{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>total_income_work</th>\n",
       "      <th>income_dependant_ratio</th>\n",
       "      <th>work_efficiency</th>\n",
       "      <th>active_work_category</th>\n",
       "      <th>work_stability_score</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EM13274</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1999-01-23</td>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>2023-02-02</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>D2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.341320e+07</td>\n",
       "      <td>3.100943e+06</td>\n",
       "      <td>1.12750</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1</td>\n",
       "      <td>3.100943e+06</td>\n",
       "      <td>3</td>\n",
       "      <td>1.033648e+06</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EM10730</td>\n",
       "      <td>Tangerang</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1998-04-12</td>\n",
       "      <td>2023-01-31</td>\n",
       "      <td>2024-03-16</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.489849e+07</td>\n",
       "      <td>1.146038e+06</td>\n",
       "      <td>1.22500</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>1</td>\n",
       "      <td>1.146038e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>1.146038e+06</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EM4510</td>\n",
       "      <td>Kabupaten Bekasi</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1981-06-10</td>\n",
       "      <td>2021-10-30</td>\n",
       "      <td>2023-12-15</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.003449e+08</td>\n",
       "      <td>2.671265e+06</td>\n",
       "      <td>1.18125</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>2.003449e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>8.013796e+06</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EM2622</td>\n",
       "      <td>Kabupaten Bekasi</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1981-07-26</td>\n",
       "      <td>2021-09-13</td>\n",
       "      <td>2023-10-31</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.537505e+08</td>\n",
       "      <td>2.537505e+06</td>\n",
       "      <td>1.22000</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>2.537505e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>1.015002e+07</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EM0633</td>\n",
       "      <td>Kota Jakarta Pusat</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1988-07-07</td>\n",
       "      <td>2022-08-22</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.312456e+07</td>\n",
       "      <td>1.274022e+06</td>\n",
       "      <td>1.18250</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.444444</td>\n",
       "      <td>1</td>\n",
       "      <td>2.548043e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>2.548043e+06</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee_id            domisili jenis_kelamin date_of_birth   join_date  \\\n",
       "0     EM13274  Kota Jakarta Timur     Perempuan    1999-01-23  2021-11-30   \n",
       "1     EM10730           Tangerang     Laki-laki    1998-04-12  2023-01-31   \n",
       "2      EM4510    Kabupaten Bekasi     Laki-laki    1981-06-10  2021-10-30   \n",
       "3      EM2622    Kabupaten Bekasi     Laki-laki    1981-07-26  2021-09-13   \n",
       "4      EM0633  Kota Jakarta Pusat     Laki-laki    1988-07-07  2022-08-22   \n",
       "\n",
       "  resign_date marriage_stat  dependant education  absent_90D  ...  \\\n",
       "0  2023-02-02        Single          0        D2         4.0  ...   \n",
       "1  2024-03-16        Single          0      SLTA         2.0  ...   \n",
       "2  2023-12-15       Married          2      SLTA         0.0  ...   \n",
       "3  2023-10-31       Married          3      SLTA         0.0  ...   \n",
       "4  2023-10-01       Married          1      SLTA         8.0  ...   \n",
       "\n",
       "   total_income_work income_dependant_ratio work_efficiency  \\\n",
       "0       4.341320e+07           3.100943e+06         1.12750   \n",
       "1       1.489849e+07           1.146038e+06         1.22500   \n",
       "2       2.003449e+08           2.671265e+06         1.18125   \n",
       "3       2.537505e+08           2.537505e+06         1.22000   \n",
       "4       3.312456e+07           1.274022e+06         1.18250   \n",
       "\n",
       "   active_work_category  work_stability_score position_score  \\\n",
       "0              Mid-term              2.800000              1   \n",
       "1              Mid-term              4.333333              1   \n",
       "2              Mid-term             25.000000              4   \n",
       "3              Mid-term             25.000000              4   \n",
       "4              Mid-term              1.444444              1   \n",
       "\n",
       "  job_income_position_score  education_score  education_income_ratio  \\\n",
       "0              3.100943e+06                3            1.033648e+06   \n",
       "1              1.146038e+06                1            1.146038e+06   \n",
       "2              2.003449e+06                1            8.013796e+06   \n",
       "3              2.537505e+06                1            1.015002e+07   \n",
       "4              2.548043e+06                1            2.548043e+06   \n",
