{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8c74636d",
   "metadata": {},
   "source": [
    "# 🏢 Hệ thống Dự đoán Số lượng Nhân sự theo Ca làm việc\n",
    "\n",
    "## Mục tiêu\n",
    "Dự đoán số lượng nhân sự cần thiết cho mỗi ca làm việc dựa trên:\n",
    "- Đặc điểm tòa nhà (diện tích, số tầng, thiết bị...)\n",
    "- Thông tin ca làm việc (giờ bắt đầu, độ dài ca...)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "088f821d",
   "metadata": {},
   "source": [
    "## 1. Import thư viện và load module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5c52f1f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Đã import thành công!\n"
     ]
    }
   ],
   "source": [
    "from predict_staff import StaffPredictor\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Cấu hình hiển thị\n",
    "pd.set_option('display.max_columns', None)\n",
    "plt.style.use('ggplot')\n",
    "sns.set_palette(\"husl\")\n",
    "\n",
    "print(\"✅ Đã import thành công!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0b8ad84f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Đã reload module predict_staff!\n"
     ]
    }
   ],
   "source": [
    "# Reload module để nhận thay đổi mới\n",
    "import importlib\n",
    "import predict_staff\n",
    "importlib.reload(predict_staff)\n",
    "from predict_staff import StaffPredictor\n",
    "\n",
    "print(\"✅ Đã reload module predict_staff!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11ed9063",
   "metadata": {},
   "source": [
    "## 2. Khởi tạo và Load dữ liệu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "95651ffb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📂 Đang đọc dữ liệu...\n",
      "✅ Đã đọc 8 tòa nhà\n",
      "✅ Đã đọc 31 records ca làm việc\n",
      "\n",
      "📊 Thống kê dữ liệu:\n",
      "  - Số tòa nhà: 8\n",
      "  - Số records ca làm việc: 31\n",
      "  - Số ca làm việc unique: 28\n"
     ]
    }
   ],
   "source": [
    "# Khởi tạo predictor\n",
    "predictor = StaffPredictor()\n",
    "\n",
    "# Load dữ liệu\n",
    "building_df, shift_df = predictor.load_data()\n",
    "\n",
    "print(f\"\\n📊 Thống kê dữ liệu:\")\n",
    "print(f\"  - Số tòa nhà: {len(building_df)}\")\n",
    "print(f\"  - Số records ca làm việc: {len(shift_df)}\")\n",
    "print(f\"  - Số ca làm việc unique: {shift_df['Ca'].nunique()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e289617",
   "metadata": {},
   "source": [
    "## 3. Khám phá dữ liệu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "11b26770",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🏢 Dữ liệu tòa nhà:\n"
     ]
    },
    {
     "data": {
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       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mã địa điểm</th>\n",
       "      <th>Loại hình</th>\n",
       "      <th>Tổng Giờ hoạt động của khách hàng mỗi tuần</th>\n",
       "      <th>Lưu lượng KH hoạt động ngày tại Tòa tháp</th>\n",
       "      <th>Diện tích ngoại cảnh Tòa tháp (m2)</th>\n",
       "      <th>Số tòa tháp</th>\n",
       "      <th>Số tầng nổi</th>\n",
       "      <th>Số tầng hầm</th>\n",
       "      <th>Tầng hầm (m2)</th>\n",
       "      <th>Sàn Sảnh (m2)</th>\n",
       "      <th>Sàn Hành lang (m2)</th>\n",
       "      <th>Sàn WC (m2)</th>\n",
       "      <th>Sàn Phòng (m2)</th>\n",
       "      <th>Thảm (m2)</th>\n",
       "      <th>Dốc hầm (m)</th>\n",
       "      <th>Viền phản quang (m)</th>\n",
       "      <th>Ốp tường (m2)</th>\n",
       "      <th>Ốp chân tường (m2)</th>\n",
       "      <th>Rãnh thoát nước (m)</th>\n",
       "      <th>Kính (m2)</th>\n",
       "      <th>Thang máy</th>\n",
       "      <th>Thang bộ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>559-1</td>\n",
       "      <td>3</td>\n",
       "      <td>168</td>\n",
       "      <td>3</td>\n",
       "      <td>400</td>\n",
       "      <td>2</td>\n",
       "      <td>59</td>\n",
       "      <td>2</td>\n",
       "      <td>2480</td>\n",
       "      <td>492</td>\n",
       "      <td>5637</td>\n",
       "      <td>214</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>350</td>\n",
       "      <td>822</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "      <td>268</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>618-1</td>\n",
       "      <td>3</td>\n",
       "      <td>168</td>\n",
       "      <td>1</td>\n",
       "      <td>2750</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>6800</td>\n",
       "      <td>9223</td>\n",
       "      <td>2384</td>\n",
       "      <td>194</td>\n",
       "      <td>177</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>850</td>\n",
       "      <td>684</td>\n",
       "      <td>1151</td>\n",
       "      <td>11</td>\n",
       "      <td>701</td>\n",
       "      <td>113</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>283-1</td>\n",
       "      <td>3</td>\n",
       "      <td>168</td>\n",
       "      <td>2</td>\n",
       "      <td>4610</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>2200</td>\n",
       "      <td>1890</td>\n",
       "      <td>1490</td>\n",
       "      <td>382</td>\n",
       "      <td>62</td>\n",
       "      <td>59</td>\n",
       "      <td>400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>275</td>\n",
       "      <td>770</td>\n",
       "      <td>61</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>337-1</td>\n",
       "      <td>3</td>\n",
       "      <td>54</td>\n",
       "      <td>2</td>\n",
       "      <td>3000</td>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>6</td>\n",
