE/ENSEMBLE MODELS TO HOLDOUT VAL\n",
+    "\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import GroupShuffleSplit\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor, HistGradientBoostingRegressor\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+    "\n",
+    "# ---------- 1) Group split (80% train, 20% val) ----------\n",
+    "groups = df[\"ma_dia_diem\"].astype(str)\n",
+    "\n",
+    "gss = GroupShuffleSplit(n_splits=1, train_size=0.8, random_state=42)\n",
+    "train_idx, val_idx = next(gss.split(X_proc, y, groups))\n",
+    "\n",
+    "X_train = X_proc.iloc[train_idx]\n",
+    "y_train = y.iloc[train_idx]\n",
+    "\n",
+    "X_val = X_proc.iloc[val_idx]\n",
+    "y_val = y.iloc[val_idx]\n",
+    "\n",
+    "print(\"Shapes:\")\n",
+    "print(\"Train:\", X_train.shape, y_train.shape)\n",
+    "print(\"Val  :\", X_val.shape, y_val.shape)\n",
+    "\n",
+    "# ---------- 2) Scale features (for linear models) ----------\n",
+    "scaler = StandardScaler()\n",
+    "X_train_s = scaler.fit_transform(X_train)\n",
+    "X_val_s   = scaler.transform(X_val)\n",
+    "\n",
+    "# ---------- 2b) No-scale matrices (for tree models) ----------\n",
+    "X_train_ns = X_train.values\n",
+    "X_val_ns   = X_val.values\n",
+    "\n",
+    "# ---------- 3) Log-transform target ----------\n",
+    "y_train_log = np.log1p(y_train)\n",
+    "y_val_log   = np.log1p(y_val)\n",
+    "\n",
+    "# ---------- 4) Evaluation helper (support scale/no-scale) ----------\n",
+    "def eval_reg_any(name, model, use_scaled_X=True):\n",
+    "    Xtr = X_train_s if use_scaled_X else X_train_ns\n",
+    "    Xva = X_val_s   if use_scaled_X else X_val_ns\n",
+    "\n",
+    "    model.fit(Xtr, y_train_log)\n",
+    "\n",
+    "    pred_train = np.maximum(0, np.expm1(model.predict(Xtr)))\n",
+    "    pred_val   = np.maximum(0, np.expm1(model.predict(Xva)))\n",
+    "\n",
+    "    def _m(y_true, y_pred):\n",
+    "        return (\n",
+    "            mean_absolute_error(y_true, y_pred),\n",
+    "            mean_squared_error(y_true, y_pred) ** 0.5,\n",
+    "            r2_score(y_true, y_pred),\n",
+    "        )\n",
+    "\n",
+    "    tr = _m(y_train, pred_train)\n",
+    "    va = _m(y_val, pred_val)\n",
+    "\n",
+    "    print(f\"\\n{name}\")\n",
+    "    print(f\" Train | MAE={tr[0]:.3f} | RMSE={tr[1]:.3f} | R2={tr[2]:.3f}\")\n",
+    "    print(f\" Val   | MAE={va[0]:.3f} | RMSE={va[1]:.3f} | R2={va[2]:.3f}\")\n",
+    "\n",
+    "    return va[0]\n",
+    "\n",
+    "# ---------- 5) Train baselines + tree/ensemble ----------\n",
+    "results = {}\n",
+    "\n",
+    "# Linear (scaled)\n",
+    "# results[\"Linear\"] = eval_reg_any(\"LinearRegression\", LinearRegression(), use_scaled_X=True)\n",
+    "# results[\"Ridge\"]  = eval_reg_any(\"Ridge(alpha=1.0)\", Ridge(alpha=1.0), use_scaled_X=True)\n",
+    "# results[\"Lasso\"]  = eval_reg_any(\"Lasso(alpha=0.01)\", Lasso(alpha=0.01, max_iter=5000), use_scaled_X=True)\n",
+    "results[\"ElasticNet\"] = eval_reg_any(\n",
+    "    \"ElasticNet(alpha=0.01, l1_ratio=0.5)\",\n",
+    "    ElasticNet(alpha=0.01, l1_ratio=0.5, max_iter=5000),\n",
+    "    use_scaled_X=True\n",
+    ")\n",
+    "\n",
+    "# Tree / Ensemble (no scale)\n",
+    "results[\"DecisionTree\"] = eval_reg_any(\n",
+    "    \"DecisionTree(max_depth=8, min_samples_leaf=5)\",\n",
+    "    DecisionTreeRegressor(max_depth=8, min_samples_leaf=5, random_state=42),\n",
+    "    use_scaled_X=False\n",
+    ")\n",
+    "\n",
+    "results[\"RandomForest\"] = eval_reg_any(\n",
+    "    \"RandomForest(n_estimators=600, min_samples_leaf=3)\",\n",
+    "    RandomForestRegressor(\n",
+    "        n_estimators=600, min_samples_leaf=3, random_state=42, n_jobs=-1\n",
+    "    ),\n",
+    "    use_scaled_X=False\n",
+    ")\n",
+    "\n",
+    "results[\"ExtraTrees\"] = eval_reg_any(\n",
+    "    \"ExtraTrees(n_estimators=800, min_samples_leaf=2)\",\n",
+    "    ExtraTreesRegressor(\n",
+    "        n_estimators=800, min_samples_leaf=2, random_state=42, n_jobs=-1\n",
+    "    ),\n",
+    "    use_scaled_X=False\n",
+    ")\n",
+    "\n",
+    "results[\"HGBR\"] = eval_reg_any(\n",
+    "    \"HistGradientBoosting(learning_rate=0.05, min_samples_leaf=20)\",\n",
+    "    HistGradientBoostingRegressor(\n",
+    "        learning_rate=0.05, min_samples_leaf=20, max_leaf_nodes=31, random_state=42\n",
+    "    ),\n",
+    "    use_scaled_X=False\n",
+    ")\n",
+    "\n",
+    "print(\"\\n=== HOLDOUT VAL MAE summary (lower is better) ===\")\n",
+    "for k, v in sorted(results.items(), key=lambda x: x[1]):\n",
+    "    print(f\"{k:12s}: Val MAE = {v:.3f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "106e557f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train for CV: (326, 46) (326,) | unique groups: 153\n",
+      "\n",
+      "=== ExtraTrees GroupKFold (TRAIN ONLY) ===\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>MAE</th>\n",
+       "      <th>RMSE</th>\n",
+       "      <th>R2</th>\n",
+       "      <th>fold</th>\n",
+       "      <th>n_val</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.677199</td>\n",
+       "      <td>7.263821</td>\n",
+       "      <td>0.048081</td>\n",
+       "      <td>1</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3.673333</td>\n",