       "\n",
       "   weighted_satisfaction_performance  \n",
       "0                                1.8  \n",
       "1                                2.6  \n",
       "2                                3.0  \n",
       "3                                4.0  \n",
       "4                                1.8  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"D:/Tugas Akhir/Codingan/Development/App/data/df_train_YESUSFIX.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns=['active_work_category', 'work_stability_score', \n",
    "                      'position_score', 'job_income_position_score',\n",
    "                      'education_score', 'education_income_ratio',\n",
    "                      'weighted_satisfaction_performance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12288"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "churn_status\n",
       "0    9265\n",
       "1    3023\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['churn_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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', 'jenis_kelamin']\n",
    "\n",
    "X = df.drop(columns=['churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date', 'active_work_months'])\n",
    "y = df['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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['domisili', 'jenis_kelamin', 'marriage_stat', 'dependant', 'education',\n",
       "       'absent_90D', 'avg_time_work', 'departemen', 'position', 'income',\n",
       "       'total_komp', 'job_satisfaction', 'performance_rating', 'age_years',\n",
       "       'active_work', 'income_3_months', 'income_6_months',\n",
       "       'total_income_work', 'income_dependant_ratio', 'work_efficiency'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>total_income_work</th>\n",
       "      <th>income_dependant_ratio</th>\n",
       "      <th>work_efficiency</th>\n",
       "      <th>active_work_category</th>\n",
       "      <th>work_stability_score</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>D2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.02</td>\n",
       "      <td>Corporate Strategy &amp; Communications</td>\n",
       "      <td>Staff</td>\n",
       "      <td>3.100943e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>4.341320e+07</td>\n",
       "      <td>3.100943e+06</td>\n",
       "      <td>1.12750</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1</td>\n",
       "      <td>3.100943e+06</td>\n",
       "      <td>3</td>\n",
       "      <td>1.033648e+06</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tangerang</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.80</td>\n",
       "      <td>Engineering &amp; IT</td>\n",
       "      <td>Staff</td>\n",
       "      <td>1.146038e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>1.489849e+07</td>\n",
       "      <td>1.146038e+06</td>\n",
       "      <td>1.22500</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>1</td>\n",
       "      <td>1.146038e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>1.146038e+06</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kabupaten Bekasi</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.45</td>\n",
       "      <td>Creative &amp; Design</td>\n",
       "      <td>Manager</td>\n",
       "      <td>8.013796e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>2.003449e+08</td>\n",
       "      <td>2.671265e+06</td>\n",
       "      <td>1.18125</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>2.003449e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>8.013796e+06</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>0.0</td>\n",
       "      <td>9.76</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Manager</td>\n",
       "      <td>1.015002e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>2.537505e+08</td>\n",
       "      <td>2.537505e+06</td>\n",
       "      <td>1.22000</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>2.537505e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>1.015002e+07</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kota Jakarta Pusat</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.46</td>\n",
       "      <td>Operations</td>\n",
       "      <td>Staff</td>\n",
       "      <td>2.548043e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>3.312456e+07</td>\n",
       "      <td>1.274022e+06</td>\n",
       "      <td>1.18250</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.444444</td>\n",
       "      <td>1</td>\n",
       "      <td>2.548043e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>2.548043e+06</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             domisili jenis_kelamin marriage_stat  dependant education  \\\n",
       "0  Kota Jakarta Timur     Perempuan        Single          0        D2   \n",
       "1           Tangerang     Laki-laki        Single          0      SLTA   \n",
       "2    Kabupaten Bekasi     Laki-laki       Married          2      SLTA   \n",
       "3    Kabupaten Bekasi     Laki-laki       Married          3      SLTA   \n",
       "4  Kota Jakarta Pusat     Laki-laki       Married          1      SLTA   \n",
       "\n",
       "   absent_90D  avg_time_work                           departemen position  \\\n",
       "0         4.0           9.02  Corporate Strategy & Communications    Staff   \n",