       "      <td>4000</td>\n",
       "      <td>900</td>\n",
       "      <td>7080</td>\n",
       "      <td>2585</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3520</td>\n",
       "      <td>1060</td>\n",
       "      <td>200</td>\n",
       "      <td>1170</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55-1</td>\n",
       "      <td>3</td>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "      <td>6800</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>4800</td>\n",
       "      <td>1217</td>\n",
       "      <td>541</td>\n",
       "      <td>671</td>\n",
       "      <td>30</td>\n",
       "      <td>2226</td>\n",
       "      <td>5</td>\n",
       "      <td>450</td>\n",
       "      <td>0</td>\n",
       "      <td>2240</td>\n",
       "      <td>650</td>\n",
       "      <td>25</td>\n",
       "      <td>148</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mã địa điểm  Loại hình  Tổng Giờ hoạt động của khách hàng mỗi tuần  \\\n",
       "0       559-1          3                                         168   \n",
       "1       618-1          3                                         168   \n",
       "2       283-1          3                                         168   \n",
       "3       337-1          3                                          54   \n",
       "4        55-1          3                                          54   \n",
       "\n",
       "   Lưu lượng KH hoạt động ngày tại Tòa tháp  \\\n",
       "0                                         3   \n",
       "1                                         1   \n",
       "2                                         2   \n",
       "3                                         2   \n",
       "4                                         3   \n",
       "\n",
       "   Diện tích ngoại cảnh Tòa tháp (m2)  Số tòa tháp  Số tầng nổi  Số tầng hầm  \\\n",
       "0                                 400            2           59            2   \n",
       "1                                2750            1           22            4   \n",
       "2                                4610            1           15            1   \n",
       "3                                3000            3           60            6   \n",
       "4                                6800            1           24            2   \n",
       "\n",
       "   Tầng hầm (m2)  Sàn Sảnh (m2)  Sàn Hành lang (m2)  Sàn WC (m2)  \\\n",
       "0           2480            492                5637          214   \n",
       "1           6800           9223                2384          194   \n",
       "2           2200           1890                1490          382   \n",
       "3           4000            900                7080         2585   \n",
       "4           4800           1217                 541          671   \n",
       "\n",
       "   Sàn Phòng (m2)   Thảm (m2)  Dốc hầm (m)   Viền phản quang (m)  \\\n",
       "0               15          0             5                  350   \n",
       "1              177          6             6                  850   \n",
       "2               62         59           400                    0   \n",
       "3               40          4             4                    0   \n",
       "4               30       2226             5                  450   \n",
       "\n",
       "   Ốp tường (m2)  Ốp chân tường (m2)  Rãnh thoát nước (m)  Kính (m2)  \\\n",
       "0            822                   0                    3         56   \n",
       "1            684                1151                   11        701   \n",
       "2              0                 100                  275        770   \n",
       "3           3520                1060                  200       1170   \n",
       "4              0                2240                  650         25   \n",
       "\n",
       "   Thang máy  Thang bộ  \n",
       "0        268       128  \n",
       "1        113        44  \n",
       "2         61        30  \n",
       "3         20        12  \n",
       "4        148        56  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Xem dữ liệu tòa nhà\n",
    "print(\"🏢 Dữ liệu tòa nhà:\")\n",
    "display(building_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2aa419f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⏰ Dữ liệu ca làm việc:\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>Mã địa điểm</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>559-1</td>\n",
       "      <td>14h00:22h00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>559-1</td>\n",
       "      <td>15h00:18h00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>559-1</td>\n",
       "      <td>15h00:21h00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>559-1</td>\n",
       "      <td>6h00:10h00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>559-1</td>\n",
       "      <td>6h00:14h00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>559-1</td>\n",
       "      <td>6h00:15h00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>618-1</td>\n",
       "      <td>6h30-14h30</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>618-1</td>\n",
       "      <td>13h30-21h30</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>618-1</td>\n",
       "      <td>14h30-21h30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>618-1</td>\n",
       "      <td>6h30-18h30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mã địa điểm           Ca  Number\n",
       "0       559-1  14h00:22h00       4\n",
       "1       559-1  15h00:18h00       2\n",
       "2       559-1  15h00:21h00       1\n",
       "3       559-1   6h00:10h00       1\n",
