From c000aec97f011ff80edb237548457dc8a27eef6e Mon Sep 17 00:00:00 2001
From: Jesselyn Mu <90194068+jesselynmu@users.noreply.github.com>
Date: Wed, 29 Jan 2025 08:08:11 +0700
Subject: [PATCH] add notebook

---
 notebook/train-rus.ipynb        | 5383 +++++++++++++++++++++++++++++++
 notebook/train_regression.ipynb | 2207 +++++++++++++
 2 files changed, 7590 insertions(+)
 create mode 100644 notebook/train-rus.ipynb
 create mode 100644 notebook/train_regression.ipynb

diff --git a/notebook/train-rus.ipynb b/notebook/train-rus.ipynb
new file mode 100644
index 0000000..4d3257d
--- /dev/null
+++ b/notebook/train-rus.ipynb
@@ -0,0 +1,5383 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "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>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>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>NaN</td>\n",
+       "      <td>Married</td>\n",
+       "      <td>2</td>\n",
+       "      <td>S1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>9.34</td>\n",
+       "      <td>Engineering &amp; IT</td>\n",
+       "      <td>Junior</td>\n",
+       "      <td>5198046</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</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>9.86</td>\n",
+       "      <td>Service &amp; Support</td>\n",
+       "      <td>Staff</td>\n",
+       "      <td>1281761</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</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>9.66</td>\n",
+       "      <td>Creative &amp; Design</td>\n",
+       "      <td>Staff</td>\n",
+       "      <td>4902208</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</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>NaN</td>\n",
+       "      <td>Married</td>\n",
+       "      <td>1</td>\n",
+       "      <td>S1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>9.54</td>\n",
+       "      <td>Marketing</td>\n",
+       "      <td>Junior</td>\n",
+       "      <td>6410492</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</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>9.14</td>\n",
+       "      <td>Operations</td>\n",
+       "      <td>Staff</td>\n",
+       "      <td>1208627</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</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",
+       "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  avg_time_work  \\\n",
+       "0         NaN       Married          2        S1         1.0           9.34   \n",
+       "1  2023-04-22       Married          3      SLTA        11.0           9.86   \n",
+       "2  2024-01-31        Single          0        D2         3.0           9.66   \n",
+       "3         NaN       Married          1        S1         1.0           9.54   \n",
+       "4  2023-02-21        Single          0      SLTA         1.0           9.14   \n",
+       "\n",
+       "          departemen position   income  total_komp  job_satisfaction  \\\n",
+       "0   Engineering & IT   Junior  5198046         NaN                 2   \n",
+       "1  Service & Support    Staff  1281761         NaN                 1   \n",
+       "2  Creative & Design    Staff  4902208         NaN                 1   \n",
+       "3          Marketing   Junior  6410492         NaN                 2   \n",
+       "4         Operations    Staff  1208627         NaN                 2   \n",
+       "\n",
+       "   performance_rating  churn_status  \n",
+       "0                   2             0  \n",
+       "1                   2             1  \n",
+       "2                   3             1  \n",
+       "3                   1             0  \n",
+       "4                   2             1  "
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "data = pd.read_csv('D:\\Tugas Akhir\\Codingan\\Development\\Data\\data_train.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": [
+       "<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>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": {
+      "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>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": {
+      "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>churn_status</th>\n",
+       "      <th>Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>11494</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4044</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "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": {
+      "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>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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+      "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",
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+      "200:\tlearn: 0.1034084\ttotal: 6.06s\tremaining: 20.4s\n",
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+      "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",
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+      "600:\tlearn: 0.0668548\ttotal: 19s\tremaining: 8.69s\n",
+      "800:\tlearn: 0.0561936\ttotal: 25.6s\tremaining: 2.4s\n",
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+      "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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+      "600:\tlearn: 0.0620306\ttotal: 18.8s\tremaining: 8.58s\n",
+      "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",
+      "400:\tlearn: 0.0817833\ttotal: 12.6s\tremaining: 14.9s\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",
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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",
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+      "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": {
+      "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>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": [
+      "Collecting streamlit-option-menu\n",
+      "  Using cached streamlit_option_menu-0.4.0-py3-none-any.whl.metadata (2.5 kB)\n",
+      "Collecting streamlit>=1.36 (from streamlit-option-menu)\n",
+      "  Downloading streamlit-1.41.1-py2.py3-none-any.whl.metadata (8.5 kB)\n",
+      "Requirement already satisfied: altair<6,>=4.0 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from streamlit>=1.36->streamlit-option-menu) (5.2.0)\n",
+      "Requirement already satisfied: blinker<2,>=1.0.0 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from streamlit>=1.36->streamlit-option-menu) (1.7.0)\n",
+      "Requirement already satisfied: cachetools<6,>=4.0 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from streamlit>=1.36->streamlit-option-menu) (4.2.2)\n",
+      "Requirement already satisfied: click<9,>=7.0 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from streamlit>=1.36->streamlit-option-menu) (7.1.2)\n",
+      "Collecting numpy<3,>=1.23 (from streamlit>=1.36->streamlit-option-menu)\n",
+      "  Downloading numpy-2.0.2-cp39-cp39-win_amd64.whl.metadata (59 kB)\n",