       "1         2.0           9.80                     Engineering & IT    Staff   \n",
       "2         0.0           9.45                    Creative & Design  Manager   \n",
       "3         0.0           9.76                            Marketing  Manager   \n",
       "4         8.0           9.46                           Operations    Staff   \n",
       "\n",
       "         income  ...  total_income_work income_dependant_ratio  \\\n",
       "0  3.100943e+06  ...       4.341320e+07           3.100943e+06   \n",
       "1  1.146038e+06  ...       1.489849e+07           1.146038e+06   \n",
       "2  8.013796e+06  ...       2.003449e+08           2.671265e+06   \n",
       "3  1.015002e+07  ...       2.537505e+08           2.537505e+06   \n",
       "4  2.548043e+06  ...       3.312456e+07           1.274022e+06   \n",
       "\n",
       "  work_efficiency  active_work_category  work_stability_score  position_score  \\\n",
       "0         1.12750              Mid-term              2.800000               1   \n",
       "1         1.22500              Mid-term              4.333333               1   \n",
       "2         1.18125              Mid-term             25.000000               4   \n",
       "3         1.22000              Mid-term             25.000000               4   \n",
       "4         1.18250              Mid-term              1.444444               1   \n",
       "\n",
       "   job_income_position_score  education_score  education_income_ratio  \\\n",
       "0               3.100943e+06                3            1.033648e+06   \n",
       "1               1.146038e+06                1            1.146038e+06   \n",
       "2               2.003449e+06                1            8.013796e+06   \n",
       "3               2.537505e+06                1            1.015002e+07   \n",
       "4               2.548043e+06                1            2.548043e+06   \n",
       "\n",
       "   weighted_satisfaction_performance  \n",
       "0                                1.8  \n",
       "1                                2.6  \n",
       "2                                3.0  \n",
       "3                                4.0  \n",
       "4                                1.8  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training:  churn_status\n",
      "0    7412\n",
      "1    2418\n",
      "Name: count, dtype: int64\n",
      "Testing:  churn_status\n",
      "0    1853\n",
      "1     605\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Training: \", y_train.value_counts())\n",
    "print(\"Testing: \", y_test.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9830"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2458"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\ttest: 0.9470753\tbest: 0.9470753 (0)\ttotal: 295ms\tremaining: 4m 54s\n",
      "200:\ttest: 0.9734485\tbest: 0.9734485 (200)\ttotal: 21.1s\tremaining: 1m 23s\n",
      "400:\ttest: 0.9768800\tbest: 0.9768889 (399)\ttotal: 40.8s\tremaining: 1m 1s\n",
      "600:\ttest: 0.9777925\tbest: 0.9777952 (599)\ttotal: 58.3s\tremaining: 38.7s\n",
      "800:\ttest: 0.9783991\tbest: 0.9783991 (800)\ttotal: 1m 17s\tremaining: 19.3s\n",
      "999:\ttest: 0.9789218\tbest: 0.9789218 (999)\ttotal: 1m 35s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.9789218288\n",
      "bestIteration = 999\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x1587df41060>"
      ]
     },
     "execution_count": 8,
     "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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting optuna\n",
      "  Downloading optuna-4.2.1-py3-none-any.whl.metadata (17 kB)\n",
      "Collecting alembic>=1.5.0 (from optuna)\n",
      "  Downloading alembic-1.15.1-py3-none-any.whl.metadata (7.2 kB)\n",
      "Collecting colorlog (from optuna)\n",
      "  Using cached colorlog-6.9.0-py3-none-any.whl.metadata (10 kB)\n",
      "Requirement already satisfied: numpy in d:\\tugas akhir\\codingan\\development\\app\\.venv\\lib\\site-packages (from optuna) (1.26.4)\n",
      "Requirement already satisfied: packaging>=20.0 in d:\\tugas akhir\\codingan\\development\\app\\.venv\\lib\\site-packages (from optuna) (24.2)\n",
      "Collecting sqlalchemy>=1.4.2 (from optuna)\n",
      "  Downloading sqlalchemy-2.0.39-cp310-cp310-win_amd64.whl.metadata (9.9 kB)\n",
      "Requirement already satisfied: tqdm in d:\\tugas akhir\\codingan\\development\\app\\.venv\\lib\\site-packages (from optuna) (4.67.1)\n",
      "Collecting PyYAML (from optuna)\n",
      "  Using cached PyYAML-6.0.2-cp310-cp310-win_amd64.whl.metadata (2.1 kB)\n",
      "Collecting Mako (from alembic>=1.5.0->optuna)\n",
      "  Downloading Mako-1.3.9-py3-none-any.whl.metadata (2.9 kB)\n",
      "Requirement already satisfied: typing-extensions>=4.12 in d:\\tugas akhir\\codingan\\development\\app\\.venv\\lib\\site-packages (from alembic>=1.5.0->optuna) (4.12.2)\n",
      "Collecting greenlet!=0.4.17 (from sqlalchemy>=1.4.2->optuna)\n",
      "  Using cached greenlet-3.1.1-cp310-cp310-win_amd64.whl.metadata (3.9 kB)\n",
      "Requirement already satisfied: colorama in d:\\tugas akhir\\codingan\\development\\app\\.venv\\lib\\site-packages (from colorlog->optuna) (0.4.6)\n",
      "Requirement already satisfied: MarkupSafe>=0.9.2 in d:\\tugas akhir\\codingan\\development\\app\\.venv\\lib\\site-packages (from Mako->alembic>=1.5.0->optuna) (3.0.2)\n",
      "Downloading optuna-4.2.1-py3-none-any.whl (383 kB)\n",
