{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jesselyn Mu\\anaconda3\\envs\\myenv\\lib\\site-packages\\numpy\\__init__.py:148: UserWarning: mkl-service package failed to import, therefore Intel(R) MKL initialization ensuring its correct out-of-the box operation under condition when Gnu OpenMP had already been loaded by Python process is not assured. Please install mkl-service package, see http://github.com/IntelPython/mkl-service\n",
      "  from . import _distributor_init\n"
     ]
    },
    {
     "ename": "ImportError",
     "evalue": "Unable to import required dependencies:\nnumpy: \n\nIMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!\n\nImporting the numpy C-extensions failed. This error can happen for\nmany reasons, often due to issues with your setup or how NumPy was\ninstalled.\n\nWe have compiled some common reasons and troubleshooting tips at:\n\n    https://numpy.org/devdocs/user/troubleshooting-importerror.html\n\nPlease note and check the following:\n\n  * The Python version is: Python3.10 from \"c:\\Users\\Jesselyn Mu\\anaconda3\\envs\\myenv\\python.exe\"\n  * The NumPy version is: \"1.21.5\"\n\nand make sure that they are the versions you expect.\nPlease carefully study the documentation linked above for further help.\n\nOriginal error was: No module named 'numpy.core._multiarray_umath'\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m      2\u001b[0m data \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mD:\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mTugas Akhir\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mCodingan\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mDevelopment\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mApp\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124motebook\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mpreprocessed_data_train_1.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      3\u001b[0m data\u001b[38;5;241m.\u001b[39mhead()\n",
      "File \u001b[1;32mc:\\Users\\Jesselyn Mu\\anaconda3\\envs\\myenv\\lib\\site-packages\\pandas\\__init__.py:16\u001b[0m\n\u001b[0;32m     13\u001b[0m         missing_dependencies\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdependency\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     15\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m missing_dependencies:\n\u001b[1;32m---> 16\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\n\u001b[0;32m     17\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnable to import required dependencies:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(missing_dependencies)\n\u001b[0;32m     18\u001b[0m     )\n\u001b[0;32m     19\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m hard_dependencies, dependency, missing_dependencies\n\u001b[0;32m     21\u001b[0m \u001b[38;5;66;03m# numpy compat\u001b[39;00m\n",
      "\u001b[1;31mImportError\u001b[0m: Unable to import required dependencies:\nnumpy: \n\nIMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!\n\nImporting the numpy C-extensions failed. This error can happen for\nmany reasons, often due to issues with your setup or how NumPy was\ninstalled.\n\nWe have compiled some common reasons and troubleshooting tips at:\n\n    https://numpy.org/devdocs/user/troubleshooting-importerror.html\n\nPlease note and check the following:\n\n  * The Python version is: Python3.10 from \"c:\\Users\\Jesselyn Mu\\anaconda3\\envs\\myenv\\python.exe\"\n  * The NumPy version is: \"1.21.5\"\n\nand make sure that they are the versions you expect.\nPlease carefully study the documentation linked above for further help.\n\nOriginal error was: No module named 'numpy.core._multiarray_umath'\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('D:\\Tugas Akhir\\Codingan\\Development\\App\\notebook\\preprocessed_data_train_1.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Engineering & IT', 'Service & Support', 'Creative & Design',\n",
       "       'Marketing', 'Operations', 'HR', 'Finance & Accounting',\n",
       "       'Corporate Strategy & Communications'], dtype=object)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['departemen'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>domisili</th>\n",
       "      <th>jenis_kelamin</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>join_date</th>\n",
       "      <th>resign_date</th>\n",
       "      <th>marriage_stat</th>\n",
       "      <th>dependant</th>\n",
       "      <th>education</th>\n",
       "      <th>absent_90D</th>\n",
       "      <th>avg_time_work</th>\n",
       "      <th>departemen</th>\n",
       "      <th>position</th>\n",
       "      <th>income</th>\n",
       "      <th>total_komp</th>\n",
       "      <th>job_satisfaction</th>\n",
       "      <th>performance_rating</th>\n",
       "      <th>churn_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11446</th>\n",
       "      <td>EM11453</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1993-05-08</td>\n",
       "      <td>2023-05-15</td>\n",
       "      <td>2024-10-01</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>D1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.81</td>\n",
       "      <td>HR</td>\n",
       "      <td>Staff</td>\n",
       "      <td>3477585</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      employee_id            domisili jenis_kelamin date_of_birth   join_date  \\\n",
       "11446     EM11453  Kota Jakarta Timur     Laki-laki    1993-05-08  2023-05-15   \n",
       "\n",
       "      resign_date marriage_stat  dependant education  absent_90D  \\\n",
       "11446  2024-10-01        Single          0        D1         5.0   \n",
       "\n",
       "       avg_time_work departemen position   income  total_komp  \\\n",
       "11446           9.81         HR    Staff  3477585         NaN   \n",
       "\n",
       "       job_satisfaction  performance_rating  churn_status  \n",
       "11446                 3                   2             1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter = data[data['employee_id'] == 'EM11453']\n",
    "filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>education</th>\n",
       "      <th>absent_90D</th>\n",
       "      <th>...</th>\n",
       "      <th>active_work_category</th>\n",
       "      <th>work_stability_score</th>\n",
       "      <th>married_dependent_ratio</th>\n",
       "      <th>position_score</th>\n",
       "      <th>job_income_position_score</th>\n",
       "      <th>education_score</th>\n",
       "      <th>education_income_ratio</th>\n",
       "      <th>weighted_satisfaction_performance</th>\n",
       "      <th>resign_risk_indicator</th>\n",
       "      <th>adjusted_work_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EM0001</td>\n",
       "      <td>Kabupaten Bogor</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1970-09-10</td>\n",
       "      <td>2024-01-04</td>\n",
       "      <td>2024-10-31</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "      <td>S1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Short-term</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2599023.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.039609e+06</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.329634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EM0002</td>\n",
       "      <td>Kota Jakarta Selatan</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1980-12-09</td>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>2023-04-22</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>2.25</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1281761.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.281761e+06</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.815385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EM0003</td>\n",
       "      <td>Tangerang</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1987-04-25</td>\n",
       "      <td>2022-01-17</td>\n",
       "      <td>2024-01-31</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>D2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4902208.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.634069e+06</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.646590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EM0004</td>\n",
       "      <td>Kepulauan Seribu</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1975-12-24</td>\n",
       "      <td>2022-01-26</td>\n",
       "      <td>2024-10-31</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>S1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>16.50</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3205246.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.282098e+06</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.536789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EM0005</td>\n",
       "      <td>Kota Jakarta Utara</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1987-06-15</td>\n",
       "      <td>2022-01-31</td>\n",
       "      <td>2023-02-21</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1208627.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.208627e+06</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.131545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee_id              domisili jenis_kelamin date_of_birth   join_date  \\\n",
       "0      EM0001       Kabupaten Bogor     Laki-laki    1970-09-10  2024-01-04   \n",
       "1      EM0002  Kota Jakarta Selatan     Laki-laki    1980-12-09  2021-01-05   \n",
       "2      EM0003             Tangerang     Laki-laki    1987-04-25  2022-01-17   \n",
       "3      EM0004      Kepulauan Seribu     Laki-laki    1975-12-24  2022-01-26   \n",
       "4      EM0005    Kota Jakarta Utara     Laki-laki    1987-06-15  2022-01-31   \n",
       "\n",
       "  resign_date marriage_stat  dependant education  absent_90D  ...  \\\n",
       "0  2024-10-31       Married          2        S1         1.0  ...   \n",
       "1  2023-04-22       Married          3      SLTA        11.0  ...   \n",
       "2  2024-01-31        Single          0        D2         3.0  ...   \n",
       "3  2024-10-31       Married          1        S1         1.0  ...   \n",
       "4  2023-02-21        Single          0      SLTA         1.0  ...   \n",
       "\n",
       "   active_work_category work_stability_score married_dependent_ratio  \\\n",
       "0            Short-term                 5.00                       3   \n",
       "1              Mid-term                 2.25                       4   \n",
       "2              Mid-term                 6.00                       1   \n",
       "3              Mid-term                16.50                       2   \n",
       "4              Mid-term                 6.00                       1   \n",
       "\n",
       "   position_score  job_income_position_score education_score  \\\n",
       "0               2                  2599023.0               5   \n",
       "1               1                  1281761.0               1   \n",
       "2               1                  4902208.0               3   \n",
       "3               2                  3205246.0               5   \n",
       "4               1                  1208627.0               1   \n",
       "\n",
       "  education_income_ratio  weighted_satisfaction_performance  \\\n",
       "0           1.039609e+06                                2.0   \n",
       "1           1.281761e+06                                1.4   \n",
       "2           1.634069e+06                                1.8   \n",
       "3           1.282098e+06                                1.6   \n",
       "4           1.208627e+06                                2.0   \n",
       "\n",
       "   resign_risk_indicator  adjusted_work_time  \n",
       "0                 Medium            9.329634  \n",
       "1                 Medium            9.815385  \n",
       "2                 Medium            9.646590  \n",
       "3                 Medium            9.536789  \n",
       "4                 Medium            9.131545  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('D:\\Tugas Akhir\\Codingan\\Development\\Data\\preprocessed_data_train.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>churn_status</th>\n",
       "      <th>Count</th>\n",
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       "      <td>4044</td>\n",
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      "text/plain": [
       "   churn_status  Count\n",
       "0             0  11494\n",
       "1             1   4044"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn = df['churn_status']\n",
    "exploded_churn = churn.explode()\n",
    "\n",
    "churn_count = exploded_churn.value_counts().reset_index()\n",
    "churn_count.columns = ['churn_status', 'Count']\n",
    "churn_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>employee_id</th>\n",
       "      <th>domisili</th>\n",
       "      <th>jenis_kelamin</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>join_date</th>\n",
       "      <th>resign_date</th>\n",
       "      <th>marriage_stat</th>\n",
       "      <th>dependant</th>\n",
       "      <th>education</th>\n",
       "      <th>absent_90D</th>\n",
       "      <th>...</th>\n",
       "      <th>active_work_category</th>\n",
       "      <th>work_stability_score</th>\n",
       "      <th>married_dependent_ratio</th>\n",
       "      <th>position_score</th>\n",
       "      <th>job_income_position_score</th>\n",
       "      <th>education_score</th>\n",
       "      <th>education_income_ratio</th>\n",
       "      <th>weighted_satisfaction_performance</th>\n",
       "      <th>resign_risk_indicator</th>\n",
       "      <th>adjusted_work_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13678</th>\n",
       "      <td>EM13783</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1976-04-03</td>\n",
       "      <td>2020-05-03</td>\n",
       "      <td>2024-10-31</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "      <td>S1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Long-term</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2804022.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.121609e+06</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Low</td>\n",
       "      <td>9.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13772</th>\n",
       "      <td>EM13877</td>\n",
       "      <td>Kota Depok</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1993-11-29</td>\n",
       "      <td>2021-12-06</td>\n",
       "      <td>2024-10-31</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>S1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>8.750000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2926778.5</td>\n",
       "      <td>5</td>\n",
       "      <td>1.170711e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.401041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11756</th>\n",
       "      <td>EM11848</td>\n",
       "      <td>Kabupaten Bogor</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1972-04-17</td>\n",
       "      <td>2020-09-30</td>\n",
       "      <td>2024-10-31</td>\n",
       "      <td>Married</td>\n",
       "      <td>4</td>\n",
       "      <td>D2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Long-term</td>\n",
       "      <td>12.250000</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3547243.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.182414e+06</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>9.523518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13464</th>\n",
       "      <td>EM13569</td>\n",
       "      <td>Kota Jakarta Pusat</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>2000-05-13</td>\n",
       "      <td>2023-05-30</td>\n",
       "      <td>2024-10-31</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>S1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3454996.5</td>\n",
       "      <td>5</td>\n",
       "      <td>1.381999e+06</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.295650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6463</th>\n",
       "      <td>EM6515</td>\n",
       "      <td>Kabupaten Bekasi</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1972-04-05</td>\n",
       "      <td>2024-02-01</td>\n",
       "      <td>2024-05-09</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Short-term</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2525476.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.525476e+06</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.265018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      employee_id            domisili jenis_kelamin date_of_birth   join_date  \\\n",
       "13678     EM13783  Kota Jakarta Timur     Laki-laki    1976-04-03  2020-05-03   \n",
       "13772     EM13877          Kota Depok     Laki-laki    1993-11-29  2021-12-06   \n",
       "11756     EM11848     Kabupaten Bogor     Laki-laki    1972-04-17  2020-09-30   \n",
       "13464     EM13569  Kota Jakarta Pusat     Laki-laki    2000-05-13  2023-05-30   \n",
       "6463       EM6515    Kabupaten Bekasi     Laki-laki    1972-04-05  2024-02-01   \n",
       "\n",
       "      resign_date marriage_stat  dependant education  absent_90D  ...  \\\n",
       "13678  2024-10-31       Married          3        S1         0.0  ...   \n",
       "13772  2024-10-31        Single          0        S1         3.0  ...   \n",
       "11756  2024-10-31       Married          4        D2         3.0  ...   \n",
       "13464  2024-10-31        Single          0        S1         4.0  ...   \n",
       "6463   2024-05-09       Married          3      SLTA        10.0  ...   \n",
       "\n",
       "       active_work_category work_stability_score married_dependent_ratio  \\\n",
       "13678             Long-term            54.000000                       4   \n",
       "13772              Mid-term             8.750000                       1   \n",
       "11756             Long-term            12.250000                       5   \n",
       "13464              Mid-term             3.400000                       1   \n",
       "6463             Short-term             0.272727                       4   \n",
       "\n",
       "       position_score  job_income_position_score education_score  \\\n",
       "13678               2                  2804022.0               5   \n",
       "13772               2                  2926778.5               5   \n",
       "11756               1                  3547243.0               3   \n",
       "13464               2                  3454996.5               5   \n",
       "6463                1                  2525476.0               1   \n",
       "\n",
       "      education_income_ratio  weighted_satisfaction_performance  \\\n",
       "13678           1.121609e+06                                2.2   \n",
       "13772           1.170711e+06                                1.0   \n",
       "11756           1.182414e+06                                3.0   \n",
       "13464           1.381999e+06                                1.8   \n",
       "6463            2.525476e+06                                1.6   \n",
       "\n",
       "       resign_risk_indicator  adjusted_work_time  \n",
       "13678                    Low            9.840000  \n",
       "13772                 Medium            9.401041  \n",
       "11756                    Low            9.523518  \n",
       "13464                 Medium            9.295650  \n",
       "6463                  Medium            9.265018  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame()\n",