+       "      <td>6.600086</td>\n",
+       "      <td>0.104487</td>\n",
+       "      <td>2</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.092924</td>\n",
+       "      <td>8.986669</td>\n",
+       "      <td>0.087366</td>\n",
+       "      <td>3</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.100641</td>\n",
+       "      <td>3.499245</td>\n",
+       "      <td>0.291277</td>\n",
+       "      <td>4</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2.550497</td>\n",
+       "      <td>4.739900</td>\n",
+       "      <td>0.407946</td>\n",
+       "      <td>5</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        MAE      RMSE        R2  fold  n_val\n",
+       "0  3.677199  7.263821  0.048081     1     66\n",
+       "1  3.673333  6.600086  0.104487     2     65\n",
+       "2  3.092924  8.986669  0.087366     3     65\n",
+       "3  2.100641  3.499245  0.291277     4     65\n",
+       "4  2.550497  4.739900  0.407946     5     65"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "=== CV SUMMARY ===\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>MAE</th>\n",
+       "      <th>RMSE</th>\n",
+       "      <th>R2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.018919</td>\n",
+       "      <td>6.217944</td>\n",
+       "      <td>0.187832</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.694572</td>\n",
+       "      <td>2.149515</td>\n",
+       "      <td>0.154694</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           MAE      RMSE        R2\n",
+       "mean  3.018919  6.217944  0.187832\n",
+       "std   0.694572  2.149515  0.154694"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Worst fold (highest MAE):\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>MAE</th>\n",
+       "      <th>RMSE</th>\n",
+       "      <th>R2</th>\n",
+       "      <th>fold</th>\n",
+       "      <th>n_val</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.677199</td>\n",
+       "      <td>7.263821</td>\n",
+       "      <td>0.048081</td>\n",
+       "      <td>1</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        MAE      RMSE        R2  fold  n_val\n",
+       "0  3.677199  7.263821  0.048081     1     66"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CELL 5 — GROUP K-FOLD CV (TRAIN ONLY) FOR EXTRA TREES (NO LEAKAGE by ma_dia_diem)\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "from sklearn.model_selection import GroupKFold\n",
+    "from sklearn.ensemble import ExtraTreesRegressor\n",
+    "from sklearn.base import clone\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+    "\n",
+    "# =========================\n",
+    "# 1) Prepare TRAIN data + groups\n",
+    "# =========================\n",
+    "X_tr = X_train.values          # no scaling for tree\n",
+    "y_tr = y_train.values.astype(float)\n",
+    "g_tr = df.loc[train_idx, \"ma_dia_diem\"].astype(str).values\n",
+    "\n",
+    "print(\"Train for CV:\", X_tr.shape, y_tr.shape, \"| unique groups:\", len(np.unique(g_tr)))\n",
+    "\n",
+    "# =========================\n",
+    "# 2) Metric helper (evaluate on original scale)\n",
+    "# =========================\n",
+    "def metrics(y_true, y_pred):\n",
+    "    y_pred = np.maximum(0, y_pred)\n",
+    "    return {\n",
+    "        \"MAE\":  mean_absolute_error(y_true, y_pred),\n",
+    "        \"RMSE\": mean_squared_error(y_true, y_pred) ** 0.5,\n",
+    "        \"R2\":   r2_score(y_true, y_pred),\n",
+    "    }\n",
+    "\n",
+    "# =========================\n",
+    "# 3) ExtraTrees config (same as your holdout baseline)\n",
+    "# =========================\n",
+    "base_model = ExtraTreesRegressor(\n",
+    "    n_estimators=800,\n",
+    "    min_samples_leaf=2,\n",
+    "    random_state=42,\n",
+    "    n_jobs=-1\n",
+    ")\n",
+    "\n",
+    "# =========================\n",
+    "# 4) GroupKFold CV (fit log1p(y) -> expm1(pred))\n",
+    "# =========================\n",
+    "gkf = GroupKFold(n_splits=5)\n",
+    "\n",
+    "rows = []\n",
+    "for fold, (tr_idx, va_idx) in enumerate(gkf.split(X_tr, y_tr, groups=g_tr), start=1):\n",
+    "    model = clone(base_model)\n",
+    "\n",
+    "    Xtr_f, Xva_f = X_tr[tr_idx], X_tr[va_idx]\n",
+    "    ytr_f, yva_f = y_tr[tr_idx], y_tr[va_idx]\n",
+    "\n",
+    "    model.fit(Xtr_f, np.log1p(ytr_f))\n",
+    "    pred_va = np.expm1(model.predict(Xva_f))\n",
+    "    pred_va = np.maximum(0, pred_va)\n",
+    "\n",
+    "    m = metrics(yva_f, pred_va)\n",
+    "    m[\"fold\"] = fold\n",
+    "    m[\"n_val\"] = len(va_idx)\n",
+    "    rows.append(m)\n",
+    "\n",
+    "cv_df = pd.DataFrame(rows)\n",
+    "\n",
+    "print(\"\\n=== ExtraTrees GroupKFold (TRAIN ONLY) ===\")\n",
+    "display(cv_df)\n",
+    "\n",
+    "summary = cv_df[[\"MAE\", \"RMSE\", \"R2\"]].agg([\"mean\", \"std\"])\n",
+    "print(\"\\n=== CV SUMMARY ===\")\n",
+    "display(summary)\n",
+    "\n",
+    "best_worst = cv_df.sort_values(\"MAE\", ascending=False)\n",
+    "print(\"\\nWorst fold (highest MAE):\")\n",
+    "display(best_worst.head(1))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ee66e389",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Linear       | MAE = 4.971 ± 2.813\n",
+      "Lasso_0.01   | MAE = 7.110 ± 7.917\n",