      "Downloading alembic-1.15.1-py3-none-any.whl (231 kB)\n",
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      "Downloading Mako-1.3.9-py3-none-any.whl (78 kB)\n",
      "Installing collected packages: PyYAML, Mako, greenlet, colorlog, sqlalchemy, alembic, optuna\n",
      "Successfully installed Mako-1.3.9 PyYAML-6.0.2 alembic-1.15.1 colorlog-6.9.0 greenlet-3.1.1 optuna-4.2.1 sqlalchemy-2.0.39\n"
     ]
    }
   ],
   "source": [
    "!pip install optuna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Tugas Akhir\\Codingan\\Development\\App\\.venv\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2025-03-20 14:04:16,096] A new study created in memory with name: no-name-a08ce526-8628-4eb7-a8f0-b10a4d1fdfcf\n",
      "[I 2025-03-20 14:05:09,743] Trial 0 finished with value: 0.9679634989942599 and parameters: {'iterations': 712, 'learning_rate': 0.00808789111583903, 'depth': 5, 'subsample': 0.7534855215992657, 'colsample_bylevel': 0.6718129120534164, 'l2_leaf_reg': 14.932552759777428, 'random_strength': 6.218629002335594}. Best is trial 0 with value: 0.9679634989942599.\n",
      "[I 2025-03-20 14:06:13,092] Trial 1 finished with value: 0.9791885394691654 and parameters: {'iterations': 997, 'learning_rate': 0.08810482292857882, 'depth': 4, 'subsample': 0.6997259087320504, 'colsample_bylevel': 0.618232755190445, 'l2_leaf_reg': 13.679013578868982, 'random_strength': 5.906123359684791}. Best is trial 1 with value: 0.9791885394691654.\n",
      "[I 2025-03-20 14:07:15,582] Trial 2 finished with value: 0.9797460450553714 and parameters: {'iterations': 729, 'learning_rate': 0.033511049626555336, 'depth': 6, 'subsample': 0.7224685767241121, 'colsample_bylevel': 0.7533870049652309, 'l2_leaf_reg': 8.860553490367955, 'random_strength': 7.587105821853806}. Best is trial 2 with value: 0.9797460450553714.\n",
      "[I 2025-03-20 14:08:13,748] Trial 3 finished with value: 0.9792295718803102 and parameters: {'iterations': 944, 'learning_rate': 0.07868698512431888, 'depth': 4, 'subsample': 0.5223746615032272, 'colsample_bylevel': 0.7033898620351601, 'l2_leaf_reg': 15.74829346697743, 'random_strength': 9.713238370277713}. Best is trial 2 with value: 0.9797460450553714.\n",
      "[I 2025-03-20 14:08:58,066] Trial 4 finished with value: 0.9676004513565226 and parameters: {'iterations': 663, 'learning_rate': 0.010937557940086157, 'depth': 6, 'subsample': 0.6711467245122322, 'colsample_bylevel': 0.5381156346917859, 'l2_leaf_reg': 5.503463881074296, 'random_strength': 8.09655801526862}. Best is trial 2 with value: 0.9797460450553714.\n",
      "[I 2025-03-20 14:09:39,130] Trial 5 finished with value: 0.9658458697756153 and parameters: {'iterations': 764, 'learning_rate': 0.0018231578420021075, 'depth': 6, 'subsample': 0.6577101894799257, 'colsample_bylevel': 0.6662020303202562, 'l2_leaf_reg': 12.906054073206946, 'random_strength': 6.862323206994869}. Best is trial 2 with value: 0.9797460450553714.\n",
      "[I 2025-03-20 14:10:18,298] Trial 6 finished with value: 0.9667209305437241 and parameters: {'iterations': 774, 'learning_rate': 0.008994651204218666, 'depth': 6, 'subsample': 0.6322676331292276, 'colsample_bylevel': 0.7650933344670046, 'l2_leaf_reg': 14.902520814369101, 'random_strength': 9.947741383362207}. Best is trial 2 with value: 0.9797460450553714.\n",
      "[I 2025-03-20 14:10:45,376] Trial 7 finished with value: 0.97690321257019 and parameters: {'iterations': 665, 'learning_rate': 0.02093868307988793, 'depth': 5, 'subsample': 0.6384311941476284, 'colsample_bylevel': 0.6402240846331038, 'l2_leaf_reg': 15.613274342420151, 'random_strength': 6.1180371709539125}. Best is trial 2 with value: 0.9797460450553714.\n",
      "[I 2025-03-20 14:11:25,916] Trial 8 finished with value: 0.9698831022286842 and parameters: {'iterations': 879, 'learning_rate': 0.009080071938714185, 'depth': 6, 'subsample': 0.5540255852522166, 'colsample_bylevel': 0.6811107913932387, 'l2_leaf_reg': 13.539933366184938, 'random_strength': 5.559986566144425}. Best is trial 2 with value: 0.9797460450553714.\n",
      "[I 2025-03-20 14:12:00,265] Trial 9 finished with value: 0.9781993015570015 and parameters: {'iterations': 693, 'learning_rate': 0.020221776374438248, 'depth': 6, 'subsample': 0.7348276606855401, 'colsample_bylevel': 0.5827275162823304, 'l2_leaf_reg': 13.571987451929706, 'random_strength': 5.754490001269322}. Best is trial 2 with value: 0.9797460450553714.\n",
      "[I 2025-03-20 14:12:26,863] Trial 10 finished with value: 0.9651144224465129 and parameters: {'iterations': 531, 'learning_rate': 0.001841128667060218, 'depth': 5, 'subsample': 0.7946638220384123, 'colsample_bylevel': 0.7928053104477072, 'l2_leaf_reg': 8.054822950225772, 'random_strength': 8.175526360465712}. Best is trial 2 with value: 0.9797460450553714.\n",
      "[I 2025-03-20 14:13:03,374] Trial 11 finished with value: 0.9792875524612757 and parameters: {'iterations': 886, 'learning_rate': 0.08828743411281516, 'depth': 4, 'subsample': 0.5064953181164747, 'colsample_bylevel': 0.7311459110707281, 'l2_leaf_reg': 19.746623436020112, 'random_strength': 9.712873146529446}. Best is trial 2 with value: 0.9797460450553714.\n",
      "[I 2025-03-20 14:13:36,657] Trial 12 finished with value: 0.9782679862452223 and parameters: {'iterations': 860, 'learning_rate': 0.03363778531524893, 'depth': 4, 'subsample': 0.5821119245249382, 'colsample_bylevel': 0.7449793101868798, 'l2_leaf_reg': 19.49496708994202, 'random_strength': 8.745069864956436}. Best is trial 2 with value: 0.9797460450553714.\n",