    "for index, row in churn_count.iterrows():\n",
    "    churn = row['churn_status']  \n",
    "    count = row['Count']\n",
    "    if count > 4048:\n",
    "        filtered_data = df[df['churn_status'] == churn].sample(4048)\n",
    "        data = pd.concat([data, filtered_data])\n",
    "\n",
    "for index, row in churn_count.iterrows():\n",
    "    churn = row['churn_status']  \n",
    "    count = row['Count']\n",
    "    if count <= 4048:\n",
    "        filtered_data = df[df['churn_status'] == churn]\n",
    "        data = pd.concat([data, filtered_data])\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "cat_feature = ['departemen', 'position', 'domisili', 'marriage_stat', 'job_satisfaction', 'performance_rating',\n",
    "               'education', 'active_work_category', 'resign_risk_indicator', 'jenis_kelamin']\n",
    "\n",
    "X = data.drop(columns=['churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date', 'active_work_months'])\n",
    "y = data['churn_status']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "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>domisili</th>\n",
       "      <th>jenis_kelamin</th>\n",
       "      <th>marriage_stat</th>\n",
       "      <th>dependant</th>\n",
       "      <th>education</th>\n",
       "      <th>absent_90D</th>\n",
       "      <th>avg_time_work</th>\n",
       "      <th>departemen</th>\n",
       "      <th>position</th>\n",
       "      <th>income</th>\n",
       "      <th>...</th>\n",
       "      <th>active_work_category</th>\n",
       "      <th>work_stability_score</th>\n",
       "      <th>married_dependent_ratio</th>\n",
       "      <th>position_score</th>\n",
       "      <th>job_income_position_score</th>\n",
       "      <th>education_score</th>\n",
       "      <th>education_income_ratio</th>\n",
       "      <th>weighted_satisfaction_performance</th>\n",
       "      <th>resign_risk_indicator</th>\n",
       "      <th>adjusted_work_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13678</th>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "      <td>S1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.84</td>\n",
       "      <td>HR</td>\n",
       "      <td>Junior</td>\n",
       "      <td>5608044</td>\n",
       "      <td>...</td>\n",
       "      <td>Long-term</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2804022.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.121609e+06</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Low</td>\n",
       "      <td>9.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13772</th>\n",
       "      <td>Kota Depok</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>S1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.41</td>\n",
       "      <td>Corporate Strategy &amp; Communications</td>\n",
       "      <td>Junior</td>\n",
       "      <td>5853557</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>8.750000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2926778.5</td>\n",
       "      <td>5</td>\n",
       "      <td>1.170711e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.401041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11756</th>\n",
       "      <td>Kabupaten Bogor</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>Married</td>\n",
       "      <td>4</td>\n",
       "      <td>D2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.53</td>\n",
       "      <td>Corporate Strategy &amp; Communications</td>\n",
       "      <td>Staff</td>\n",
       "      <td>3547243</td>\n",
       "      <td>...</td>\n",
       "      <td>Long-term</td>\n",
       "      <td>12.250000</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3547243.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.182414e+06</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>9.523518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13464</th>\n",
       "      <td>Kota Jakarta Pusat</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>S1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.32</td>\n",
       "      <td>Operations</td>\n",
       "      <td>Junior</td>\n",
       "      <td>6909993</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3454996.5</td>\n",
       "      <td>5</td>\n",
       "      <td>1.381999e+06</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.295650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6463</th>\n",
       "      <td>Kabupaten Bekasi</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.62</td>\n",
       "      <td>Engineering &amp; IT</td>\n",
       "      <td>Staff</td>\n",
       "      <td>2525476</td>\n",
       "      <td>...</td>\n",
       "      <td>Short-term</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2525476.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.525476e+06</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.265018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 domisili jenis_kelamin marriage_stat  dependant education  \\\n",
       "13678  Kota Jakarta Timur     Laki-laki       Married          3        S1   \n",
       "13772          Kota Depok     Laki-laki        Single          0        S1   \n",
       "11756     Kabupaten Bogor     Laki-laki       Married          4        D2   \n",
       "13464  Kota Jakarta Pusat     Laki-laki        Single          0        S1   \n",
       "6463     Kabupaten Bekasi     Laki-laki       Married          3      SLTA   \n",
       "\n",
       "       absent_90D  avg_time_work                           departemen  \\\n",
       "13678         0.0           9.84                                   HR   \n",
       "13772         3.0           9.41  Corporate Strategy & Communications   \n",
       "11756         3.0           9.53  Corporate Strategy & Communications   \n",
       "13464         4.0           9.32                           Operations   \n",
       "6463         10.0           9.62                     Engineering & IT   \n",
       "\n",
       "      position   income  ...  active_work_category work_stability_score  \\\n",
       "13678   Junior  5608044  ...             Long-term            54.000000   \n",
       "13772   Junior  5853557  ...              Mid-term             8.750000   \n",
       "11756    Staff  3547243  ...             Long-term            12.250000   \n",
       "13464   Junior  6909993  ...              Mid-term             3.400000   \n",
       "6463     Staff  2525476  ...            Short-term             0.272727   \n",
       "\n",
       "      married_dependent_ratio  position_score  job_income_position_score  \\\n",
       "13678                       4               2                  2804022.0   \n",
       "13772                       1               2                  2926778.5   \n",
       "11756                       5               1                  3547243.0   \n",
       "13464                       1               2                  3454996.5   \n",
       "6463                        4               1                  2525476.0   \n",
       "\n",
       "       education_score  education_income_ratio  \\\n",
       "13678                5            1.121609e+06   \n",
       "13772                5            1.170711e+06   \n",
       "11756                3            1.182414e+06   \n",
       "13464                5            1.381999e+06   \n",
       "6463                 1            2.525476e+06   \n",
       "\n",
       "       weighted_satisfaction_performance  resign_risk_indicator  \\\n",
       "13678                                2.2                    Low   \n",
       "13772                                1.0                 Medium   \n",
       "11756                                3.0                    Low   \n",
       "13464                                1.8                 Medium   \n",
       "6463                                 1.6                 Medium   \n",
       "\n",
       "       adjusted_work_time  \n",
       "13678            9.840000  \n",
       "13772            9.401041  \n",
       "11756            9.523518  \n",
       "13464            9.295650  \n",
       "6463             9.265018  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.to_csv(r\"D:\\Tugas Akhir\\Codingan\\Development\\App\\X_train.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\ttest: 0.9691762\tbest: 0.9691762 (0)\ttotal: 197ms\tremaining: 3m 16s\n",
      "200:\ttest: 0.9851730\tbest: 0.9851730 (200)\ttotal: 6.91s\tremaining: 27.5s\n",
      "400:\ttest: 0.9857376\tbest: 0.9857407 (389)\ttotal: 14s\tremaining: 20.9s\n",
      "600:\ttest: 0.9861924\tbest: 0.9861954 (599)\ttotal: 22.1s\tremaining: 14.7s\n",
      "800:\ttest: 0.9865571\tbest: 0.9865571 (800)\ttotal: 30.2s\tremaining: 7.5s\n",
      "999:\ttest: 0.9867128\tbest: 0.9867189 (984)\ttotal: 38.4s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.9867188573\n",
      "bestIteration = 984\n",
      "\n",
      "Shrink model to first 985 iterations.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x1f8d7afaa90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from catboost import CatBoostClassifier\n",
    "import pandas as pd\n",
    "\n",
    "model = CatBoostClassifier(\n",
    "    iterations=1000,\n",
    "    learning_rate=0.01,\n",
    "    depth=6,\n",
    "    cat_features= cat_feature,\n",
    "    loss_function='Logloss',\n",
    "    eval_metric='AUC',\n",
    "    scale_pos_weight=len(y_train[y_train == 0]) / len(y_train[y_train == 1]),\n",
    "    verbose=200\n",
    ")\n",
    "\n",
    "# Melatih model\n",
    "model.fit(X_train, y_train, eval_set=(X_test, y_test), use_best_model=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import optuna\n",
    "from catboost import CatBoostClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# Fungsi objective untuk Optuna\n",
    "def objective(trial):\n",
    "    # Definisikan parameter yang akan dioptimasi\n",
    "    params = {\n",
    "        'iterations': trial.suggest_int('iterations', 500, 1000),\n",
    "        'learning_rate': trial.suggest_float('learning_rate', 0.001, 0.1, log=True),\n",
    "        'depth': trial.suggest_int('depth', 4, 6),\n",
    "        'subsample': trial.suggest_float('subsample', 0.5, 0.8),\n",
    "        'colsample_bylevel': trial.suggest_float('colsample_bylevel', 0.5, 0.8),\n",
    "        'l2_leaf_reg': trial.suggest_float('l2_leaf_reg', 5, 20),\n",
    "        'random_strength': trial.suggest_float('random_strength', 5, 10),\n",
    "        'cat_features': cat_feature,\n",
    "        'loss_function': 'Logloss',\n",
    "        'random_state': 42,\n",
    "        'verbose': 0\n",
    "    }\n",
    "\n",
    "    # Inisialisasi model dengan parameter yang dioptimasi\n",
    "    model = CatBoostClassifier(**params)\n",
    "\n",
    "    # Melatih model dengan validasi\n",
    "    model.fit(X_train, y_train, eval_set=(X_test, y_test), use_best_model=True)\n",
    "\n",
    "    # Prediksi probabilitas untuk menghitung AUC\n",
    "    y_pred = model.predict_proba(X_test)[:, 1]\n",
    "    auc = roc_auc_score(y_test, y_pred)\n",
    "\n",
    "    return auc  # Mengembalikan AUC sebagai skor yang ingin dimaksimalkan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2025-01-19 21:36:46,269] A new study created in memory with name: no-name-24998c48-7ab3-460c-a73b-9aa6f43bfdc8\n",
      "[I 2025-01-19 21:37:01,527] Trial 0 finished with value: 0.9771551526804926 and parameters: {'iterations': 649, 'learning_rate': 0.002356531904328771, 'depth': 4, 'subsample': 0.5287211911341743, 'colsample_bylevel': 0.5247194897918462, 'l2_leaf_reg': 10.976330879790167, 'random_strength': 7.413435700780713}. Best is trial 0 with value: 0.9771551526804926.\n",
      "[I 2025-01-19 21:37:17,438] Trial 1 finished with value: 0.9826473774969859 and parameters: {'iterations': 555, 'learning_rate': 0.020382394285856864, 'depth': 5, 'subsample': 0.5386628374413537, 'colsample_bylevel': 0.7011677630678268, 'l2_leaf_reg': 5.113099196547044, 'random_strength': 9.893079897354522}. Best is trial 1 with value: 0.9826473774969859.\n",
      "[I 2025-01-19 21:37:43,352] Trial 2 finished with value: 0.9776419600482229 and parameters: {'iterations': 707, 'learning_rate': 0.0011523374691728523, 'depth': 6, 'subsample': 0.6896392981436401, 'colsample_bylevel': 0.7243498051042692, 'l2_leaf_reg': 11.135729944501549, 'random_strength': 5.798833903834322}. Best is trial 1 with value: 0.9826473774969859.\n",
      "[I 2025-01-19 21:38:04,812] Trial 3 finished with value: 0.9772055120633614 and parameters: {'iterations': 693, 'learning_rate': 0.0017201567283983315, 'depth': 5, 'subsample': 0.5042183778157456, 'colsample_bylevel': 0.6629079988150055, 'l2_leaf_reg': 8.005649715124765, 'random_strength': 9.665094335744072}. Best is trial 1 with value: 0.9826473774969859.\n",
      "[I 2025-01-19 21:38:26,834] Trial 4 finished with value: 0.9871202063208656 and parameters: {'iterations': 714, 'learning_rate': 0.07759650357267997, 'depth': 5, 'subsample': 0.6922961321024227, 'colsample_bylevel': 0.5016545646731999, 'l2_leaf_reg': 6.281386045275517, 'random_strength': 6.552793489199638}. Best is trial 4 with value: 0.9871202063208656.\n",
      "[I 2025-01-19 21:38:48,372] Trial 5 finished with value: 0.9774695173129455 and parameters: {'iterations': 585, 'learning_rate': 0.006305531863851279, 'depth': 6, 'subsample': 0.5494390031333488, 'colsample_bylevel': 0.6030414898973065, 'l2_leaf_reg': 15.99220681185335, 'random_strength': 9.681170797727894}. Best is trial 4 with value: 0.9871202063208656.\n",
      "[I 2025-01-19 21:39:16,998] Trial 6 finished with value: 0.9792366738390637 and parameters: {'iterations': 809, 'learning_rate': 0.007336368376617573, 'depth': 6, 'subsample': 0.5923133899133239, 'colsample_bylevel': 0.7660581946641654, 'l2_leaf_reg': 12.3919091988477, 'random_strength': 7.903883438874353}. Best is trial 4 with value: 0.9871202063208656.\n",
      "[I 2025-01-19 21:39:38,529] Trial 7 finished with value: 0.9866410291626608 and parameters: {'iterations': 803, 'learning_rate': 0.03653076905048038, 'depth': 4, 'subsample': 0.593006037174206, 'colsample_bylevel': 0.7217340416753967, 'l2_leaf_reg': 6.735445361426357, 'random_strength': 6.666832401546532}. Best is trial 4 with value: 0.9871202063208656.\n",
      "[I 2025-01-19 21:39:53,527] Trial 8 finished with value: 0.9787513925132385 and parameters: {'iterations': 558, 'learning_rate': 0.009038699931330333, 'depth': 4, 'subsample': 0.7739155986246009, 'colsample_bylevel': 0.7659878632753714, 'l2_leaf_reg': 17.602739957093824, 'random_strength': 5.95160701740704}. Best is trial 4 with value: 0.9871202063208656.\n",
      "[I 2025-01-19 21:40:19,266] Trial 9 finished with value: 0.9834943307543226 and parameters: {'iterations': 1000, 'learning_rate': 0.01261295852486322, 'depth': 4, 'subsample': 0.7157271802792031, 'colsample_bylevel': 0.6315619597261601, 'l2_leaf_reg': 17.944347781213253, 'random_strength': 9.929119793767951}. Best is trial 4 with value: 0.9871202063208656.\n",
      "[I 2025-01-19 21:40:48,327] Trial 10 finished with value: 0.9870500083932305 and parameters: {'iterations': 912, 'learning_rate': 0.055912059976930245, 'depth': 5, 'subsample': 0.7975917646291624, 'colsample_bylevel': 0.5262611133152493, 'l2_leaf_reg': 8.87110873995303, 'random_strength': 5.119907612313103}. Best is trial 4 with value: 0.9871202063208656.\n",
      "[I 2025-01-19 21:41:18,306] Trial 11 finished with value: 0.9869172427474858 and parameters: {'iterations': 933, 'learning_rate': 0.09718913327176688, 'depth': 5, 'subsample': 0.7982309193265602, 'colsample_bylevel': 0.5002400453374314, 'l2_leaf_reg': 8.786070958278003, 'random_strength': 5.063101250696956}. Best is trial 4 with value: 0.9871202063208656.\n",
      "[I 2025-01-19 21:41:47,830] Trial 12 finished with value: 0.9873658990675883 and parameters: {'iterations': 876, 'learning_rate': 0.09562605966721016, 'depth': 5, 'subsample': 0.7409456779566318, 'colsample_bylevel': 0.5586195847075694, 'l2_leaf_reg': 8.961877370933633, 'random_strength': 5.0170217614609705}. Best is trial 12 with value: 0.9873658990675883.\n",
      "[I 2025-01-19 21:42:14,352] Trial 13 finished with value: 0.9867310656350623 and parameters: {'iterations': 795, 'learning_rate': 0.0995697164401411, 'depth': 5, 'subsample': 0.7319878962098416, 'colsample_bylevel': 0.5760242041639352, 'l2_leaf_reg': 5.986660072960932, 'random_strength': 8.215667096275425}. Best is trial 12 with value: 0.9873658990675883.\n",
      "[I 2025-01-19 21:42:42,930] Trial 14 finished with value: 0.9864701124692885 and parameters: {'iterations': 872, 'learning_rate': 0.04085465674100252, 'depth': 5, 'subsample': 0.6632564341922649, 'colsample_bylevel': 0.5630613240622155, 'l2_leaf_reg': 9.819997956892475, 'random_strength': 6.714947133168835}. Best is trial 12 with value: 0.9873658990675883.\n",
      "[I 2025-01-19 21:43:11,978] Trial 15 finished with value: 0.9864029666254635 and parameters: {'iterations': 747, 'learning_rate': 0.023214134477676424, 'depth': 6, 'subsample': 0.7421705975249314, 'colsample_bylevel': 0.5553355257613636, 'l2_leaf_reg': 14.111430604723884, 'random_strength': 6.101808740209048}. Best is trial 12 with value: 0.9873658990675883.\n",
      "[I 2025-01-19 21:43:40,334] Trial 16 finished with value: 0.9864441697569015 and parameters: {'iterations': 859, 'learning_rate': 0.059234189592353055, 'depth': 5, 'subsample': 0.6377060538599335, 'colsample_bylevel': 0.6047103318025887, 'l2_leaf_reg': 7.1997352695871655, 'random_strength': 8.924012606144876}. Best is trial 12 with value: 0.9873658990675883.\n",
      "[I 2025-01-19 21:43:57,429] Trial 17 finished with value: 0.9774527308519891 and parameters: {'iterations': 645, 'learning_rate': 0.004006779484929066, 'depth': 4, 'subsample': 0.6856992963709286, 'colsample_bylevel': 0.5027601083651831, 'l2_leaf_reg': 19.917989160304266, 'random_strength': 6.909485015846398}. Best is trial 12 with value: 0.9873658990675883.\n",
      "[I 2025-01-19 21:44:34,140] Trial 18 finished with value: 0.9862381540997116 and parameters: {'iterations': 1000, 'learning_rate': 0.018012681624400343, 'depth': 6, 'subsample': 0.6306681187250449, 'colsample_bylevel': 0.5373205314655686, 'l2_leaf_reg': 13.371000422511258, 'random_strength': 5.527555543553592}. Best is trial 12 with value: 0.9873658990675883.\n",
      "[I 2025-01-19 21:44:59,325] Trial 19 finished with value: 0.9866257687436097 and parameters: {'iterations': 769, 'learning_rate': 0.06575427694702385, 'depth': 5, 'subsample': 0.7521482678491676, 'colsample_bylevel': 0.5889844417940283, 'l2_leaf_reg': 10.274232272344193, 'random_strength': 6.301954961914125}. Best is trial 12 with value: 0.9873658990675883.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Trial:\n",
      "AUC: 0.9873658990675883\n",
      "Params:\n",
      "  iterations: 876\n",
      "  learning_rate: 0.09562605966721016\n",
      "  depth: 5\n",
      "  subsample: 0.7409456779566318\n",
      "  colsample_bylevel: 0.5586195847075694\n",
      "  l2_leaf_reg: 8.961877370933633\n",
      "  random_strength: 5.0170217614609705\n"
     ]
    }
   ],
   "source": [
    "# Buat studi Optuna untuk memaksimalkan AUC\n",
    "study = optuna.create_study(direction='maximize')\n",
    "study.optimize(objective, n_trials=20)  # Lakukan 20 percobaan\n",
    "\n",
    "# Tampilkan hasil terbaik\n",
    "print(\"Best Trial:\")\n",
    "print(f\"AUC: {study.best_trial.value}\")\n",
    "print(\"Params:\")\n",
    "for key, value in study.best_trial.params.items():\n",