+      "ElasticNet_0.01 | MAE = 6.386 ± 6.240\n",
+      "\n",
+      "=== CV SUMMARY ===\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Linear</th>\n",
+       "      <td>4.970868</td>\n",
+       "      <td>3.145106</td>\n",
+       "      <td>2.279954</td>\n",
+       "      <td>10.321992</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ElasticNet_0.01</th>\n",
+       "      <td>6.386372</td>\n",
+       "      <td>6.976535</td>\n",
+       "      <td>2.104239</td>\n",
+       "      <td>18.765868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lasso_0.01</th>\n",
+       "      <td>7.110267</td>\n",
+       "      <td>8.851455</td>\n",
+       "      <td>2.113510</td>\n",
+       "      <td>22.876806</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     mean       std       min        max\n",
+       "model                                                   \n",
+       "Linear           4.970868  3.145106  2.279954  10.321992\n",
+       "ElasticNet_0.01  6.386372  6.976535  2.104239  18.765868\n",
+       "Lasso_0.01       7.110267  8.851455  2.113510  22.876806"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CELL 5 — GROUP K-FOLD CONFIRMATION (TOP LINEAR MODELS)\n",
+    "\n",
+    "from sklearn.model_selection import GroupKFold\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.linear_model import LinearRegression, Lasso, ElasticNet\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "models = {\n",
+    "    \"Linear\": LinearRegression(),\n",
+    "    \"Lasso_0.01\": Lasso(alpha=0.01, max_iter=5000),\n",
+    "    \"ElasticNet_0.01\": ElasticNet(alpha=0.01, l1_ratio=0.5, max_iter=5000),\n",
+    "}\n",
+    "\n",
+    "groups = df[\"ma_dia_diem\"].astype(str)\n",
+    "gkf = GroupKFold(n_splits=5)\n",
+    "\n",
+    "rows = []\n",
+    "\n",
+    "for name, model in models.items():\n",
+    "    maes = []\n",
+    "    for fold, (tr_idx, va_idx) in enumerate(gkf.split(X_proc, y, groups)):\n",
+    "        X_tr, X_va = X_proc.iloc[tr_idx], X_proc.iloc[va_idx]\n",
+    "        y_tr, y_va = y.iloc[tr_idx], y.iloc[va_idx]\n",
+    "\n",
+    "        scaler = StandardScaler()\n",
+    "        X_tr_s = scaler.fit_transform(X_tr)\n",
+    "        X_va_s = scaler.transform(X_va)\n",
+    "\n",
+    "        model.fit(X_tr_s, np.log1p(y_tr))\n",
+    "        pred_va = np.maximum(0, np.expm1(model.predict(X_va_s)))\n",
+    "\n",
+    "        mae = mean_absolute_error(y_va, pred_va)\n",
+    "        maes.append(mae)\n",
+    "\n",
+    "        rows.append({\n",
+    "            \"model\": name,\n",
+    "            \"fold\": fold,\n",
+    "            \"MAE\": mae,\n",
+    "            \"n_val\": len(va_idx),\n",
+    "        })\n",
+    "\n",
+    "    print(f\"{name:12s} | MAE = {np.mean(maes):.3f} ± {np.std(maes):.3f}\")\n",
+    "\n",
+    "cv_df = pd.DataFrame(rows)\n",
+    "\n",
+    "print(\"\\n=== CV SUMMARY ===\")\n",
+    "display(\n",
+    "    cv_df.groupby(\"model\")[\"MAE\"]\n",
+    "    .agg([\"mean\", \"std\", \"min\", \"max\"])\n",
+    "    .sort_values(\"mean\")\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "73a31e6e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== GROUP K-FOLD CV LEADERBOARD (TRAIN ONLY) ===\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>MAE_mean</th>\n",
+       "      <th>MAE_std</th>\n",
+       "      <th>RMSE_mean</th>\n",
+       "      <th>R2_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ExtraTrees</td>\n",
+       "      <td>3.018919</td>\n",
+       "      <td>0.694572</td>\n",
+       "      <td>6.217944</td>\n",
+       "      <td>0.187832</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>3.052084</td>\n",
+       "      <td>0.715409</td>\n",
+       "      <td>6.243409</td>\n",
+       "      <td>0.187735</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>HGBR</td>\n",
+       "      <td>3.105344</td>\n",
+       "      <td>0.673739</td>\n",
+       "      <td>6.344961</td>\n",
+       "      <td>0.156316</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DecisionTree</td>\n",
+       "      <td>3.681985</td>\n",
+       "      <td>0.668149</td>\n",
+       "      <td>6.861043</td>\n",
+       "      <td>-0.017019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Lasso</td>\n",
+       "      <td>4.366601</td>\n",
+       "      <td>1.709182</td>\n",
+       "      <td>10.584715</td>\n",
+       "      <td>-2.703761</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ElasticNet</td>\n",
+       "      <td>4.918275</td>\n",
+       "      <td>2.414753</td>\n",
+       "      <td>13.602627</td>\n",
+       "      <td>-15.883303</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          model  MAE_mean   MAE_std  RMSE_mean    R2_mean\n",
+       "4    ExtraTrees  3.018919  0.694572   6.217944   0.187832\n",
+       "3  RandomForest  3.052084  0.715409   6.243409   0.187735\n",
+       "5          HGBR  3.105344  0.673739   6.344961   0.156316\n",
+       "2  DecisionTree  3.681985  0.668149   6.861043  -0.017019\n",
+       "0         Lasso  4.366601  1.709182  10.584715  -2.703761\n",
+       "1    ElasticNet  4.918275  2.414753  13.602627 -15.883303"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "=== Per-fold details (worst MAE first) ===\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>MAE</th>\n",