      "[I 2025-03-20 14:14:10,360] Trial 13 finished with value: 0.9790083536637038 and parameters: {'iterations': 839, 'learning_rate': 0.045887802352489515, 'depth': 4, 'subsample': 0.5023121637588966, 'colsample_bylevel': 0.7291218854317933, 'l2_leaf_reg': 9.402278082979281, 'random_strength': 9.039922562458145}. Best is trial 2 with value: 0.9797460450553714.\n",
      "[I 2025-03-20 14:14:45,073] Trial 14 finished with value: 0.9790600901821037 and parameters: {'iterations': 595, 'learning_rate': 0.05147478816103685, 'depth': 5, 'subsample': 0.5901507546819968, 'colsample_bylevel': 0.797181790956148, 'l2_leaf_reg': 19.776893509299732, 'random_strength': 7.107892489869574}. Best is trial 2 with value: 0.9797460450553714.\n",
      "[I 2025-03-20 14:15:26,433] Trial 15 finished with value: 0.9787844594202834 and parameters: {'iterations': 803, 'learning_rate': 0.025473064394081438, 'depth': 5, 'subsample': 0.7153245442551085, 'colsample_bylevel': 0.7166214230614575, 'l2_leaf_reg': 10.186072438562274, 'random_strength': 7.800910785193279}. Best is trial 2 with value: 0.9797460450553714.\n",
      "[I 2025-03-20 14:16:00,342] Trial 16 finished with value: 0.9673551488985921 and parameters: {'iterations': 909, 'learning_rate': 0.003564744966856029, 'depth': 4, 'subsample': 0.7893884226735706, 'colsample_bylevel': 0.755387068489751, 'l2_leaf_reg': 6.127909456792237, 'random_strength': 5.027365413104045}. Best is trial 2 with value: 0.9797460450553714.\n",
      "[I 2025-03-20 14:16:57,295] Trial 17 finished with value: 0.9793874574623237 and parameters: {'iterations': 993, 'learning_rate': 0.0597142220820913, 'depth': 5, 'subsample': 0.6046372008163389, 'colsample_bylevel': 0.6981448419100035, 'l2_leaf_reg': 17.677064335921635, 'random_strength': 9.127715251270327}. Best is trial 2 with value: 0.9797460450553714.\n",
      "[I 2025-03-20 14:17:26,367] Trial 18 finished with value: 0.9792527641126965 and parameters: {'iterations': 601, 'learning_rate': 0.05081581522472726, 'depth': 5, 'subsample': 0.6167962634213932, 'colsample_bylevel': 0.6166390281331033, 'l2_leaf_reg': 10.84167216925184, 'random_strength': 8.773077118659113}. Best is trial 2 with value: 0.9797460450553714.\n",
      "[I 2025-03-20 14:18:17,349] Trial 19 finished with value: 0.977029877839376 and parameters: {'iterations': 999, 'learning_rate': 0.01505650943617022, 'depth': 5, 'subsample': 0.6832696529030774, 'colsample_bylevel': 0.5280518508085718, 'l2_leaf_reg': 17.45765221042104, 'random_strength': 7.3502244628405276}. Best is trial 2 with value: 0.9797460450553714.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Trial:\n",
      "AUC: 0.9797460450553714\n",
      "Params:\n",
      "  iterations: 729\n",
      "  learning_rate: 0.033511049626555336\n",
      "  depth: 6\n",
      "  subsample: 0.7224685767241121\n",
      "  colsample_bylevel: 0.7533870049652309\n",
      "  l2_leaf_reg: 8.860553490367955\n",
      "  random_strength: 7.587105821853806\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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\ttest: 0.9386249\tbest: 0.9386249 (0)\ttotal: 135ms\tremaining: 1m 37s\n",
      "100:\ttest: 0.9658378\tbest: 0.9660823 (80)\ttotal: 8.33s\tremaining: 51.8s\n",
      "200:\ttest: 0.9678520\tbest: 0.9678672 (199)\ttotal: 18.7s\tremaining: 49.2s\n",
      "300:\ttest: 0.9710061\tbest: 0.9710302 (297)\ttotal: 26.4s\tremaining: 37.5s\n",
      "400:\ttest: 0.9775258\tbest: 0.9775258 (400)\ttotal: 33.7s\tremaining: 27.6s\n",
      "500:\ttest: 0.9789557\tbest: 0.9789557 (500)\ttotal: 42.5s\tremaining: 19.3s\n",
      "600:\ttest: 0.9794062\tbest: 0.9794347 (579)\ttotal: 51.7s\tremaining: 11s\n",
      "700:\ttest: 0.9796720\tbest: 0.9797166 (680)\ttotal: 1m 2s\tremaining: 2.5s\n",
      "728:\ttest: 0.9797443\tbest: 0.9797460 (722)\ttotal: 1m 5s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.9797460451\n",
      "bestIteration = 722\n",
      "\n",
      "Shrink model to first 723 iterations.\n",
      "Learn AUC: 0.9869 | Test AUC: 0.9797\n"
     ]
    }
   ],
   "source": [
    "from catboost import CatBoostClassifier\n",
    "from sklearn.metrics import roc_auc_score\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': 'Logloss',  # Masih pakai Logloss untuk training\n",
    "    'eval_metric': 'AUC',        # Pakai AUC untuk evaluasi\n",
    "    'cat_features': cat_feature,\n",
    "    'random_state': 42,\n",
    "    'verbose': 100,  # Set verbose ke 100 agar terlihat AUC setiap 100 iterasi\n",
    "    'od_type': 'Iter',\n",
    "    'od_wait': 50\n",
    "})\n",
    "\n",
    "# Latih model dengan parameter terbaik\n",
    "final_model = CatBoostClassifier(**best_params)\n",
    "\n",
    "final_model.fit(\n",
    "    X_train, y_train,\n",
    "    eval_set=(X_test, y_test),\n",
    "    use_best_model=True, \n",
    "    verbose=100  # AUC akan ditampilkan setiap 100 iterasi\n",
    ")\n",
    "\n",
    "# Dapatkan prediksi probabilitas\n",
    "y_pred_train = final_model.predict_proba(X_train)[:, 1]  # Untuk training set\n",
    "y_pred_test = final_model.predict_proba(X_test)[:, 1]  # Untuk testing set\n",
    "\n",
    "# Hitung AUC untuk training dan testing\n",
    "train_auc = roc_auc_score(y_train, y_pred_train)\n",
    "test_auc = roc_auc_score(y_test, y_pred_test)\n",
    "\n",