    "    print(f\"  {key}: {value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.5496446\ttest: 0.5501316\tbest: 0.5501316 (0)\ttotal: 24.4ms\tremaining: 21.3s\n",
      "200:\tlearn: 0.1013691\ttest: 0.1337989\tbest: 0.1337962 (199)\ttotal: 5.2s\tremaining: 17.4s\n",
      "400:\tlearn: 0.0785841\ttest: 0.1305082\tbest: 0.1304245 (367)\ttotal: 11s\tremaining: 13s\n",
      "Stopped by overfitting detector  (50 iterations wait)\n",
      "\n",
      "bestTest = 0.1302927779\n",
      "bestIteration = 406\n",
      "\n",
      "Shrink model to first 407 iterations.\n",
      "Final AUC: 0.9874498313723695\n"
     ]
    }
   ],
   "source": [
    "# Ambil parameter terbaik dari Optuna\n",
    "best_params = study.best_trial.params\n",
    "\n",
    "# Tambahkan parameter tetap (yang tidak dioptimasi)\n",
    "best_params.update({\n",
    "    'loss_function': 'Logloss',  # Gunakan Logloss sebagai loss function\n",
    "    'cat_features': cat_feature,\n",
    "    'random_state': 42,\n",
    "    'verbose': 200,  # Aktifkan output verbose\n",
    "    'od_type': 'Iter',\n",
    "    'od_wait': 50\n",
    "})\n",
    "\n",
    "# Latih model dengan parameter terbaik\n",
    "final_model = CatBoostClassifier(**best_params)\n",
    "final_model.fit(X_train, y_train, eval_set=(X_test, y_test), use_best_model=True)\n",
    "\n",
    "# Evaluasi model final\n",
    "y_pred = final_model.predict_proba(X_test)[:, 1]\n",
    "final_auc = roc_auc_score(y_test, y_pred)\n",
    "print(f\"Final AUC: {final_auc}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CatBoost Classification model saved to 'clasification_model.sav'\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "with open('clasification_model.sav', 'wb') as f:\n",
    "    pickle.dump(final_model, f)\n",
    "print(\"CatBoost Classification model saved to 'clasification_model.sav'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Training Logloss: 0.07160162089283625\n",
      "Final Validation Logloss: 0.13042008928172583\n"
     ]
    }
   ],
   "source": [
    "evals_result = final_model.get_evals_result()\n",
    "\n",
    "# Menampilkan skor terakhir\n",
    "train_score = evals_result['learn']['Logloss'][-1]\n",
    "val_score = evals_result['validation']['Logloss'][-1]\n",
    "\n",
    "print(f\"Final Training Logloss: {train_score}\")\n",
    "print(f\"Final Validation Logloss: {val_score}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Ambil skor training dan validation dari evals_result\n",
    "train_logloss = evals_result['learn']['Logloss']\n",
    "val_logloss = evals_result['validation']['Logloss']\n",
    "\n",
    "# Plot learning curve\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train_logloss, label='Training Logloss')\n",
    "plt.plot(val_logloss, label='Validation Logloss')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Logloss')\n",
    "plt.title('Learning Curve')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\ttotal: 20.5ms\tremaining: 20.5s\n",
      "200:\ttotal: 6.73s\tremaining: 26.8s\n",
      "400:\ttotal: 15s\tremaining: 22.4s\n",
      "600:\ttotal: 23.9s\tremaining: 15.9s\n",
      "800:\ttotal: 33.2s\tremaining: 8.24s\n",
      "999:\ttotal: 43.5s\tremaining: 0us\n",
      "0:\ttotal: 26.3ms\tremaining: 26.2s\n",
      "200:\ttotal: 10.4s\tremaining: 41.3s\n",
      "400:\ttotal: 20.6s\tremaining: 30.7s\n",
      "600:\ttotal: 30.3s\tremaining: 20.1s\n",
      "800:\ttotal: 39.7s\tremaining: 9.85s\n",
      "999:\ttotal: 48.5s\tremaining: 0us\n",
      "0:\ttotal: 35.6ms\tremaining: 35.5s\n",
      "200:\ttotal: 9.03s\tremaining: 35.9s\n",
      "400:\ttotal: 18.7s\tremaining: 27.9s\n",
      "600:\ttotal: 28.4s\tremaining: 18.8s\n",
      "800:\ttotal: 38.1s\tremaining: 9.47s\n",
      "999:\ttotal: 46.5s\tremaining: 0us\n",
      "0:\ttotal: 39.9ms\tremaining: 39.9s\n",
      "200:\ttotal: 8.15s\tremaining: 32.4s\n",
      "400:\ttotal: 16.2s\tremaining: 24.2s\n",
      "600:\ttotal: 24.3s\tremaining: 16.1s\n",
      "800:\ttotal: 33s\tremaining: 8.19s\n",
      "999:\ttotal: 41.7s\tremaining: 0us\n",
      "0:\ttotal: 40.1ms\tremaining: 40.1s\n",
      "200:\ttotal: 8.38s\tremaining: 33.3s\n",
      "400:\ttotal: 16.9s\tremaining: 25.3s\n",
      "600:\ttotal: 25.4s\tremaining: 16.8s\n",
      "800:\ttotal: 33.5s\tremaining: 8.33s\n",
      "999:\ttotal: 41.8s\tremaining: 0us\n",
      "0:\ttotal: 36.3ms\tremaining: 36.3s\n",
      "200:\ttotal: 8.16s\tremaining: 32.4s\n",
      "400:\ttotal: 16.1s\tremaining: 24.1s\n",
      "600:\ttotal: 24.3s\tremaining: 16.1s\n",
      "800:\ttotal: 32.5s\tremaining: 8.08s\n",
      "999:\ttotal: 40.9s\tremaining: 0us\n",
      "0:\ttotal: 28.1ms\tremaining: 28.1s\n",
      "200:\ttotal: 7.96s\tremaining: 31.6s\n",
      "400:\ttotal: 16.1s\tremaining: 24s\n",
      "600:\ttotal: 24s\tremaining: 15.9s\n",
      "800:\ttotal: 32.1s\tremaining: 7.98s\n",
      "999:\ttotal: 40.7s\tremaining: 0us\n",
      "0:\ttotal: 30.9ms\tremaining: 30.9s\n",
      "200:\ttotal: 8.28s\tremaining: 32.9s\n",
      "400:\ttotal: 16.9s\tremaining: 25.3s\n",
      "600:\ttotal: 25.5s\tremaining: 16.9s\n",
      "800:\ttotal: 33.8s\tremaining: 8.39s\n",
      "999:\ttotal: 42.5s\tremaining: 0us\n",
      "0:\ttotal: 36.3ms\tremaining: 36.2s\n",
      "200:\ttotal: 8.29s\tremaining: 32.9s\n",
      "400:\ttotal: 16.8s\tremaining: 25.2s\n",
      "600:\ttotal: 25.6s\tremaining: 17s\n",
      "800:\ttotal: 34.5s\tremaining: 8.56s\n",
      "999:\ttotal: 44.2s\tremaining: 0us\n",
      "0:\ttotal: 51.8ms\tremaining: 51.7s\n",
      "200:\ttotal: 8.16s\tremaining: 32.4s\n",
      "400:\ttotal: 16.5s\tremaining: 24.7s\n",
      "600:\ttotal: 24.7s\tremaining: 16.4s\n",
      "800:\ttotal: 33.7s\tremaining: 8.38s\n",
      "999:\ttotal: 42.5s\tremaining: 0us\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Scores for each fold: [0.94691358 0.94814815 0.95550062 0.95302843 0.96044499 0.95550062\n",
      " 0.94684796 0.96415328 0.95673671 0.96044499]\n",
      "Mean Accuracy: 0.95\n",
      "Standard Deviation: 0.01\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score, StratifiedKFold\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Fungsi untuk menghitung skor cross-validation dan visualisasi\n",
    "def cross_validate_and_visualize_accuracy(model, X, y, cv=10):\n",
    "    # Stratified K-Fold untuk mempertahankan distribusi label\n",
    "    skf = StratifiedKFold(n_splits=cv, shuffle=True, random_state=42)\n",
    "\n",
    "    # Hitung skor cross-validation dengan metrik akurasi\n",
    "    scores = cross_val_score(model, X, y, scoring='accuracy', cv=skf)\n",
    "\n",
    "    # Rata-rata dan standar deviasi\n",
    "    mean_score = np.mean(scores)\n",
    "    std_score = np.std(scores)\n",
    "\n",
    "    # Visualisasi hasil cross-validation\n",
    "    plt.figure(figsize=(8, 5))\n",
    "    plt.plot(range(1, cv + 1), scores, marker='o', linestyle='-', color='b', label='Fold Score')\n",
    "    plt.axhline(y=mean_score, color='r', linestyle='--', label=f'Mean Accuracy: {mean_score:.2f}')\n",
    "    plt.fill_between(range(1, cv + 1), mean_score - std_score, mean_score + std_score, color='r', alpha=0.2, label='±1 Std Dev')\n",
    "    plt.title('Cross-Validation Scores (Accuracy)')\n",
    "    plt.xlabel('Fold')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.legend()\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "\n",
    "    # Cetak hasil skor\n",
    "    print(f'Accuracy Scores for each fold: {scores}')\n",
    "    print(f'Mean Accuracy: {mean_score:.2f}')\n",
    "    print(f'Standard Deviation: {std_score:.2f}')\n",
    "\n",
    "# Contoh penggunaan\n",
    "# Ganti model dengan model Anda, misalnya `model`\n",
    "cross_validate_and_visualize_accuracy(model, X, y, cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "200:\tlearn: 0.1015757\ttotal: 6.25s\tremaining: 21s\n",
      "400:\tlearn: 0.0766872\ttotal: 13s\tremaining: 15.4s\n",
      "600:\tlearn: 0.0614839\ttotal: 20.1s\tremaining: 9.21s\n",
      "800:\tlearn: 0.0512545\ttotal: 26.8s\tremaining: 2.51s\n",
      "875:\tlearn: 0.0477551\ttotal: 29.4s\tremaining: 0us\n",
      "0:\tlearn: 0.5481219\ttotal: 35.6ms\tremaining: 31.1s\n",
      "200:\tlearn: 0.0981562\ttotal: 6.35s\tremaining: 21.3s\n",
      "400:\tlearn: 0.0772036\ttotal: 13.3s\tremaining: 15.8s\n",
      "600:\tlearn: 0.0599954\ttotal: 19.8s\tremaining: 9.08s\n",
      "800:\tlearn: 0.0473294\ttotal: 26.3s\tremaining: 2.47s\n",
      "875:\tlearn: 0.0440178\ttotal: 28.9s\tremaining: 0us\n",
      "0:\tlearn: 0.5523868\ttotal: 37.9ms\tremaining: 33.1s\n",
      "200:\tlearn: 0.1038157\ttotal: 6.43s\tremaining: 21.6s\n",
      "400:\tlearn: 0.0795523\ttotal: 13.3s\tremaining: 15.7s\n",
      "600:\tlearn: 0.0626088\ttotal: 20.1s\tremaining: 9.19s\n",
      "800:\tlearn: 0.0498160\ttotal: 27s\tremaining: 2.53s\n",
      "875:\tlearn: 0.0456413\ttotal: 29.4s\tremaining: 0us\n",
      "0:\tlearn: 0.5464817\ttotal: 27.7ms\tremaining: 24.3s\n",
      "200:\tlearn: 0.1034084\ttotal: 6.06s\tremaining: 20.4s\n",
      "400:\tlearn: 0.0815560\ttotal: 12.5s\tremaining: 14.8s\n",
      "600:\tlearn: 0.0679180\ttotal: 19.2s\tremaining: 8.8s\n",
      "800:\tlearn: 0.0558956\ttotal: 25.6s\tremaining: 2.4s\n",
      "875:\tlearn: 0.0536034\ttotal: 28s\tremaining: 0us\n",
      "0:\tlearn: 0.5526390\ttotal: 24.7ms\tremaining: 21.6s\n",
      "200:\tlearn: 0.1043072\ttotal: 6.11s\tremaining: 20.5s\n",
      "400:\tlearn: 0.0811592\ttotal: 12.6s\tremaining: 15s\n",
      "600:\tlearn: 0.0668548\ttotal: 19s\tremaining: 8.69s\n",
      "800:\tlearn: 0.0561936\ttotal: 25.6s\tremaining: 2.4s\n",
      "875:\tlearn: 0.0526947\ttotal: 28.1s\tremaining: 0us\n",
      "0:\tlearn: 0.5448904\ttotal: 32.9ms\tremaining: 28.8s\n",
      "200:\tlearn: 0.1033182\ttotal: 6.22s\tremaining: 20.9s\n",
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      "800:\tlearn: 0.0533145\ttotal: 25.3s\tremaining: 2.36s\n",
      "875:\tlearn: 0.0498717\ttotal: 27.6s\tremaining: 0us\n",
      "0:\tlearn: 0.5433682\ttotal: 26.9ms\tremaining: 23.6s\n",
      "200:\tlearn: 0.1033114\ttotal: 6.36s\tremaining: 21.4s\n",
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      "800:\tlearn: 0.0549548\ttotal: 25.9s\tremaining: 2.42s\n",
      "875:\tlearn: 0.0509236\ttotal: 28.2s\tremaining: 0us\n",
      "0:\tlearn: 0.5449640\ttotal: 29.9ms\tremaining: 26.2s\n",
      "200:\tlearn: 0.1057254\ttotal: 6.25s\tremaining: 21s\n",
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      "600:\tlearn: 0.0676233\ttotal: 19.4s\tremaining: 8.88s\n",
      "800:\tlearn: 0.0533185\ttotal: 26.6s\tremaining: 2.49s\n",
      "875:\tlearn: 0.0489685\ttotal: 29.3s\tremaining: 0us\n",
      "0:\tlearn: 0.5432646\ttotal: 26ms\tremaining: 22.8s\n",
      "200:\tlearn: 0.1014616\ttotal: 6.33s\tremaining: 21.3s\n",
      "400:\tlearn: 0.0794462\ttotal: 13.3s\tremaining: 15.7s\n",
      "600:\tlearn: 0.0651259\ttotal: 20s\tremaining: 9.14s\n",
      "800:\tlearn: 0.0530310\ttotal: 26.6s\tremaining: 2.5s\n",
      "875:\tlearn: 0.0486757\ttotal: 29.3s\tremaining: 0us\n",
      "0:\tlearn: 0.5577186\ttotal: 31ms\tremaining: 27.1s\n",
      "200:\tlearn: 0.1005977\ttotal: 6.83s\tremaining: 22.9s\n",
      "400:\tlearn: 0.0783775\ttotal: 13.7s\tremaining: 16.2s\n",
      "600:\tlearn: 0.0642211\ttotal: 20.6s\tremaining: 9.44s\n",
      "800:\tlearn: 0.0528759\ttotal: 27.2s\tremaining: 2.54s\n",
      "875:\tlearn: 0.0501950\ttotal: 29.5s\tremaining: 0us\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Scores for each fold: [0.94567901 0.94444444 0.95426452 0.96044499 0.9592089  0.96044499\n",
      " 0.94561187 0.96291718 0.9592089  0.96538937]\n",
      "Mean Accuracy: 0.96\n",
      "Standard Deviation: 0.01\n"
     ]
    }
   ],
   "source": [
    "cross_validate_and_visualize_accuracy(final_model, X, y, cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Metrics:\n",
      "Accuracy: 0.97\n",
      "Precision: 0.95\n",
      "Recall: 0.99\n",
      "F1 Score: 0.97\n",
      "------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Metrics:\n",
      "Accuracy: 0.96\n",
      "Precision: 0.94\n",
      "Recall: 0.98\n",
      "F1 Score: 0.96\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Fungsi untuk menampilkan confusion matrix dan metrik evaluasi\n",
    "def evaluate_model(y_true, y_pred, dataset_name):\n",
    "    # Confusion matrix\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    \n",
    "    # Plot confusion matrix\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Not Churn', 'Churn'], yticklabels=['Not Churn', 'Churn'])\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('Actual')\n",
    "    plt.title(f'Confusion Matrix ({dataset_name})')\n",
    "    plt.show()\n",
    "    \n",
    "    # Hitung metrik evaluasi\n",
    "    accuracy = accuracy_score(y_true, y_pred)\n",
    "    precision = precision_score(y_true, y_pred, zero_division=0)\n",
    "    recall = recall_score(y_true, y_pred, zero_division=0)\n",
    "    f1 = f1_score(y_true, y_pred, zero_division=0)\n",
    "    \n",
    "    print(f'{dataset_name} Metrics:')\n",
    "    print(f'Accuracy: {accuracy:.2f}')\n",
    "    print(f'Precision: {precision:.2f}')\n",
    "    print(f'Recall: {recall:.2f}')\n",
    "    print(f'F1 Score: {f1:.2f}')\n",
    "    print('-' * 30)\n",
    "\n",
    "# Prediksi untuk data training dan testing\n",
    "y_train_pred = final_model.predict(X_train)\n",
    "y_test_pred = final_model.predict(X_test)\n",
    "\n",
    "# Evaluasi untuk data training\n",
    "evaluate_model(y_train, y_train_pred, 'Training')\n",
    "\n",
    "# Evaluasi untuk data testing\n",
    "evaluate_model(y_test, y_test_pred, 'Testing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Metrics:\n",
      "Accuracy: 0.96\n",
      "Precision: 0.94\n",
      "Recall: 0.99\n",
      "F1 Score: 0.96\n",
      "------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Metrics:\n",
      "Accuracy: 0.96\n",
      "Precision: 0.94\n",
      "Recall: 0.98\n",
      "F1 Score: 0.96\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = model.predict(X_train)\n",
    "y_test_pred = model.predict(X_test)\n",
    "\n",
    "# Evaluasi untuk data training\n",
    "evaluate_model(y_train, y_train_pred, 'Training')\n",
    "\n",
    "# Evaluasi untuk data testing\n",
    "evaluate_model(y_test, y_test_pred, 'Testing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Training Logloss: 0.09655999575829563\n",
      "Final Validation Logloss: 0.13220269163650872\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evals_result = model.get_evals_result()\n",
    "\n",
    "# Menampilkan skor terakhir\n",
    "train_score = evals_result['learn']['Logloss'][-1]\n",
    "val_score = evals_result['validation']['Logloss'][-1]\n",
    "\n",
    "print(f\"Final Training Logloss: {train_score}\")\n",
    "print(f\"Final Validation Logloss: {val_score}\")\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Ambil skor training dan validation dari evals_result\n",
    "train_logloss = evals_result['learn']['Logloss']\n",
    "val_logloss = evals_result['validation']['Logloss']\n",
    "\n",
    "# Plot learning curve\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train_logloss, label='Training Logloss')\n",
    "plt.plot(val_logloss, label='Validation Logloss')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Logloss')\n",
    "plt.title('Learning Curve')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "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>employee_id</th>\n",
       "      <th>domisili</th>\n",
       "      <th>jenis_kelamin</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>join_date</th>\n",
       "      <th>resign_date</th>\n",
       "      <th>marriage_stat</th>\n",
       "      <th>dependant</th>\n",
       "      <th>education</th>\n",
       "      <th>absent_90D</th>\n",
       "      <th>...</th>\n",
       "      <th>active_work_category</th>\n",
       "      <th>work_stability_score</th>\n",
       "      <th>married_dependent_ratio</th>\n",
       "      <th>position_score</th>\n",
       "      <th>job_income_position_score</th>\n",
       "      <th>education_score</th>\n",
       "      <th>education_income_ratio</th>\n",
       "      <th>weighted_satisfaction_performance</th>\n",
       "      <th>resign_risk_indicator</th>\n",
       "      <th>adjusted_work_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EM9660</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1970-07-27</td>\n",
       "      <td>2023-10-09</td>\n",
       "      <td>2024-10-31</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>D3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4843236.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1210809.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.182979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EM12614</td>\n",
       "      <td>Tangerang</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1972-03-13</td>\n",
       "      <td>2023-12-19</td>\n",
       "      <td>2024-10-31</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>D1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Short-term</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3491432.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1745716.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.529412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EM2150</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1986-12-27</td>\n",
       "      <td>2022-05-22</td>\n",
       "      <td>2023-07-05</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1727468.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1727468.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.288164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EM6733</td>\n",
       "      <td>Kabupaten Bogor</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1978-07-12</td>\n",
       "      <td>2022-12-07</td>\n",
       "      <td>2024-10-31</td>\n",
       "      <td>Married</td>\n",
       "      <td>4</td>\n",
       "      <td>D3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>3.285714</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3745375.0</td>\n",
       "      <td>4</td>\n",
       "      <td>936343.75</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.053694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EM9133</td>\n",
       "      <td>Kabupaten Bekasi</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1994-06-03</td>\n",
       "      <td>2023-05-29</td>\n",
       "      <td>2023-10-20</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Short-term</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2658503.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2658503.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3103</th>\n",
       "      <td>EM7715</td>\n",
       "      <td>Kabupaten Bekasi</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1985-04-11</td>\n",
       "      <td>2021-02-07</td>\n",
       "      <td>2023-02-25</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1197442.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1197442.00</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.728385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3104</th>\n",
       "      <td>EM2762</td>\n",
       "      <td>Kabupaten Bogor</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1984-05-22</td>\n",
       "      <td>2021-11-11</td>\n",
       "      <td>2024-10-31</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>5.142857</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2192338.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2192338.00</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.622154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3105</th>\n",