+       "      <th>RMSE</th>\n",
+       "      <th>R2</th>\n",
+       "      <th>model</th>\n",
+       "      <th>fold</th>\n",
+       "      <th>n_val</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8.972213</td>\n",
+       "      <td>37.201319</td>\n",
+       "      <td>-79.102177</td>\n",
+       "      <td>ElasticNet</td>\n",
+       "      <td>4</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.342893</td>\n",
+       "      <td>19.028144</td>\n",
+       "      <td>-5.532246</td>\n",
+       "      <td>Lasso</td>\n",
+       "      <td>1</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5.347237</td>\n",
+       "      <td>9.838825</td>\n",
+       "      <td>-0.746450</td>\n",
+       "      <td>ElasticNet</td>\n",
+       "      <td>1</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>4.347409</td>\n",
+       "      <td>7.407078</td>\n",
+       "      <td>0.010163</td>\n",
+       "      <td>DecisionTree</td>\n",
+       "      <td>1</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.288895</td>\n",
+       "      <td>12.729106</td>\n",
+       "      <td>-8.378285</td>\n",
+       "      <td>Lasso</td>\n",
+       "      <td>4</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>4.144338</td>\n",
+       "      <td>9.678611</td>\n",
+       "      <td>-0.058584</td>\n",
+       "      <td>DecisionTree</td>\n",
+       "      <td>3</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>3.772958</td>\n",
+       "      <td>7.109340</td>\n",
+       "      <td>-0.039038</td>\n",
+       "      <td>DecisionTree</td>\n",
+       "      <td>2</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>3.742208</td>\n",
+       "      <td>6.724452</td>\n",
+       "      <td>0.070421</td>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>2</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>3.721214</td>\n",
+       "      <td>7.376454</td>\n",
+       "      <td>0.018331</td>\n",
+       "      <td>HGBR</td>\n",
+       "      <td>1</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>3.684392</td>\n",
+       "      <td>6.736473</td>\n",
+       "      <td>0.067094</td>\n",
+       "      <td>HGBR</td>\n",
+       "      <td>2</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>3.677199</td>\n",
+       "      <td>7.263821</td>\n",
+       "      <td>0.048081</td>\n",
+       "      <td>ExtraTrees</td>\n",
+       "      <td>1</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>3.673333</td>\n",
+       "      <td>6.600086</td>\n",
+       "      <td>0.104487</td>\n",
+       "      <td>ExtraTrees</td>\n",
+       "      <td>2</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>3.557926</td>\n",
+       "      <td>7.090878</td>\n",
+       "      <td>0.092870</td>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>1</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3.511097</td>\n",
+       "      <td>6.344130</td>\n",
+       "      <td>0.172598</td>\n",
+       "      <td>Lasso</td>\n",
+       "      <td>2</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>3.507451</td>\n",
+       "      <td>5.627415</td>\n",
+       "      <td>0.165473</td>\n",
+       "      <td>DecisionTree</td>\n",
+       "      <td>5</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         MAE       RMSE         R2         model  fold  n_val\n",
+       "8   8.972213  37.201319 -79.102177    ElasticNet     4     65\n",
+       "0   7.342893  19.028144  -5.532246         Lasso     1     66\n",
+       "5   5.347237   9.838825  -0.746450    ElasticNet     1     66\n",
+       "10  4.347409   7.407078   0.010163  DecisionTree     1     66\n",
+       "3   4.288895  12.729106  -8.378285         Lasso     4     65\n",
+       "12  4.144338   9.678611  -0.058584  DecisionTree     3     65\n",
+       "11  3.772958   7.109340  -0.039038  DecisionTree     2     65\n",
+       "16  3.742208   6.724452   0.070421  RandomForest     2     65\n",
+       "25  3.721214   7.376454   0.018331          HGBR     1     66\n",
+       "26  3.684392   6.736473   0.067094          HGBR     2     65\n",
+       "20  3.677199   7.263821   0.048081    ExtraTrees     1     66\n",
+       "21  3.673333   6.600086   0.104487    ExtraTrees     2     65\n",
+       "15  3.557926   7.090878   0.092870  RandomForest     1     66\n",
+       "1   3.511097   6.344130   0.172598         Lasso     2     65\n",
+       "14  3.507451   5.627415   0.165473  DecisionTree     5     65"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CELL 6 — GROUP K-FOLD CV: TRY MULTIPLE MODELS (FAIR COMPARISON)\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "from sklearn.model_selection import GroupKFold\n",
+    "from sklearn.base import clone\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+    "\n",
+    "from sklearn.linear_model import Lasso, ElasticNet\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor, HistGradientBoostingRegressor\n",
+    "\n",
+    "# =========================\n",
+    "# 1) Prepare TRAIN data + groups\n",
+    "# =========================\n",
+    "X_tr_df = X_train.copy()  # keep dataframe\n",
+    "y_tr = y_train.astype(float).values\n",
+    "g_tr = df.loc[train_idx, \"ma_dia_diem\"].astype(str).values\n",
+    "\n",