    "# Cetak skor AUC setelah training selesai\n",
    "print(f\"Learn AUC: {train_auc:.4f} | Test AUC: {test_auc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Training Logloss: 0.1271368948276341\n",
      "Final Validation Logloss: 0.1558541739979401\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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}\")\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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Training Logloss: 0.14482801515145513\n",
      "Final Validation Logloss: 0.1703206692296592\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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\ttotal: 75.1ms\tremaining: 1m 15s\n",
      "200:\ttotal: 11.2s\tremaining: 44.7s\n",
      "400:\ttotal: 22.6s\tremaining: 33.8s\n",
      "600:\ttotal: 37.4s\tremaining: 24.8s\n",
      "800:\ttotal: 55.8s\tremaining: 13.9s\n",
      "999:\ttotal: 1m 8s\tremaining: 0us\n",
      "0:\ttotal: 47.9ms\tremaining: 47.8s\n",
      "200:\ttotal: 11.2s\tremaining: 44.6s\n",
      "400:\ttotal: 23.9s\tremaining: 35.7s\n",
      "600:\ttotal: 45.2s\tremaining: 30s\n",
      "800:\ttotal: 56.4s\tremaining: 14s\n",
      "999:\ttotal: 1m 8s\tremaining: 0us\n",
      "0:\ttotal: 49.7ms\tremaining: 49.6s\n",
      "200:\ttotal: 11.2s\tremaining: 44.3s\n",
      "400:\ttotal: 24.4s\tremaining: 36.4s\n",
      "600:\ttotal: 44.3s\tremaining: 29.4s\n",
      "800:\ttotal: 59.1s\tremaining: 14.7s\n",
      "999:\ttotal: 1m 10s\tremaining: 0us\n",
      "0:\ttotal: 56.3ms\tremaining: 56.2s\n",
      "200:\ttotal: 11.7s\tremaining: 46.7s\n",
      "400:\ttotal: 28.1s\tremaining: 42s\n",
      "600:\ttotal: 47.9s\tremaining: 31.8s\n",
      "800:\ttotal: 1m\tremaining: 15.1s\n",
      "999:\ttotal: 1m 11s\tremaining: 0us\n",
      "0:\ttotal: 47.6ms\tremaining: 47.5s\n",
      "200:\ttotal: 11.4s\tremaining: 45.3s\n",
      "400:\ttotal: 28.8s\tremaining: 43.1s\n",
      "600:\ttotal: 49.4s\tremaining: 32.8s\n",
      "800:\ttotal: 1m 1s\tremaining: 15.3s\n",
      "999:\ttotal: 1m 16s\tremaining: 0us\n",
      "0:\ttotal: 57.2ms\tremaining: 57.1s\n",
      "200:\ttotal: 15.5s\tremaining: 1m 1s\n",
      "400:\ttotal: 36.2s\tremaining: 54.1s\n",
      "600:\ttotal: 55.3s\tremaining: 36.7s\n",
      "800:\ttotal: 1m 6s\tremaining: 16.6s\n",
      "999:\ttotal: 1m 23s\tremaining: 0us\n",
      "0:\ttotal: 65.7ms\tremaining: 1m 5s\n",
      "200:\ttotal: 22s\tremaining: 1m 27s\n",
      "400:\ttotal: 45.9s\tremaining: 1m 8s\n",
      "600:\ttotal: 1m 1s\tremaining: 41.1s\n",
      "800:\ttotal: 1m 17s\tremaining: 19.3s\n",
      "999:\ttotal: 1m 37s\tremaining: 0us\n",
      "0:\ttotal: 79.7ms\tremaining: 1m 19s\n",
      "200:\ttotal: 13.4s\tremaining: 53.3s\n",
      "400:\ttotal: 26.4s\tremaining: 39.4s\n",
      "600:\ttotal: 45.3s\tremaining: 30.1s\n",
      "800:\ttotal: 1m 5s\tremaining: 16.3s\n",
      "999:\ttotal: 1m 20s\tremaining: 0us\n",
      "0:\ttotal: 49ms\tremaining: 49s\n",
      "200:\ttotal: 12.3s\tremaining: 49.1s\n",
      "400:\ttotal: 34.7s\tremaining: 51.8s\n",
      "600:\ttotal: 53.4s\tremaining: 35.4s\n",
      "800:\ttotal: 1m 5s\tremaining: 16.4s\n",
      "999:\ttotal: 1m 18s\tremaining: 0us\n",
      "0:\ttotal: 71.8ms\tremaining: 1m 11s\n",
      "200:\ttotal: 12.4s\tremaining: 49.3s\n",
      "400:\ttotal: 33.7s\tremaining: 50.3s\n",
      "600:\ttotal: 54.5s\tremaining: 36.2s\n",
      "800:\ttotal: 1m 8s\tremaining: 16.9s\n",
      "999:\ttotal: 1m 24s\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: [0.9194467  0.91619203 0.90398698 0.90968267 0.9275834  0.90561432\n",
      " 0.91537836 0.91049634 0.91449511 0.90635179]\n",
      "Mean Accuracy: 0.91\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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\ttotal: 67.4ms\tremaining: 49.1s\n",
      "100:\ttotal: 9.39s\tremaining: 58.4s\n",
      "200:\ttotal: 18.2s\tremaining: 47.8s\n",
      "300:\ttotal: 26.9s\tremaining: 38.3s\n",
      "400:\ttotal: 34.6s\tremaining: 28.3s\n",
      "500:\ttotal: 42.3s\tremaining: 19.3s\n",
      "600:\ttotal: 51.5s\tremaining: 11s\n",
      "700:\ttotal: 1m 5s\tremaining: 2.62s\n",
      "728:\ttotal: 1m 9s\tremaining: 0us\n",
      "0:\ttotal: 117ms\tremaining: 1m 25s\n",
      "100:\ttotal: 15.1s\tremaining: 1m 33s\n",
      "200:\ttotal: 22.7s\tremaining: 59.8s\n",
      "300:\ttotal: 28.5s\tremaining: 40.5s\n",
      "400:\ttotal: 34.7s\tremaining: 28.4s\n",
      "500:\ttotal: 41.8s\tremaining: 19s\n",
      "600:\ttotal: 48.6s\tremaining: 10.4s\n",
      "700:\ttotal: 56.4s\tremaining: 2.25s\n",
      "728:\ttotal: 59.5s\tremaining: 0us\n",
      "0:\ttotal: 101ms\tremaining: 1m 13s\n",
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      "200:\ttotal: 18.7s\tremaining: 49.1s\n",
      "300:\ttotal: 30.8s\tremaining: 43.8s\n",
      "400:\ttotal: 41.4s\tremaining: 33.9s\n",
      "500:\ttotal: 51.6s\tremaining: 23.5s\n",
      "600:\ttotal: 59.2s\tremaining: 12.6s\n",
      "700:\ttotal: 1m 7s\tremaining: 2.69s\n",
      "728:\ttotal: 1m 10s\tremaining: 0us\n",
      "0:\ttotal: 83.9ms\tremaining: 1m 1s\n",
      "100:\ttotal: 9.63s\tremaining: 59.9s\n",
      "200:\ttotal: 18.8s\tremaining: 49.4s\n",
      "300:\ttotal: 26.1s\tremaining: 37.2s\n",
      "400:\ttotal: 33s\tremaining: 27s\n",
      "500:\ttotal: 41.1s\tremaining: 18.7s\n",
      "600:\ttotal: 48.3s\tremaining: 10.3s\n",
      "700:\ttotal: 57.1s\tremaining: 2.28s\n",