       "4       559-1   6h00:14h00       4\n",
       "5       559-1   6h00:15h00       4\n",
       "6       618-1   6h30-14h30       5\n",
       "7       618-1  13h30-21h30       3\n",
       "8       618-1  14h30-21h30       1\n",
       "9       618-1   6h30-18h30       1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📈 Thống kê số nhân sự:\n",
      "count    31.000000\n",
      "mean      5.129032\n",
      "std       5.129925\n",
      "min       1.000000\n",
      "25%       1.000000\n",
      "50%       4.000000\n",
      "75%       6.000000\n",
      "max      25.000000\n",
      "Name: Number, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Xem dữ liệu ca làm việc\n",
    "print(\"⏰ Dữ liệu ca làm việc:\")\n",
    "display(shift_df.head(10))\n",
    "\n",
    "print(\"\\n📈 Thống kê số nhân sự:\")\n",
    "print(shift_df['Number'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d9304340",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tXrxYbdu21eDBg5Wdna3p06dby1etWvWKb+QDAABAwTRp0kQDBw7UJ598ojfffNM638/PTwMHDlSTJk1cFxwAAIADXDUplfNQ84cffljPP/+8/Pz8dPjwYd1///15lvf391daWlqhgli0aJG2bNmiI0eOyNvbWzExMerTp48iIiKsZdLT0/X555/r559/1rlz5xQaGqo77rhDt956q822/v77b82ZM0d79uyRp6enatasqeeee07e3t5XrH/GjBnatWuXDh06pKpVq2ry5MmFih8AAKA4tG/fXi1bttTff/+t1NRUBQQEKCYmRj4+Pq4ODQAAoMgK9fa9I0eOqEaNGipXrpwuXLiQZ5nk5GSVL1++UEEkJCTotttuU3R0tLKysvTFF19o/PjxmjJlinXQNXPmTO3YsUNDhw5VSEiItm3bpo8++kjBwcG67rrrJP2TkJowYYLi4uLUv39/mc1mHThw4KoPAjUMQ+3atdOePXt04MCBQsUOAABQnHx8fNSoUSNXhwEAAOBwV32mVI7x48dr0aJFSk1NVWxsrJYvXy7DMHKV+/7771W/fv1CBTFq1Ci1bdtW1apVU82aNRUfH69Tp05p37591jK7d+9WmzZt1KBBA4WGhqpDhw6qUaOG9u7day0za9Ys3XHHHeratauqVaum8PBwtWjRQl5eXvnW379/f91+++0KDQ0tVNwAAAAAAACwT4GTUlWqVNFLL70kHx8fdevWTbt379Zzzz2npUuXSpK2bNmiV155RTt37tQ999xTpKBy7sLy9/e3zouNjdWvv/6q5ORkGYah7du369ixY9bnKZw9e1a7d+9WYGCgnn/+eT388MMaM2aMdu3aVaRYAAAAAAAA4HiF+vpezlfpoqOjNXLkSE2fPl2ffvqppH+eCxUWFqaRI0eqevXqdgdkGIZmzZqlunXr2mynf//+mjZtmgYNGiRPT0+ZTCYNGjRIdevWlSQdP35ckjR//nz95z//Uc2aNbVu3Tq9+OKLev311xUeHm53TP9msVhksVis0yaTSb6+vg7bPgDAvV3ta+EFWbco2yiraDsAAIDSpVBJqcs1bNhQU6dOVVJSks6ePasKFSrYPJjcXtOnT9fBgwf14osv2sxfunSpdu/erREjRigkJEQ7d+7URx99pKCgIDVq1Mj6VcIOHTqoXbt2kqSoqCht375da9euVe/evTVx4kTt3LlTkhQSEqIpU6bYFeOiRYu0YMEC63RUVJQmTZpk17YAACWPIy50hIWFOSCSsom2AwAAKB3sTkrlCAsLc9jg8OOPP9avv/6qcePGqVKlStb5GRkZmjNnjp5++mk1a9ZMklSjRg0lJibq66+/VqNGjRQcHCxJioyMtNlm1apVderUKUnSoEGDlJGRIUny9PS0O864uDh16tTJOs0VWwAoW44dO2b3uiaTSWFhYUpKSsrz2Yy4MndvO7PZrJCQEFeHAQAAUGIUOCmVkJCgWrVqycfHRwkJCVctX5iHnRuGoY8//lhbtmzR2LFjcz1wPDMzU1lZWbmSPx4eHtZBaUhIiIKDg3X06FGbMpc/d6pixYoFjik/Xl5eV314OgCg9HJEQsQwDLdMrJQEtB0AAEDpUOCk1Lhx4zRhwgTVrl1b48aNu2r5uXPnFjiI6dOna8OGDRoxYoR8fX2VkpIiSfLz85O3t7f8/PxUv359ffbZZ/L29lZISIgSEhK0bt069evXT9I/V087d+6sefPmqWbNmqpZs6a+//57HTlyRE8++WS+9SclJSk9PV0pKSnKyMhQYmKipH/uujKbi3wzGQAAQKH16NHjistMJpPKly+vWrVqqUuXLmrYsKETIwMAAHCMAmdcxowZo6pVq1p/d6SVK1dKksaOHWszPz4+Xm3btpUkDRs2TLNnz9Zbb72ltLQ0hYSEqFevXurYsaO1/F133SWLxaJZs2YpLS1NNWrU0OjRo6/69cJp06bZ3P01YsQISdI777yT664tAAAAZ7j33nu1fv16Xbx4Uddee62CgoJ05swZbd26Vb6+vmrevLn+/PNPjR8/Xs8995waNWrk6pABAAAKpcBJqcu/jleYr+YVxLx5865aJigoSPHx8Vct17VrV3Xt2rVQ9f87GQYAAOBq/v7+CgoK0muvvWZ9A7Ikpaen66WXXlLFihX16quvavz48Vq4cCFJKQAAUOJ4FLRgdna2Hn30UR0+fLg44wEAAICkZcuW6e6777ZJSEmSj4+P7r77bq1YsUKenp7q2LGj9u/f76IoAQAA7FfgO6Xef/99nTx5UrNnz5a/v3++ZU0mkwYPHlzk4AAAAMqq5OTkK74t2NPT0/oMzqCgIGVmZjoxMgAAAMco1Nv3JGnv3r0ym826cOGCLly4IA8PD1WoUEHnzp1Tdna2/Pz8VL58+WILGAAAoCyIiIjQsmXL1LRpU5vkVFZWlpYuXaqIiAhJUkpKigICAlwVJgAAgN0KnJR666231Lt3bz3zzDPKzs7W66+/rgEDBqhly5by8PBQdna2Nm3apM8++0zDhg0rxpABAABKv+7du+v111/XY489puuuu06BgYE6e/asfv75ZyUnJ+upp56SJG3btk0xMTEujhYAAKDwCpyUMplM1t8//fRT3X333WrdurV1noeHh1q3bq2UlBTNmjVLL730kmMjBQAAKEOuu+46Pfvss5o7d66WLVtmnR8dHa2HH35YTZo0kSQNGjTIRRECAAAUTaGSUt26dVNwcLD27dunbt265VmuevXqmjt3rsMCBAAAKKuaNGmiJk2a6NKlSzp//rzKly+vcuXKuTosAAAAhyhUUuq+++6TJPn6+urPP//UNddck6vcn3/+KV9fX8dFWIKk9r1TFovF1WGUKCaTSeHh4Tp27JgMw3B1OCUO7Wc/2s5+tB3gfOXKlSMZBQAASp0CJ6Uud/PNN+urr75SVlaWWrduraCgIKWkpOiHH37Q0qVL1alTJ0fHCQAAUOacOHFCmzdv1smTJ5WRkWGzrLjedpycnKzPPvtMv//+uzIyMhQeHq7BgwerVq1aDq8LAACUbXYlpXr16qWzZ8/qm2++0TfffGOz7KabblKvXr0cEhwAAEBZtXXrVr322mvKzs5WYGCgzGbbYdvlz/t0lLS0NI0ePVoNGjTQc889p4CAAB0/flx+fn4OrwsAAMCupJSnp6eGDBmiuLg4bd++XWlpafL391eDBg1UtWpVR8cIAABQ5syZM0exsbEaNmyYAgMDnVLnkiVLVKlSJcXHx1vnhYaGOqVuAABQ9tiVlMoRERGhiIgIR8UCAACA/0lKStJTTz3ltISUJP3yyy9q3LixpkyZooSEBFWsWFG33nqrOnTokGd5i8Vi8zxNk8lkfbaoyWSy3s1VHHd1lVX2tCX94B7oB/dAP7gH+sE9uEM/FCkpBQAAgOJRuXJlpaenO7XOEydO6LvvvtNdd92luLg47dmzRzNmzJCXl5fatGmTq/yiRYu0YMEC63RUVJQmTZqkkJAQm3JhYWEOi9HT7MLhq4v/d/I0mxUeHm73+o7sB9iPfnAP9IN7oB/cgyv7gaQUAACAG4qLi9PXX3+tpk2bOu3Ne9nZ2YqOjlbv3r0l/ZNkOnTokFauXJlnUiouLs7mBTc5V1pPnjypzMxMmUwmhYWFKSkpyWFv68zKzHTIduzi4heOZmVm6tixY4Verzj6AYVHP7gH+sE90A/uoTj7wWw257pIlWc5h9YKAAAAh9izZ49SU1P12GOPqUGDBvL397dZbjKZ9OCDDzq0zuDgYEVGRtrMi4yM1E8//ZRneS8vL3l5eeW57PLBrWEY/NPhIEVpR/rBPdAP7oF+cA/0g3twZT+QlAIAAHBDK1assP6+cePGPMs4OikVGxuro0eP2sw7evRoga50AgAAFBZJKQAAADc0d+5cp9d51113afTo0Vq4cKFatmypPXv2aPXq1XrkkUecHgsAACj9SEoBAAC4md9++02XLl1S3bp1FRQU5LR6a9eureHDh2v27Nn68ssvFRoaqn79+ummm25yWgwAAKDssDsplZCQoGXLlunIkSPKyMiwWWYymfT2228XOTgAAICyaOnSpcrMzNSHH36oIUOGaNeuXUpLS1OFChV0zTXXqGHDhsVW97XXXqtrr7222LYPAACQw66k1K5du/TSSy+pfv36OnLkiJo0aaKLFy/q77//VpUqVRQbG+voOAEAAMqMUaNG6cKFC3rwwQc1adIkSZKHh4eys7O1ePFiNWvWTE899ZTMZm56BwAAJZddI5l58+apbdu2evjhh9WrVy/16NFDtWrV0oEDBzRx4kRdf/31jo4TAACgTHn66aclSffff7/atm2rgIAApaam6vvvv9cXX3yhBQsWqGfPni6OEgAAwH4e9qx06NAhm8RTdna2JKlGjRq699579eWXXzomOgAAgDLKw8ND9957rzp37qyAgABJUkBAgDp37qyuXbtqw4YNkqRTp04pKyvLlaECAADYxa6k1KVLl+Tj4yMPDw+ZzWadO3fOuiwiIkKHDx92WIAAAABl0enTp1WvXr08l9WrV0/JycnKzs7WkCFDdODAASdHBwAAUHR2JaUqV66ss2fPSpIiIyO1detW67KEhAT5+/s7JjoAAIAyKiAgQAcPHsxz2cGDBxUQECAPDw8NHjxYoaGhTo4OAACg6Ox6plT9+vW1Y8cOtWjRQrfccoumT5+uI0eOyMvLS3/88Yc6derk6DgBAADKlObNm2vevHmqXLmybrjhBuv8n3/+WfPnz1fr1q0lSW3btnVRhAAAAEVjV1Kqe/fuSktLkyTdeuutysjI0A8//CCTyaR77rlH99xzj0ODBAAAKGt69uypv/76S1OmTJGPj4+CgoKUkpKi9PR0Va9eXb169XJ1iAAAAEViV1IqICDA+sBNSerUqRN3RwEAADiQv7+/Jk6cqO+//147duzQuXPnFBUVpYYNG6pNmzby8vJydYgAAABFYldSCgAAAMVnwIABslgsat6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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Phân bố số nhân sự theo tòa nhà\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "shift_df.groupby('Mã địa điểm')['Number'].sum().sort_values().plot(kind='barh')\n",