+      "Requirement already satisfied: packaging<25,>=20 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from streamlit>=1.36->streamlit-option-menu) (23.1)\n",
+      "Requirement already satisfied: pandas<3,>=1.4.0 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from streamlit>=1.36->streamlit-option-menu) (1.4.2)\n",
+      "Requirement already satisfied: pillow<12,>=7.1.0 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from streamlit>=1.36->streamlit-option-menu) (10.0.1)\n",
+      "Requirement already satisfied: protobuf<6,>=3.20 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from streamlit>=1.36->streamlit-option-menu) (3.20.3)\n",
+      "Requirement already satisfied: pyarrow>=7.0 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from streamlit>=1.36->streamlit-option-menu) (14.0.2)\n",
+      "Requirement already satisfied: requests<3,>=2.27 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from streamlit>=1.36->streamlit-option-menu) (2.31.0)\n",
+      "Requirement already satisfied: rich<14,>=10.14.0 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from streamlit>=1.36->streamlit-option-menu) (13.7.0)\n",
+      "Requirement already satisfied: tenacity<10,>=8.1.0 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from streamlit>=1.36->streamlit-option-menu) (8.2.2)\n",
+      "Requirement already satisfied: toml<2,>=0.10.1 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from streamlit>=1.36->streamlit-option-menu) (0.10.2)\n",
+      "Requirement already satisfied: typing-extensions<5,>=4.3.0 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from streamlit>=1.36->streamlit-option-menu) (4.7.1)\n",
+      "Requirement already satisfied: watchdog<7,>=2.1.5 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from streamlit>=1.36->streamlit-option-menu) (2.1.6)\n",
+      "Requirement already satisfied: gitpython!=3.1.19,<4,>=3.0.7 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from streamlit>=1.36->streamlit-option-menu) (3.1.41)\n",
+      "Requirement already satisfied: pydeck<1,>=0.8.0b4 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from streamlit>=1.36->streamlit-option-menu) (0.8.1b0)\n",
+      "Requirement already satisfied: tornado<7,>=6.0.3 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from streamlit>=1.36->streamlit-option-menu) (6.3.3)\n",
+      "Requirement already satisfied: jinja2 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from altair<6,>=4.0->streamlit>=1.36->streamlit-option-menu) (3.1.2)\n",
+      "Requirement already satisfied: jsonschema>=3.0 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from altair<6,>=4.0->streamlit>=1.36->streamlit-option-menu) (3.2.0)\n",
+      "Requirement already satisfied: toolz in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from altair<6,>=4.0->streamlit>=1.36->streamlit-option-menu) (0.12.0)\n",
+      "Requirement already satisfied: gitdb<5,>=4.0.1 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from gitpython!=3.1.19,<4,>=3.0.7->streamlit>=1.36->streamlit-option-menu) (4.0.11)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from pandas<3,>=1.4.0->streamlit>=1.36->streamlit-option-menu) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from pandas<3,>=1.4.0->streamlit>=1.36->streamlit-option-menu) (2023.3.post1)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from requests<3,>=2.27->streamlit>=1.36->streamlit-option-menu) (2.0.4)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from requests<3,>=2.27->streamlit>=1.36->streamlit-option-menu) (2.10)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from requests<3,>=2.27->streamlit>=1.36->streamlit-option-menu) (1.26.16)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from requests<3,>=2.27->streamlit>=1.36->streamlit-option-menu) (2024.6.2)\n",
+      "Requirement already satisfied: markdown-it-py>=2.2.0 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from rich<14,>=10.14.0->streamlit>=1.36->streamlit-option-menu) (3.0.0)\n",
+      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from rich<14,>=10.14.0->streamlit>=1.36->streamlit-option-menu) (2.15.1)\n",
+      "Requirement already satisfied: smmap<6,>=3.0.1 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from gitdb<5,>=4.0.1->gitpython!=3.1.19,<4,>=3.0.7->streamlit>=1.36->streamlit-option-menu) (5.0.1)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from jinja2->altair<6,>=4.0->streamlit>=1.36->streamlit-option-menu) (2.1.1)\n",
+      "Requirement already satisfied: attrs>=17.4.0 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit>=1.36->streamlit-option-menu) (23.1.0)\n",
+      "Requirement already satisfied: pyrsistent>=0.14.0 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit>=1.36->streamlit-option-menu) (0.18.0)\n",
+      "Requirement already satisfied: setuptools in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit>=1.36->streamlit-option-menu) (68.0.0)\n",
+      "Requirement already satisfied: six>=1.11.0 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit>=1.36->streamlit-option-menu) (1.16.0)\n",
+      "Requirement already satisfied: mdurl~=0.1 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from markdown-it-py>=2.2.0->rich<14,>=10.14.0->streamlit>=1.36->streamlit-option-menu) (0.1.2)\n",
+      "Downloading streamlit_option_menu-0.4.0-py3-none-any.whl (829 kB)\n",
+      "   ---------------------------------------- 0.0/829.3 kB ? eta -:--:--\n",
+      "   ------------------------- -------------- 524.3/829.3 kB 4.2 MB/s eta 0:00:01\n",
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+      "Downloading streamlit-1.41.1-py2.py3-none-any.whl (9.1 MB)\n",
+      "   ---------------------------------------- 0.0/9.1 MB ? eta -:--:--\n",
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+      "   ---------------------------------------- 9.1/9.1 MB 3.7 MB/s eta 0:00:00\n",
+      "Downloading numpy-2.0.2-cp39-cp39-win_amd64.whl (15.9 MB)\n",
+      "   ---------------------------------------- 0.0/15.9 MB ? eta -:--:--\n",
+      "   - -------------------------------------- 0.5/15.9 MB 3.3 MB/s eta 0:00:05\n",
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+      "   ---------------------------------------- 15.9/15.9 MB 4.7 MB/s eta 0:00:00\n",
+      "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"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebook/train_regression.ipynb b/notebook/train_regression.ipynb
new file mode 100644
index 0000000..2eb73d7
--- /dev/null
+++ b/notebook/train_regression.ipynb
@@ -0,0 +1,2207 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 365
+    },
+    "executionInfo": {
+     "elapsed": 668,
+     "status": "ok",
+     "timestamp": 1735311271540,
+     "user": {
+      "displayName": "kelompok bersama",
+      "userId": "01911350349879401396"
+     },
+     "user_tz": -420
+    },
+    "id": "A9JXeRGCC1Fg",
+    "outputId": "fe6008e2-e5e3-4083-8899-7fe5b6bc4067"