+    "# =========================\n",
+    "# 2) Metric helper\n",
+    "# =========================\n",
+    "def metrics(y_true, y_pred):\n",
+    "    y_pred = np.maximum(0, y_pred)\n",
+    "    return {\n",
+    "        \"MAE\":  mean_absolute_error(y_true, y_pred),\n",
+    "        \"RMSE\": mean_squared_error(y_true, y_pred) ** 0.5,\n",
+    "        \"R2\":   r2_score(y_true, y_pred),\n",
+    "    }\n",
+    "\n",
+    "def cv_eval_model(name, model, X_df, y, groups, n_splits=5, use_log=True):\n",
+    "    gkf = GroupKFold(n_splits=n_splits)\n",
+    "    rows = []\n",
+    "\n",
+    "    for fold, (tr_i, va_i) in enumerate(gkf.split(X_df, y, groups=groups), start=1):\n",
+    "        m = clone(model)\n",
+    "\n",
+    "        Xtr = X_df.iloc[tr_i]\n",
+    "        Xva = X_df.iloc[va_i]\n",
+    "        ytr = y[tr_i]\n",
+    "        yva = y[va_i]\n",
+    "\n",
+    "        if use_log:\n",
+    "            m.fit(Xtr, np.log1p(ytr))\n",
+    "            pred = np.expm1(m.predict(Xva))\n",
+    "        else:\n",
+    "            m.fit(Xtr, ytr)\n",
+    "            pred = m.predict(Xva)\n",
+    "\n",
+    "        mm = metrics(yva, pred)\n",
+    "        mm.update({\"model\": name, \"fold\": fold, \"n_val\": len(va_i)})\n",
+    "        rows.append(mm)\n",
+    "\n",
+    "    out = pd.DataFrame(rows)\n",
+    "    summ = out[[\"MAE\", \"RMSE\", \"R2\"]].agg([\"mean\", \"std\"])\n",
+    "    return out, summ\n",
+    "\n",
+    "# =========================\n",
+    "# 3) Define candidates\n",
+    "# =========================\n",
+    "candidates = []\n",
+    "\n",
+    "# Linear (need scale)\n",
+    "candidates.append((\"Lasso\", Pipeline([\n",
+    "    (\"scaler\", StandardScaler()),\n",
+    "    (\"model\", Lasso(alpha=0.01, max_iter=5000, random_state=42))\n",
+    "]), True))\n",
+    "\n",
+    "candidates.append((\"ElasticNet\", Pipeline([\n",
+    "    (\"scaler\", StandardScaler()),\n",
+    "    (\"model\", ElasticNet(alpha=0.01, l1_ratio=0.5, max_iter=8000, random_state=42))\n",
+    "]), True))\n",
+    "\n",
+    "# Tree/Ensemble (no scale needed)\n",
+    "candidates.append((\"DecisionTree\", DecisionTreeRegressor(\n",
+    "    max_depth=8, min_samples_leaf=5, random_state=42\n",
+    "), True))\n",
+    "\n",
+    "candidates.append((\"RandomForest\", RandomForestRegressor(\n",
+    "    n_estimators=600, min_samples_leaf=3, random_state=42, n_jobs=-1\n",
+    "), True))\n",
+    "\n",
+    "candidates.append((\"ExtraTrees\", ExtraTreesRegressor(\n",
+    "    n_estimators=800, min_samples_leaf=2, random_state=42, n_jobs=-1\n",
+    "), True))\n",
+    "\n",
+    "candidates.append((\"HGBR\", HistGradientBoostingRegressor(\n",
+    "    learning_rate=0.05, min_samples_leaf=20, max_leaf_nodes=31, random_state=42\n",
+    "), True))\n",
+    "\n",
+    "# =========================\n",
+    "# 4) Run CV for all models\n",
+    "# =========================\n",
+    "all_fold = []\n",
+    "rows_lb = []\n",
+    "\n",
+    "for name, model, use_log in candidates:\n",
+    "    fold_df, summ = cv_eval_model(name, model, X_tr_df, y_tr, g_tr, n_splits=5, use_log=use_log)\n",
+    "    all_fold.append(fold_df)\n",
+    "\n",
+    "    rows_lb.append({\n",
+    "        \"model\": name,\n",
+    "        \"MAE_mean\":  summ.loc[\"mean\", \"MAE\"],\n",
+    "        \"MAE_std\":   summ.loc[\"std\",  \"MAE\"],\n",
+    "        \"RMSE_mean\": summ.loc[\"mean\", \"RMSE\"],\n",
+    "        \"R2_mean\":   summ.loc[\"mean\", \"R2\"],\n",
+    "    })\n",
+    "\n",
+    "cv_all = pd.concat(all_fold, ignore_index=True)\n",
+    "leaderboard = pd.DataFrame(rows_lb).sort_values([\"MAE_mean\", \"RMSE_mean\"], ascending=True)\n",
+    "\n",
+    "print(\"=== GROUP K-FOLD CV LEADERBOARD (TRAIN ONLY) ===\")\n",
+    "display(leaderboard)\n",
+    "\n",
+    "print(\"\\n=== Per-fold details (worst MAE first) ===\")\n",
+    "display(cv_all.sort_values(\"MAE\", ascending=False).head(15))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "1d408c02",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== FINAL MODEL: ExtraTrees (fit on TRAIN, eval on HOLDOUT VAL) ===\n",
+      "\n",
+      "[VAL (raw)]\n",
+      "MAE : 1.968\n",
+      "RMSE: 3.461\n",
+      "R2  : 0.614\n",
+      "\n",
+      "[VAL (rounded)]\n",
+      "MAE : 1.971\n",
+      "RMSE: 3.502\n",
+      "R2  : 0.604\n",
+      "\n",
+      "Worst 15 samples (by abs error):\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>y_true</th>\n",
+       "      <th>y_pred</th>\n",
+       "      <th>y_pred_round</th>\n",
+       "      <th>abs_err</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>16.876830</td>\n",
+       "      <td>17</td>\n",
+       "      <td>15.123170</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>29.0</td>\n",
+       "      <td>16.242619</td>\n",
+       "      <td>16</td>\n",