      "728:\ttotal: 1m\tremaining: 0us\n",
      "0:\ttotal: 109ms\tremaining: 1m 19s\n",
      "100:\ttotal: 10.8s\tremaining: 1m 7s\n",
      "200:\ttotal: 19.9s\tremaining: 52.2s\n",
      "300:\ttotal: 31.5s\tremaining: 44.7s\n",
      "400:\ttotal: 39.2s\tremaining: 32s\n",
      "500:\ttotal: 47.8s\tremaining: 21.7s\n",
      "600:\ttotal: 56.3s\tremaining: 12s\n",
      "700:\ttotal: 1m 8s\tremaining: 2.72s\n",
      "728:\ttotal: 1m 11s\tremaining: 0us\n",
      "0:\ttotal: 128ms\tremaining: 1m 33s\n",
      "100:\ttotal: 21s\tremaining: 2m 10s\n",
      "200:\ttotal: 28.7s\tremaining: 1m 15s\n",
      "300:\ttotal: 35.1s\tremaining: 50s\n",
      "400:\ttotal: 42.5s\tremaining: 34.7s\n",
      "500:\ttotal: 48.8s\tremaining: 22.2s\n",
      "600:\ttotal: 55.4s\tremaining: 11.8s\n",
      "700:\ttotal: 1m 3s\tremaining: 2.53s\n",
      "728:\ttotal: 1m 5s\tremaining: 0us\n",
      "0:\ttotal: 92.4ms\tremaining: 1m 7s\n",
      "100:\ttotal: 8.57s\tremaining: 53.3s\n",
      "200:\ttotal: 15.7s\tremaining: 41.2s\n",
      "300:\ttotal: 25s\tremaining: 35.6s\n",
      "400:\ttotal: 34s\tremaining: 27.8s\n",
      "500:\ttotal: 42.5s\tremaining: 19.3s\n",
      "600:\ttotal: 49.4s\tremaining: 10.5s\n",
      "700:\ttotal: 56.2s\tremaining: 2.24s\n",
      "728:\ttotal: 58.2s\tremaining: 0us\n",
      "0:\ttotal: 55.7ms\tremaining: 40.5s\n",
      "100:\ttotal: 6.49s\tremaining: 40.3s\n",
      "200:\ttotal: 12.2s\tremaining: 32s\n",
      "300:\ttotal: 21.3s\tremaining: 30.3s\n",
      "400:\ttotal: 31.5s\tremaining: 25.8s\n",
      "500:\ttotal: 41.6s\tremaining: 18.9s\n",
      "600:\ttotal: 49.8s\tremaining: 10.6s\n",
      "700:\ttotal: 56.2s\tremaining: 2.25s\n",
      "728:\ttotal: 57.9s\tremaining: 0us\n",
      "0:\ttotal: 90.7ms\tremaining: 1m 6s\n",
      "100:\ttotal: 5.42s\tremaining: 33.7s\n",
      "200:\ttotal: 11.7s\tremaining: 30.7s\n",
      "300:\ttotal: 18.6s\tremaining: 26.4s\n",
      "400:\ttotal: 29.2s\tremaining: 23.9s\n",
      "500:\ttotal: 40.9s\tremaining: 18.6s\n",
      "600:\ttotal: 50s\tremaining: 10.6s\n",
      "700:\ttotal: 57.5s\tremaining: 2.3s\n",
      "728:\ttotal: 59.4s\tremaining: 0us\n",
      "0:\ttotal: 55.8ms\tremaining: 40.7s\n",
      "100:\ttotal: 4.87s\tremaining: 30.3s\n",
      "200:\ttotal: 10s\tremaining: 26.3s\n",
      "300:\ttotal: 16.7s\tremaining: 23.8s\n",
      "400:\ttotal: 25.8s\tremaining: 21.1s\n",
      "500:\ttotal: 37.3s\tremaining: 17s\n",
      "600:\ttotal: 47.3s\tremaining: 10.1s\n",
      "700:\ttotal: 56.9s\tremaining: 2.27s\n",
      "728:\ttotal: 1m\tremaining: 0us\n"
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    },
    {
     "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: [0.93083808 0.93490643 0.93653377 0.92839707 0.94060212 0.92595606\n",
      " 0.9357201  0.92595606 0.93892508 0.92996743]\n",
      "Mean Accuracy: 0.93\n",
      "Standard Deviation: 0.01\n"
     ]
    }
   ],
   "source": [
    "cross_validate_and_visualize_accuracy(final_model, X, y, cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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: 0.95\n",
      "Precision: 0.86\n",
      "Recall: 0.94\n",
      "F1 Score: 0.90\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: 0.93\n",
      "Precision: 0.82\n",
      "Recall: 0.93\n",
      "F1 Score: 0.87\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": 19,
   "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: 0.92\n",
      "Precision: 0.76\n",
      "Recall: 0.99\n",
      "F1 Score: 0.86\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: 0.91\n",
      "Precision: 0.74\n",
      "Recall: 0.98\n",
      "F1 Score: 0.84\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": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('D:/Tugas Akhir/Codingan/Development/App/model/clasification_final_model_biasa.sav', 'wb') as f:\n",
    "    pickle.dump(final_model, f)\n",
    "\n",
    "with open('D:/Tugas Akhir/Codingan/Development/App/model/clasification_model_biasa.sav', 'wb') as f:\n",
    "    pickle.dump(model, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>total_income_work</th>\n",
       "      <th>income_dependant_ratio</th>\n",
       "      <th>work_efficiency</th>\n",
       "      <th>active_work_category</th>\n",
       "      <th>work_stability_score</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EM6407</td>\n",
       "      <td>Kota Jakarta Selatan</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1981-03-05</td>\n",
       "      <td>2022-03-13</td>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "      <td>D3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.169413e+08</td>\n",
       "      <td>1.719725e+06</td>\n",
       "      <td>1.22500</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>4.250000</td>\n",
       "      <td>4</td>\n",
       "      <td>1.719725e+06</td>\n",
       "      <td>4</td>\n",
       "      <td>1.719725e+06</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EM6881</td>\n",
       "      <td>Tangerang</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1974-04-26</td>\n",
       "      <td>2022-04-11</td>\n",
       "      <td>2023-05-31</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>D3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.369110e+08</td>\n",
       "      <td>1.053162e+07</td>\n",
       "      <td>1.17375</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>4</td>\n",