+   },
+   "outputs": [
+    {
+     "data": {
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+       "      <td>Kota Jakarta Selatan</td>\n",
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+       "      <td>2024-01-31</td>\n",
+       "      <td>Single</td>\n",
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+       "      <td>Medium</td>\n",
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+       "      <td>Laki-laki</td>\n",
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+       "      <td>2022-01-26</td>\n",
+       "      <td>2024-10-31</td>\n",
+       "      <td>Married</td>\n",
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+       "      <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": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Join Date - Min: 2020-01-02 00:00:00\n",
+      "Join Date - Max: 2024-10-30 00:00:00\n",
+      "Resign Date - Min: 2020-05-05 00:00:00\n",
+      "Resign Date - Max: 2024-10-31 00:00:00\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['join_date'] = pd.to_datetime(df['join_date'])\n",
+    "df['resign_date'] = pd.to_datetime(df['resign_date'])\n",
+    "\n",
+    "min_join_date = df['join_date'].min()\n",
+    "max_join_date = df['join_date'].max()\n",
+    "\n",
+    "min_resign_date = df['resign_date'].min()\n",
+    "max_resign_date = df['resign_date'].max()\n",
+    "\n",
+    "print(\"Join Date - Min:\", min_join_date)\n",
+    "print(\"Join Date - Max:\", max_join_date)\n",
+    "print(\"Resign Date - Min:\", min_resign_date)\n",
+    "print(\"Resign Date - Max:\", max_resign_date)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.dropna(subset=['marriage_stat'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "qT7W_7qbUmtZ"
+   },
+   "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 = df.drop(columns=['active_work_months','churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
+    "y = df['active_work_months']\n",
+    "\n",
+    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "df['join_date'] = pd.to_datetime(df['join_date'])\n",
+    "\n",
+    "train_data = df[df['join_date'] < '2023-01-01']\n",
+    "valid_data = df[df['join_date'] >= '2023-01-01']\n",
+    "\n",
+    "X_train = train_data.drop(columns=['active_work_months', 'churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
+    "y_train = train_data['active_work_months']\n",
+    "\n",
+    "X_valid = valid_data.drop(columns=['active_work_months', 'churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
+    "y_valid = valid_data['active_work_months']\n",
+    "\n",
+    "cat_feature = ['departemen', 'position', 'domisili', 'marriage_stat', 'job_satisfaction', 'performance_rating',\n",
+    "               'education', 'active_work_category', 'resign_risk_indicator', 'jenis_kelamin']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 29830,
+     "status": "ok",
+     "timestamp": 1735311423993,
+     "user": {
+      "displayName": "kelompok bersama",
+      "userId": "01911350349879401396"
+     },
+     "user_tz": -420
+    },
+    "id": "Q8deDWqJY1oC",
+    "outputId": "e3fd20f2-2385-4514-c1c6-058f047b7221"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0:\tlearn: 13.5633497\ttest: 22.5835323\tbest: 22.5835323 (0)\ttotal: 258ms\tremaining: 4m 17s\n",
+      "200:\tlearn: 2.2131170\ttest: 4.9022408\tbest: 4.9022408 (200)\ttotal: 8.86s\tremaining: 35.2s\n",
+      "400:\tlearn: 0.5838665\ttest: 2.2138070\tbest: 2.2138070 (400)\ttotal: 17.6s\tremaining: 26.2s\n",
+      "600:\tlearn: 0.3577892\ttest: 1.7168738\tbest: 1.7168738 (600)\ttotal: 26.6s\tremaining: 17.7s\n",
+      "800:\tlearn: 0.3073068\ttest: 1.5536284\tbest: 1.5536284 (800)\ttotal: 35.5s\tremaining: 8.82s\n",
+      "999:\tlearn: 0.2891487\ttest: 1.4757698\tbest: 1.4757678 (998)\ttotal: 44.5s\tremaining: 0us\n",
+      "\n",
+      "bestTest = 1.475767819\n",
+      "bestIteration = 998\n",
+      "\n",
+      "Shrink model to first 999 iterations.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<catboost.core.CatBoostRegressor at 0x22f08be3e50>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from catboost import CatBoostRegressor\n",
+    "\n",
+    "model = CatBoostRegressor(\n",
+    "    iterations=1000,\n",
+    "    learning_rate=0.01,\n",
+    "    depth=6,\n",
+    "    cat_features=cat_feature,\n",
+    "    loss_function='RMSE',  # Fungsi kerugian regresi, seperti RMSE atau MAE\n",
+    "    eval_metric='RMSE',    # Metrik evaluasi regresi\n",
+    "    verbose=200\n",
+    ")\n",
+    "\n",
+    "model.fit(X_train, y_train, eval_set=(X_valid, y_valid), use_best_model=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 175
+    },
+    "executionInfo": {
+     "elapsed": 321,
+     "status": "ok",
+     "timestamp": 1735311559697,
+     "user": {
+      "displayName": "kelompok bersama",
+      "userId": "01911350349879401396"
+     },
+     "user_tz": -420
+    },
+    "id": "0AN-WzVOZrtG",
+    "outputId": "0aed7f15-c856-4524-c9ec-89119a498d39"
+   },
+   "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>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>MSE</th>\n",
+       "      <td>2.177891</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MAE</th>\n",
+       "      <td>0.834776</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RMSE</th>\n",
+       "      <td>1.475768</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>R2 Score</th>\n",
+       "      <td>0.930862</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Score\n",
+       "MSE       2.177891\n",
+       "MAE       0.834776\n",
+       "RMSE      1.475768\n",
+       "R2 Score  0.930862"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
+    "import numpy as np\n",
+    "\n",
+    "# Prediksi pada data valid\n",
+    "y_pred = model.predict(X_valid)\n",
+    "\n",
+    "# Menghitung metrik regresi\n",
+    "mse = mean_squared_error(y_valid, y_pred)\n",
+    "mae = mean_absolute_error(y_valid, y_pred)\n",
+    "rmse = np.sqrt(mse)\n",
+    "r2 = r2_score(y_valid, y_pred)\n",
+    "\n",
+    "# Membuat dataframe hasil metrik\n",
+    "metrics = {\n",
+    "    \"MSE\": mse,\n",
+    "    \"MAE\": mae,\n",
+    "    \"RMSE\": rmse,\n",
+    "    \"R2 Score\": r2\n",
+    "}\n",
+    "\n",
+    "metrics_df = pd.DataFrame.from_dict(metrics, orient='index', columns=['Score'])\n",
+    "metrics_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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",
+    "# Membuat DataFrame untuk mempermudah visualisasi\n",
+    "comparison_df = pd.DataFrame({'Actual': y_valid, 'Predicted': y_pred})\n",
+    "\n",
+    "# Membatasi hanya pada 20 indeks pertama\n",
+    "comparison_df_subset = comparison_df.iloc[:20]\n",
+    "\n",
+    "# Scatter plot untuk membandingkan prediksi dan nilai asli\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(range(len(comparison_df_subset)), comparison_df_subset['Actual'], label='Actual Values', alpha=0.7, color='blue')\n",