       "      <td>EM1927</td>\n",
       "      <td>Kota Jakarta Barat</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1968-12-11</td>\n",
       "      <td>2020-06-21</td>\n",
       "      <td>2024-10-31</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "      <td>S1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Long-term</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3001594.5</td>\n",
       "      <td>5</td>\n",
       "      <td>1200637.80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>9.710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3106</th>\n",
       "      <td>EM7271</td>\n",
       "      <td>Kota Jakarta Barat</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1977-05-09</td>\n",
       "      <td>2021-05-26</td>\n",
       "      <td>2024-10-31</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "      <td>S1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Long-term</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3153785.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1261514.00</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Low</td>\n",
       "      <td>9.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3107</th>\n",
       "      <td>EM4803</td>\n",
       "      <td>Kota Bogor</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1993-05-19</td>\n",
       "      <td>2023-06-15</td>\n",
       "      <td>2024-10-31</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>S1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3122322.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1248928.80</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.686537</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3108 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     employee_id            domisili jenis_kelamin date_of_birth   join_date  \\\n",
       "0         EM9660  Kota Jakarta Timur     Perempuan    1970-07-27  2023-10-09   \n",
       "1        EM12614           Tangerang     Laki-laki    1972-03-13  2023-12-19   \n",
       "2         EM2150  Kota Jakarta Timur     Laki-laki    1986-12-27  2022-05-22   \n",
       "3         EM6733     Kabupaten Bogor     Laki-laki    1978-07-12  2022-12-07   \n",
       "4         EM9133    Kabupaten Bekasi     Perempuan    1994-06-03  2023-05-29   \n",
       "...          ...                 ...           ...           ...         ...   \n",
       "3103      EM7715    Kabupaten Bekasi     Perempuan    1985-04-11  2021-02-07   \n",
       "3104      EM2762     Kabupaten Bogor     Perempuan    1984-05-22  2021-11-11   \n",
       "3105      EM1927  Kota Jakarta Barat     Perempuan    1968-12-11  2020-06-21   \n",
       "3106      EM7271  Kota Jakarta Barat     Perempuan    1977-05-09  2021-05-26   \n",
       "3107      EM4803          Kota Bogor     Perempuan    1993-05-19  2023-06-15   \n",
       "\n",
       "     resign_date marriage_stat  dependant education  absent_90D  ...  \\\n",
       "0     2024-10-31        Single          0        D3         2.0  ...   \n",
       "1     2024-10-31       Married          1        D1         1.0  ...   \n",
       "2     2023-07-05       Married          1      SLTA         4.0  ...   \n",
       "3     2024-10-31       Married          4        D3         6.0  ...   \n",
       "4     2023-10-20       Married          0      SLTA         0.0  ...   \n",
       "...          ...           ...        ...       ...         ...  ...   \n",
       "3103  2023-02-25       Married          2      SLTA         7.0  ...   \n",
       "3104  2024-10-31       Married          2      SLTA         6.0  ...   \n",
       "3105  2024-10-31       Married          2        S1         0.0  ...   \n",
       "3106  2024-10-31       Married          3        S1         0.0  ...   \n",
       "3107  2024-10-31       Married          0        S1         2.0  ...   \n",
       "\n",
       "      active_work_category work_stability_score married_dependent_ratio  \\\n",
       "0                 Mid-term             4.000000                       1   \n",
       "1               Short-term             5.000000                       2   \n",
       "2                 Mid-term             2.600000                       2   \n",
       "3                 Mid-term             3.285714                       5   \n",
       "4               Short-term             4.000000                       1   \n",
       "...                    ...                  ...                     ...   \n",
       "3103              Mid-term             3.000000                       3   \n",
       "3104              Mid-term             5.142857                       3   \n",
       "3105             Long-term            53.000000                       3   \n",
       "3106             Long-term            41.000000                       4   \n",
       "3107              Mid-term             5.333333                       1   \n",
       "\n",
       "      position_score  job_income_position_score education_score  \\\n",
       "0                  1                  4843236.0               4   \n",
       "1                  1                  3491432.0               2   \n",
       "2                  1                  1727468.0               1   \n",
       "3                  1                  3745375.0               4   \n",
       "4                  1                  2658503.0               1   \n",
       "...              ...                        ...             ...   \n",
       "3103               1                  1197442.0               1   \n",
       "3104               1                  2192338.0               1   \n",
       "3105               2                  3001594.5               5   \n",
       "3106               2                  3153785.0               5   \n",
       "3107               2                  3122322.0               5   \n",
       "\n",
       "     education_income_ratio  weighted_satisfaction_performance  \\\n",
       "0                1210809.00                                2.0   \n",
       "1                1745716.00                                2.6   \n",
       "2                1727468.00                                3.0   \n",
       "3                 936343.75                                1.6   \n",
       "4                2658503.00                                1.0   \n",
       "...                     ...                                ...   \n",
       "3103             1197442.00                                2.2   \n",
       "3104             2192338.00                                2.2   \n",
       "3105             1200637.80                                1.0   \n",
       "3106             1261514.00                                1.4   \n",
       "3107             1248928.80                                3.0   \n",
       "\n",
       "      resign_risk_indicator  adjusted_work_time  \n",
       "0                    Medium            9.182979  \n",
       "1                    Medium            9.529412  \n",
       "2                    Medium            9.288164  \n",
       "3                    Medium            9.053694  \n",
       "4                    Medium            9.540000  \n",
       "...                     ...                 ...  \n",
       "3103                 Medium            9.728385  \n",
       "3104                 Medium            9.622154  \n",
       "3105                    Low            9.710000  \n",
       "3106                    Low            9.070000  \n",
       "3107                 Medium            9.686537  \n",
       "\n",
       "[3108 rows x 37 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv('D:\\Tugas Akhir\\Codingan\\Development\\Data\\data_testing.csv')\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2299\n",
       "1     809\n",
       "Name: churn_status, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['churn_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tanggal terlama (minimum): 2020-01-02 00:00:00\n",
      "Tanggal terbaru (maksimum): 2024-10-30 00:00:00\n"
     ]
    }
   ],
   "source": [
    "# Konversi kolom join_date ke datetime\n",
    "df_test['join_date'] = pd.to_datetime(df_test['join_date'])\n",
    "\n",
    "# Cari tanggal terlama (minimum) dan terbaru (maksimum)\n",
    "oldest_date = df_test['join_date'].min()\n",
    "latest_date = df_test['join_date'].max()\n",
    "\n",
    "# Cetak hasil\n",
    "print(f\"Tanggal terlama (minimum): {oldest_date}\")\n",
    "print(f\"Tanggal terbaru (maksimum): {latest_date}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# end_date = pd.to_datetime(\"2024-10-31\")\n",
    "# df_test[\"date_of_birth\"] = pd.to_datetime(df_test[\"date_of_birth\"], errors='coerce')\n",
    "# df_test[\"age_years\"] = (end_date - df_test[\"date_of_birth\"]).dt.days // 365\n",
    "\n",
    "# df_test[\"join_date\"] = pd.to_datetime(df_test[\"join_date\"])\n",
    "# df_test[\"resign_date\"] = pd.to_datetime(df_test[\"resign_date\"])\n",
    "\n",
    "# df_test[\"resign_date\"].fillna(end_date, inplace=True)\n",
    "\n",
    "# df_test[\"total_komp\"].fillna(0, inplace=True)\n",
    "# df_test[\"absent_90D\"].fillna(0, inplace=True)\n",
    "\n",
    "# df_test[\"active_work\"] = (df_test[\"resign_date\"] - df_test[\"join_date\"]).dt.days\n",
    "\n",
    "# df_test[\"active_work_months\"] = df_test[\"active_work\"] // 30\n",
    "# df_test[\"income_3_months\"] = df_test[\"income\"] * 3\n",
    "# df_test[\"income_6_months\"] = df_test[\"income\"] * 6\n",
    "# df_test[\"total_income_work\"] = df_test[\"income\"] * df_test[\"active_work_months\"]\n",
    "\n",
    "# df_test[\"absence_ratio\"] = df_test[\"absent_90D\"] / (df_test[\"active_work\"] / 90)\n",
    "# df_test[\"income_dependant_ratio\"] = df_test[\"income\"] / (df_test[\"dependant\"] + 1)\n",
    "# df_test[\"work_efficiency\"] = df_test[\"avg_time_work\"] / 8\n",
    "\n",
    "# def categorize_work_duration_months(months):\n",
    "#     if months < 12:\n",
    "#         return \"Short-term\"\n",
    "#     elif 12 <= months <= 36:\n",
    "#         return \"Mid-term\"\n",
    "#     else:\n",
    "#         return \"Long-term\"\n",
    "\n",
    "# df_test['active_work_category'] = df_test['active_work_months'].apply(categorize_work_duration_months)\n",
    "\n",
    "# # Work Stability Score\n",
    "# df_test['work_stability_score'] = df_test['active_work_months'] / (df_test['absent_90D'] + 1)\n",
    "\n",
    "# # Married-Dependent Ratio\n",
    "# def married_dependent_ratio(row):\n",
    "#     if row['marriage_stat'] == 'Married':\n",
    "#         return row['dependant'] + 1\n",
    "#     else:\n",
    "#         return 1\n",
    "\n",
    "# df_test['married_dependent_ratio'] = df_test.apply(married_dependent_ratio, axis=1)\n",
    "\n",
    "# # Job Income to Position Score\n",
    "# position_score_mapping = {'Junior': 2, 'Staff': 1, 'Senior': 3, 'Manager': 4}\n",
    "# df_test['position_score'] = df_test['position'].map(position_score_mapping)\n",
    "# df_test['job_income_position_score'] = df_test['income'] / df_test['position_score']\n",
    "\n",
    "# # Education-Adjusted Income\n",
    "# education_score_mapping = {'SLTA': 1, 'D1': 2, 'D2': 3, 'D3': 4, 'S1': 5, 'S2': 6, 'S3': 7}\n",
    "# df_test['education_score'] = df_test['education'].map(education_score_mapping)\n",
    "# df_test['education_income_ratio'] = df_test['income'] / df_test['education_score']\n",
    "\n",
    "# # Weighted Satisfaction-Performance Score\n",
    "# df_test['weighted_satisfaction_performance'] = (\n",
    "#     0.6 * df_test['job_satisfaction'] + 0.4 * df_test['performance_rating']\n",
    "# )\n",
    "\n",
    "# # Resign Risk Indicator\n",
    "# def resign_risk_indicator(row):\n",
    "#     if row['age_years'] < 30 and row['active_work_months'] < 12:\n",
    "#         return \"High\"\n",
    "#     elif 1 <= row['active_work_months'] <= 36:\n",
    "#         return \"Medium\"\n",
    "#     else:\n",
    "#         return \"Low\"\n",
    "\n",
    "# df_test['resign_risk_indicator'] = df_test.apply(resign_risk_indicator, axis=1)\n",
    "\n",
    "# # Adjusted Work Time\n",
    "# df_test['adjusted_work_time'] = df_test['avg_time_work'] * (1 - (df_test['absent_90D'] / ((df_test['active_work_months'] * 90) + 1)))\n",
    "\n",
    "# job_satisfaction_mapping = {1.0: 'Low', 2.0: 'Medium', 3.0: 'High', 4.0: 'Very High'}\n",
    "# df_test['job_satisfaction'] = df_test['job_satisfaction'].map(job_satisfaction_mapping)\n",
    "\n",
    "# performance_rating_mapping = {1.0: 'Low', 2.0: 'Good', 3.0: 'Excellent', 4.0: 'Outstanding'}\n",
    "# df_test['performance_rating'] = df_test['performance_rating'].map(performance_rating_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jumlah baris sebelum filter: 3108\n",
      "Jumlah baris setelah filter: 3108\n"
     ]
    }
   ],
   "source": [
    "# Memilih hanya kolom numerik\n",
    "numerical_columns = df_test.select_dtypes(include=['int64', 'float64']).columns\n",
    "\n",
    "# Filter: Hanya menyimpan baris yang tidak memiliki nilai negatif\n",
    "df_test_filtered = df_test[(df_test[numerical_columns] >= 0).all(axis=1)]\n",
    "\n",
    "# Menampilkan hasil\n",
    "print(\"Jumlah baris sebelum filter:\", df_test.shape[0])\n",
    "print(\"Jumlah baris setelah filter:\", df_test_filtered.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = df_test.dropna(subset=['marriage_stat'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9453024453024453\n",
      "Precision: 0.8310880829015544\n",
      "Recall: 0.9913473423980222\n",
      "F1 Score: 0.9041713641488162\n"
     ]
    }
   ],
   "source": [
    "X_test = df_test.drop(['churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date', 'active_work_months'], axis=1)\n",
    "\n",
    "cat_feature = ['departemen', 'position', 'domisili', 'marriage_stat', 'job_satisfaction', 'performance_rating',\n",
    "               'education', 'active_work_category', 'resign_risk_indicator', 'jenis_kelamin']\n",
    "\n",
    "y_pred = final_model.predict(X_test)\n",
    "\n",
    "X_test['predicted_churn'] = y_pred\n",
    "\n",
    "accuracy = accuracy_score(df_test['churn_status'], y_pred)\n",
    "precision = precision_score(df_test['churn_status'], y_pred, zero_division=0)\n",
    "recall = recall_score(df_test['churn_status'], y_pred, zero_division=0)\n",
    "f1 = f1_score(df_test['churn_status'], y_pred, zero_division=0)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1 Score:\", f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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>employee_id</th>\n",
       "      <th>domisili</th>\n",
       "      <th>jenis_kelamin</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>join_date</th>\n",
       "      <th>resign_date</th>\n",
       "      <th>marriage_stat</th>\n",
       "      <th>dependant</th>\n",
       "      <th>education</th>\n",
       "      <th>absent_90D</th>\n",
       "      <th>...</th>\n",
       "      <th>active_work_category</th>\n",
       "      <th>work_stability_score</th>\n",
       "      <th>married_dependent_ratio</th>\n",
       "      <th>position_score</th>\n",
       "      <th>job_income_position_score</th>\n",
       "      <th>education_score</th>\n",
       "      <th>education_income_ratio</th>\n",
       "      <th>weighted_satisfaction_performance</th>\n",
       "      <th>resign_risk_indicator</th>\n",
       "      <th>adjusted_work_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EM13260</td>\n",
       "      <td>Kota Jakarta Barat</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1976-12-02</td>\n",
       "      <td>2020-10-25</td>\n",
       "      <td>2023-02-16</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>3.111111</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2252793.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.252793e+06</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.280456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EM0533</td>\n",
       "      <td>Tangerang</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1970-10-08</td>\n",
       "      <td>2022-10-05</td>\n",
       "      <td>2024-03-25</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.133333</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1784520.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.784520e+06</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.809471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EM7296</td>\n",
       "      <td>Kota Depok</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1980-05-10</td>\n",
       "      <td>2022-07-21</td>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1291410.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.291410e+06</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.534629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EM9032</td>\n",
       "      <td>Kota Depok</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1993-10-24</td>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>2024-01-25</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "      <td>D2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3909283.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.303094e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.468933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EM11615</td>\n",
       "      <td>Tangerang</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1987-02-02</td>\n",
       "      <td>2022-09-14</td>\n",
       "      <td>2023-11-03</td>\n",
       "      <td>Divorce</td>\n",
       "      <td>0</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.444444</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2615265.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.615265e+06</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Medium</td>\n",
       "      <td>8.968309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>EM12172</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1985-12-27</td>\n",
       "      <td>2022-05-31</td>\n",
       "      <td>2023-07-04</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1666355.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.666355e+06</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.517378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>EM1528</td>\n",
       "      <td>Kabupaten Bekasi</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1982-02-08</td>\n",
       "      <td>2022-02-15</td>\n",
       "      <td>2024-04-06</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "      <td>D1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4090506.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.045253e+06</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.214229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>EM12674</td>\n",
       "      <td>Kabupaten Bogor</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1994-10-19</td>\n",
       "      <td>2021-08-02</td>\n",
       "      <td>2023-07-07</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.769231</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2400606.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.400606e+06</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.693506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>807</th>\n",
       "      <td>EM13279</td>\n",
       "      <td>Kabupaten Bogor</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1985-07-16</td>\n",
       "      <td>2021-12-12</td>\n",