+       "      <td>12.757381</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>5.239388</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7.760612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.994058</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6.994058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>7.210027</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6.789973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>5.706020</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6.293980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.108304</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5.108304</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.948221</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4.948221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>6.426550</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4.426550</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>11.0</td>\n",
+       "      <td>6.758044</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4.241956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>6.354210</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.645790</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2.356056</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3.643944</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>3.442200</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.557800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>5.504394</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.504394</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.087934</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.087934</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    y_true     y_pred  y_pred_round    abs_err\n",
+       "34    32.0  16.876830            17  15.123170\n",
+       "0     29.0  16.242619            16  12.757381\n",
+       "14    13.0   5.239388             5   7.760612\n",
+       "65     1.0   7.994058             8   6.994058\n",
+       "28    14.0   7.210027             7   6.789973\n",
+       "29    12.0   5.706020             6   6.293980\n",
+       "60     1.0   6.108304             6   5.108304\n",
+       "61     1.0   5.948221             6   4.948221\n",
+       "56     2.0   6.426550             6   4.426550\n",
+       "17    11.0   6.758044             7   4.241956\n",
+       "58    10.0   6.354210             6   3.645790\n",
+       "10     6.0   2.356056             2   3.643944\n",
+       "23     7.0   3.442200             3   3.557800\n",
+       "20     2.0   5.504394             6   3.504394\n",
+       "62     3.0   6.087934             6   3.087934"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Saved:\n",
+      " - Model  : ./artifacts\\extratrees_log1p.joblib\n",
+      " - Columns: ./artifacts\\X_proc_columns.joblib\n"
+     ]
+    }
+   ],
+   "source": [
+    "# CELL 7 — FINALIZE BEST MODEL (ExtraTrees) + EVAL ON HOLDOUT VAL + SAVE ARTIFACTS\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "from sklearn.ensemble import ExtraTreesRegressor\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+    "import joblib\n",
+    "\n",
+    "# =========================\n",
+    "# 1) Train final ExtraTrees on FULL TRAIN\n",
+    "# =========================\n",
+    "final_model = ExtraTreesRegressor(\n",
+    "    n_estimators=800,\n",
+    "    min_samples_leaf=2,\n",
+    "    random_state=42,\n",
+    "    n_jobs=-1\n",
+    ")\n",
+    "\n",
+    "final_model.fit(X_train.values, np.log1p(y_train.values))\n",
+    "\n",
+    "# =========================\n",
+    "# 2) Predict on HOLDOUT VAL\n",
+    "# =========================\n",
+    "pred_val = np.expm1(final_model.predict(X_val.values))\n",
+    "pred_val = np.maximum(0, pred_val)\n",
+    "\n",
+    "# optional: round to headcount integer\n",
+    "pred_val_round = np.rint(pred_val).astype(int)\n",
+    "\n",
+    "# =========================\n",
+    "# 3) Metrics (raw vs rounded)\n",
+    "# =========================\n",
+    "def print_metrics(tag, y_true, y_pred):\n",
+    "    mae  = mean_absolute_error(y_true, y_pred)\n",
+    "    rmse = mean_squared_error(y_true, y_pred) ** 0.5\n",
+    "    r2   = r2_score(y_true, y_pred)\n",
+    "    print(f\"\\n[{tag}]\")\n",
+    "    print(f\"MAE : {mae:.3f}\")\n",
+    "    print(f\"RMSE: {rmse:.3f}\")\n",
+    "    print(f\"R2  : {r2:.3f}\")\n",
+    "    return {\"MAE\": mae, \"RMSE\": rmse, \"R2\": r2}\n",
+    "\n",
+    "print(\"=== FINAL MODEL: ExtraTrees (fit on TRAIN, eval on HOLDOUT VAL) ===\")\n",
+    "m_raw = print_metrics(\"VAL (raw)\", y_val.values, pred_val)\n",
+    "m_int = print_metrics(\"VAL (rounded)\", y_val.values, pred_val_round)\n",
+    "\n",
+    "# =========================\n",
+    "# 4) Quick error analysis\n",
+    "# =========================\n",
+    "err_df = pd.DataFrame({\n",
+    "    \"y_true\": y_val.values,\n",
+    "    \"y_pred\": pred_val,\n",
+    "    \"y_pred_round\": pred_val_round,\n",
+    "    \"abs_err\": np.abs(y_val.values - pred_val),\n",
+    "})\n",
+    "print(\"\\nWorst 15 samples (by abs error):\")\n",
+    "display(err_df.sort_values(\"abs_err\", ascending=False).head(15))\n",
+    "\n",
+    "# =========================\n",
+    "# 5) Save model + schema (columns)\n",
+    "# =========================\n",
+    "ARTIFACT_DIR = \"./artifacts\"\n",
+    "import os\n",
+    "os.makedirs(ARTIFACT_DIR, exist_ok=True)\n",
+    "\n",
+    "model_path = os.path.join(ARTIFACT_DIR, \"extratrees_log1p.joblib\")\n",
+    "cols_path  = os.path.join(ARTIFACT_DIR, \"X_proc_columns.joblib\")\n",
+    "\n",
+    "joblib.dump(final_model, model_path)\n",