+    "plt.scatter(range(len(comparison_df_subset)), comparison_df_subset['Predicted'], label='Predicted Values', alpha=0.7, color='orange')\n",
+    "plt.title('Comparison of Actual vs Predicted Active Work Months (First 20)')\n",
+    "plt.xlabel('Index')\n",
+    "plt.ylabel('Active Work Months')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "# Line plot untuk membandingkan prediksi dan nilai asli\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(range(len(comparison_df_subset)), comparison_df_subset['Actual'], label='Actual Values', marker='o', linestyle='-', color='blue')\n",
+    "plt.plot(range(len(comparison_df_subset)), comparison_df_subset['Predicted'], label='Predicted Values', marker='x', linestyle='-', color='orange')\n",
+    "plt.title('Actual vs Predicted Active Work Months (First 20 - Line Plot)')\n",
+    "plt.xlabel('Index')\n",
+    "plt.ylabel('Active Work Months')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2025-01-15 20:13:29,609] A new study created in memory with name: no-name-08bf7bda-2a69-4819-aee4-c38c972943cd\n",
+      "[I 2025-01-15 20:14:05,596] Trial 0 finished with value: 2.7842129590357327 and parameters: {'iterations': 947, 'learning_rate': 0.021474274017260056, 'depth': 6, 'subsample': 0.6597772630375474, 'colsample_bylevel': 0.6725654633870543, 'l2_leaf_reg': 11.904064563391351, 'random_strength': 6.636852794260673}. Best is trial 0 with value: 2.7842129590357327.\n",
+      "[I 2025-01-15 20:14:18,537] Trial 1 finished with value: 3.3604966459792873 and parameters: {'iterations': 532, 'learning_rate': 0.014997699392015939, 'depth': 4, 'subsample': 0.5066064744995207, 'colsample_bylevel': 0.6769577723016567, 'l2_leaf_reg': 18.54766425803946, 'random_strength': 7.313362982904383}. Best is trial 0 with value: 2.7842129590357327.\n",
+      "[I 2025-01-15 20:14:35,244] Trial 2 finished with value: 10.731547937509777 and parameters: {'iterations': 745, 'learning_rate': 0.0018382281881892761, 'depth': 4, 'subsample': 0.6467320760058648, 'colsample_bylevel': 0.557736500080584, 'l2_leaf_reg': 14.828021101711611, 'random_strength': 9.019716501749155}. Best is trial 0 with value: 2.7842129590357327.\n",
+      "[I 2025-01-15 20:14:49,463] Trial 3 finished with value: 1.70690993706933 and parameters: {'iterations': 567, 'learning_rate': 0.03781343917057186, 'depth': 4, 'subsample': 0.6039387723621907, 'colsample_bylevel': 0.6808598591491113, 'l2_leaf_reg': 11.477573214087545, 'random_strength': 9.74704206793275}. Best is trial 3 with value: 1.70690993706933.\n",
+      "[I 2025-01-15 20:15:13,235] Trial 4 finished with value: 6.377846207599941 and parameters: {'iterations': 682, 'learning_rate': 0.003970846204103649, 'depth': 6, 'subsample': 0.782742848821905, 'colsample_bylevel': 0.6991305457084517, 'l2_leaf_reg': 11.814370178020795, 'random_strength': 7.978490486571175}. Best is trial 3 with value: 1.70690993706933.\n",
+      "[I 2025-01-15 20:15:34,758] Trial 5 finished with value: 3.8532653988417978 and parameters: {'iterations': 703, 'learning_rate': 0.009627989777082258, 'depth': 5, 'subsample': 0.6758350820650804, 'colsample_bylevel': 0.6845137435168039, 'l2_leaf_reg': 14.54643516179267, 'random_strength': 7.928157458442849}. Best is trial 3 with value: 1.70690993706933.\n",
+      "[I 2025-01-15 20:15:56,121] Trial 6 finished with value: 7.911327625120516 and parameters: {'iterations': 617, 'learning_rate': 0.003161788308676907, 'depth': 6, 'subsample': 0.655849596099901, 'colsample_bylevel': 0.5907470643588891, 'l2_leaf_reg': 15.953005158515333, 'random_strength': 6.33160838409998}. Best is trial 3 with value: 1.70690993706933.\n",
+      "[I 2025-01-15 20:16:20,651] Trial 7 finished with value: 2.6472143447255028 and parameters: {'iterations': 762, 'learning_rate': 0.0651233495346411, 'depth': 5, 'subsample': 0.6045406434832912, 'colsample_bylevel': 0.6408137062270973, 'l2_leaf_reg': 7.3636217948679885, 'random_strength': 5.056590959434105}. Best is trial 3 with value: 1.70690993706933.\n",
+      "[I 2025-01-15 20:16:40,416] Trial 8 finished with value: 3.904345034752064 and parameters: {'iterations': 836, 'learning_rate': 0.0063081178897640665, 'depth': 4, 'subsample': 0.6209534648047793, 'colsample_bylevel': 0.6735288310668713, 'l2_leaf_reg': 14.619073756706687, 'random_strength': 7.447109529616001}. Best is trial 3 with value: 1.70690993706933.\n",
+      "[I 2025-01-15 20:17:12,248] Trial 9 finished with value: 2.675917946189127 and parameters: {'iterations': 995, 'learning_rate': 0.029430371458377378, 'depth': 5, 'subsample': 0.7838327288912854, 'colsample_bylevel': 0.6968730335788775, 'l2_leaf_reg': 6.590606976608566, 'random_strength': 7.1462545755187366}. Best is trial 3 with value: 1.70690993706933.\n",
+      "[I 2025-01-15 20:17:25,774] Trial 10 finished with value: 2.4922635454584756 and parameters: {'iterations': 503, 'learning_rate': 0.08959558982916915, 'depth': 4, 'subsample': 0.5117214866989651, 'colsample_bylevel': 0.7761028884115426, 'l2_leaf_reg': 9.546619424302936, 'random_strength': 9.468866323105104}. Best is trial 3 with value: 1.70690993706933.\n",
+      "[I 2025-01-15 20:17:38,867] Trial 11 finished with value: 1.744493954106988 and parameters: {'iterations': 504, 'learning_rate': 0.09492600457554262, 'depth': 4, 'subsample': 0.5004298952554207, 'colsample_bylevel': 0.7922138691597381, 'l2_leaf_reg': 9.734927799742529, 'random_strength': 9.936390327287459}. Best is trial 3 with value: 1.70690993706933.\n",
+      "[I 2025-01-15 20:17:53,899] Trial 12 finished with value: 2.151305735187685 and parameters: {'iterations': 590, 'learning_rate': 0.04527434848399387, 'depth': 4, 'subsample': 0.5613306996433728, 'colsample_bylevel': 0.7844782145581627, 'l2_leaf_reg': 8.71724115650851, 'random_strength': 9.767586689208404}. Best is trial 3 with value: 1.70690993706933.\n",
+      "[I 2025-01-15 20:18:08,769] Trial 13 finished with value: 2.20265785283312 and parameters: {'iterations': 590, 'learning_rate': 0.041259515389968866, 'depth': 4, 'subsample': 0.7140194720599207, 'colsample_bylevel': 0.7444231994330105, 'l2_leaf_reg': 10.07294144120628, 'random_strength': 8.771995918460187}. Best is trial 3 with value: 1.70690993706933.\n",
+      "[I 2025-01-15 20:18:23,736] Trial 14 finished with value: 2.6079691048763585 and parameters: {'iterations': 502, 'learning_rate': 0.08460466557013521, 'depth': 5, 'subsample': 0.5587677216450273, 'colsample_bylevel': 0.516048785445198, 'l2_leaf_reg': 11.017167930786023, 'random_strength': 9.982359815218986}. Best is trial 3 with value: 1.70690993706933.\n",
+      "[I 2025-01-15 20:18:39,599] Trial 15 finished with value: 2.132731119368835 and parameters: {'iterations': 624, 'learning_rate': 0.046020548752808504, 'depth': 4, 'subsample': 0.5628765445757447, 'colsample_bylevel': 0.7389480544787516, 'l2_leaf_reg': 5.290599965354091, 'random_strength': 8.656602115357634}. Best is trial 3 with value: 1.70690993706933.\n",