    "plt.title('Tổng số nhân sự theo tòa nhà')\n",
    "plt.xlabel('Tổng số nhân sự')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "shift_df['Number'].hist(bins=15, edgecolor='black')\n",
    "plt.title('Phân bố số nhân sự mỗi ca')\n",
    "plt.xlabel('Số nhân sự')\n",
    "plt.ylabel('Số lượng ca')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd7275eb",
   "metadata": {},
   "source": [
    "## 4. Chuẩn bị Features và Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "461ce935",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🔧 Chuẩn bị features...\n",
      "✅ Đã chuẩn bị 21 features\n",
      "📊 Dataset shape: (31, 21)\n",
      "\n",
      "📊 Shape của dữ liệu:\n",
      "  X (features): (31, 21)\n",
      "  y (target): (31,)\n"
     ]
    }
   ],
   "source": [
    "# Chuẩn bị features\n",
    "X, y, df_full = predictor.prepare_features()\n",
    "\n",
    "print(f\"\\n📊 Shape của dữ liệu:\")\n",
    "print(f\"  X (features): {X.shape}\")\n",
    "print(f\"  y (target): {y.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "85bb2ca4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📋 Danh sách các features (21 features):\n",
      "   1. Tổng Giờ hoạt động của khách hàng mỗi tuần\n",
      "   2. Lưu lượng KH hoạt động ngày tại Tòa tháp\n",
      "   3. Diện tích ngoại cảnh Tòa tháp (m2)\n",
      "   4. Số tòa tháp\n",
      "   5. Số tầng nổi\n",
      "   6. Số tầng hầm\n",
      "   7. Tầng hầm (m2)\n",
      "   8. Sàn Sảnh (m2)\n",
      "   9. Sàn Hành lang (m2)\n",
      "  10. Sàn WC (m2)\n",
      "  11. Sàn Phòng (m2) \n",
      "  12. Thảm (m2)\n",
      "  13. Kính (m2)\n",
      "  14. Thang máy\n",
      "  15. Thang bộ\n",
      "  16. total_shifts_per_building\n",
      "  17. start_hour\n",
      "  18. shift_duration\n",
      "  19. is_morning\n",
      "  20. is_afternoon\n",
      "  21. is_evening\n",
      "\n",
      "🔍 Mẫu dữ liệu features:\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tổng Giờ hoạt động của khách hàng mỗi tuần</th>\n",
       "      <th>Lưu lượng KH hoạt động ngày tại Tòa tháp</th>\n",
       "      <th>Diện tích ngoại cảnh Tòa tháp (m2)</th>\n",
       "      <th>Số tòa tháp</th>\n",
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       "      <th>Số tầng hầm</th>\n",
       "      <th>Tầng hầm (m2)</th>\n",
       "      <th>Sàn Sảnh (m2)</th>\n",
       "      <th>Sàn Hành lang (m2)</th>\n",
       "      <th>Sàn WC (m2)</th>\n",
       "      <th>Sàn Phòng (m2)</th>\n",
       "      <th>Thảm (m2)</th>\n",
       "      <th>Kính (m2)</th>\n",
       "      <th>Thang máy</th>\n",
       "      <th>Thang bộ</th>\n",
       "      <th>total_shifts_per_building</th>\n",
       "      <th>start_hour</th>\n",
       "      <th>shift_duration</th>\n",
       "      <th>is_morning</th>\n",
       "      <th>is_afternoon</th>\n",
       "      <th>is_evening</th>\n",
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       "      <td>2480</td>\n",
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       "      <td>5637</td>\n",
       "      <td>214</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>268</td>\n",
       "      <td>128</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>6.0</td>\n",
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      "text/plain": [
       "   Tổng Giờ hoạt động của khách hàng mỗi tuần  \\\n",
       "0                                         168   \n",
       "1                                         168   \n",
       "2                                         168   \n",
       "3                                         168   \n",
       "4                                         168   \n",
       "\n",
       "   Lưu lượng KH hoạt động ngày tại Tòa tháp  \\\n",
       "0                                         3   \n",
       "1                                         3   \n",
       "2                                         3   \n",
       "3                                         3   \n",
       "4                                         3   \n",
       "\n",
       "   Diện tích ngoại cảnh Tòa tháp (m2)  Số tòa tháp  Số tầng nổi  Số tầng hầm  \\\n",
       "0                                 400            2           59            2   \n",
       "1                                 400            2           59            2   \n",
       "2                                 400            2           59            2   \n",
       "3                                 400            2           59            2   \n",
       "4                                 400            2           59            2   \n",
       "\n",
       "   Tầng hầm (m2)  Sàn Sảnh (m2)  Sàn Hành lang (m2)  Sàn WC (m2)  \\\n",
       "0           2480            492                5637          214   \n",
       "1           2480            492                5637          214   \n",
       "2           2480            492                5637          214   \n",
       "3           2480            492                5637          214   \n",
