{
 "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>...</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": 1,
     "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": 2,
   "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', \n",
    "                     '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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "cat_indices = [X.columns.get_loc(col) for col in cat_feature]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
    "\n",
    "rus = RandomUnderSampler(random_state=42, replacement=False, sampling_strategy=1)\n",
    "X_train_res, y_train_res = rus.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training:  churn_status\n",
      "0    2418\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_res.value_counts())\n",
    "print(\"Testing: \", y_test.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "churn_status\n",
      "0    2418\n",
      "1    2418\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(y_train_res.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4836, 27)\n",
      "(2458, 27)\n"
     ]
    }
   ],
   "source": [
    "print(X_train_res.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\ttest: 0.9398496\tbest: 0.9398496 (0)\ttotal: 245ms\tremaining: 4m 5s\n",
      "200:\ttest: 0.9734663\tbest: 0.9734663 (200)\ttotal: 13.5s\tremaining: 53.9s\n",
      "400:\ttest: 0.9757927\tbest: 0.9758025 (399)\ttotal: 26.9s\tremaining: 40.2s\n",
      "600:\ttest: 0.9765562\tbest: 0.9765562 (600)\ttotal: 39s\tremaining: 25.9s\n",
      "800:\ttest: 0.9767819\tbest: 0.9768533 (752)\ttotal: 50.7s\tremaining: 12.6s\n",
      "999:\ttest: 0.9773778\tbest: 0.9773787 (997)\ttotal: 1m 5s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.9773786533\n",
      "bestIteration = 997\n",
      "\n",
      "Shrink model to first 998 iterations.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x1b008816770>"
      ]
     },
     "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_res[y_train_res == 0]) / len(y_train_res[y_train_res == 1]),\n",
    "    verbose=200\n",
    ")\n",
    "\n",
    "# Melatih model\n",
    "model.fit(X_train_res, y_train_res, eval_set=(X_test, y_test), use_best_model=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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_res, y_train_res, 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 2025-03-20 19:46:34,097] A new study created in memory with name: no-name-3132f69d-f955-4596-aebd-04fd0127d83b\n",
      "[I 2025-03-20 19:47:20,025] Trial 0 finished with value: 0.9771074826169758 and parameters: {'iterations': 884, 'learning_rate': 0.04789064250307746, 'depth': 4, 'subsample': 0.5444942322178251, 'colsample_bylevel': 0.7187433717151654, 'l2_leaf_reg': 8.167915544904256, 'random_strength': 6.060135891254852}. Best is trial 0 with value: 0.9771074826169758.\n",
      "[I 2025-03-20 19:47:56,014] Trial 1 finished with value: 0.9646639579328585 and parameters: {'iterations': 548, 'learning_rate': 0.0011293412756373017, 'depth': 6, 'subsample': 0.6034494360432621, 'colsample_bylevel': 0.5320984475456583, 'l2_leaf_reg': 5.12474681308636, 'random_strength': 9.338142545067281}. Best is trial 0 with value: 0.9771074826169758.\n",
      "[I 2025-03-20 19:48:33,610] Trial 2 finished with value: 0.9733842373100577 and parameters: {'iterations': 540, 'learning_rate': 0.02149319112897647, 'depth': 6, 'subsample': 0.771279112391235, 'colsample_bylevel': 0.7971406034343576, 'l2_leaf_reg': 17.14330875783048, 'random_strength': 6.601968623521204}. Best is trial 0 with value: 0.9771074826169758.\n",