+      "[I 2025-01-15 20:18:56,192] Trial 16 finished with value: 3.5482068496788455 and parameters: {'iterations': 560, 'learning_rate': 0.020510851473925385, 'depth': 5, 'subsample': 0.7195393184364242, 'colsample_bylevel': 0.6243422046941179, 'l2_leaf_reg': 13.064507268703021, 'random_strength': 9.245627139927496}. Best is trial 3 with value: 1.70690993706933.\n",
+      "[I 2025-01-15 20:19:12,951] Trial 17 finished with value: 3.3575114872171645 and parameters: {'iterations': 667, 'learning_rate': 0.012523349283214951, 'depth': 4, 'subsample': 0.5344273642074554, 'colsample_bylevel': 0.7448170199649261, 'l2_leaf_reg': 7.893350620289716, 'random_strength': 8.369812485816734}. Best is trial 3 with value: 1.70690993706933.\n",
+      "[I 2025-01-15 20:19:40,464] Trial 18 finished with value: 1.8672095214450317 and parameters: {'iterations': 844, 'learning_rate': 0.09708642998838826, 'depth': 5, 'subsample': 0.6022210392087399, 'colsample_bylevel': 0.7962309080793653, 'l2_leaf_reg': 19.69295037962641, 'random_strength': 9.505062844821552}. Best is trial 3 with value: 1.70690993706933.\n",
+      "[I 2025-01-15 20:19:53,762] Trial 19 finished with value: 2.499504916116985 and parameters: {'iterations': 551, 'learning_rate': 0.03006684196464984, 'depth': 4, 'subsample': 0.5855833584969841, 'colsample_bylevel': 0.6087741643186144, 'l2_leaf_reg': 16.820613373939256, 'random_strength': 9.989894548577027}. Best is trial 3 with value: 1.70690993706933.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best trial:\n",
+      "  RMSE: 1.70690993706933\n",
+      "  Params: {'iterations': 567, 'learning_rate': 0.03781343917057186, 'depth': 4, 'subsample': 0.6039387723621907, 'colsample_bylevel': 0.6808598591491113, 'l2_leaf_reg': 11.477573214087545, 'random_strength': 9.74704206793275}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import optuna\n",
+    "from catboost import CatBoostRegressor\n",
+    "from sklearn.metrics import mean_squared_error\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': 'RMSE',  # Fungsi kerugian untuk regresi\n",
+    "        'random_state': 42,\n",
+    "        'verbose': 0\n",
+    "    }\n",
+    "\n",
+    "    # Inisialisasi model dengan parameter yang dioptimasi\n",
+    "    model = CatBoostRegressor(**params)\n",
+    "\n",
+    "    # Melatih model dengan validasi\n",
+    "    model.fit(X_train, y_train, eval_set=(X_valid, y_valid), use_best_model=True)\n",
+    "\n",
+    "    # Prediksi nilai target\n",
+    "    y_pred = model.predict(X_valid)\n",
+    "\n",
+    "    # Hitung RMSE\n",
+    "    rmse = mean_squared_error(y_valid, y_pred, squared=False)\n",
+    "\n",
+    "    return rmse  # Mengembalikan RMSE sebagai skor yang ingin diminimalkan\n",
+    "\n",
+    "# Membuat studi Optuna\n",
+    "study = optuna.create_study(direction=\"minimize\")  # Minimalkan RMSE\n",
+    "study.optimize(objective, n_trials=20)\n",
+    "\n",
+    "# Menampilkan hasil terbaik\n",
+    "print(\"Best trial:\")\n",
+    "print(f\"  RMSE: {study.best_value}\")\n",
+    "print(f\"  Params: {study.best_params}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0:\tlearn: 13.3189474\ttest: 22.4201913\tbest: 22.4201913 (0)\ttotal: 30.4ms\tremaining: 17.2s\n",
+      "200:\tlearn: 1.3383498\ttest: 3.1531773\tbest: 3.1531773 (200)\ttotal: 4.53s\tremaining: 8.25s\n",
+      "400:\tlearn: 0.5436172\ttest: 1.9572286\tbest: 1.9572286 (400)\ttotal: 9.49s\tremaining: 3.93s\n",
+      "566:\tlearn: 0.4106783\ttest: 1.7069099\tbest: 1.7069099 (566)\ttotal: 13.6s\tremaining: 0us\n",
+      "\n",
+      "bestTest = 1.706909937\n",
+      "bestIteration = 566\n",
+      "\n",
+      "Final RMSE: 1.70690993706933\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "# 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': 'RMSE',  # Gunakan RMSE 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 = CatBoostRegressor(**best_params)\n",
+    "final_model.fit(X_train, y_train, eval_set=(X_valid, y_valid), use_best_model=True)\n",
+    "\n",
+    "# Evaluasi model final\n",
+    "y_pred = final_model.predict(X_valid)\n",
+    "final_rmse = mean_squared_error(y_valid, y_pred, squared=False)  # Hitung RMSE\n",
+    "print(f\"Final RMSE: {final_rmse}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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",
+    "# Membuat DataFrame untuk mempermudah visualisasi\n",
+    "comparison_df = pd.DataFrame({'Actual': y_valid, 'Predicted': y_pred})\n",
+    "\n",
+    "# Membatasi hanya pada 20 indeks pertama\n",
+    "comparison_df_subset = comparison_df.iloc[:20]\n",
+    "\n",
+    "# Scatter plot untuk membandingkan prediksi dan nilai asli\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(range(len(comparison_df_subset)), comparison_df_subset['Actual'], label='Actual Values', alpha=0.7, color='blue')\n",
+    "plt.scatter(range(len(comparison_df_subset)), comparison_df_subset['Predicted'], label='Predicted Values', alpha=0.7, color='orange')\n",
+    "plt.title('Comparison of Actual vs Predicted Active Work Months (First 20)')\n",
+    "plt.xlabel('Index')\n",
+    "plt.ylabel('Active Work Months')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "# Line plot untuk membandingkan prediksi dan nilai asli\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(range(len(comparison_df_subset)), comparison_df_subset['Actual'], label='Actual Values', marker='o', linestyle='-', color='blue')\n",
+    "plt.plot(range(len(comparison_df_subset)), comparison_df_subset['Predicted'], label='Predicted Values', marker='x', linestyle='-', color='orange')\n",
+    "plt.title('Actual vs Predicted Active Work Months (First 20 - Line Plot)')\n",
+    "plt.xlabel('Index')\n",
+    "plt.ylabel('Active Work Months')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final Training RMSE: 0.2891486734682186\n",
+      "Final Validation RMSE: 1.4757698154285221\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Ambil hasil evaluasi dari model\n",
+    "evals_result = model.get_evals_result()\n",
+    "\n",
+    "# Menampilkan skor terakhir\n",
+    "train_score = evals_result['learn']['RMSE'][-1]\n",
+    "val_score = evals_result['validation']['RMSE'][-1]\n",
+    "\n",
+    "print(f\"Final Training RMSE: {train_score}\")\n",
+    "print(f\"Final Validation RMSE: {val_score}\")\n",
+    "\n",
+    "# Import library untuk visualisasi\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Ambil skor training dan validation dari evals_result\n",
+    "train_rmse = evals_result['learn']['RMSE']\n",
+    "val_rmse = evals_result['validation']['RMSE']\n",
+    "\n",
+    "# Plot learning curve\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(train_rmse, label='Training RMSE', color='blue')\n",
+    "plt.plot(val_rmse, label='Validation RMSE', color='orange')\n",
+    "plt.xlabel('Iteration')\n",
+    "plt.ylabel('RMSE')\n",
+    "plt.title('Learning Curve')\n",