       "4           2480            492                5637          214   \n",
       "\n",
       "   Sàn Phòng (m2)   Thảm (m2)  Kính (m2)  Thang máy  Thang bộ  \\\n",
       "0               15          0         56        268       128   \n",
       "1               15          0         56        268       128   \n",
       "2               15          0         56        268       128   \n",
       "3               15          0         56        268       128   \n",
       "4               15          0         56        268       128   \n",
       "\n",
       "   total_shifts_per_building  start_hour  shift_duration  is_morning  \\\n",
       "0                          6          14             8.0           0   \n",
       "1                          6          15             3.0           0   \n",
       "2                          6          15             6.0           0   \n",
       "3                          6           6             4.0           1   \n",
       "4                          6           6             8.0           1   \n",
       "\n",
       "   is_afternoon  is_evening  \n",
       "0             1           0  \n",
       "1             1           0  \n",
       "2             1           0  \n",
       "3             0           0  \n",
       "4             0           0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hiển thị các features hiện có\n",
    "print(f\"\\n📋 Danh sách các features ({len(predictor.feature_columns)} features):\")\n",
    "for i, feature in enumerate(predictor.feature_columns, 1):\n",
    "    print(f\"  {i:2d}. {feature}\")\n",
    "    \n",
    "# Hiển thị mẫu dữ liệu\n",
    "print(f\"\\n🔍 Mẫu dữ liệu features:\")\n",
    "display(pd.DataFrame(X, columns=predictor.feature_columns).head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "070cf114",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🤖 Training model: random_forest...\n",
      "\n",
      "📈 Kết quả đánh giá:\n",
      "Train RMSE: 1.88, MAE: 1.02, R²: 0.871\n",
      "Test RMSE:  2.89, MAE: 2.17, R²: 0.535\n",
      "Cross-validation RMSE: 5.77 (+/- 0.92)\n"
     ]
    }
   ],
   "source": [
    "# Train model với Random Forest\n",
    "results_rf = predictor.train_model(X, y, model_type='random_forest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ace148ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Feature Importance (Top 10):\n",
      "  shift_duration: 0.2365\n",
      "  Sàn WC (m2): 0.1750\n",
      "  Diện tích ngoại cảnh Tòa tháp (m2): 0.1658\n",
      "  total_shifts_per_building: 0.0999\n",
      "  Kính (m2): 0.0674\n",
      "  start_hour: 0.0347\n",
      "  Số tầng nổi: 0.0305\n",
      "  Tầng hầm (m2): 0.0243\n",
      "  Thảm (m2): 0.0220\n",
      "  Sàn Sảnh (m2): 0.0216\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hiển thị feature importance\n",
    "predictor.show_feature_importance()\n",
    "\n",
    "# Vẽ biểu đồ feature importance\n",
    "if hasattr(predictor.model, 'feature_importances_'):\n",
    "    importances = pd.DataFrame({\n",
    "        'feature': predictor.feature_columns,\n",
    "        'importance': predictor.model.feature_importances_\n",
    "    }).sort_values('importance', ascending=False)\n",
    "    \n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.barh(importances['feature'].head(10), importances['importance'].head(10))\n",
    "    plt.xlabel('Importance')\n",
    "    plt.title('Top 10 Features quan trọng nhất')\n",
    "    plt.gca().invert_yaxis()\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "843bd15c",
   "metadata": {},
   "source": [
    "## 5. So sánh các Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cf2fcffa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "📂 Đang đọc dữ liệu...\n",
      "✅ Đã đọc 8 tòa nhà\n",
      "✅ Đã đọc 31 records ca làm việc\n",
      "\n",
      "🔧 Chuẩn bị features...\n",
      "✅ Đã chuẩn bị 21 features\n",
      "📊 Dataset shape: (31, 21)\n",
      "\n",
      "🤖 Training model: random_forest...\n",
      "\n",
      "📈 Kết quả đánh giá:\n",
      "Train RMSE: 1.88, MAE: 1.02, R²: 0.871\n",
      "Test RMSE:  2.89, MAE: 2.17, R²: 0.535\n",
      "Cross-validation RMSE: 5.77 (+/- 0.92)\n",
      "\n",
      "============================================================\n",
      "📂 Đang đọc dữ liệu...\n",
      "✅ Đã đọc 8 tòa nhà\n",
      "✅ Đã đọc 31 records ca làm việc\n",
      "\n",
      "🔧 Chuẩn bị features...\n",
      "✅ Đã chuẩn bị 21 features\n",
      "📊 Dataset shape: (31, 21)\n",
      "\n",
      "🤖 Training model: gradient_boosting...\n",
      "\n",
      "📈 Kết quả đánh giá:\n",
      "Train RMSE: 0.00, MAE: 0.00, R²: 1.000\n",
      "Test RMSE:  1.87, MAE: 1.41, R²: 0.805\n",
      "Cross-validation RMSE: 6.63 (+/- 1.00)\n",
      "\n",
      "============================================================\n",
      "📂 Đang đọc dữ liệu...\n",
      "✅ Đã đọc 8 tòa nhà\n",
      "✅ Đã đọc 31 records ca làm việc\n",
      "\n",
      "🔧 Chuẩn bị features...\n",
      "✅ Đã chuẩn bị 21 features\n",
      "📊 Dataset shape: (31, 21)\n",
      "\n",
      "🤖 Training model: ridge...\n",
      "\n",
      "📈 Kết quả đánh giá:\n",
      "Train RMSE: 2.19, MAE: 1.55, R²: 0.825\n",
      "Test RMSE:  3.23, MAE: 2.12, R²: 0.418\n",
      "Cross-validation RMSE: 6.43 (+/- 0.35)\n",
      "\n",
      "📊 So sánh các Model:\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>test_rmse</th>\n",
       "      <th>test_mae</th>\n",
       "      <th>test_r2</th>\n",
       "      <th>cv_rmse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>random_forest</td>\n",