      "[I 2025-03-20 19:49:21,804] Trial 3 finished with value: 0.9670786261278338 and parameters: {'iterations': 733, 'learning_rate': 0.007222838292932871, 'depth': 6, 'subsample': 0.6631386128964532, 'colsample_bylevel': 0.60309273266455, 'l2_leaf_reg': 5.596552563463778, 'random_strength': 5.044497175394075}. Best is trial 0 with value: 0.9771074826169758.\n",
      "[I 2025-03-20 19:49:52,565] Trial 4 finished with value: 0.963834389620584 and parameters: {'iterations': 658, 'learning_rate': 0.0011972434888641932, 'depth': 4, 'subsample': 0.7392187119109812, 'colsample_bylevel': 0.7178871877247741, 'l2_leaf_reg': 12.159917096432405, 'random_strength': 9.628760083373821}. Best is trial 0 with value: 0.9771074826169758.\n",
      "[I 2025-03-20 19:50:31,076] Trial 5 finished with value: 0.9643606748939624 and parameters: {'iterations': 692, 'learning_rate': 0.002535042934651015, 'depth': 5, 'subsample': 0.6303648007369818, 'colsample_bylevel': 0.5096840223052727, 'l2_leaf_reg': 19.947140086696123, 'random_strength': 8.003922590226598}. Best is trial 0 with value: 0.9771074826169758.\n",
      "[I 2025-03-20 19:51:23,831] Trial 6 finished with value: 0.9652732000374643 and parameters: {'iterations': 762, 'learning_rate': 0.001558683364145829, 'depth': 6, 'subsample': 0.6141944757086006, 'colsample_bylevel': 0.6138798328106461, 'l2_leaf_reg': 16.524041805791324, 'random_strength': 8.560269936663886}. Best is trial 0 with value: 0.9771074826169758.\n",
      "[I 2025-03-20 19:52:24,829] Trial 7 finished with value: 0.9767542470775556 and parameters: {'iterations': 851, 'learning_rate': 0.05333122174168492, 'depth': 6, 'subsample': 0.6201729737799783, 'colsample_bylevel': 0.5196786532252674, 'l2_leaf_reg': 7.561662462719584, 'random_strength': 8.501118338394015}. Best is trial 0 with value: 0.9771074826169758.\n",
      "[I 2025-03-20 19:53:33,585] Trial 8 finished with value: 0.9767676272116245 and parameters: {'iterations': 999, 'learning_rate': 0.04129372305865798, 'depth': 6, 'subsample': 0.5930805869812731, 'colsample_bylevel': 0.5020922354924633, 'l2_leaf_reg': 10.104945925764227, 'random_strength': 7.871186164643426}. Best is trial 0 with value: 0.9771074826169758.\n",
      "[I 2025-03-20 19:54:04,696] Trial 9 finished with value: 0.9650956902588164 and parameters: {'iterations': 570, 'learning_rate': 0.0010055758217727892, 'depth': 5, 'subsample': 0.5928677164870811, 'colsample_bylevel': 0.5570025253648548, 'l2_leaf_reg': 13.672354515145718, 'random_strength': 5.818547303793943}. Best is trial 0 with value: 0.9771074826169758.\n",
      "[I 2025-03-20 19:54:44,080] Trial 10 finished with value: 0.9707528109431657 and parameters: {'iterations': 921, 'learning_rate': 0.011928447377089596, 'depth': 4, 'subsample': 0.5205192252276996, 'colsample_bylevel': 0.705455983874488, 'l2_leaf_reg': 8.961700563479646, 'random_strength': 6.793473180814787}. Best is trial 0 with value: 0.9771074826169758.\n",
      "[I 2025-03-20 19:55:28,337] Trial 11 finished with value: 0.9768256077925901 and parameters: {'iterations': 994, 'learning_rate': 0.07517765440141273, 'depth': 4, 'subsample': 0.5183747771311006, 'colsample_bylevel': 0.7101736741538399, 'l2_leaf_reg': 10.739958317341673, 'random_strength': 7.166842680545109}. Best is trial 0 with value: 0.9771074826169758.\n",