+    "joblib.dump(list(X_train.columns), cols_path)\n",
+    "\n",
+    "print(\"\\nSaved:\")\n",
+    "print(\" - Model  :\", model_path)\n",
+    "print(\" - Columns:\", cols_path)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "9cff151d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Preview:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ma_dia_diem</th>\n",
+       "      <th>so_luong_thuc_te</th>\n",
+       "      <th>so_luong_du_doan_raw</th>\n",
+       "      <th>so_luong_du_doan_round</th>\n",
+       "      <th>abs_error</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>579-1</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>16.876830</td>\n",
+       "      <td>17</td>\n",
+       "      <td>15.123170</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>114-1</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>16.242619</td>\n",
+       "      <td>16</td>\n",
+       "      <td>12.757381</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>121-3</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>5.239388</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7.760612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>227-1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.994058</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6.994058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>55-1</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>7.210027</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6.789973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>55-1</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>5.706020</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6.293980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>236-1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.108304</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5.108304</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>236-1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.948221</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4.948221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>236-1</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>6.426550</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4.426550</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>121-4</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>6.758044</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4.241956</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ma_dia_diem  so_luong_thuc_te  so_luong_du_doan_raw  \\\n",
+       "34       579-1              32.0             16.876830   \n",
+       "0        114-1              29.0             16.242619   \n",
+       "14       121-3              13.0              5.239388   \n",
+       "65       227-1               1.0              7.994058   \n",
+       "28        55-1              14.0              7.210027   \n",
+       "29        55-1              12.0              5.706020   \n",
+       "60       236-1               1.0              6.108304   \n",
+       "61       236-1               1.0              5.948221   \n",
+       "56       236-1               2.0              6.426550   \n",
+       "17       121-4              11.0              6.758044   \n",
+       "\n",
+       "    so_luong_du_doan_round  abs_error  \n",
+       "34                      17  15.123170  \n",
+       "0                       16  12.757381  \n",
+       "14                       5   7.760612  \n",
+       "65                       8   6.994058  \n",
+       "28                       7   6.789973  \n",
+       "29                       6   6.293980  \n",
+       "60                       6   5.108304  \n",
+       "61                       6   4.948221  \n",
+       "56                       6   4.426550  \n",
+       "17                       7   4.241956  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Saved CSV to: ./outputs\\val_predictions_extratrees.csv\n",
+      "Rows: 68\n"
+     ]
+    }
+   ],
+   "source": [
+    "# CELL 8 — EXPORT VAL PREDICTIONS TO CSV (FOR ANALYSIS / BUSINESS REVIEW)\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "\n",
+    "# =========================\n",
+    "# 1) Recompute predictions (safety, explicit)\n",
+    "# =========================\n",
+    "pred_val_raw = np.expm1(final_model.predict(X_val.values))\n",
+    "pred_val_raw = np.maximum(0, pred_val_raw)\n",
+    "\n",
+    "pred_val_round = np.rint(pred_val_raw).astype(int)\n",
+    "\n",
+    "# =========================\n",
+    "# 2) Build result DataFrame\n",
+    "# =========================\n",
+    "val_result = pd.DataFrame({\n",
+    "    \"ma_dia_diem\": df.loc[val_idx, \"ma_dia_diem\"].values,\n",
+    "    \"so_luong_thuc_te\": y_val.values,\n",
+    "    \"so_luong_du_doan_raw\": pred_val_raw,\n",
+    "    \"so_luong_du_doan_round\": pred_val_round,\n",
+    "    \"abs_error\": np.abs(y_val.values - pred_val_raw),\n",
+    "})\n",
+    "\n",
+    "# (optional) sort by error to review bad cases first\n",
+    "val_result = val_result.sort_values(\"abs_error\", ascending=False)\n",
+    "\n",
+    "print(\"Preview:\")\n",
+    "display(val_result.head(10))\n",
+    "\n",
+    "# =========================\n",
+    "# 3) Save to CSV\n",
+    "# =========================\n",