       "      <td>2.886147</td>\n",
       "      <td>2.172857</td>\n",
       "      <td>0.535123</td>\n",
       "      <td>5.771715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gradient_boosting</td>\n",
       "      <td>1.867781</td>\n",
       "      <td>1.409457</td>\n",
       "      <td>0.805306</td>\n",
       "      <td>6.631231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ridge</td>\n",
       "      <td>3.229793</td>\n",
       "      <td>2.122169</td>\n",
       "      <td>0.417828</td>\n",
       "      <td>6.432632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model  test_rmse  test_mae   test_r2   cv_rmse\n",
       "0      random_forest   2.886147  2.172857  0.535123  5.771715\n",
       "1  gradient_boosting   1.867781  1.409457  0.805306  6.631231\n",
       "2              ridge   3.229793  2.122169  0.417828  6.432632"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train và so sánh các model khác nhau\n",
    "models = ['random_forest', 'gradient_boosting', 'ridge']\n",
    "results_comparison = []\n",
    "\n",
    "for model_type in models:\n",
    "    print(f\"\\n{'='*60}\")\n",
    "    predictor_temp = StaffPredictor()\n",
    "    predictor_temp.load_data()\n",
    "    X_temp, y_temp, _ = predictor_temp.prepare_features()\n",
    "    results = predictor_temp.train_model(X_temp, y_temp, model_type=model_type)\n",
    "    results['model'] = model_type\n",
    "    results_comparison.append(results)\n",
    "\n",
    "# Tạo DataFrame so sánh\n",
    "comparison_df = pd.DataFrame(results_comparison)\n",
    "print(\"\\n📊 So sánh các Model:\")\n",
    "display(comparison_df[['model', 'test_rmse', 'test_mae', 'test_r2', 'cv_rmse']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "998e463a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WrVrZMDoAAAAAAMq2Qt+JLkkuLi7q27cvd50DAGAHBg0apP/+97967rnn1KRJE0nSN998oxMnTsjR0VEDBgywcYQAAAAAAJRdRSqiX+/QoUOKi4tT48aNFRgYaI1NAgAAK/Hy8tI777yj7777Tv/973/l4OCgY8eOqWXLlrr//vtVpUoVW4cIlGnleegAqXwPH8DQAQAAALCGQhfRP//8c2VlZZkfGLZ582Z9/PHHVzfm5KQJEyZYfHgZAACwDS8vL40cOdLWYQAAAAAAYHcKXUT/888/c3X7/v7779W8eXM99NBDmjVrlr7//nu99NJLBd7ekiVLtGPHDp06dUouLi4KDQ3V4MGDFRAQcMP1YmNjNXv2bJ08eVLe3t7q3bu3unXrVtjDAQAAAACgTDlw4ID++ecfBQYGqnbt2nnmJyUlac2aNQzJBgBAKSl0ET0lJUW+vr6SribukydPasSIEQoODlbPnj311VdfFWp7sbGx6t69u+rVq6esrCwtWLBAb775pj788EO5urrmu058fLzeeecddenSRU8//bQOHDigr7/+Wh4eHmrbtm1hDwkAgHLv/Pnz2rRpk06ePKkrV67kmmcymfTkk0/aKDIAAJAjIyNDUVFR+uOPP8zTbrvtNj3++OOqXLmyedo///yjhQsXUkQHAKCUFLqI7ujoaP7j+8CBA3J2dlaDBg0kSZUrV9aFCxcKtb1XXnkl1+unnnpKI0aM0JEjR9S4ceN814mJiZGPj4+GDRsmSQoMDNThw4e1dOlSiugAAFwnMTFRERERunz5si5fviwPDw+dP39e2dnZqly5stzd3W0dIgAAkPTzzz/rr7/+0uDBg1W/fn398ccfWrp0qV599VW99tpr8vYur08wAACgbHMo7Aq1atXShg0bdOnSJa1Zs0YNGzaUk9PVWnxSUpI8PDyKFdDFixcl6YYPOTt06JDCw8NzTWvevLmOHDmizMzMYu0fAIDyZv78+QoMDDT3FouIiNDcuXM1fPhwOTs7F2oYNgAAUHI2btyovn376t5771VYWJgGDRqkN998U+np6ZowYYL++ecfW4cIAECFVOgieq9evbR582YNGzZMe/fu1d13322e98cffyg4OLjIwRiGodmzZ6tRo0b5jvuWIyUlRZ6enrmmeXp6KisrS2lpafmuk5GRoYsXL5p/Ll26ZJ5nMpmK/QP7ZY3Pn5///R7YOoby8gP7VRbbwcGDB9WtWzc5Ozubpzk5OalHjx7q3Lmz5s2bZ7V9AQCAoouPj1dYWFiuacHBwYqMjFRWVpYmTpyohIQEG0UHAEDFVejhXP71r3+pevXqOnjwoOrVq5crwVevXl233XZbkYOZPn26jh8/rtdff/2my15fXDAMI9/pOZYsWaJFixaZX9etW1dRUVHm8d2LK90qW4Et1KxZ09YhlCv+/v62DqFc4Jxiv8riOeXcuXPy9vaWg4ODHBwczL2+JKlx48ZasWKFDaMDAAA5XF1dlZ6e95ugn5+fJk6caP558MEHbRAdAAAVV6GL6JIUGhqq0NDQPNMHDRpU5EBmzJihnTt3KjIyUtWrV7/hsl5eXkpJSck1LTU1VY6OjhaHgenXr5969eplfp1TbE9ISLDKEDCMTGe/Tp8+besQygWTySR/f3+dOXPGfFELRcc5xX5Z85zi5ORklYu9np6eOn/+vCTJ19dXR44cUdOmTSVdzYOOjo7F3gcAACi+WrVqKTY2Vq1atcozz9fX11xE//zzz20QHQAAFVeRiug5Ll++rIyMjDzTbzSe+fUMw9CMGTO0Y8cOTZw4UX5+fjddp0GDBtq5c2euaXv27FFISIh5fPbrOTs75+rGfn0MqLj4/K3LMAzeU1RoZbH9N2jQQHFxcWrdurXatGmjRYsWKSMjQ05OTvrpp5/UpEkTW4cIAAAk3XLLLVqxYoXuv/9+ubi45Jnv6+uryMhIRUZGKj4+3gYRAgBQMRW6iH758mV988032rhxo/mutut9++23Bd7e9OnTtWnTJo0fP15ubm7mO8zd3d3NXxqio6OVlJSk0aNHS5K6deumlStXavbs2erSpYsOHjyoNWvWaMyYMYU9HAAAyr3evXub/9AeMGCATp06pe+++06SFBYWpuHDh9syPAAA8P/dfffduvXWW2+4jI+Pj9566y2dPHmylKICAACFLqJPnz5dGzduVKtWrVSrVi2Ld34XVExMjCRp4sSJuaY/9dRT6tSpkyQpOTlZiYmJ5nl+fn6KiIjQ7NmztXLlSnl7e2v48OFq27ZtsWIBAKA8CgkJUUhIiKSrY62++OKLunjxokwmk9zc3GwcHQAAyOHm5qagoCBduXJF0dHRatu2rTmHX8vDw0ONGze2QYQAAFRMha6A79y5Uw8++KB69+5tlQBy7oS7kVGjRuWZ1rhxY0VFRVklBgAAKhp3d3dbhwAAACxwcXHR8uXL1bx5c1uHAgAAVMQx0evWrWvtOAAAQAnKyMjQH3/8oYSEhHyfZ3Ltw7cBAIDtBQYGKj4+njvOAQAoAwpdRL/tttu0Z88e3XLLLSURDwAAsLIjR44oKirK/NyR/FBEBwCgbLnvvvs0b948NWrUSP7+/rYOBwCACq3QRfQhQ4bogw8+0OzZs9WiRQtVqVIlzzL5jdkGAABs4+uvv5abm5see+wxqzzPBAAAlLy1a9fq8uXLeu655xQcHCwvLy+ZTCbzfJPJpPHjx9swQgAAKo5C/xV95coVZWVl6eeff9bPP/+c7zLffvttsQMDyqtq78+zdQglJl2St62DKEFJLwy2dQhAkZw4cUJjxoxR69atbR0KAAAooOPHj8vJyUnVqlVTWlqa0tLScs2/tqAOAABKVqGL6NOmTdPhw4fVs2dP7mYDAMAO+Pj42DoEAABQSFOnTrV1CAAA4P8rdAX8zz//1JAhQ3TXXXeVRDwAAMDK+vTpo6VLl6pZs2Zydna2dTgAAMDKsrOz9cYbb2jkyJGqWbOmrcMBAKDcKXQR3c3NTX5+fiURCwAAKAGdOnVSfHy8nn76aTVu3DjP80xMJpOGDx9uo+gAAIA1xMbG6tKlS7YOAwCAcqnQRfQ77rhDmzdvVnh4eEnEAwAArGzXrl364YcflJWVpc2bN+e7DEV0AAAAAADyV+gienBwsBYsWKD33ntPLVu2zHM3myTddtttVgkOAAAU39y5c1W3bl2NHDmS55kAAAAAAFBIhf4r+pNPPpEkJSQk6Pfff893mW+//bZ4UQEAAKtJSEjQCy+8oODgYFuHAgAAAACA3Sl0EX3ChAklEQcAACghtWrVYoxUAAAAAACKqFBF9CtXrmj37t1q27atQkJCSiomAABgRffff7+++eYbhYWFycvLy9bhAAAAAABgVwpVRHdxcdHy5cvVvHnzEgoHAABY26pVq3T+/Hk9/fTTqlOnTp7nmZhMJo0fP95G0QEAAAAAULYVejiXwMBAxcfHq3HjxiURDwAAsLLjx4/LwcFBHh4eSkpKUlJSUq75JpPJRpEBAABr8fHxkbOzs63DAACgXCp0Ef2+++7TvHnz1KhRI/n7+5dETAAAwIqmTp1q6xAAAEAhjR49Wi+88ILq1KmTZ97x48c1adIkffrpp5IkBwcH8j0AACWo0EX0tWvX6vLly3ruuecUHBwsLy+vXHew0SUcAAD7lZ2drTfeeEMjR45UzZo1bR0OAAAVVkJCgjIzM/Odl5GRoYSEhFKOCACAiqvQRfTjx4/LyclJ1apVU1pamtLS0nLNp0s4AAD2LTY2VpcuXbJ1GAAAwIKzZ8/Kzc3N1mEAAFBhFLqIThcxAAAAAACsb926