      "[I 2025-03-20 19:56:16,219] Trial 12 finished with value: 0.97730818462801 and parameters: {'iterations': 979, 'learning_rate': 0.09789678865086295, 'depth': 4, 'subsample': 0.5093273501815935, 'colsample_bylevel': 0.7091406956756546, 'l2_leaf_reg': 11.480254165984475, 'random_strength': 6.860455722015773}. Best is trial 12 with value: 0.97730818462801.\n",
      "[I 2025-03-20 19:56:57,865] Trial 13 finished with value: 0.9771360269029896 and parameters: {'iterations': 860, 'learning_rate': 0.09221683368000552, 'depth': 4, 'subsample': 0.5458835365109643, 'colsample_bylevel': 0.7621626033215507, 'l2_leaf_reg': 13.464865112092241, 'random_strength': 6.006320379707552}. Best is trial 12 with value: 0.97730818462801.\n",
      "[I 2025-03-20 19:57:47,121] Trial 14 finished with value: 0.9774874784245338 and parameters: {'iterations': 822, 'learning_rate': 0.09966577750874556, 'depth': 5, 'subsample': 0.5504102718870579, 'colsample_bylevel': 0.7871988270336528, 'l2_leaf_reg': 13.494142122621247, 'random_strength': 5.281812292329937}. Best is trial 14 with value: 0.9774874784245338.\n",
      "[I 2025-03-20 19:58:35,853] Trial 15 finished with value: 0.9767863593993212 and parameters: {'iterations': 798, 'learning_rate': 0.02552190081599913, 'depth': 5, 'subsample': 0.6727618770567871, 'colsample_bylevel': 0.6744922862469303, 'l2_leaf_reg': 15.717862102763855, 'random_strength': 5.289373799542309}. Best is trial 14 with value: 0.9774874784245338.\n",
      "[I 2025-03-20 19:59:33,531] Trial 16 finished with value: 0.9772662602079274 and parameters: {'iterations': 940, 'learning_rate': 0.09976080892954645, 'depth': 5, 'subsample': 0.5616116526625916, 'colsample_bylevel': 0.7725005905838425, 'l2_leaf_reg': 11.950912864973626, 'random_strength': 6.407725465104204}. Best is trial 14 with value: 0.9774874784245338.\n",
      "[I 2025-03-20 20:00:22,719] Trial 17 finished with value: 0.9766159856921768 and parameters: {'iterations': 798, 'learning_rate': 0.02248013759447714, 'depth': 5, 'subsample': 0.5028568024165431, 'colsample_bylevel': 0.7526415483680209, 'l2_leaf_reg': 14.654655382550676, 'random_strength': 7.276627975369472}. Best is trial 14 with value: 0.9774874784245338.\n",
      "[I 2025-03-20 20:01:15,344] Trial 18 finished with value: 0.9660876041977939 and parameters: {'iterations': 937, 'learning_rate': 0.004857929807295402, 'depth': 5, 'subsample': 0.7074787718965035, 'colsample_bylevel': 0.6713820233430615, 'l2_leaf_reg': 18.82163009523071, 'random_strength': 5.599384492142793}. Best is trial 14 with value: 0.9774874784245338.\n",
      "[I 2025-03-20 20:01:49,214] Trial 19 finished with value: 0.977257340118548 and parameters: {'iterations': 669, 'learning_rate': 0.03551087503697094, 'depth': 4, 'subsample': 0.569277537512543, 'colsample_bylevel': 0.7949414551736295, 'l2_leaf_reg': 10.713153536535007, 'random_strength': 6.930251830865581}. Best is trial 14 with value: 0.9774874784245338.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Trial:\n",
      "AUC: 0.9774874784245338\n",
      "Params:\n",
      "  iterations: 822\n",
      "  learning_rate: 0.09966577750874556\n",
      "  depth: 5\n",
      "  subsample: 0.5504102718870579\n",
      "  colsample_bylevel: 0.7871988270336528\n",
      "  l2_leaf_reg: 13.494142122621247\n",
      "  random_strength: 5.281812292329937\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:\ttest: 0.9373952\tbest: 0.9373952 (0)\ttotal: 48.2ms\tremaining: 39.6s\n",
      "100:\ttest: 0.9705316\tbest: 0.9705316 (100)\ttotal: 6.09s\tremaining: 43.5s\n",
      "200:\ttest: 0.9772636\tbest: 0.9772698 (199)\ttotal: 12.9s\tremaining: 40s\n",
      "Stopped by overfitting detector  (50 iterations wait)\n",
      "\n",