+    "OUTPUT_DIR = \"./outputs\"\n",
+    "os.makedirs(OUTPUT_DIR, exist_ok=True)\n",
+    "\n",
+    "csv_path = os.path.join(OUTPUT_DIR, \"val_predictions_extratrees.csv\")\n",
+    "val_result.to_csv(csv_path, index=False, encoding=\"utf-8-sig\")\n",
+    "\n",
+    "print(f\"\\nSaved CSV to: {csv_path}\")\n",
+    "print(f\"Rows: {len(val_result)}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "8cb3cde1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== MODEL SAVED SUCCESSFULLY ===\n",
+      "Model file   : ./artifacts\\extratrees_staff_model.joblib\n",
+      "Columns file : ./artifacts\\X_proc_columns.joblib\n",
+      "Meta file    : ./artifacts\\model_meta.joblib\n"
+     ]
+    }
+   ],
+   "source": [
+    "# CELL 8 — SAVE TRAINED MODEL (NO PREDICT YET)\n",
+    "\n",
+    "import os\n",
+    "import joblib\n",
+    "\n",
+    "# =========================\n",
+    "# 1) Create artifact directory\n",
+    "# =========================\n",
+    "ARTIFACT_DIR = \"./artifacts\"\n",
+    "os.makedirs(ARTIFACT_DIR, exist_ok=True)\n",
+    "\n",
+    "# =========================\n",
+    "# 2) Define paths\n",
+    "# =========================\n",
+    "MODEL_PATH = os.path.join(ARTIFACT_DIR, \"extratrees_staff_model.joblib\")\n",
+    "COLUMNS_PATH = os.path.join(ARTIFACT_DIR, \"X_proc_columns.joblib\")\n",
+    "META_PATH = os.path.join(ARTIFACT_DIR, \"model_meta.joblib\")\n",
+    "\n",
+    "# =========================\n",
+    "# 3) Save model\n",
+    "# =========================\n",
+    "joblib.dump(final_model, MODEL_PATH)\n",
+    "\n",
+    "# =========================\n",
+    "# 4) Save feature schema (VERY IMPORTANT)\n",
+    "# =========================\n",
+    "joblib.dump(list(X_train.columns), COLUMNS_PATH)\n",
+    "\n",
+    "# =========================\n",
+    "# 5) Save metadata (optional but professional)\n",
+    "# =========================\n",
+    "meta = {\n",
+    "    \"model_type\": \"ExtraTreesRegressor\",\n",
+    "    \"target\": \"so_luong\",\n",
+    "    \"target_transform\": \"log1p -> expm1\",\n",
+    "    \"train_size\": len(X_train),\n",
+    "    \"val_size\": len(X_val),\n",
+    "    \"features\": X_train.shape[1],\n",
+    "    \"note\": \"Predict staff headcount per shift\",\n",
+    "}\n",
+    "\n",
+    "joblib.dump(meta, META_PATH)\n",
+    "\n",
+    "print(\"=== MODEL SAVED SUCCESSFULLY ===\")\n",
+    "print(\"Model file   :\", MODEL_PATH)\n",
+    "print(\"Columns file :\", COLUMNS_PATH)\n",
+    "print(\"Meta file    :\", META_PATH)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "0eab135b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['tong_gio_lam', 'so_ca_cua_toa', 'num_tasks', 'num_cleaning_tasks',\n",
+       "       'num_trash_collection_tasks', 'num_monitoring_tasks',\n",
+       "       'num_deep_cleaning_tasks', 'num_support_tasks', 'num_other_tasks',\n",
+       "       'num_wc_tasks', 'num_hallway_tasks', 'num_lobby_tasks',\n",
+       "       'num_outdoor_tasks', 'num_elevator_tasks', 'cleaning_ratio',\n",
+       "       'trash_collection_ratio', 'monitoring_ratio', 'area_diversity',\n",
+       "       'so_tang', 'so_cua_thang_may', 'dien_tich_ngoai_canh', 'dien_tich_sanh',\n",
+       "       'dien_tich_hanh_lang', 'dien_tich_wc', 'dien_tich_phong',\n",
+       "       'dien_tich_tham', 'doc_ham', 'vien_phan_quang', 'op_tuong',\n",
+       "       'op_chan_tuong', 'ranh_thoat_nuoc', 'dien_tich_kinh',\n",
+       "       'num_medical_tasks_total', 'num_indoor_room_tasks', 'hour_start',\n",
+       "       'hour_end', 'shift_length', 'is_cross_day', 'loai_ca_24/24',\n",
+       "       'loai_ca_Ca chiều', 'loai_ca_Ca gãy', 'loai_ca_Ca sáng',\n",
+       "       'loai_ca_Ca đêm', 'loai_ca_Hành chính', 'loai_ca_Part time',\n",
+       "       'loai_ca_nan'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_val.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "1dd44caa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['ma_dia_diem', 'all_task_normal', 'all_task_dinhky', 'loai_ca',\n",
+       "       'bat_dau', 'ket_thuc', 'tong_gio_lam', 'so_ca_cua_toa', 'so_luong',\n",
+       "       'num_tasks', 'num_cleaning_tasks', 'num_trash_collection_tasks',\n",
+       "       'num_monitoring_tasks', 'num_deep_cleaning_tasks', 'num_support_tasks',\n",
+       "       'num_other_tasks', 'num_wc_tasks', 'num_hallway_tasks',\n",
+       "       'num_lobby_tasks', 'num_outdoor_tasks', 'num_elevator_tasks',\n",
+       "       'cleaning_ratio', 'trash_collection_ratio', 'monitoring_ratio',\n",
+       "       'area_diversity', 'so_tang', 'so_cua_thang_may', 'dien_tich_ngoai_canh',\n",
+       "       'dien_tich_sanh', 'dien_tich_hanh_lang', 'dien_tich_wc',\n",
+       "       'dien_tich_phong', 'dien_tich_tham', 'doc_ham', 'vien_phan_quang',\n",
+       "       'op_tuong', 'op_chan_tuong', 'ranh_thoat_nuoc', 'dien_tich_kinh',\n",
+       "       'num_medical_tasks_total', 'num_indoor_room_tasks',\n",
+       "       'is_tasks_text_missing'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7036167",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8dbd670",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ebac85b",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22866fc4",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}