dVq/fr359ddff52nWH7lyhUdO3asSM8pW7lypX766SelpKQoMDBQw4YNU1hY2E3X++uvvzRx4kQFBQXpvffeK/R+AaCsqvb+PFuHUGLSJXnbOogSlPTC4FLdn0NJbjw7O1uRkZE6ffp0Se4GAAAAAAC7d+XKFaWmpio1NVWSdOHCBfPrnJ+srCy1a9dOI0eOLNS2t2zZolmzZql///6KiopSWFiY3n77bSUmJt5wvYsXL2rq1Km65ZZbinxcAADYu0LfiV5YdAkHAAAAAODmunXrpm7dukmSRo0apbFjx+b7YNGiWLZsmTp37qwuXbpIkoYNG6Y9e/YoJiZGDz30kMX1vvzyS7Vv314ODg767bffrBILAAD2pkTvRAcAAAAAAIU3depUqxXQMzMzdeTIETVr1izX9PDwcB04cMDiemvXrtXZs2c1cOBAq8QBAIC9KvE70QEAAAAAQOEcO3ZMFy5cMI99np6ernnz5ikuLk7h4eEaNGiQTCZTgbaVmpqq7OxseXp65pru6emplJSUfNc5ffq0oqOjFRkZKUdHxwLtJyMjQxkZGebXJpPJPKZ7QWNF+UUbQEHQTlBQpd1WKKIDAAAAAFDGzJkzR3Xr1jUX0b/55hutXr1atWvX1g8//CAPDw/dfffdhdpmfgWH/KZlZ2fr448/1sCBAxUQEFDg7S9ZskSLFi0yv65bt66ioqLk6+tbqDhvJN1qW0Jpq1mzZqnti3Ziv0qznUi0FXtW2m2FIjoAAMjFx8dHzs7Otg4DAIAK7fjx4+revbskyTAMbdq0SQMHDlT//v21YMECrV27tsBFdA8PDzk4OOS56/zcuXN57k6XpEuXLunw4cOKi4vTjBkzzDEYhqEHHnhAr776qpo2bZpnvX79+qlXr17m1zkF+oSEBGVmZhYo1pvxtspWYAunT58utX3RTuxXabYTibZiz6zVVpycnAp0sZciOgAA5dzo0aP1wgsv5Duu6vHjxzVp0iR9+umnkiQHBwdNnTq1lCMEAADXu3jxojw8PCRdHdrl/PnzateunSSpadOmWrFiRYG35eTkpJCQEO3du1dt2rQxT9+7d69uvfXWPMu7ubnp/fffzzUtJiZG+/bt0/PPPy8/P7989+Ps7GzxQrxhGAWOF+UTbQAFQTtBQZV2WykTRfTY2Fj99NNPiou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",
      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vẽ biểu đồ so sánh\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
    "\n",
    "metrics = ['test_rmse', 'test_mae', 'test_r2']\n",
    "titles = ['Test RMSE (Lower is better)', 'Test MAE (Lower is better)', 'Test R² (Higher is better)']\n",
    "\n",
    "for idx, (metric, title) in enumerate(zip(metrics, titles)):\n",
    "    axes[idx].bar(comparison_df['model'], comparison_df[metric])\n",
    "    axes[idx].set_title(title)\n",
    "    axes[idx].set_ylabel(metric)\n",
    "    axes[idx].tick_params(axis='x', rotation=45)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83a8019b",
   "metadata": {},
   "source": [
    "## 6. Dự đoán cho các trường hợp cụ thể"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "71b3ee8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🎯 DEMO DỰ ĐOÁN\n",
      "============================================================\n",
      "\n",
      "🏢 Tòa: 559-1 | ⏰ Ca: 6h00:14h00 | 👥 Nhân sự: 4 người\n",
      "\n",
      "🏢 Tòa: 559-1 | ⏰ Ca: 14h00:22h00 | 👥 Nhân sự: 4 người\n",
      "\n",
      "🏢 Tòa: 618-1 | ⏰ Ca: 6h00:14h00 | 👥 Nhân sự: 5 người\n",
      "\n",
      "🏢 Tòa: 283-1 | ⏰ Ca: 6h00:14h00 | 👥 Nhân sự: 8 người\n",
      "\n",
      "🏢 Tòa: 337-1 | ⏰ Ca: 13h00-21h00 | 👥 Nhân sự: 10 người\n",
      "\n",
      "📋 Bảng kết quả dự đoán:\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>Mã địa điểm</th>\n",
       "      <th>Ca làm việc</th>\n",
       "      <th>Số nhân sự dự đoán</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>559-1</td>\n",
       "      <td>6h00:14h00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>559-1</td>\n",
       "      <td>14h00:22h00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>618-1</td>\n",
       "      <td>6h00:14h00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>283-1</td>\n",
       "      <td>6h00:14h00</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>337-1</td>\n",
       "      <td>13h00-21h00</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mã địa điểm  Ca làm việc  Số nhân sự dự đoán\n",
       "0       559-1   6h00:14h00                   4\n",
       "1       559-1  14h00:22h00                   4\n",
       "2       618-1   6h00:14h00                   5\n",
       "3       283-1   6h00:14h00                   8\n",
       "4       337-1  13h00-21h00                  10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Demo dự đoán\n",
    "print(\"🎯 DEMO DỰ ĐOÁN\\n\" + \"=\"*60)\n",
    "\n",
    "test_cases = [\n",
    "    ('559-1', '6h00:14h00'),\n",
    "    ('559-1', '14h00:22h00'),\n",
    "    ('618-1', '6h00:14h00'),\n",
    "    ('283-1', '6h00:14h00'),\n",
    "    ('337-1', '13h00-21h00'),\n",
    "]\n",
    "\n",
    "predictions = []\n",
    "for building_code, shift_time in test_cases:\n",
    "    predicted_staff = predictor.predict_staff(building_code, shift_time)\n",
    "    print(f\"\\n🏢 Tòa: {building_code} | ⏰ Ca: {shift_time} | 👥 Nhân sự: {predicted_staff} người\")\n",
    "    predictions.append({\n",
    "        'Mã địa điểm': building_code,\n",
    "        'Ca làm việc': shift_time,\n",
    "        'Số nhân sự dự đoán': predicted_staff\n",
    "    })\n",
    "\n",
    "# Tạo DataFrame kết quả\n",
    "predictions_df = pd.DataFrame(predictions)\n",
    "print(\"\\n📋 Bảng kết quả dự đoán:\")\n",
    "display(predictions_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "088dcf73",
   "metadata": {},
   "source": [
    "## 7. Chức năng Dự đoán Tương tác"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "fd2aebcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🏢 Danh sách tòa nhà có sẵn:\n",
      "  1. 559-1\n",
      "  2. 618-1\n",
      "  3. 283-1\n",
      "  4. 337-1\n",
      "  5. 55-1\n",
      "  6. 33-1\n",
      "  7. 213-1\n",
      "  8. 286-1\n"
     ]
    }
   ],
   "source": [
    "# Danh sách tòa nhà có sẵn\n",
    "available_buildings = building_df['Mã địa điểm'].tolist()\n",
    "print(\"🏢 Danh sách tòa nhà có sẵn:\")\n",
    "for i, building in enumerate(available_buildings, 1):\n",
    "    print(f\"  {i}. {building}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2afe83da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "🎯 KẾT QUẢ DỰ ĐOÁN\n",
      "============================================================\n",
      "🏢 Tòa nhà: 559-1\n",
      "⏰ Ca làm việc: 6h00:14h00\n",
