predict_caLamviec_nhansu/ML_Training_Pipeline.ipynb

{
 "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": {
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       "</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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",
      "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": {
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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'\")"
   ]
  }
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