       "      <td>2024-03-19</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "      <td>D2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3852210.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.284070e+06</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.278536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808</th>\n",
       "      <td>EM8022</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1978-02-02</td>\n",
       "      <td>2022-07-07</td>\n",
       "      <td>2024-09-03</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "      <td>D2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4131962.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.377321e+06</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.417915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>809 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    employee_id            domisili jenis_kelamin date_of_birth   join_date  \\\n",
       "0       EM13260  Kota Jakarta Barat     Perempuan    1976-12-02  2020-10-25   \n",
       "1        EM0533           Tangerang     Laki-laki    1970-10-08  2022-10-05   \n",
       "2        EM7296          Kota Depok     Perempuan    1980-05-10  2022-07-21   \n",
       "3        EM9032          Kota Depok     Perempuan    1993-10-24  2022-07-05   \n",
       "4       EM11615           Tangerang     Laki-laki    1987-02-02  2022-09-14   \n",
       "..          ...                 ...           ...           ...         ...   \n",
       "804     EM12172  Kota Jakarta Timur     Perempuan    1985-12-27  2022-05-31   \n",
       "805      EM1528    Kabupaten Bekasi     Perempuan    1982-02-08  2022-02-15   \n",
       "806     EM12674     Kabupaten Bogor     Perempuan    1994-10-19  2021-08-02   \n",
       "807     EM13279     Kabupaten Bogor     Perempuan    1985-07-16  2021-12-12   \n",
       "808      EM8022  Kota Jakarta Timur     Laki-laki    1978-02-02  2022-07-07   \n",
       "\n",
       "    resign_date marriage_stat  dependant education  absent_90D  ...  \\\n",
       "0    2023-02-16       Married          2      SLTA         8.0  ...   \n",
       "1    2024-03-25       Married          3      SLTA        14.0  ...   \n",
       "2    2023-09-01       Married          2      SLTA        14.0  ...   \n",
       "3    2024-01-25       Married          2        D2         7.0  ...   \n",
       "4    2023-11-03       Divorce          0      SLTA         8.0  ...   \n",
       "..          ...           ...        ...       ...         ...  ...   \n",
       "804  2023-07-04       Married          2      SLTA         4.0  ...   \n",
       "805  2024-04-06       Married          2        D1         4.0  ...   \n",
       "806  2023-07-07       Married          0      SLTA        12.0  ...   \n",
       "807  2024-03-19       Married          2        D2         3.0  ...   \n",
       "808  2024-09-03       Married          3        D2         3.0  ...   \n",
       "\n",
       "     active_work_category work_stability_score married_dependent_ratio  \\\n",
       "0                Mid-term             3.111111                       3   \n",
       "1                Mid-term             1.133333                       4   \n",
       "2                Mid-term             0.866667                       3   \n",
       "3                Mid-term             2.250000                       3   \n",
       "4                Mid-term             1.444444                       1   \n",
       "..                    ...                  ...                     ...   \n",
       "804              Mid-term             2.600000                       3   \n",
       "805              Mid-term             5.200000                       3   \n",
       "806              Mid-term             1.769231                       1   \n",
       "807              Mid-term             6.750000                       3   \n",
       "808              Mid-term             6.500000                       4   \n",
       "\n",
       "     position_score  job_income_position_score education_score  \\\n",
       "0                 1                  2252793.0               1   \n",
       "1                 1                  1784520.0               1   \n",
       "2                 1                  1291410.0               1   \n",
       "3                 1                  3909283.0               3   \n",
       "4                 1                  2615265.0               1   \n",
       "..              ...                        ...             ...   \n",
       "804               1                  1666355.0               1   \n",
       "805               1                  4090506.0               2   \n",
       "806               1                  2400606.0               1   \n",
       "807               1                  3852210.0               3   \n",
       "808               1                  4131962.0               3   \n",
       "\n",
       "    education_income_ratio  weighted_satisfaction_performance  \\\n",
       "0             2.252793e+06                                2.0   \n",
       "1             1.784520e+06                                1.8   \n",
       "2             1.291410e+06                                1.6   \n",
       "3             1.303094e+06                                1.0   \n",
       "4             2.615265e+06                                2.2   \n",
       "..                     ...                                ...   \n",
       "804           1.666355e+06                                2.2   \n",
       "805           2.045253e+06                                1.6   \n",
       "806           2.400606e+06                                2.0   \n",
       "807           1.284070e+06                                2.2   \n",
       "808           1.377321e+06                                2.4   \n",
       "\n",
       "     resign_risk_indicator  adjusted_work_time  \n",
       "0                   Medium            9.280456  \n",
       "1                   Medium            9.809471  \n",
       "2                   Medium            9.534629  \n",
       "3                   Medium            9.468933  \n",
       "4                   Medium            8.968309  \n",
       "..                     ...                 ...  \n",
       "804                 Medium            9.517378  \n",
       "805                 Medium            9.214229  \n",
       "806                 Medium            9.693506  \n",
       "807                 Medium            9.278536  \n",
       "808                 Medium            9.417915  \n",
       "\n",
       "[809 rows x 37 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv('D:\\Tugas Akhir\\Codingan\\Development\\Data\\data_testing_resign.csv')\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jumlah baris sebelum filter: 809\n",
      "Jumlah baris setelah filter: 809\n"
     ]
    }
   ],
   "source": [
    "# Memilih hanya kolom numerik\n",
    "numerical_columns = df_test.select_dtypes(include=['int64', 'float64']).columns\n",
    "\n",
    "# Filter: Hanya menyimpan baris yang tidak memiliki nilai negatif\n",
    "df_test_filtered = df_test[(df_test[numerical_columns] >= 0).all(axis=1)]\n",
    "\n",
    "# Menampilkan hasil\n",
    "print(\"Jumlah baris sebelum filter:\", df_test.shape[0])\n",
    "print(\"Jumlah baris setelah filter:\", df_test_filtered.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = df_test.dropna(subset=['marriage_stat'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9913473423980222\n",
      "Precision: 1.0\n",
      "Recall: 0.9913473423980222\n",
      "F1 Score: 0.9956548727498449\n"
     ]
    }
   ],
   "source": [
    "X_test = df_test.drop(['churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date', 'active_work_months'], axis=1)\n",
    "\n",
    "cat_feature = ['departemen', 'position', 'domisili', 'marriage_stat', 'job_satisfaction', 'performance_rating',\n",
    "               'education', 'active_work_category', 'resign_risk_indicator', 'jenis_kelamin']\n",
    "\n",
    "y_pred = final_model.predict(X_test)\n",
    "\n",
    "X_test['predicted_churn'] = y_pred\n",
    "\n",
    "accuracy = accuracy_score(df_test['churn_status'], y_pred)\n",
    "precision = precision_score(df_test['churn_status'], y_pred, zero_division=0)\n",
    "recall = recall_score(df_test['churn_status'], y_pred, zero_division=0)\n",
    "f1 = f1_score(df_test['churn_status'], y_pred, zero_division=0)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1 Score:\", f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>domisili</th>\n",
       "      <th>jenis_kelamin</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>join_date</th>\n",
       "      <th>resign_date</th>\n",
       "      <th>marriage_stat</th>\n",
       "      <th>dependant</th>\n",
       "      <th>education</th>\n",
       "      <th>absent_90D</th>\n",
       "      <th>...</th>\n",
       "      <th>active_work_category</th>\n",
       "      <th>work_stability_score</th>\n",
       "      <th>married_dependent_ratio</th>\n",
       "      <th>position_score</th>\n",
       "      <th>job_income_position_score</th>\n",
       "      <th>education_score</th>\n",
       "      <th>education_income_ratio</th>\n",
       "      <th>weighted_satisfaction_performance</th>\n",
       "      <th>resign_risk_indicator</th>\n",
       "      <th>adjusted_work_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EM0012</td>\n",
       "      <td>Tangerang</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1970-12-21</td>\n",
       "      <td>2023-02-23</td>\n",
       "      <td>2024-08-07</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "      <td>D3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4708861.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.177215e+06</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.857106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EM0026</td>\n",
       "      <td>Kota Depok</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1986-11-14</td>\n",
       "      <td>2022-04-17</td>\n",
       "      <td>2024-08-04</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1430853.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.430853e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.694593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EM0041</td>\n",
       "      <td>Kota Jakarta Barat</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1983-03-16</td>\n",
       "      <td>2023-06-15</td>\n",
       "      <td>2024-09-06</td>\n",
       "      <td>Divorce</td>\n",
       "      <td>3</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1379381.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.379381e+06</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.059429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EM0053</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1979-07-13</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>2024-09-21</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1911583.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.911583e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.842189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EM0057</td>\n",
       "      <td>Kota Jakarta Barat</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>2000-03-13</td>\n",
       "      <td>2022-07-14</td>\n",
       "      <td>2024-08-29</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>D2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>2.777778</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3724157.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.241386e+06</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.047730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>EM0058</td>\n",
       "      <td>Tangerang</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1996-04-23</td>\n",
       "      <td>2023-07-18</td>\n",
       "      <td>2024-09-26</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2229928.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.229928e+06</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.114481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>EM0064</td>\n",
       "      <td>Kota Jakarta Utara</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1987-04-20</td>\n",
       "      <td>2022-07-25</td>\n",
       "      <td>2024-08-23</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1257855.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.257855e+06</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>EM0180</td>\n",
       "      <td>Kota Jakarta Utara</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>2000-06-25</td>\n",
       "      <td>2022-09-04</td>\n",
       "      <td>2024-10-07</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>D2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3034058.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.011353e+06</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.091639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EM0259</td>\n",
       "      <td>Kepulauan Seribu</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1993-10-04</td>\n",
       "      <td>2023-06-08</td>\n",
       "      <td>2024-08-29</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>D1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4513378.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.256689e+06</td>\n",
       "      <td>2.6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.479833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>EM0263</td>\n",
       "      <td>Kabupaten Bogor</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1995-02-15</td>\n",
       "      <td>2022-06-15</td>\n",
       "      <td>2024-07-27</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1599099.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.599099e+06</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.077272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>EM0268</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1984-09-25</td>\n",
       "      <td>2023-07-04</td>\n",
       "      <td>2024-09-21</td>\n",
       "      <td>Married</td>\n",
       "      <td>5</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2869178.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.869178e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.599453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>EM0274</td>\n",
       "      <td>Kota Bogor</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1995-07-09</td>\n",
       "      <td>2023-07-15</td>\n",
       "      <td>2024-10-02</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>D1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3040879.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.520440e+06</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.781063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>EM0360</td>\n",
       "      <td>Tangerang</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1979-08-13</td>\n",
       "      <td>2022-04-17</td>\n",
       "      <td>2024-09-19</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "      <td>D3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>3.625000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4658718.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.164680e+06</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.494477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>EM0368</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1979-12-25</td>\n",
       "      <td>2022-05-25</td>\n",
       "      <td>2024-08-02</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>D1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>2.888889</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3326206.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.663103e+06</td>\n",
       "      <td>2.6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.786442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>EM0384</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1976-08-11</td>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>2024-09-21</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>D3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3215076.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8.037690e+05</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.773272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>EM0388</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1970-11-15</td>\n",
       "      <td>2023-07-10</td>\n",
       "      <td>2024-09-07</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.076923</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1178459.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.178459e+06</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.072831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>EM0398</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1999-05-03</td>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>2024-10-12</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1527441.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.527441e+06</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>EM0481</td>\n",
       "      <td>Kabupaten Bekasi</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1997-12-24</td>\n",
       "      <td>2022-06-08</td>\n",
       "      <td>2024-09-27</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>9.333333</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2890639.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.890639e+06</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.562408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>EM0483</td>\n",
       "      <td>Kabupaten Bogor</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1975-08-05</td>\n",
       "      <td>2023-06-14</td>\n",
       "      <td>2024-07-25</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1193560.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.193560e+06</td>\n",
       "      <td>2.6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.313570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>EM0491</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1969-04-10</td>\n",
       "      <td>2023-06-28</td>\n",
       "      <td>2024-09-14</td>\n",
       "      <td>Married</td>\n",
       "      <td>5</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2048458.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.048458e+06</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.372688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>EM0493</td>\n",
       "      <td>Tangerang</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1996-08-05</td>\n",
       "      <td>2023-07-04</td>\n",
       "      <td>2024-07-25</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1267701.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.267701e+06</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.190102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>EM0499</td>\n",
       "      <td>Kota Jakarta Pusat</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1990-10-23</td>\n",
       "      <td>2022-07-20</td>\n",
       "      <td>2024-09-25</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.857143</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1544522.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.544522e+06</td>\n",
       "      <td>2.6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.825135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>EM0504</td>\n",