      "👥 Số nhân sự cần thiết: 4 người\n",
      "============================================================\n"
     ]
    }
   ],
   "source": [
    "# DỰ ĐOÁN TÙY CHỈNH - Thay đổi giá trị bên dưới\n",
    "building_code = '559-1'  # <-- Thay đổi mã tòa nhà ở đây\n",
    "shift_time = '6h00:14h00'  # <-- Thay đổi ca làm việc ở đây (vd: '6h00:14h00', '14h00:22h00')\n",
    "\n",
    "try:\n",
    "    predicted_staff = predictor.predict_staff(building_code, shift_time)\n",
    "    print(\"\\n\" + \"=\"*60)\n",
    "    print(\"🎯 KẾT QUẢ DỰ ĐOÁN\")\n",
    "    print(\"=\"*60)\n",
    "    print(f\"🏢 Tòa nhà: {building_code}\")\n",
    "    print(f\"⏰ Ca làm việc: {shift_time}\")\n",
    "    print(f\"👥 Số nhân sự cần thiết: {predicted_staff} người\")\n",
    "    print(\"=\"*60)\n",
    "except Exception as e:\n",
    "    print(f\"❌ Lỗi: {e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30e092f2",
   "metadata": {},
   "source": [
    "## 8. Phân tích sâu hơn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c17bef95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 Ma trận dự đoán nhân sự:\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>Ca</th>\n",
       "      <th>13h00-21h00</th>\n",
       "      <th>14h00:22h00</th>\n",
       "      <th>6h00:14h00</th>\n",
       "      <th>6h30-14h30</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tòa nhà</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>213-1</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283-1</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286-1</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33-1</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337-1</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55-1</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559-1</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618-1</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Ca       13h00-21h00  14h00:22h00  6h00:14h00  6h30-14h30\n",
       "Tòa nhà                                                  \n",
       "213-1              5            5           7           7\n",
       "283-1              6            6           8           8\n",
       "286-1              3            3           3           3\n",
       "33-1               5            5           6           6\n",
       "337-1             10           10          13          13\n",
       "55-1              10           10          12          12\n",
       "559-1              4            4           4           4\n",
       "618-1              4            4           5           5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dự đoán cho tất cả các tòa nhà với các ca phổ biến\n",
    "common_shifts = ['6h00:14h00', '14h00:22h00', '6h30-14h30', '13h00-21h00']\n",
    "all_predictions = []\n",
    "\n",
    "for building in available_buildings:\n",
    "    for shift in common_shifts:\n",
    "        try:\n",
    "            pred = predictor.predict_staff(building, shift)\n",
    "            all_predictions.append({\n",
    "                'Tòa nhà': building,\n",
    "                'Ca': shift,\n",
    "                'Nhân sự': pred\n",
    "            })\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "all_pred_df = pd.DataFrame(all_predictions)\n",
    "\n",
    "# Tạo pivot table\n",
    "pivot_df = all_pred_df.pivot(index='Tòa nhà', columns='Ca', values='Nhân sự')\n",
    "print(\"📊 Ma trận dự đoán nhân sự:\")\n",
    "display(pivot_df)\n",
    "\n",
    "# Vẽ heatmap\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.heatmap(pivot_df, annot=True, fmt='.0f', cmap='YlOrRd', cbar_kws={'label': 'Số nhân sự'})\n",
    "plt.title('Ma trận Dự đoán Nhân sự theo Tòa nhà và Ca làm việc')\n",
    "plt.xlabel('Ca làm việc')\n",
    "plt.ylabel('Tòa nhà')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7042bb94",
   "metadata": {},
   "source": [
    "## 9. Xuất kết quả"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "07c05f28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Đã xuất kết quả ra file: du_doan_nhan_su.csv\n",
      "✅ Đã xuất ma trận ra file: ma_tran_nhan_su.csv\n"
     ]
    }
   ],
   "source": [
    "# Xuất kết quả ra file CSV\n",
    "output_file = 'du_doan_nhan_su.csv'\n",
    "all_pred_df.to_csv(output_file, index=False, encoding='utf-8-sig')\n",
    "print(f\"✅ Đã xuất kết quả ra file: {output_file}\")\n",
    "\n",
    "# Xuất ma trận pivot\n",
    "pivot_file = 'ma_tran_nhan_su.csv'\n",
    "pivot_df.to_csv(pivot_file, encoding='utf-8-sig')\n",
    "print(f\"✅ Đã xuất ma trận ra file: {pivot_file}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92ca69a0",
   "metadata": {},
   "source": [
    "## 10. Hướng dẫn sử dụng\n",
    "\n",
    "### Để dự đoán cho một trường hợp mới:\n",
    "\n",
    "```python\n",
    "# Ví dụ:\n",
    "building_code = '559-1'  # Mã tòa nhà\n",
    "shift_time = '14h00:22h00'  # Ca làm việc\n",
    "\n",
    "predicted_staff = predictor.predict_staff(building_code, shift_time)\n",
    "print(f\"Số nhân sự cần thiết: {predicted_staff} người\")\n",
    "```\n",
    "\n",
    "### Các format ca làm việc được hỗ trợ:\n",
    "- `6h00:14h00`\n",
    "- `14h00:22h00`\n",
    "- `6h30-14h30`\n",
    "- `13h00-21h00`\n",
    "- `6:30-14:30`\n",
    "- `07:00-17:00`\n",
    "- và các format tương tự\n",
    "\n",
    "### Lưu ý:\n",
    "- Model được train trên 8 tòa nhà với 31 records\n",
    "- Độ chính xác: Test RMSE ~3, R² ~0.5\n",
    "- Features quan trọng nhất: Độ dài ca làm việc, Diện tích ngoại cảnh, Diện tích WC"
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