      "bestTest = 0.9775811394\n",
      "bestIteration = 236\n",
      "\n",
      "Shrink model to first 237 iterations.\n",
      "Learn AUC: 0.9879 | Test AUC: 0.9776\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_res, y_train_res,\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_res)[:, 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_res, 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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('D:/Tugas Akhir/Codingan/Development/App/model/clasification_final_model.sav', 'wb') as f:\n",
    "    pickle.dump(final_model, f)\n",
    "\n",
    "with open('D:/Tugas Akhir/Codingan/Development/App/model/clasification_model.sav', 'wb') as f:\n",
    "    pickle.dump(model, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Training Logloss: 0.12062964889437237\n",
      "Final Validation Logloss: 0.20434872914018204\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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Training Logloss: 0.12681468319092534\n",
      "Final Validation Logloss: 0.20724008601459717\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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\ttotal: 99.6ms\tremaining: 1m 39s\n",
      "200:\ttotal: 17.5s\tremaining: 1m 9s\n",
      "400:\ttotal: 34.2s\tremaining: 51.1s\n",
      "600:\ttotal: 51s\tremaining: 33.8s\n",
      "800:\ttotal: 1m 7s\tremaining: 16.8s\n",
      "999:\ttotal: 1m 22s\tremaining: 0us\n",
      "0:\ttotal: 141ms\tremaining: 2m 21s\n",
      "200:\ttotal: 15.9s\tremaining: 1m 3s\n",
      "400:\ttotal: 33.7s\tremaining: 50.4s\n",
      "600:\ttotal: 49.7s\tremaining: 33s\n",
      "800:\ttotal: 1m 5s\tremaining: 16.3s\n",
      "999:\ttotal: 1m 20s\tremaining: 0us\n",
      "0:\ttotal: 100ms\tremaining: 1m 39s\n",
      "200:\ttotal: 17.1s\tremaining: 1m 7s\n",
      "400:\ttotal: 34.4s\tremaining: 51.4s\n",
      "600:\ttotal: 51s\tremaining: 33.9s\n",
      "800:\ttotal: 1m 6s\tremaining: 16.5s\n",
      "999:\ttotal: 1m 21s\tremaining: 0us\n",
      "0:\ttotal: 143ms\tremaining: 2m 22s\n",
      "200:\ttotal: 16.5s\tremaining: 1m 5s\n",
      "400:\ttotal: 32.8s\tremaining: 49s\n",
      "600:\ttotal: 49s\tremaining: 32.6s\n",
      "800:\ttotal: 1m 5s\tremaining: 16.4s\n",
      "999:\ttotal: 1m 22s\tremaining: 0us\n",
      "0:\ttotal: 91.1ms\tremaining: 1m 31s\n",
      "200:\ttotal: 17.4s\tremaining: 1m 9s\n",
      "400:\ttotal: 32.4s\tremaining: 48.4s\n",
      "600:\ttotal: 48.1s\tremaining: 32s\n",
      "800:\ttotal: 1m 3s\tremaining: 15.7s\n",
      "999:\ttotal: 1m 20s\tremaining: 0us\n",
      "0:\ttotal: 136ms\tremaining: 2m 16s\n",
      "200:\ttotal: 15.4s\tremaining: 1m 1s\n",
      "400:\ttotal: 32.6s\tremaining: 48.7s\n",
      "600:\ttotal: 49.7s\tremaining: 33s\n",
      "800:\ttotal: 1m 6s\tremaining: 16.5s\n",
      "999:\ttotal: 1m 21s\tremaining: 0us\n",
      "0:\ttotal: 135ms\tremaining: 2m 14s\n",
      "200:\ttotal: 17.5s\tremaining: 1m 9s\n",
      "400:\ttotal: 34.6s\tremaining: 51.6s\n",
      "600:\ttotal: 48.6s\tremaining: 32.2s\n",
      "800:\ttotal: 1m 4s\tremaining: 15.9s\n",
      "999:\ttotal: 1m 20s\tremaining: 0us\n",
      "0:\ttotal: 88.5ms\tremaining: 1m 28s\n",
      "200:\ttotal: 17s\tremaining: 1m 7s\n",
      "400:\ttotal: 32.3s\tremaining: 48.3s\n",
      "600:\ttotal: 47.6s\tremaining: 31.6s\n",
      "800:\ttotal: 1m 2s\tremaining: 15.6s\n",
      "999:\ttotal: 1m 19s\tremaining: 0us\n",
      "0:\ttotal: 170ms\tremaining: 2m 49s\n",
      "200:\ttotal: 17.4s\tremaining: 1m 8s\n",