+    "plt.legend()\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final Training RMSE: 0.4106782777280611\n",
+      "Final Validation RMSE: 1.706909937069329\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Ambil hasil evaluasi dari model\n",
+    "evals_result = final_model.get_evals_result()\n",
+    "\n",
+    "# Menampilkan skor terakhir\n",
+    "train_score = evals_result['learn']['RMSE'][-1]\n",
+    "val_score = evals_result['validation']['RMSE'][-1]\n",
+    "\n",
+    "print(f\"Final Training RMSE: {train_score}\")\n",
+    "print(f\"Final Validation RMSE: {val_score}\")\n",
+    "\n",
+    "# Import library untuk visualisasi\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Ambil skor training dan validation dari evals_result\n",
+    "train_rmse = evals_result['learn']['RMSE']\n",
+    "val_rmse = evals_result['validation']['RMSE']\n",
+    "\n",
+    "# Plot learning curve\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(train_rmse, label='Training RMSE', color='blue')\n",
+    "plt.plot(val_rmse, label='Validation RMSE', color='orange')\n",
+    "plt.xlabel('Iteration')\n",
+    "plt.ylabel('RMSE')\n",
+    "plt.title('Learning Curve')\n",
+    "plt.legend()\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>MSE</th>\n",
+       "      <td>2.913542</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MAE</th>\n",
+       "      <td>0.893629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RMSE</th>\n",
+       "      <td>1.706910</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>R2 Score</th>\n",
+       "      <td>0.907509</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Score\n",
+       "MSE       2.913542\n",
+       "MAE       0.893629\n",
+       "RMSE      1.706910\n",
+       "R2 Score  0.907509"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
+    "import numpy as np\n",
+    "\n",
+    "# Prediksi pada data valid\n",
+    "y_pred = final_model.predict(X_valid)\n",
+    "\n",
+    "# Menghitung metrik regresi\n",
+    "mse = mean_squared_error(y_valid, y_pred)\n",
+    "mae = mean_absolute_error(y_valid, y_pred)\n",
+    "rmse = np.sqrt(mse)\n",
+    "r2 = r2_score(y_valid, y_pred)\n",
+    "\n",
+    "# Membuat dataframe hasil metrik\n",
+    "metrics = {\n",
+    "    \"MSE\": mse,\n",
+    "    \"MAE\": mae,\n",
+    "    \"RMSE\": rmse,\n",
+    "    \"R2 Score\": r2\n",
+    "}\n",
+    "\n",
+    "metrics_df = pd.DataFrame.from_dict(metrics, orient='index', columns=['Score'])\n",
+    "metrics_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CatBoost Regression model saved to 'regression_model.sav'\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "with open('regression_model.sav', 'wb') as f:\n",
+    "    pickle.dump(model, f)\n",
+    "print(\"CatBoost Regression model saved to 'regression_model.sav'\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CatBoost Regression model saved to 'regression_model.sav'\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "with open('regression_model_final.sav', 'wb') as f:\n",
+    "    pickle.dump(final_model, f)\n",
+    "print(\"CatBoost Regression model saved to 'regression_model.sav'\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Mengurutkan data berdasarkan waktu (join_date)\n",
+    "df = df.sort_values('join_date')\n",
+    "X = df.drop(columns=['active_work_months', 'churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
+    "y = df['active_work_months']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "600:\tlearn: 0.4269301\ttotal: 27.9s\tremaining: 18.5s\n",
+      "800:\tlearn: 0.3636209\ttotal: 34.1s\tremaining: 8.46s\n",
+      "999:\tlearn: 0.3239213\ttotal: 45.2s\tremaining: 0us\n",
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+      "999:\tlearn: 0.3056851\ttotal: 47.8s\tremaining: 0us\n",
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+      "200:\tlearn: 2.0424293\ttotal: 16.1s\tremaining: 1m 4s\n",
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+      "600:\tlearn: 0.3540443\ttotal: 37.5s\tremaining: 24.9s\n",
+      "800:\tlearn: 0.3048528\ttotal: 53s\tremaining: 13.2s\n",
+      "999:\tlearn: 0.2854924\ttotal: 1m 3s\tremaining: 0us\n",
+      "0:\tlearn: 13.6327729\ttotal: 32.6ms\tremaining: 32.6s\n",
+      "200:\tlearn: 2.2277348\ttotal: 16s\tremaining: 1m 3s\n",
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+      "800:\tlearn: 0.3067813\ttotal: 58.2s\tremaining: 14.5s\n",
+      "999:\tlearn: 0.2882435\ttotal: 1m 9s\tremaining: 0us\n",
+      "0:\tlearn: 14.5619725\ttotal: 36ms\tremaining: 36s\n",
+      "200:\tlearn: 2.3283745\ttotal: 12s\tremaining: 47.7s\n",
+      "400:\tlearn: 0.6073001\ttotal: 27.8s\tremaining: 41.6s\n",
+      "600:\tlearn: 0.3486209\ttotal: 37.7s\tremaining: 25s\n",
+      "800:\tlearn: 0.2916105\ttotal: 54.1s\tremaining: 13.5s\n",
+      "999:\tlearn: 0.2717463\ttotal: 1m 10s\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": [
+      "R² Scores for each fold: [0.99040942 0.98790673 0.99653286 0.99102248 0.88548406]\n",
+      "Mean R²: 0.97\n",
+      "Standard Deviation: 0.04\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import TimeSeriesSplit, cross_val_score\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Fungsi untuk menghitung skor cross-validation dengan TimeSeriesSplit\n",
+    "def time_series_cross_validate_and_visualize_r2(model, X, y, n_splits=5):\n",
+    "    # TimeSeriesSplit untuk data terkait waktu\n",
+    "    tscv = TimeSeriesSplit(n_splits=n_splits)\n",
+    "\n",
+    "    # Hitung skor cross-validation dengan metrik R²\n",
+    "    scores = cross_val_score(model, X, y, scoring='r2', cv=tscv)\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, n_splits + 1), scores, marker='o', linestyle='-', color='b', label='Fold Score')\n",
+    "    plt.axhline(y=mean_score, color='r', linestyle='--', label=f'Mean R²: {mean_score:.2f}')\n",
+    "    plt.fill_between(range(1, n_splits + 1), mean_score - std_score, mean_score + std_score, color='r', alpha=0.2, label='±1 Std Dev')\n",
+    "    plt.title('Time-Based Cross-Validation Scores (R²)')\n",
+    "    plt.xlabel('Fold')\n",
+    "    plt.ylabel('R² Score')\n",
+    "    plt.legend()\n",
+    "    plt.grid()\n",
+    "    plt.show()\n",
+    "\n",
+    "    # Cetak hasil skor\n",
+    "    print(f'R² Scores for each fold: {scores}')\n",
+    "    print(f'Mean R²: {mean_score:.2f}')\n",
+    "    print(f'Standard Deviation: {std_score:.2f}')\n",
+    "\n",
+    "# Contoh penggunaan\n",
+    "time_series_cross_validate_and_visualize_r2(model, X, y, n_splits=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0:\tlearn: 8.7841225\ttotal: 17.9ms\tremaining: 12.3s\n",
+      "200:\tlearn: 0.6414325\ttotal: 4.79s\tremaining: 11.6s\n",
+      "400:\tlearn: 0.4070658\ttotal: 9.53s\tremaining: 6.82s\n",
+      "600:\tlearn: 0.3136415\ttotal: 14.6s\tremaining: 2.11s\n",
+      "687:\tlearn: 0.2871957\ttotal: 18.3s\tremaining: 0us\n",
+      "0:\tlearn: 9.6341897\ttotal: 38.6ms\tremaining: 26.5s\n",