       "      <td>2.632904e+06</td>\n",
       "      <td>4</td>\n",
       "      <td>2.632904e+06</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EM9588</td>\n",
       "      <td>Kota Depok</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1980-01-08</td>\n",
       "      <td>2022-02-22</td>\n",
       "      <td>2023-08-30</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "      <td>D1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.408170e+08</td>\n",
       "      <td>1.955791e+06</td>\n",
       "      <td>1.18625</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>4</td>\n",
       "      <td>1.955791e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>3.911582e+06</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EM6817</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1985-06-15</td>\n",
       "      <td>2021-09-04</td>\n",
       "      <td>2023-01-13</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.969525e+07</td>\n",
       "      <td>8.269843e+05</td>\n",
       "      <td>1.13125</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.454545</td>\n",
       "      <td>1</td>\n",
       "      <td>2.480953e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>2.480953e+06</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</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>2.918537e+08</td>\n",
       "      <td>4.864228e+06</td>\n",
       "      <td>1.14125</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>4</td>\n",
       "      <td>2.432114e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>9.728456e+06</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee_id              domisili jenis_kelamin date_of_birth   join_date  \\\n",
       "0      EM6407  Kota Jakarta Selatan     Laki-laki    1981-03-05  2022-03-13   \n",
       "1      EM6881             Tangerang     Laki-laki    1974-04-26  2022-04-11   \n",
       "2      EM9588            Kota Depok     Perempuan    1980-01-08  2022-02-22   \n",
       "3      EM6817    Kota Jakarta Timur     Perempuan    1985-06-15  2021-09-04   \n",
       "4      EM0933    Kota Jakarta Timur     Laki-laki    1981-10-31  2022-03-20   \n",
       "\n",
       "  resign_date marriage_stat  dependant education  absent_90D  ...  \\\n",
       "0  2023-08-08       Married          3        D3         3.0  ...   \n",
       "1  2023-05-31       Married          0        D3         2.0  ...   \n",
       "2  2023-08-30       Married          3        D1         4.0  ...   \n",
       "3  2023-01-13       Married          2      SLTA        10.0  ...   \n",
       "4  2024-09-08       Married          1      SLTA         7.0  ...   \n",
       "\n",
       "   total_income_work income_dependant_ratio work_efficiency  \\\n",
       "0       1.169413e+08           1.719725e+06         1.22500   \n",
       "1       1.369110e+08           1.053162e+07         1.17375   \n",
       "2       1.408170e+08           1.955791e+06         1.18625   \n",
       "3       3.969525e+07           8.269843e+05         1.13125   \n",
       "4       2.918537e+08           4.864228e+06         1.14125   \n",
       "\n",
       "   active_work_category  work_stability_score position_score  \\\n",
       "0              Mid-term              4.250000              4   \n",
       "1              Mid-term              4.333333              4   \n",
       "2              Mid-term              3.600000              4   \n",
       "3              Mid-term              1.454545              1   \n",
       "4              Mid-term              3.750000              4   \n",
       "\n",
       "  job_income_position_score  education_score  education_income_ratio  \\\n",
       "0              1.719725e+06                4            1.719725e+06   \n",
       "1              2.632904e+06                4            2.632904e+06   \n",
       "2              1.955791e+06                2            3.911582e+06   \n",
       "3              2.480953e+06                1            2.480953e+06   \n",
       "4              2.432114e+06                1            9.728456e+06   \n",
       "\n",
       "   weighted_satisfaction_performance  \n",
       "0                                3.4  \n",
       "1                                4.0  \n",
       "2                                3.6  \n",
       "3                                1.0  \n",
       "4                                4.0  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv('D:/Tugas Akhir/Codingan/Development/App/data/df_test_YESUSFIX.csv')\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "churn_status\n",
       "1    335\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['churn_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8955223880597015\n",
      "Precision: 1.0\n",
      "Recall: 0.8955223880597015\n",
      "F1 Score: 0.9448818897637795\n"
     ]
    }
   ],
   "source": [
    "X_test = df_test.drop(['churn_status', 'employee_id', 'date_of_birth', 'join_date', \n",
    "                       '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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.982089552238806\n",
      "Precision: 1.0\n",
      "Recall: 0.982089552238806\n",
      "F1 Score: 0.9909638554216867\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 = 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)"
   ]
  }
 ],
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