      "400:\ttotal: 33.5s\tremaining: 50.1s\n",
      "600:\ttotal: 48.2s\tremaining: 32s\n",
      "800:\ttotal: 1m 4s\tremaining: 16s\n",
      "999:\ttotal: 1m 20s\tremaining: 0us\n",
      "0:\ttotal: 74.5ms\tremaining: 1m 14s\n",
      "200:\ttotal: 16.5s\tremaining: 1m 5s\n",
      "400:\ttotal: 33.2s\tremaining: 49.6s\n",
      "600:\ttotal: 49.1s\tremaining: 32.6s\n",
      "800:\ttotal: 1m 5s\tremaining: 16.3s\n",
      "999:\ttotal: 1m 21s\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: [0.9357201  0.92839707 0.93409276 0.93002441 0.94141579 0.92676973\n",
      " 0.93409276 0.92107404 0.93403909 0.92508143]\n",
      "Mean Accuracy: 0.93\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": [
      "0:\ttotal: 40.3ms\tremaining: 33.1s\n",
      "100:\ttotal: 5.75s\tremaining: 41.1s\n",
      "200:\ttotal: 13.7s\tremaining: 42.3s\n",
      "300:\ttotal: 19.8s\tremaining: 34.3s\n",
      "400:\ttotal: 25.3s\tremaining: 26.5s\n",
      "500:\ttotal: 31.7s\tremaining: 20.3s\n",
      "600:\ttotal: 39.4s\tremaining: 14.5s\n",
      "700:\ttotal: 46.2s\tremaining: 7.97s\n",
      "800:\ttotal: 52.9s\tremaining: 1.39s\n",
      "821:\ttotal: 54.3s\tremaining: 0us\n",
      "0:\ttotal: 60.3ms\tremaining: 49.5s\n",
      "100:\ttotal: 4.98s\tremaining: 35.6s\n",
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      "500:\ttotal: 31.4s\tremaining: 20.1s\n",
      "600:\ttotal: 38.4s\tremaining: 14.1s\n",
      "700:\ttotal: 44.7s\tremaining: 7.71s\n",
      "800:\ttotal: 51.3s\tremaining: 1.34s\n",
      "821:\ttotal: 52.5s\tremaining: 0us\n",
      "0:\ttotal: 107ms\tremaining: 1m 27s\n",
      "100:\ttotal: 5.72s\tremaining: 40.8s\n",
      "200:\ttotal: 12.2s\tremaining: 37.7s\n",
      "300:\ttotal: 18.3s\tremaining: 31.7s\n",
      "400:\ttotal: 25.3s\tremaining: 26.5s\n",
      "500:\ttotal: 32.7s\tremaining: 20.9s\n",
      "600:\ttotal: 39.9s\tremaining: 14.7s\n",
      "700:\ttotal: 45.9s\tremaining: 7.92s\n",
      "800:\ttotal: 53s\tremaining: 1.39s\n",
      "821:\ttotal: 54.3s\tremaining: 0us\n",
      "0:\ttotal: 39.9ms\tremaining: 32.8s\n",
      "100:\ttotal: 6.15s\tremaining: 43.9s\n",
      "200:\ttotal: 12.8s\tremaining: 39.6s\n",
      "300:\ttotal: 19.6s\tremaining: 33.9s\n",
      "400:\ttotal: 26.1s\tremaining: 27.4s\n",
      "500:\ttotal: 32.3s\tremaining: 20.7s\n",
      "600:\ttotal: 39.2s\tremaining: 14.4s\n",
      "700:\ttotal: 46.8s\tremaining: 8.08s\n",
      "800:\ttotal: 54s\tremaining: 1.42s\n",
      "821:\ttotal: 55.1s\tremaining: 0us\n",
      "0:\ttotal: 113ms\tremaining: 1m 33s\n",
      "100:\ttotal: 6.19s\tremaining: 44.2s\n",
      "200:\ttotal: 13.4s\tremaining: 41.4s\n",
      "300:\ttotal: 20.4s\tremaining: 35.3s\n",
      "400:\ttotal: 27.8s\tremaining: 29.2s\n",
      "500:\ttotal: 35s\tremaining: 22.4s\n",
      "600:\ttotal: 41.7s\tremaining: 15.3s\n",
      "700:\ttotal: 49s\tremaining: 8.46s\n",
      "800:\ttotal: 56s\tremaining: 1.47s\n",
      "821:\ttotal: 57.4s\tremaining: 0us\n",
      "0:\ttotal: 45.9ms\tremaining: 37.7s\n",
      "100:\ttotal: 5.72s\tremaining: 40.8s\n",
      "200:\ttotal: 12.2s\tremaining: 37.6s\n",
      "300:\ttotal: 18.9s\tremaining: 32.8s\n",
      "400:\ttotal: 25.8s\tremaining: 27.1s\n",
      "500:\ttotal: 32.7s\tremaining: 20.9s\n",
      "600:\ttotal: 39.1s\tremaining: 14.4s\n",
      "700:\ttotal: 45.2s\tremaining: 7.8s\n",
      "800:\ttotal: 52.2s\tremaining: 1.37s\n",
      "821:\ttotal: 53.5s\tremaining: 0us\n",
      "0:\ttotal: 66.6ms\tremaining: 54.6s\n",
      "100:\ttotal: 5.63s\tremaining: 40.2s\n",