+      "200:\tlearn: 0.5292219\ttotal: 8.49s\tremaining: 20.6s\n",
+      "400:\tlearn: 0.3513814\ttotal: 16.8s\tremaining: 12s\n",
+      "600:\tlearn: 0.2840241\ttotal: 21.7s\tremaining: 3.13s\n",
+      "687:\tlearn: 0.2635121\ttotal: 24.4s\tremaining: 0us\n",
+      "0:\tlearn: 11.3736677\ttotal: 29.5ms\tremaining: 20.3s\n",
+      "200:\tlearn: 0.5146587\ttotal: 5.86s\tremaining: 14.2s\n",
+      "400:\tlearn: 0.3822767\ttotal: 10.9s\tremaining: 7.81s\n",
+      "600:\tlearn: 0.3077429\ttotal: 16.1s\tremaining: 2.33s\n",
+      "687:\tlearn: 0.2894420\ttotal: 18.5s\tremaining: 0us\n",
+      "0:\tlearn: 12.7272643\ttotal: 26.4ms\tremaining: 18.1s\n",
+      "200:\tlearn: 0.5280327\ttotal: 6.21s\tremaining: 15s\n",
+      "400:\tlearn: 0.3907724\ttotal: 13.1s\tremaining: 9.38s\n",
+      "600:\tlearn: 0.3223581\ttotal: 22.6s\tremaining: 3.27s\n",
+      "687:\tlearn: 0.3042779\ttotal: 27.1s\tremaining: 0us\n",
+      "0:\tlearn: 13.4616767\ttotal: 61.1ms\tremaining: 42s\n",
+      "200:\tlearn: 0.5121746\ttotal: 6.71s\tremaining: 16.3s\n",
+      "400:\tlearn: 0.3754235\ttotal: 13s\tremaining: 9.29s\n",
+      "600:\tlearn: 0.3125012\ttotal: 24.6s\tremaining: 3.56s\n",
+      "687:\tlearn: 0.2947761\ttotal: 29.9s\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": [
+      "R² Scores for each fold: [0.97547565 0.97730142 0.99540966 0.98816158 0.30451961]\n",
+      "Mean R²: 0.85\n",
+      "Standard Deviation: 0.27\n"
+     ]
+    }
+   ],
+   "source": [
+    "time_series_cross_validate_and_visualize_r2(final_model, X, y, n_splits=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "YHxMZUUgarGa"
+   },
+   "source": [
+    "# Testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 348
+    },
+    "executionInfo": {
+     "elapsed": 772,
+     "status": "ok",
+     "timestamp": 1735311729780,
+     "user": {
+      "displayName": "kelompok bersama",
+      "userId": "01911350349879401396"
+     },
+     "user_tz": -420
+    },
+    "id": "hwyHyRX_ap0R",
+    "outputId": "f98c2683-1c1e-437b-fc49-9f61f5dbba4f"
+   },
+   "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",
+       "  </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      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",
+       "  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",
+       "   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",
+       "   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",
+       "  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",
+       "   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",
+       "[5 rows x 37 columns]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_test = pd.read_csv('D:\\Tugas Akhir\\Codingan\\Development\\Data\\data_testing.csv')\n",
+    "df_test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 561,
+     "status": "ok",
+     "timestamp": 1735309156053,
+     "user": {
+      "displayName": "kelompok bersama",
+      "userId": "01911350349879401396"
+     },
+     "user_tz": -420
+    },
+    "id": "VX58wjd8bSfB",
+    "outputId": "6d7dd536-8981-4528-8fb5-4fb3e0fec7f4"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R² Score: 0.9951390052323061\n",
+      "Mean Absolute Error (MAE): 0.44852516212478744\n",
+      "Mean Squared Error (MSE): 1.198342479918863\n",
+      "Root Mean Squared Error (RMSE): 1.094688302631787\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
+    "import numpy as np\n",
+    "\n",
+    "# Menghapus kolom yang tidak diperlukan untuk prediksi\n",
+    "X_test = df_test.drop(columns=['active_work_months','churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
+    "\n",
+    "# Lakukan prediksi menggunakan model final\n",
+    "y_pred = final_model.predict(X_test)\n",
+    "\n",
+    "# Tambahkan prediksi ke DataFrame\n",
+    "X_test['predicted_active_work'] = y_pred\n",
+    "\n",
+    "# Hitung metrik evaluasi\n",
+    "r2 = r2_score(df_test['active_work_months'], y_pred)\n",
+    "mae = mean_absolute_error(df_test['active_work_months'], y_pred)\n",
+    "mse = mean_squared_error(df_test['active_work_months'], y_pred)\n",
+    "rmse = np.sqrt(mse)\n",
+    "\n",
+    "# Cetak hasil\n",
+    "print(\"R² Score:\", r2)\n",
+    "print(\"Mean Absolute Error (MAE):\", mae)\n",
+    "print(\"Mean Squared Error (MSE):\", mse)\n",
+    "print(\"Root Mean Squared Error (RMSE):\", rmse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "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",
+       "  </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     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",
+       "  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",
+       "   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",
+       "   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",
+       "  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",
+       "   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",
+       "[5 rows x 37 columns]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_test = pd.read_csv('D:\\Tugas Akhir\\Codingan\\Development\\Data\\data_testing_resign.csv')\n",
+    "df_test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "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": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R² Score: 0.9982375650520496\n",
+      "Mean Absolute Error (MAE): 0.2113292077085458\n",
+      "Mean Squared Error (MSE): 0.08052087321650396\n",
+      "Root Mean Squared Error (RMSE): 0.2837620010087749\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
+    "import numpy as np\n",
+    "\n",
+    "# Menghapus kolom yang tidak diperlukan untuk prediksi\n",
+    "X_test = df_test.drop(columns=['active_work_months','churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
+    "\n",
+    "# Lakukan prediksi menggunakan model final\n",
+    "y_pred = final_model.predict(X_test)\n",
+    "\n",
+    "# Tambahkan prediksi ke DataFrame\n",
+    "X_test['predicted_active_work'] = y_pred\n",
+    "\n",
+    "# Hitung metrik evaluasi\n",
+    "r2 = r2_score(df_test['active_work_months'], y_pred)\n",
+    "mae = mean_absolute_error(df_test['active_work_months'], y_pred)\n",
+    "mse = mean_squared_error(df_test['active_work_months'], y_pred)\n",
+    "rmse = np.sqrt(mse)\n",
+    "\n",
+    "# Cetak hasil\n",
+    "print(\"R² Score:\", r2)\n",
+    "print(\"Mean Absolute Error (MAE):\", mae)\n",
+    "print(\"Mean Squared Error (MSE):\", mse)\n",
+    "print(\"Root Mean Squared Error (RMSE):\", rmse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "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>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",
+       "  </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      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",
+       "\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",
+       "\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",
+       "\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",