       "      <td>Kabupaten Bekasi</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>2000-04-19</td>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>2024-09-21</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1486463.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.486463e+06</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.705073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>EM0509</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1992-11-23</td>\n",
       "      <td>2023-08-15</td>\n",
       "      <td>2024-10-02</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1214155.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.214155e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.733698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>EM0520</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>2000-09-11</td>\n",
       "      <td>2022-09-12</td>\n",
       "      <td>2024-10-13</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1098601.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.098601e+06</td>\n",
       "      <td>2.6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.087215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>EM0590</td>\n",
       "      <td>Kota Jakarta Pusat</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1980-06-14</td>\n",
       "      <td>2023-04-13</td>\n",
       "      <td>2024-08-29</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>D3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4646268.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.161567e+06</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.108286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>EM0597</td>\n",
       "      <td>Kabupaten Bekasi</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1980-11-30</td>\n",
       "      <td>2023-05-15</td>\n",
       "      <td>2024-09-21</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "      <td>D1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3975285.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.987642e+06</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.580014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>EM0602</td>\n",
       "      <td>Kota Bekasi</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1990-07-28</td>\n",
       "      <td>2023-05-29</td>\n",
       "      <td>2024-08-26</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>D2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3496995.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.165665e+06</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.188379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>EM0606</td>\n",
       "      <td>Kota Bogor</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1987-08-01</td>\n",
       "      <td>2023-06-12</td>\n",
       "      <td>2024-08-07</td>\n",
       "      <td>Divorce</td>\n",
       "      <td>0</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2928866.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.928866e+06</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.357764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>EM0621</td>\n",
       "      <td>Kabupaten Bekasi</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>2000-05-14</td>\n",
       "      <td>2022-07-25</td>\n",
       "      <td>2024-09-07</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1374872.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.374872e+06</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.107099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>EM0626</td>\n",
       "      <td>Kota Depok</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1978-08-28</td>\n",
       "      <td>2023-08-14</td>\n",
       "      <td>2024-09-21</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.181818</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2436465.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.436465e+06</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.250325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>EM0638</td>\n",
       "      <td>Kabupaten Bogor</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1991-06-05</td>\n",
       "      <td>2023-09-11</td>\n",
       "      <td>2024-10-02</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.090909</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1191009.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.191009e+06</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.134709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>EM0640</td>\n",
       "      <td>Tangerang</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1986-05-10</td>\n",
       "      <td>2023-09-25</td>\n",
       "      <td>2024-10-22</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1106988.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.106988e+06</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.220359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>EM0722</td>\n",
       "      <td>Kota Jakarta Selatan</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1978-05-29</td>\n",
       "      <td>2022-07-10</td>\n",
       "      <td>2024-10-26</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "      <td>D2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>3.375000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3502617.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.167539e+06</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.133624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>EM0726</td>\n",
       "      <td>Kabupaten Bekasi</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1980-09-08</td>\n",
       "      <td>2023-07-20</td>\n",
       "      <td>2024-08-31</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.083333</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1592248.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.592248e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.668318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>EM0728</td>\n",
       "      <td>Kota Bekasi</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1983-04-20</td>\n",
       "      <td>2023-07-25</td>\n",
       "      <td>2024-09-11</td>\n",
       "      <td>Divorce</td>\n",
       "      <td>2</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1798264.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.798264e+06</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>EM0730</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1978-08-22</td>\n",
       "      <td>2023-08-03</td>\n",
       "      <td>2024-09-14</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1658463.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.658463e+06</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>EM0732</td>\n",
       "      <td>Kota Jakarta Selatan</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1981-03-17</td>\n",
       "      <td>2022-08-04</td>\n",
       "      <td>2024-09-13</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>2.777778</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1461380.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.461380e+06</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.695420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>EM0733</td>\n",
       "      <td>Kota Jakarta Utara</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1975-05-11</td>\n",
       "      <td>2023-08-04</td>\n",
       "      <td>2024-09-18</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.857143</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2041027.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.041027e+06</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.550811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>EM0736</td>\n",
       "      <td>Kota Depok</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1999-12-24</td>\n",
       "      <td>2022-08-08</td>\n",
       "      <td>2024-09-30</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>D3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4568518.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.142130e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.773272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>EM0741</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1997-01-24</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>2024-09-23</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>D1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.857143</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3317052.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.658526e+06</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.371734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>EM0819</td>\n",
       "      <td>Kota Depok</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1998-10-15</td>\n",
       "      <td>2022-05-09</td>\n",
       "      <td>2024-10-11</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>D1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>4.833333</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3966514.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.983257e+06</td>\n",
       "      <td>2.6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.342076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>EM0837</td>\n",
       "      <td>Kota Jakarta Selatan</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1991-10-25</td>\n",
       "      <td>2023-07-03</td>\n",
       "      <td>2024-09-15</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>D1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3765986.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.882993e+06</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.327930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>EM0845</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1998-10-17</td>\n",
       "      <td>2022-07-24</td>\n",
       "      <td>2024-08-23</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1258904.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.258904e+06</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.798676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>EM0865</td>\n",
       "      <td>Kota Depok</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1996-10-04</td>\n",
       "      <td>2023-09-05</td>\n",
       "      <td>2024-09-27</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1126688.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.126688e+06</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>EM0868</td>\n",
       "      <td>Kabupaten Bogor</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1977-06-10</td>\n",
       "      <td>2023-09-07</td>\n",
       "      <td>2024-10-26</td>\n",
       "      <td>Married</td>\n",
       "      <td>5</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1144246.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.144246e+06</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>EM0930</td>\n",
       "      <td>Kota Depok</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1970-08-04</td>\n",
       "      <td>2023-03-08</td>\n",
       "      <td>2024-09-18</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "      <td>D3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3918148.0</td>\n",
       "      <td>4</td>\n",
       "      <td>9.795370e+05</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.478291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>EM0933</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1981-10-31</td>\n",
       "      <td>2022-03-20</td>\n",
       "      <td>2024-09-08</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2490863.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.490863e+06</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.106338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>EM0957</td>\n",
       "      <td>Kota Jakarta Selatan</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1998-11-24</td>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>2024-10-31</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>2.545455</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2615137.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.615137e+06</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.342793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>EM0967</td>\n",
       "      <td>Kabupaten Bogor</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1996-02-01</td>\n",
       "      <td>2023-08-07</td>\n",
       "      <td>2024-10-03</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1745824.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.745824e+06</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Medium</td>\n",
       "      <td>9.208596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   employee_id              domisili jenis_kelamin date_of_birth   join_date  \\\n",
       "0       EM0012             Tangerang     Laki-laki    1970-12-21  2023-02-23   \n",
       "1       EM0026            Kota Depok     Laki-laki    1986-11-14  2022-04-17   \n",
       "2       EM0041    Kota Jakarta Barat     Laki-laki    1983-03-16  2023-06-15   \n",
       "3       EM0053    Kota Jakarta Timur     Laki-laki    1979-07-13  2023-07-11   \n",
       "4       EM0057    Kota Jakarta Barat     Perempuan    2000-03-13  2022-07-14   \n",
       "5       EM0058             Tangerang     Perempuan    1996-04-23  2023-07-18   \n",
       "6       EM0064    Kota Jakarta Utara     Perempuan    1987-04-20  2022-07-25   \n",
       "7       EM0180    Kota Jakarta Utara     Perempuan    2000-06-25  2022-09-04   \n",
       "8       EM0259      Kepulauan Seribu     Laki-laki    1993-10-04  2023-06-08   \n",
       "9       EM0263       Kabupaten Bogor     Laki-laki    1995-02-15  2022-06-15   \n",
       "10      EM0268    Kota Jakarta Timur     Laki-laki    1984-09-25  2023-07-04   \n",
       "11      EM0274            Kota Bogor     Laki-laki    1995-07-09  2023-07-15   \n",
       "12      EM0360             Tangerang     Perempuan    1979-08-13  2022-04-17   \n",
       "13      EM0368    Kota Jakarta Timur     Laki-laki    1979-12-25  2022-05-25   \n",
       "14      EM0384    Kota Jakarta Timur     Laki-laki    1976-08-11  2022-07-05   \n",
       "15      EM0388    Kota Jakarta Timur     Laki-laki    1970-11-15  2023-07-10   \n",
       "16      EM0398    Kota Jakarta Timur     Laki-laki    1999-05-03  2023-09-01   \n",
       "17      EM0481      Kabupaten Bekasi     Perempuan    1997-12-24  2022-06-08   \n",
       "18      EM0483       Kabupaten Bogor     Perempuan    1975-08-05  2023-06-14   \n",
       "19      EM0491    Kota Jakarta Timur     Perempuan    1969-04-10  2023-06-28   \n",
       "20      EM0493             Tangerang     Perempuan    1996-08-05  2023-07-04   \n",
       "21      EM0499    Kota Jakarta Pusat     Laki-laki    1990-10-23  2022-07-20   \n",
       "22      EM0504      Kabupaten Bekasi     Laki-laki    2000-04-19  2023-08-01   \n",
       "23      EM0509    Kota Jakarta Timur     Laki-laki    1992-11-23  2023-08-15   \n",
       "24      EM0520    Kota Jakarta Timur     Perempuan    2000-09-11  2022-09-12   \n",
       "25      EM0590    Kota Jakarta Pusat     Perempuan    1980-06-14  2023-04-13   \n",
       "26      EM0597      Kabupaten Bekasi     Perempuan    1980-11-30  2023-05-15   \n",
       "27      EM0602           Kota Bekasi     Perempuan    1990-07-28  2023-05-29   \n",
       "28      EM0606            Kota Bogor     Laki-laki    1987-08-01  2023-06-12   \n",
       "29      EM0621      Kabupaten Bekasi     Laki-laki    2000-05-14  2022-07-25   \n",
       "30      EM0626            Kota Depok     Laki-laki    1978-08-28  2023-08-14   \n",
       "31      EM0638       Kabupaten Bogor     Laki-laki    1991-06-05  2023-09-11   \n",
       "32      EM0640             Tangerang     Laki-laki    1986-05-10  2023-09-25   \n",
       "33      EM0722  Kota Jakarta Selatan     Perempuan    1978-05-29  2022-07-10   \n",
       "34      EM0726      Kabupaten Bekasi     Laki-laki    1980-09-08  2023-07-20   \n",
       "35      EM0728           Kota Bekasi     Laki-laki    1983-04-20  2023-07-25   \n",
       "36      EM0730    Kota Jakarta Timur     Perempuan    1978-08-22  2023-08-03   \n",
       "37      EM0732  Kota Jakarta Selatan     Laki-laki    1981-03-17  2022-08-04   \n",
       "38      EM0733    Kota Jakarta Utara     Laki-laki    1975-05-11  2023-08-04   \n",
       "39      EM0736            Kota Depok     Laki-laki    1999-12-24  2022-08-08   \n",
       "40      EM0741    Kota Jakarta Timur     Laki-laki    1997-01-24  2023-08-21   \n",
       "41      EM0819            Kota Depok     Laki-laki    1998-10-15  2022-05-09   \n",
       "42      EM0837  Kota Jakarta Selatan     Perempuan    1991-10-25  2023-07-03   \n",
       "43      EM0845    Kota Jakarta Timur     Laki-laki    1998-10-17  2022-07-24   \n",
       "44      EM0865            Kota Depok     Laki-laki    1996-10-04  2023-09-05   \n",
       "45      EM0868       Kabupaten Bogor     Laki-laki    1977-06-10  2023-09-07   \n",
       "46      EM0930            Kota Depok     Laki-laki    1970-08-04  2023-03-08   \n",
       "47      EM0933    Kota Jakarta Timur     Laki-laki    1981-10-31  2022-03-20   \n",
       "48      EM0957  Kota Jakarta Selatan     Perempuan    1998-11-24  2022-07-05   \n",
       "49      EM0967       Kabupaten Bogor     Laki-laki    1996-02-01  2023-08-07   \n",
       "\n",
       "   resign_date marriage_stat  dependant education  absent_90D  ...  \\\n",
       "0   2024-08-07       Married          3        D3         2.0  ...   \n",
       "1   2024-08-04       Married          2      SLTA         4.0  ...   \n",
       "2   2024-09-06       Divorce          3      SLTA         7.0  ...   \n",
       "3   2024-09-21        Single          0      SLTA         1.0  ...   \n",
       "4   2024-08-29        Single          0        D2         8.0  ...   \n",
       "5   2024-09-26        Single          0      SLTA         9.0  ...   \n",
       "6   2024-08-23       Married          3      SLTA         0.0  ...   \n",
       "7   2024-10-07        Single          0        D2         7.0  ...   \n",
       "8   2024-08-29        Single          0        D1         4.0  ...   \n",
       "9   2024-07-27       Married          1      SLTA        13.0  ...   \n",
       "10  2024-09-21       Married          5      SLTA         4.0  ...   \n",
       "11  2024-10-02       Married          0        D1         5.0  ...   \n",
       "12  2024-09-19       Married          3        D3         7.0  ...   \n",
       "13  2024-08-02       Married          1        D1         8.0  ...   \n",
       "14  2024-09-21       Married          1        D3         4.0  ...   \n",
       "15  2024-09-07       Married          2      SLTA        12.0  ...   \n",
       "16  2024-10-12        Single          0      SLTA         0.0  ...   \n",
       "17  2024-09-27        Single          0      SLTA         2.0  ...   \n",
       "18  2024-07-25       Married          1      SLTA        12.0  ...   \n",
       "19  2024-09-14       Married          5      SLTA         5.0  ...   \n",
       "20  2024-07-25       Married          1      SLTA         7.0  ...   \n",
       "21  2024-09-25       Married          1      SLTA        13.0  ...   \n",
       "22  2024-09-21        Single          0      SLTA         3.0  ...   \n",
       "23  2024-10-02       Married          1      SLTA        15.0  ...   \n",
       "24  2024-10-13        Single          0      SLTA        13.0  ...   \n",
       "25  2024-08-29       Married          0        D3         5.0  ...   \n",