      "200:\ttotal: 12.2s\tremaining: 37.7s\n",
      "300:\ttotal: 18.3s\tremaining: 31.7s\n",
      "400:\ttotal: 25.2s\tremaining: 26.4s\n",
      "500:\ttotal: 32.4s\tremaining: 20.8s\n",
      "600:\ttotal: 41.1s\tremaining: 15.1s\n",
      "700:\ttotal: 48.1s\tremaining: 8.31s\n",
      "800:\ttotal: 55.3s\tremaining: 1.45s\n",
      "821:\ttotal: 56.7s\tremaining: 0us\n",
      "0:\ttotal: 161ms\tremaining: 2m 11s\n",
      "100:\ttotal: 5.72s\tremaining: 40.9s\n",
      "200:\ttotal: 9.37s\tremaining: 28.9s\n",
      "300:\ttotal: 13.1s\tremaining: 22.7s\n",
      "400:\ttotal: 17.5s\tremaining: 18.3s\n",
      "500:\ttotal: 21.8s\tremaining: 14s\n",
      "600:\ttotal: 26s\tremaining: 9.57s\n",
      "700:\ttotal: 31.4s\tremaining: 5.42s\n",
      "800:\ttotal: 36.3s\tremaining: 952ms\n",
      "821:\ttotal: 37.3s\tremaining: 0us\n",
      "0:\ttotal: 76.2ms\tremaining: 1m 2s\n",
      "100:\ttotal: 4.33s\tremaining: 30.9s\n",
      "200:\ttotal: 8.78s\tremaining: 27.1s\n",
      "300:\ttotal: 13.7s\tremaining: 23.7s\n",
      "400:\ttotal: 18.5s\tremaining: 19.4s\n",
      "500:\ttotal: 23.1s\tremaining: 14.8s\n",
      "600:\ttotal: 27.6s\tremaining: 10.1s\n",
      "700:\ttotal: 32.2s\tremaining: 5.56s\n",
      "800:\ttotal: 40.4s\tremaining: 1.06s\n",
      "821:\ttotal: 42.9s\tremaining: 0us\n",
      "0:\ttotal: 54.5ms\tremaining: 44.7s\n",
      "100:\ttotal: 6.78s\tremaining: 48.4s\n",
      "200:\ttotal: 13s\tremaining: 40.2s\n",
      "300:\ttotal: 19s\tremaining: 32.9s\n",
      "400:\ttotal: 24.5s\tremaining: 25.7s\n",
      "500:\ttotal: 29s\tremaining: 18.6s\n",
      "600:\ttotal: 34s\tremaining: 12.5s\n",
      "700:\ttotal: 39s\tremaining: 6.74s\n",
      "800:\ttotal: 44.8s\tremaining: 1.17s\n",
      "821:\ttotal: 46.1s\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: [0.93490643 0.93083808 0.93165175 0.92351505 0.94304312 0.92839707\n",
      " 0.93816111 0.92921074 0.93322476 0.91775244]\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": 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: 0.95\n",
      "Precision: 0.91\n",
      "Recall: 0.99\n",
      "F1 Score: 0.95\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: 0.73\n",
      "Recall: 0.98\n",
      "F1 Score: 0.84\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_res)\n",
    "y_test_pred = final_model.predict(X_test)\n",
    "\n",
    "# Evaluasi untuk data training\n",
    "evaluate_model(y_train_res, 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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S0rB//3588803MlVMlHMcMSC0b98eZ86cQZcuXfDnn3/Cz88P48aNw82bNzF37lwsWrRI7PvLL79g6tSpOHHiBIYNG4aIiAiMHz8ev/32W4HWVKJECWzZsgXFihXDmDFjEBoaiqCgIPHOeq/WXrZsWfz666/w8/PDkiVL0KhRI0RERLz1N7PmzZtj9+7dKFGiBCZPnow5c+agfv36OHToUK5/qOrDhAkTMHLkSOzZswdDhw7FqVOnsHPnTpQpU0ann5GREUJDQ2FgYIBBgwbhiy++wP79+3O1rydPnqBv376oVauWzu2KGzZsiKFDh2Lu3Lk4evToW7fx1VdfISMjA9u2bcvVvt+kadOmWLFiBeLj4zFs2DCsX78es2bNQseOHQtk+wXB398fixYtwu3btzFq1CgcOHAA27Ztg5WVleTOjeHh4Xj06BF8fX1lqpYo51RCfmbTEBH9v379+uHKlSs4cOCA3KXIJjMzE7a2tujUqRN+/vlnsd3HxwcqlUpyKozofcRTCURUIKZMmYJKlSrh0KFDaNCggdzl6N2LFy+g0Wh0ThusWrUKjx490rkl8sWLF7Fjx4734gmYRDnBEQMiojyIjIzE8OHD0bVrV5QoUQKnTp3CihUrUKVKFURHRxfaA5qIChpHDIiI8qBcuXIoU6YMFi1ahEePHsHGxga9evXCzJkzGQrog8YRAyIiIhLxqgQiIiISMRgQERGRiMGAiIiIREVy8qFpy3lyl0Ckd9c3+MtdApHeOVrqdyKnaa28/3/0/PQPBVjJ+6NIBgMiIqIcUXHg/HUMBkREpFwF+ITSooLBgIiIlIsjBhL8RIiIiEjEEQMiIlIunkqQYDAgIiLl4qkECQYDIiJSLo4YSDAYEBGRcnHEQILBgIiIlIsjBhKMSkRERCTiiAERESkXTyVIMBgQEZFy8VSCBIMBEREpF0cMJBgMiIhIuThiIMFgQEREysURAwl+IkRERCTiiAERESkXRwwkGAyIiEi51Jxj8DoGAyIiUi6OGEgwGBARkXLxqgQJBgMiIlIujhhI8BMhIiIiEUcMiIhIuXgqQYLBgIiIlIunEiQYDIiISLk4YiDBYEBERMrFEQMJBgMiIlIujhhIMCoRERGRiCMGRESkXDyVIMFgQEREysVTCRIMBkREpFwcMZBgMCAiIuViMJBgMCAiIuXiqQQJRiUiIiISccSAiIiUi6cSJBgMiIhIuXgqQYLBgIiIlIsjBhIMBkREpFwcMZBgMCAiIsVSMRhIcAyFiIiIRBwxICIixeKIgRSDARERKRdzgcR7EwxSU1ORkJCAzMxMnfayZcvKVBERERV1HDGQkj0YXL16FX379sXhw4d12gVBgEqlQkZGhkyVERFRUcdgICV7MOjduzcMDQ2xY8cOODo68i+JiIgKDX/mSMkeDGJiYhAdHY3KlSvLXQoREZHiyX65opubGx48eCB3GUREpEAqlSrPr9wICgpC3bp1Ubx4cdjZ2cHHxweXL1/W6fPixQv4+fmhRIkSMDc3R+fOnXHv3j2dPrdv30abNm1QrFgx2NnZYfTo0UhPT9fpExkZidq1a0Oj0cDFxQUhISG5qlX2YDBr1iyMGTMGkZGRePjwIZKTk3VeREREeqPKxysX9u/fDz8/Pxw9ehRhYWFIS0tDixYt8PTpU7HP8OHDsX37dvz+++/Yv38/7t69i06dOonLMzIy0KZNG6SmpuLw4cMIDQ1FSEgIJk+eLPa5ceMG2rRpAy8vL8TExGDYsGHo378/9uzZk/OPRBAEIXeHV7DU6pfZ5PX0lZ/Jh6Yt5xVIbUTvs+sb/OUugUjvHC2N9bp9q55r8rxu4tov87zu/fv3YWdnh/3796NRo0ZISkqCra0t1q1bhy5dugAALl26hCpVquDIkSOoX78+/vrrL7Rt2xZ3796Fvb09AGD58uUYO3Ys7t+/D2NjY4wdOxY7d+7EuXPnxH11794diYmJ2L17d45qk32Owb59++QugYiIFCo/kw+1Wi20Wq1Om0ajgUajeee6SUlJAAAbGxsAQHR0NNLS0tC8eXOxT+XKlVG2bFkxGBw5cgTu7u5iKAAAb29vDB48GOfPn0etWrVw5MgRnW1k9Rk2bFiOj0vWYJCWlobAwEAsX74cFStWlLMUIiJSoPwEg6CgIEydOlWnbcqUKQgICHjrepmZmRg2bBgaNGiAatWqAQDi4+NhbGwMKysrnb729vaIj48X+7waCrKWZy17W5/k5GQ8f/4cpqam7zwuWYOBkZERzpw5I2cJREREeTJ+/HiMGDFCpy0nowV+fn44d+4cDh48qK/S8kX2yYdffvklVqxYIXcZRESkQPm5KkGj0cDCwkLn9a5g4O/vjx07dmDfvn0oXbq02O7g4IDU1FQkJibq9L937x4cHBzEPq9fpZD1/l19LCwscjRaALwHcwzS09Px66+/4u+//4aHhwfMzMx0ls+bx4mERESkJ4V0fyNBEDBkyBBs2bIFkZGR+Oijj3SWe3h4wMjICOHh4ejcuTMA4PLly7h9+zY8PT0BAJ6enpg+fToSEhJgZ2cHAAgLC4OFhQXc3NzEPrt27dLZdlhYmLiNnJA9GJw7dw61a9cGAFy5ckVnGe9IRURE+lRYP2f8/Pywbt06/PnnnyhevLg4J8DS0hKmpqawtLREv379MGLECNjY2MDCwgJDhgyBp6cn6tevDwBo0aIF3Nzc8NVXXyE4OBjx8fH47rvv4OfnJ45UDBo0CD/88APGjBmDvn37IiIiAhs3bsTOnTtzXKvslyvqAy9XJCXg5YqkBPq+XNG2z4Y8r3t/5ec57vumALJy5Ur07t0bwMsbHI0cORLr16+HVquFt7c3li5dKp4mAIBbt25h8ODBiIyMhJmZGXx9fTFz5kwYGv7v9/zIyEgMHz4cFy5cQOnSpTFp0iRxHzmqlcGA6MPEYEBKoO9gYNd3Y57XTfi1WwFW8v6Q/VSCl5fXW4dyIiIiCrEaIiIiZZM9GNSsWVPnfVpaGmJiYnDu3Dn4+vrKUxQRESkDp7JJyB4M5s+fn217QEAAUlJSCrkaIiJSEk5yl5L9PgZv8uWXX+LXX3+VuwwiIirCCuvpih8S2UcM3uTIkSMwMTGRuwwiIirCivIP+LySPRi8+khJ4OVNIOLi4nDy5ElMmjRJpqqIiEgJGAykZA8GlpaWOu/VajVcXV0RGBiIFi1ayFQVERGRMskeDFauXCl3CUREpFQcMJCQPRhkSU1NRUJCAjIzM3Xay5YtK1NFRERU1PFUgpTsweDKlSvo168fDh8+rNMuCAJUKhUyMjJkqoyIiIo6BgMp2YNBnz59YGhoiB07dsDR0ZF/SUREVGj4M0dK9mAQExOD6OhoVK5cWe5SiIiIFE/2YODm5oYHDx7IXQYRESkRBwwkZAkGycnJ4p9nzZqFMWPGYMaMGXB3d4eRkZFOXwsLi8IuT5FGfV4XPg0qolJpGzxPTcexC3cx8dcDuHrnsdinbyt3fO5VGTUr2MHCTAOHzkuQ9FSrsx2XUlaY0b8RPN1KwdhQjXM3H2Bq6GFEnflXp9+Xn7nh204eqFjKGsnPUrH5wBUMX8IHZlHhWhvyC6L2/Y3bt25AozFBVfca+HrIcJR1/kjsMzdoKqKPH8WDB/dhaloM1arXwED/4XAuVx4AEHvlMtatWoGzMaeQlJQIB0cntO/UDV26fynXYVEu8FSClCzBwMrKSucvQxAENGvWTKcPJx8WrobuZbB8ewyir9yDoVqFqX0+xY7pnVFrYAieadMBAMU0hgg7eRNhJ29iWt+G2W5n89SOiL37GK3G/Y7n2nT4d6yNzYE+qNpnBe49fgYA+LZTbQztVAcTfonC8ctxMDMxgrM9AyAVvphTJ+HTtTsqV6mGjIwM/LJsIUYP+RohG7bC1LQYAKBSZTc0924DOwdHPElOQsjPyzB6yNdYv3U3DAwMcOXSBVhb22BiYBDs7B1w7kwM5s4IhFqtRqduPWQ+QnoXBgMpWYLBvn375NgtvUWH7zbrvB84dw/+3TAYtSra49C5/wAAP2w9DQBoWL10ttsoYWGCiqWtMXj+Xpy78fL00KRfD2BQu5pwK1cS9x7fhpW5BlN6NUDngK2IjPnfKEJWf6LCNHvRcp334yZ/Dx/vxrhy8QJq1K4DAGjXsau43NGpFPoN8ke/nl0QH3cXpUqXQev2HXW24VSqDC6c/QcH9oUzGHwAGAykZAkGjRs3lmO3lAsWxTQAgMdPXuR4nYfJL3D530fo0dwNp2PvQZuWgf6tq+Pe46c4ffUeAKBZLWeo1So4lTDH6Z98UdzUGEcv3sW4n/bjzgM+TZPklfVE1+Kv3ZE1y/Pnz/DX9q1wdCoFO3uHt27nTdug9wuDgZRsT1e8evUqvvjiC535BlmSkpLQo0cPXL9+XYbKSKUCZg9qgsPn/8OFWw9ztW6b8ZtQo4It7m8ZgsTtQ/FtJw90+G4zElNezkX4yNESapUKY7rXw+gfI9Fj+g5YFzfBjqAuMDJ8bx/2SQqQmZmJH+bNQrUatVC+QkWdZVs3/YaWjT9Gq8b1cOzIQcz54