+       "\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",
+       "\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",
+       "\n",
+       "[5 rows x 37 columns]"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_test = pd.read_csv('D:\\Tugas Akhir\\Codingan\\Development\\Data\\data_testing_resign_6.csv')\n",
+    "df_test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R² Score: 0.9983869290912244\n",
+      "Mean Absolute Error (MAE): 0.18480799751480997\n",
+      "Mean Squared Error (MSE): 0.06445121600267545\n",
+      "Root Mean Squared Error (RMSE): 0.25387244041580304\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
+    "import numpy as np\n",
+    "\n",
+    "# Menghapus kolom yang tidak diperlukan untuk prediksi\n",
+    "X_test = df_test.drop(columns=['active_work_months','churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
+    "\n",
+    "# Lakukan prediksi menggunakan model final\n",
+    "y_pred = final_model.predict(X_test)\n",
+    "\n",
+    "# Tambahkan prediksi ke DataFrame\n",
+    "X_test['predicted_active_work'] = y_pred\n",
+    "\n",
+    "# Hitung metrik evaluasi\n",
+    "r2 = r2_score(df_test['active_work_months'], y_pred)\n",
+    "mae = mean_absolute_error(df_test['active_work_months'], y_pred)\n",
+    "mse = mean_squared_error(df_test['active_work_months'], y_pred)\n",
+    "rmse = np.sqrt(mse)\n",
+    "\n",
+    "# Cetak hasil\n",
+    "print(\"R² Score:\", r2)\n",
+    "print(\"Mean Absolute Error (MAE):\", mae)\n",
+    "print(\"Mean Squared Error (MSE):\", mse)\n",
+    "print(\"Root Mean Squared Error (RMSE):\", rmse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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",
+    "import pandas as pd\n",
+    "\n",
+    "# Nilai aktual dan prediksi\n",
+    "y_actual = df_test['active_work_months']  # Nilai aktual dari active_work_months\n",
+    "y_pred = X_test['predicted_active_work']  # Nilai prediksi dari model regresi\n",
+    "\n",
+    "# Membuat DataFrame untuk mempermudah visualisasi\n",
+    "comparison_df = pd.DataFrame({'Actual': y_actual, 'Predicted': y_pred})\n",
+    "\n",
+    "# Scatter plot untuk membandingkan prediksi dan nilai asli\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(range(len(y_actual)), y_actual, label='Actual Values', alpha=0.7, color='blue')\n",
+    "plt.scatter(range(len(y_pred)), y_pred, label='Predicted Values', alpha=0.7, color='orange')\n",
+    "plt.title('Comparison of Actual vs Predicted Active Work Months')\n",
+    "plt.xlabel('Index')\n",
+    "plt.ylabel('Active Work Months')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "# Line plot untuk membandingkan prediksi dan nilai asli\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(range(len(y_actual)), y_actual, label='Actual Values', marker='o', linestyle='-', color='blue')\n",
+    "plt.plot(range(len(y_pred)), y_pred, label='Predicted Values', marker='x', linestyle='-', color='orange')\n",
+    "plt.title('Actual vs Predicted Active Work Months (Line Plot)')\n",
+    "plt.xlabel('Index')\n",
+    "plt.ylabel('Active Work Months')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CatBoost Regression model saved to 'regression_model.sav'\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "with open('regression_model.sav', 'wb') as f:\n",
+    "    pickle.dump(final_model, f)\n",
+    "print(\"CatBoost Regression model saved to 'regression_model.sav'\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting shap\n",
+      "  Obtaining dependency information for shap from https://files.pythonhosted.org/packages/02/48/033ab9a2dee26d3de7e57cf532ab1d8408a608544c85ff98e6ea65775bdf/shap-0.46.0-cp39-cp39-win_amd64.whl.metadata\n",
+      "  Downloading shap-0.46.0-cp39-cp39-win_amd64.whl.metadata (25 kB)\n",
+      "Requirement already satisfied: numpy in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from shap) (1.22.4)\n",
+      "Requirement already satisfied: scipy in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from shap) (1.9.3)\n",
+      "Requirement already satisfied: scikit-learn in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from shap) (1.0.2)\n",
+      "Requirement already satisfied: pandas in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from shap) (1.4.2)\n",
+      "Requirement already satisfied: tqdm>=4.27.0 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from shap) (4.65.0)\n",
+      "Requirement already satisfied: packaging>20.9 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from shap) (23.1)\n",
+      "Collecting slicer==0.0.8 (from shap)\n",
+      "  Obtaining dependency information for slicer==0.0.8 from https://files.pythonhosted.org/packages/63/81/9ef641ff4e12cbcca30e54e72fb0951a2ba195d0cda0ba4100e532d929db/slicer-0.0.8-py3-none-any.whl.metadata\n",
+      "  Downloading slicer-0.0.8-py3-none-any.whl.metadata (4.0 kB)\n",
+      "Requirement already satisfied: numba in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from shap) (0.58.1)\n",
+      "Requirement already satisfied: cloudpickle in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from shap) (1.6.0)\n",
+      "Requirement already satisfied: colorama in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from tqdm>=4.27.0->shap) (0.4.6)\n",
+      "Requirement already satisfied: llvmlite<0.42,>=0.41.0dev0 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from numba->shap) (0.41.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from pandas->shap) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from pandas->shap) (2023.3.post1)\n",
+      "Requirement already satisfied: joblib>=0.11 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from scikit-learn->shap) (1.2.0)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from scikit-learn->shap) (2.2.0)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.1->pandas->shap) (1.16.0)\n",
+      "Downloading shap-0.46.0-cp39-cp39-win_amd64.whl (456 kB)\n",
+      "   ---------------------------------------- 0.0/456.1 kB ? eta -:--:--\n",
+      "    --------------------------------------- 10.2/456.1 kB ? eta -:--:--\n",
+      "   --------- ------------------------------ 112.6/456.1 kB 1.6 MB/s eta 0:00:01\n",
+      "   ------------------------------------ --- 419.8/456.1 kB 3.8 MB/s eta 0:00:01\n",
+      "   ---------------------------------------- 456.1/456.1 kB 3.6 MB/s eta 0:00:00\n",
+      "Downloading slicer-0.0.8-py3-none-any.whl (15 kB)\n",
+      "Installing collected packages: slicer, shap\n",
+      "Successfully installed shap-0.46.0 slicer-0.0.8\n",
+      "Note: you may need to restart the kernel to use updated packages.\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"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install shap"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "authorship_tag": "ABX9TyNQ761UqVawGErIP7JLgmhK",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}