       "26  2024-09-21       Married          3        D1         3.0  ...   \n",
       "27  2024-08-26        Single          0        D2         9.0  ...   \n",
       "28  2024-08-07       Divorce          0      SLTA         7.0  ...   \n",
       "29  2024-09-07       Married          0      SLTA        13.0  ...   \n",
       "30  2024-09-21       Married          2      SLTA        10.0  ...   \n",
       "31  2024-10-02        Single          0      SLTA        10.0  ...   \n",
       "32  2024-10-22       Married          1      SLTA        15.0  ...   \n",
       "33  2024-10-26       Married          3        D2         7.0  ...   \n",
       "34  2024-08-31       Married          2      SLTA        11.0  ...   \n",
       "35  2024-09-11       Divorce          2      SLTA         0.0  ...   \n",
       "36  2024-09-14       Married          3      SLTA         0.0  ...   \n",
       "37  2024-09-13       Married          2      SLTA         8.0  ...   \n",
       "38  2024-09-18       Married          2      SLTA         6.0  ...   \n",
       "39  2024-09-30        Single          0        D3         4.0  ...   \n",
       "40  2024-09-23        Single          0        D1         6.0  ...   \n",
       "41  2024-10-11        Single          0        D1         5.0  ...   \n",
       "42  2024-09-15       Married          1        D1         7.0  ...   \n",
       "43  2024-08-23        Single          0      SLTA        14.0  ...   \n",
       "44  2024-09-27        Single          0      SLTA         0.0  ...   \n",
       "45  2024-10-26       Married          5      SLTA         0.0  ...   \n",
       "46  2024-09-18       Married          2        D3         2.0  ...   \n",
       "47  2024-09-08       Married          1      SLTA         7.0  ...   \n",
       "48  2024-10-31       Married          2      SLTA        10.0  ...   \n",
       "49  2024-10-03        Single          0      SLTA         7.0  ...   \n",
       "\n",
       "    active_work_category work_stability_score married_dependent_ratio  \\\n",
       "0               Mid-term             5.666667                       4   \n",
       "1               Mid-term             5.600000                       3   \n",
       "2               Mid-term             1.750000                       1   \n",
       "3               Mid-term             7.000000                       1   \n",
       "4               Mid-term             2.777778                       1   \n",
       "5               Mid-term             1.400000                       1   \n",
       "6               Mid-term            25.000000                       4   \n",
       "7               Mid-term             3.125000                       1   \n",
       "8               Mid-term             2.800000                       1   \n",
       "9               Mid-term             1.785714                       2   \n",
       "10              Mid-term             2.800000                       6   \n",
       "11              Mid-term             2.333333                       1   \n",
       "12              Mid-term             3.625000                       4   \n",
       "13              Mid-term             2.888889                       2   \n",
       "14              Mid-term             5.200000                       2   \n",
       "15              Mid-term             1.076923                       3   \n",
       "16              Mid-term            13.000000                       1   \n",
       "17              Mid-term             9.333333                       1   \n",
       "18              Mid-term             1.000000                       2   \n",
       "19              Mid-term             2.333333                       6   \n",
       "20              Mid-term             1.500000                       2   \n",
       "21              Mid-term             1.857143                       2   \n",
       "22              Mid-term             3.250000                       1   \n",
       "23              Mid-term             0.812500                       2   \n",
       "24              Mid-term             1.785714                       1   \n",
       "25              Mid-term             2.666667                       1   \n",
       "26              Mid-term             4.000000                       4   \n",
       "27              Mid-term             1.500000                       1   \n",
       "28              Mid-term             1.750000                       1   \n",
       "29              Mid-term             1.785714                       1   \n",
       "30              Mid-term             1.181818                       3   \n",
       "31              Mid-term             1.090909                       1   \n",
       "32              Mid-term             0.812500                       2   \n",
       "33              Mid-term             3.375000                       4   \n",
       "34              Mid-term             1.083333                       3   \n",
       "35              Mid-term            13.000000                       1   \n",
       "36              Mid-term            13.000000                       4   \n",
       "37              Mid-term             2.777778                       3   \n",
       "38              Mid-term             1.857143                       3   \n",
       "39              Mid-term             5.200000                       1   \n",
       "40              Mid-term             1.857143                       1   \n",
       "41              Mid-term             4.833333                       1   \n",
       "42              Mid-term             1.750000                       2   \n",
       "43              Mid-term             1.666667                       1   \n",
       "44              Mid-term            12.000000                       1   \n",
       "45              Mid-term            13.000000                       6   \n",
       "46              Mid-term             6.000000                       3   \n",
       "47              Mid-term             3.750000                       2   \n",
       "48              Mid-term             2.545455                       3   \n",
       "49              Mid-term             1.750000                       1   \n",
       "\n",
       "    position_score  job_income_position_score education_score  \\\n",
       "0                1                  4708861.0               4   \n",
       "1                1                  1430853.0               1   \n",
       "2                1                  1379381.0               1   \n",
       "3                1                  1911583.0               1   \n",
       "4                1                  3724157.0               3   \n",
       "5                1                  2229928.0               1   \n",
       "6                1                  1257855.0               1   \n",
       "7                1                  3034058.0               3   \n",
       "8                1                  4513378.0               2   \n",
       "9                1                  1599099.0               1   \n",
       "10               1                  2869178.0               1   \n",
       "11               1                  3040879.0               2   \n",
       "12               1                  4658718.0               4   \n",
       "13               1                  3326206.0               2   \n",
       "14               1                  3215076.0               4   \n",
       "15               1                  1178459.0               1   \n",
       "16               1                  1527441.0               1   \n",
       "17               1                  2890639.0               1   \n",
       "18               1                  1193560.0               1   \n",
       "19               1                  2048458.0               1   \n",
       "20               1                  1267701.0               1   \n",
       "21               1                  1544522.0               1   \n",
       "22               1                  1486463.0               1   \n",
       "23               1                  1214155.0               1   \n",
       "24               1                  1098601.0               1   \n",
       "25               1                  4646268.0               4   \n",
       "26               1                  3975285.0               2   \n",
       "27               1                  3496995.0               3   \n",
       "28               1                  2928866.0               1   \n",
       "29               1                  1374872.0               1   \n",
       "30               1                  2436465.0               1   \n",
       "31               1                  1191009.0               1   \n",
       "32               1                  1106988.0               1   \n",
       "33               1                  3502617.0               3   \n",
       "34               1                  1592248.0               1   \n",
       "35               1                  1798264.0               1   \n",
       "36               1                  1658463.0               1   \n",
       "37               1                  1461380.0               1   \n",
       "38               1                  2041027.0               1   \n",
       "39               1                  4568518.0               4   \n",
       "40               1                  3317052.0               2   \n",
       "41               1                  3966514.0               2   \n",
       "42               1                  3765986.0               2   \n",
       "43               1                  1258904.0               1   \n",
       "44               1                  1126688.0               1   \n",
       "45               1                  1144246.0               1   \n",
       "46               1                  3918148.0               4   \n",
       "47               1                  2490863.0               1   \n",
       "48               1                  2615137.0               1   \n",
       "49               1                  1745824.0               1   \n",
       "\n",
       "   education_income_ratio  weighted_satisfaction_performance  \\\n",
       "0            1.177215e+06                                1.4   \n",
       "1            1.430853e+06                                1.0   \n",
       "2            1.379381e+06                                2.4   \n",
       "3            1.911583e+06                                1.0   \n",
       "4            1.241386e+06                                2.0   \n",
       "5            2.229928e+06                                1.4   \n",
       "6            1.257855e+06                                2.2   \n",
       "7            1.011353e+06                                2.2   \n",
       "8            2.256689e+06                                2.6   \n",
       "9            1.599099e+06                                1.6   \n",
       "10           2.869178e+06                                1.0   \n",
       "11           1.520440e+06                                1.6   \n",
       "12           1.164680e+06                                3.0   \n",
       "13           1.663103e+06                                2.6   \n",
       "14           8.037690e+05                                2.0   \n",
       "15           1.178459e+06                                1.6   \n",
       "16           1.527441e+06                                3.0   \n",
       "17           2.890639e+06                                1.6   \n",
       "18           1.193560e+06                                2.6   \n",
       "19           2.048458e+06                                1.6   \n",
       "20           1.267701e+06                                1.4   \n",
       "21           1.544522e+06                                2.6   \n",
       "22           1.486463e+06                                1.8   \n",
       "23           1.214155e+06                                1.0   \n",
       "24           1.098601e+06                                2.6   \n",
       "25           1.161567e+06                                1.4   \n",
       "26           1.987642e+06                                1.6   \n",
       "27           1.165665e+06                                2.2   \n",
       "28           2.928866e+06                                2.4   \n",
       "29           1.374872e+06                                1.6   \n",
       "30           2.436465e+06                                3.0   \n",
       "31           1.191009e+06                                2.2   \n",
       "32           1.106988e+06                                2.2   \n",
       "33           1.167539e+06                                1.8   \n",
       "34           1.592248e+06                                1.0   \n",
       "35           1.798264e+06                                2.0   \n",
       "36           1.658463e+06                                1.6   \n",
       "37           1.461380e+06                                2.4   \n",
       "38           2.041027e+06                                1.8   \n",
       "39           1.142130e+06                                1.0   \n",
       "40           1.658526e+06                                2.4   \n",
       "41           1.983257e+06                                2.6   \n",
       "42           1.882993e+06                                3.0   \n",
       "43           1.258904e+06                                2.2   \n",
       "44           1.126688e+06                                1.4   \n",
       "45           1.144246e+06                                1.4   \n",
       "46           9.795370e+05                                2.4   \n",
       "47           2.490863e+06                                2.0   \n",
       "48           2.615137e+06                                2.0   \n",
       "49           1.745824e+06                                1.4   \n",
       "\n",
       "    resign_risk_indicator  adjusted_work_time  \n",
       "0                  Medium            9.857106  \n",
       "1                  Medium            9.694593  \n",
       "2                  Medium            9.059429  \n",
       "3                  Medium            9.842189  \n",
       "4                  Medium            9.047730  \n",
       "5                  Medium            9.114481  \n",
       "6                  Medium            9.320000  \n",
       "7                  Medium            9.091639  \n",
       "8                  Medium            9.479833  \n",
       "9                  Medium            9.077272  \n",
       "10                 Medium            9.599453  \n",
       "11                 Medium            9.781063  \n",
       "12                 Medium            9.494477  \n",
       "13                 Medium            9.786442  \n",
       "14                 Medium            9.773272  \n",
       "15                 Medium            9.072831  \n",
       "16                 Medium            9.390000  \n",
       "17                 Medium            9.562408  \n",
       "18                 Medium            9.313570  \n",
       "19                 Medium            9.372688  \n",
       "20                 Medium            9.190102  \n",
       "21                 Medium            9.825135  \n",
       "22                 Medium            9.705073  \n",
       "23                 Medium            9.733698  \n",
       "24                 Medium            9.087215  \n",
       "25                 Medium            9.108286  \n",
       "26                 Medium            9.580014  \n",
       "27                 Medium            9.188379  \n",
       "28                 Medium            9.357764  \n",
       "29                 Medium            9.107099  \n",
       "30                 Medium            9.250325  \n",
       "31                 Medium            9.134709  \n",
       "32                 Medium            9.220359  \n",
       "33                 Medium            9.133624  \n",
       "34                 Medium            9.668318  \n",
       "35                 Medium            9.720000  \n",
       "36                 Medium            9.150000  \n",
       "37                 Medium            9.695420  \n",
       "38                 Medium            9.550811  \n",
       "39                 Medium            9.773272  \n",
       "40                 Medium            9.371734  \n",
       "41                 Medium            9.342076  \n",
       "42                 Medium            9.327930  \n",
       "43                 Medium            9.798676  \n",
       "44                 Medium            9.400000  \n",
       "45                 Medium            9.140000  \n",
       "46                 Medium            9.478291  \n",
       "47                 Medium            9.106338  \n",
       "48                 Medium            9.342793  \n",
       "49                 Medium            9.208596  \n",
       "\n",
       "[50 rows x 37 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_test = pd.read_csv('D:\\Tugas Akhir\\Codingan\\Development\\Data\\data_testing_resign_6.csv')\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "employee_id                          0\n",
       "domisili                             0\n",
       "jenis_kelamin                        0\n",
       "date_of_birth                        0\n",
       "join_date                            0\n",
       "resign_date                          0\n",
       "marriage_stat                        0\n",
       "dependant                            0\n",
       "education                            0\n",
       "absent_90D                           0\n",
       "avg_time_work                        0\n",
       "departemen                           0\n",
       "position                             0\n",
       "income                               0\n",
       "total_komp                           0\n",
       "job_satisfaction                     0\n",
       "performance_rating                   0\n",
       "churn_status                         0\n",
       "age_years                            0\n",
       "active_work                          0\n",
       "active_work_months                   0\n",
       "income_3_months                      0\n",
       "income_6_months                      0\n",
       "total_income_work                    0\n",
       "absence_ratio                        0\n",
       "income_dependant_ratio               0\n",
       "work_efficiency                      0\n",
       "active_work_category                 0\n",
       "work_stability_score                 0\n",
       "married_dependent_ratio              0\n",
       "position_score                       0\n",
       "job_income_position_score            0\n",
       "education_score                      0\n",
       "education_income_ratio               0\n",
       "weighted_satisfaction_performance    0\n",
       "resign_risk_indicator                0\n",
       "adjusted_work_time                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "final_model = pickle.load(open('clasification_model.sav', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['departemen', 'position', 'income', 'domisili', 'marriage_stat', 'dependant', 'education', 'absent_90D', 'avg_time_work', 'total_komp', 'job_satisfaction', 'performance_rating', 'age_years', 'active_work', 'active_work_months', 'income_3_months', 'income_6_months', 'total_income_work', 'absence_ratio', 'income_dependant_ratio', 'work_efficiency', 'active_work_category', 'work_stability_score', 'married_dependent_ratio', 'position_score', 'job_income_position_score', 'education_score', 'education_income_ratio', 'weighted_satisfaction_performance', 'resign_risk_indicator', 'adjusted_work_time']\n"
     ]
    }
   ],
   "source": [
    "expected_columns = final_model.feature_names_\n",
    "print(expected_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n",
      "Precision: 1.0\n",
      "Recall: 1.0\n",
      "F1 Score: 1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from catboost import Pool\n",
    "\n",
    "# Drop kolom yang tidak relevan\n",
    "X_test = df_test.drop(['churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date', 'active_work_months'], axis=1)\n",
    "\n",
    "# Konversi semua kolom kategori ke string\n",
    "cat_feature = ['departemen', 'position', 'domisili', 'marriage_stat', 'job_satisfaction', \n",
    "               'performance_rating', 'education', 'active_work_category', 'resign_risk_indicator', 'jenis_kelamin']\n",
    "\n",
    "# Pastikan semua fitur kategori adalah string\n",
    "for col in cat_feature:\n",
    "    if col in X_test.columns:\n",
    "        X_test[col] = X_test[col].astype(str)\n",
    "\n",
    "# Buat Pool untuk data uji\n",
    "test_pool = Pool(data=X_test, cat_features=cat_feature)\n",
    "\n",
    "# Prediksi dengan model menggunakan Pool\n",
    "y_pred = final_model.predict(test_pool)\n",
    "\n",
    "# Evaluasi\n",
    "accuracy = accuracy_score(df_test['churn_status'], y_pred)\n",
    "precision = precision_score(df_test['churn_status'], y_pred, zero_division=0)\n",
    "recall = recall_score(df_test['churn_status'], y_pred, zero_division=0)\n",
    "f1 = f1_score(df_test['churn_status'], y_pred, zero_division=0)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1 Score:\", f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "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>income</th>\n",
       "      <th>dependant</th>\n",
       "      <th>absent_90D</th>\n",
       "      <th>avg_time_work</th>\n",
       "      <th>total_komp</th>\n",
       "      <th>churn_status</th>\n",
       "      <th>age_years</th>\n",
       "      <th>active_work</th>\n",
       "      <th>active_work_months</th>\n",
       "      <th>income_3_months</th>\n",
       "      <th>...</th>\n",
       "      <th>income_dependant_ratio</th>\n",
       "      <th>work_efficiency</th>\n",
       "      <th>work_stability_score</th>\n",
       "      <th>married_dependent_ratio</th>\n",
       "      <th>position_score</th>\n",
       "      <th>job_income_position_score</th>\n",
       "      <th>education_score</th>\n",
       "      <th>education_income_ratio</th>\n",
       "      <th>weighted_satisfaction_performance</th>\n",
       "      <th>adjusted_work_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.120000e+02</td>\n",
       "      <td>812.000000</td>\n",
       "      <td>812.000000</td>\n",
       "      <td>812.0</td>\n",
       "      <td>812.000000</td>\n",
       "      <td>812.0</td>\n",
       "      <td>812.000000</td>\n",
       "      <td>812.000000</td>\n",
       "      <td>812.000000</td>\n",
       "      <td>8.120000e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>8.120000e+02</td>\n",
       "      <td>812.000</td>\n",
       "      <td>812.000000</td>\n",
       "      <td>812.000000</td>\n",
       "      <td>812.000000</td>\n",
       "      <td>8.120000e+02</td>\n",
       "      <td>812.000000</td>\n",
       "      <td>8.120000e+02</td>\n",
       "      <td>812.000000</td>\n",
       "      <td>812.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.704077e+06</td>\n",
       "      <td>1.443350</td>\n",
       "      <td>5.703202</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.116995</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.488916</td>\n",
       "      <td>601.076355</td>\n",
       "      <td>19.556650</td>\n",
       "      <td>8.112230e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>1.494665e+06</td>\n",
       "      <td>1.125</td>\n",
       "      <td>5.940580</td>\n",
       "      <td>2.387931</td>\n",
       "      <td>1.008621</td>\n",
       "      <td>2.676485e+06</td>\n",
       "      <td>1.838670</td>\n",
       "      <td>1.650454e+06</td>\n",
       "      <td>2.018966</td>\n",
       "      <td>8.967059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.217016e+06</td>\n",
       "      <td>1.272101</td>\n",
       "      <td>4.420202</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.543866</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.914092</td>\n",
       "      <td>195.811635</td>\n",
       "      <td>6.545148</td>\n",
       "      <td>3.651049e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>1.103701e+06</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6.556615</td>\n",
       "      <td>1.283876</td>\n",
       "      <td>0.092504</td>\n",
       "      <td>1.165877e+06</td>\n",
       "      <td>1.136691</td>\n",
       "      <td>5.549310e+05</td>\n",
       "      <td>0.580476</td>\n",
       "      <td>0.029627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.015570e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.046710e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>1.907077e+05</td>\n",
       "      <td>1.125</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.015570e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.524518e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.866790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.570747e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>428.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.712242e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>6.466449e+05</td>\n",
       "      <td>1.125</td>\n",
       "      <td>1.854396</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.570747e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.200502e+06</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>8.950040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.561418e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>496.500000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>7.684256e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>1.168337e+06</td>\n",
       "      <td>1.125</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.561418e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.548386e+06</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.973353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.692924e+06</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>798.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>1.107877e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>1.879129e+06</td>\n",
       "      <td>1.125</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.679853e+06</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.048790e+06</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>8.991674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.855813e+06</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>1095.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>2.356744e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>7.855813e+06</td>\n",
       "      <td>1.125</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.982560e+06</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.996378e+06</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             income   dependant  absent_90D  avg_time_work  total_komp  \\\n",
       "count  8.120000e+02  812.000000  812.000000          812.0  812.000000   \n",
       "mean   2.704077e+06    1.443350    5.703202            9.0    0.116995   \n",
       "std    1.217016e+06    1.272101    4.420202            0.0    0.543866   \n",
       "min    1.015570e+06    0.000000    0.000000            9.0    0.000000   \n",
       "25%    1.570747e+06    0.000000    2.000000            9.0    0.000000   \n",
       "50%    2.561418e+06    1.000000    5.000000            9.0    0.000000   \n",
       "75%    3.692924e+06    2.000000    9.000000            9.0    0.000000   \n",
       "max    7.855813e+06    6.000000   16.000000            9.0   12.000000   \n",
       "\n",
       "       churn_status   age_years  active_work  active_work_months  \\\n",
       "count         812.0  812.000000   812.000000          812.000000   \n",
       "mean            1.0   37.488916   601.076355           19.556650   \n",
       "std             0.0    8.914092   195.811635            6.545148   \n",
       "min             1.0    1.000000   365.000000           12.000000   \n",
       "25%             1.0   30.000000   428.000000           14.000000   \n",
       "50%             1.0   37.000000   496.500000           16.000000   \n",
       "75%             1.0   44.000000   798.000000           26.000000   \n",
       "max             1.0   57.000000  1095.000000           36.000000   \n",
       "\n",
       "       income_3_months  ...  income_dependant_ratio  work_efficiency  \\\n",
       "count     8.120000e+02  ...            8.120000e+02          812.000   \n",
       "mean      8.112230e+06  ...            1.494665e+06            1.125   \n",
       "std       3.651049e+06  ...            1.103701e+06            0.000   \n",
       "min       3.046710e+06  ...            1.907077e+05            1.125   \n",
       "25%       4.712242e+06  ...            6.466449e+05            1.125   \n",
       "50%       7.684256e+06  ...            1.168337e+06            1.125   \n",
       "75%       1.107877e+07  ...            1.879129e+06            1.125   \n",
       "max       2.356744e+07  ...            7.855813e+06            1.125   \n",
       "\n",
       "       work_stability_score  married_dependent_ratio  position_score  \\\n",
       "count            812.000000               812.000000      812.000000   \n",
       "mean               5.940580                 2.387931        1.008621   \n",
       "std                6.556615                 1.283876        0.092504   \n",
       "min                0.705882                 1.000000        1.000000   \n",
       "25%                1.854396                 1.000000        1.000000   \n",
       "50%                3.200000                 2.000000        1.000000   \n",
       "75%                7.000000                 3.000000        1.000000   \n",
       "max               33.000000                 7.000000        2.000000   \n",
       "\n",
       "       job_income_position_score  education_score  education_income_ratio  \\\n",
       "count               8.120000e+02       812.000000            8.120000e+02   \n",
       "mean                2.676485e+06         1.838670            1.650454e+06   \n",
       "std                 1.165877e+06         1.136691            5.549310e+05   \n",
       "min                 1.015570e+06         1.000000            7.524518e+05   \n",
       "25%                 1.570747e+06         1.000000            1.200502e+06   \n",
       "50%                 2.561418e+06         1.000000            1.548386e+06   \n",
       "75%                 3.679853e+06         3.000000            2.048790e+06   \n",
       "max                 4.982560e+06         5.000000            2.996378e+06   \n",
       "\n",
       "       weighted_satisfaction_performance  adjusted_work_time  \n",
       "count                         812.000000          812.000000  \n",
       "mean                            2.018966            8.967059  \n",
       "std                             0.580476            0.029627  \n",
       "min                             1.000000            8.866790  \n",
       "25%                             1.600000            8.950040  \n",
       "50%                             2.000000            8.973353  \n",
       "75%                             2.400000            8.991674  \n",
       "max                             3.000000            9.000000  \n",
       "\n",
       "[8 rows x 23 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "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>Feature</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>active_work_category</td>\n",
       "      <td>54.255294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>active_work</td>\n",
       "      <td>7.777737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>position</td>\n",
       "      <td>7.155448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>active_work_months</td>\n",
       "      <td>4.230350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>position_score</td>\n",
       "      <td>3.667581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>education_score</td>\n",
       "      <td>3.106486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>income_6_months</td>\n",
       "      <td>3.089712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>education</td>\n",
       "      <td>2.826131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>income</td>\n",
       "      <td>2.385703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>total_income_work</td>\n",
       "      <td>2.048091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>income_3_months</td>\n",
       "      <td>1.777421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>resign_risk_indicator</td>\n",
       "      <td>1.190863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>job_income_position_score</td>\n",
       "      <td>1.018409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>age_years</td>\n",
       "      <td>0.762253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>income_dependant_ratio</td>\n",
       "      <td>0.684176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>marriage_stat</td>\n",
       "      <td>0.508788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>domisili</td>\n",
       "      <td>0.435528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>married_dependent_ratio</td>\n",
       "      <td>0.357841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>education_income_ratio</td>\n",
       "      <td>0.335609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>work_stability_score</td>\n",
       "      <td>0.309915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>adjusted_work_time</td>\n",
       "      <td>0.308920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>weighted_satisfaction_performance</td>\n",
       "      <td>0.283635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>absence_ratio</td>\n",
       "      <td>0.282780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>departemen</td>\n",
       "      <td>0.259452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dependant</td>\n",
       "      <td>0.241856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>absent_90D</td>\n",
       "      <td>0.223434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>performance_rating</td>\n",
       "      <td>0.206559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>job_satisfaction</td>\n",
       "      <td>0.148087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>total_komp</td>\n",
       "      <td>0.119943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>avg_time_work</td>\n",
       "      <td>0.001582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>work_efficiency</td>\n",
       "      <td>0.000416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Feature  Importance\n",
       "21               active_work_category   54.255294\n",
       "13                        active_work    7.777737\n",
       "1                            position    7.155448\n",
       "14                 active_work_months    4.230350\n",
       "24                     position_score    3.667581\n",
       "26                    education_score    3.106486\n",
       "16                    income_6_months    3.089712\n",
       "6                           education    2.826131\n",
       "2                              income    2.385703\n",
       "17                  total_income_work    2.048091\n",
       "15                    income_3_months    1.777421\n",
       "29              resign_risk_indicator    1.190863\n",
       "25          job_income_position_score    1.018409\n",
       "12                          age_years    0.762253\n",
       "19             income_dependant_ratio    0.684176\n",
       "4                       marriage_stat    0.508788\n",
       "3                            domisili    0.435528\n",
       "23            married_dependent_ratio    0.357841\n",
       "27             education_income_ratio    0.335609\n",
       "22               work_stability_score    0.309915\n",
       "30                 adjusted_work_time    0.308920\n",
       "28  weighted_satisfaction_performance    0.283635\n",
       "18                      absence_ratio    0.282780\n",
       "0                          departemen    0.259452\n",
       "5                           dependant    0.241856\n",
       "7                          absent_90D    0.223434\n",
       "11                 performance_rating    0.206559\n",
       "10                   job_satisfaction    0.148087\n",
       "9                          total_komp    0.119943\n",
       "8                       avg_time_work    0.001582\n",
       "20                    work_efficiency    0.000416"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names = X_train.columns.tolist()\n",
    "feature_importance = model.get_feature_importance()\n",
    "\n",
    "feature_importance_df = pd.DataFrame({\n",
    "    'Feature': feature_names,\n",
    "    'Importance': feature_importance\n",
    "}).sort_values(by='Importance', ascending=False)\n",
    "feature_importance_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CatBoost Classification model saved to 'clasification_model.sav'\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "with open('clasification_model.sav', 'wb') as f:\n",
    "    pickle.dump(final_model, f)\n",
    "print(\"CatBoost Classification model saved to 'clasification_model.sav'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: numpy, streamlit, streamlit-option-menu\n",
      "  Attempting uninstall: numpy\n",
      "    Found existing installation: numpy 1.22.4\n",
      "    Uninstalling numpy-1.22.4:\n",
      "      Successfully uninstalled numpy-1.22.4\n",
      "  Attempting uninstall: streamlit\n",
      "    Found existing installation: streamlit 1.31.0\n",
      "    Uninstalling streamlit-1.31.0:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -pencv-python (c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -treamlit (c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -pencv-python (c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -treamlit (c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages)\n",
      "  WARNING: Failed to remove contents in a temporary directory 'C:\\Users\\Jesselyn Mu\\anaconda3\\Lib\\site-packages\\~umpy'.\n",
      "  You can safely remove it manually.\n",
      "ERROR: Could not install packages due to an OSError: [WinError 32] The process cannot access the file because it is being used by another process: 'c:\\\\users\\\\jesselyn mu\\\\anaconda3\\\\scripts\\\\streamlit.exe'\n",
      "Consider using the `--user` option or check the permissions.\n",
      "\n",
      "\n",
      "[notice] A new release of pip is available: 23.2.1 -> 24.3.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "%pip install streamlit-option-menu"
   ]
  }
 ],
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