WfJfKgs587EYF/YHrTz6VIYZRMVONn+JZ49ezbKlCmT7eRCS0tLlClTBrNnz37ndrRaLZKTk3VeQma6PkpWjAV+zVC1XAn0CtqZ63Xn+zXF/cTnaD5qAxoOXYdth2PxR4APHGzMALxM58ZGBhi5bB/+jr6F45fi4DtzF1ycrNC4RpmCPhSiHFsQPB03rsdi8vfBkmXNW7bBL6t/x8LlK1GmbDlMnTASWq1W0u/6tauYOOpb+PYfhLr1PymMsim/VPl4FVGyBYP9+/eja9eub1zerVs3RES8e5Z6UFAQLC0tdV7p18MLslRFmf9NU7SuVx7eY37Hf7kc2m9Sswxaf1wevWbuxJELdxETm4BhSyLwPDUdXzZ3AwDEP3oKALh0+38jEQ+SnuNB8nOUseUERJLHgtnTceTgfixYuiLbUwTm5sVRuqwzatSug6kz5+H2zZs4GKn778zN69cw0q8/2vl0Qa9+XxdW6ZRPKpUqz6+iSrZgcPv2bdjZ2b1xecmSJfHvv/++cXmW8ePHIykpSedlWL7ZO9cjqfnfNEX7T1zQcuzvuHVPeornXYppXg6tZmYKOu2Z/3+FCQAcufByImPF0tbicmtzE5S0MMXthNzvkyg/BEHAgtnTcTAyAvOXroBjqewn1r6+jiAISE1LFdtuXIvF8G/6wrt1B/T/5lt9lkwFjMFASrYbHFlaWuLatWtwdnbOdnlsbGyO7mGg0Wig0Wh02lRq2e/b9MFZ4NcUn3tVRtep25DyPBX21i8v1Up6mooXqS9PzdhbF4O9tRkqOFkBAKqVK4knz1Pxb8ITPE55gWMX7+Jxiha/jGqJGWuP4HlqOvq2qo5y9pbYffzlfJHY/xKx/XAs5gzygv/CMCQ/S0Vgn09x+c4j7P/n3UGQqCAtCJ6Ov/fswvQ5C2FazAwP//9ma+bm5tCYmODuf/9iX9ge1KnnCStrG9xPuId1oSug0WhQ/5OXl+xev3YVI77pj7r1P0HXHr3EbRgYqGFlbSPbsVHOFOGf73mmEgRBeHe3gtetWzekpaVhy5Yt2S7v0KEDjI2N8fvvv+d626Yt5+W3PMV5vntEtu0D5u7GmrALAICJX3riuy8939qndkV7BPRugNoV7WFkoMbF2w8xY+1R7D15U+xfvJgxgr9ugg6fuCBTEHDw7B2MWraPVyXk0vUN/nKX8MFr8rF7tu1jJ09Dq7Y+eHA/AbOnT8GVSxfwJDkZ1jYlUKOWB3r1HyTeBGnlT0sR+ssyyTbsHZ2w4c89eq1fCRwtjfW6/Yqjd+d53auzWxZgJe8P2YLB6dOn4enpibZt22LMmDFwdXUFAFy6dAnBwcHYuXMnDh8+jNq1a+d62wwGpAQMBqQEDAaFT7Yx91q1amHTpk3o27evZNSgRIkS2LhxY55CARERUU7xVIKUrCfj27Zti1u3bmH37t2IjY2FIAioVKkSWrRogWLFislZGhERKUBRnkSYV7LP0jM1NUXHjh3f3ZGIiKiAMRdIyR4MiIiI5KJWMxm8jsGAiIgUiyMGUrw5PREREYk4YkBERIrFyYdSso8YGBgYICEhQdL+8OFDGBgYyFAREREphUqV91dRJfuIwZvur6TVamFsrN8bWxARkbJxxEBKtmCwaNEiAC//Un755ReYm5uLyzIyMhAVFYXKlSvLVR4RESkAg4GUbMFg/vz5AF6OGCxfvlzntIGxsTHKlSuH5cuXy1UeEREpAHOBlGzB4MaNGwAALy8vbN68GdbW1u9Yg4iIiPRN9jkG+/btE/+cNd+AQztERFQY+PNGSvarEgBg1apVcHd3h6mpKUxNTVG9enWsXr1a7rKIiKiI41UJUrKPGMybNw+TJk2Cv78/GjRoAAA4ePAgBg0ahAcPHmD48OEyV0hEREUVRwykZA8GixcvxrJly9CrVy+xrX379qhatSoCAgIYDIiISG+YC6RkDwZxcXH45JNPJO2ffPIJ4uLiZKiIiIiUgiMGUrLPMXBxccHGjRsl7Rs2bEDFihVlqIiIiEi5ZB8xmDp1Kj7//HNERUWJcwwOHTqE8PDwbAMDERFRQeGAgZTswaBz5844duwY5s+fj61btwIAqlSpguPHj6NWrVryFkdEREUaTyVIyR4MAMDDwwNr1qyRuwwiIlIY5gKp9yIYEBERyYEjBlKyBQO1Wv3OvxCVSoX09PRCqoiIiJSGuUBKtmCwZcuWNy47cuQIFi1ahMzMzEKsiIiIiGQLBh06dJC0Xb58GePGjcP27dvRs2dPBAYGylAZEREpBU8lSMl+HwMAuHv3LgYMGAB3d3ekp6cjJiYGoaGhcHZ2lrs0IiIqwvisBClZg0FSUhLGjh0LFxcXnD9/HuHh4di+fTuqVasmZ1lERKQQKpUqz6+iSrZTCcHBwZg1axYcHBywfv36bE8tEBER6VNR/gGfV7IFg3HjxsHU1BQuLi4IDQ1FaGhotv02b95cyJUREZFSMBdIyRYMevXqxaRGRET0npEtGISEhMi1ayIiIgCFdyohKioKs2fPRnR0NOLi4rBlyxb4+PiIy3v37i0ZOff29sbu3bvF948ePcKQIUOwfft2qNVqdO7cGQsXLoS5ubnY58yZM/Dz88OJEydga2uLIUOGYMyYMbmq9b24KoGIiEgOhXVVwtOnT1GjRg0sWbLkjX1atmyJuLg48bV+/Xqd5T179sT58+cRFhaGHTt2ICoqCgMHDhSXJycno0WLFnB2dkZ0dDRmz56NgIAA/PTTT7mqlbdEJiIixSqsEYNWrVqhVatWb+2j0Wjg4OCQ7bKLFy9i9+7dOHHiBOrUqQMAWLx4MVq3bo05c+bAyckJa9euRWpqKn799VcYGxujatWqiImJwbx583QCxLtwxICIiBQrPyMGWq0WycnJOi+tVpvnWiIjI2FnZwdXV1cMHjwYDx8+FJcdOXIEVlZWYigAgObNm0OtVuPYsWNin0aNGsHY2Fjs4+3tjcuXL+Px48c5roPBgIiIFEutUuX5FRQUBEtLS51XUFBQnupo2bIlVq1ahfDwcMyaNQv79+9Hq1atkJGRAQCIj4+HnZ2dzjqGhoawsbFBfHy82Mfe3l6nT9b7rD45wVMJREREeTB+/HiMGDFCp02j0eRpW927dxf/7O7ujurVq6NChQqIjIxEs2bN8lVnbnHEgIiIFCs/pxI0Gg0sLCx0XnkNBq8rX748SpYsidjYWACAg4MDEhISdPqkp6fj0aNH4rwEBwcH3Lt3T6dP1vs3zV3IDoMBEREp1vt6S+Q7d+7g4cOHcHR0BAB4enoiMTER0dHRYp+IiAhkZmaiXr16Yp+oqCikpaWJfcLCwuDq6gpra+sc75vBgIiIFEutyvsrN1JSUhATE4OYmBgAwI0bNxATE4Pbt28jJSUFo0ePxtGjR3Hz5k2Eh4ejQ4cOcHFxgbe3NwCgSpUqaNmyJQYMGIDjx4/j0KFD8Pf3R/fu3eHk5AQA6NGjB4yNjdGvXz+cP38eGzZswMKFCyWnO96FcwyIiEixCutyxZMnT8LLy0t8n/XD2tfXF8uWLcOZM2cQGhqKxMREODk5oUWLFpg2bZrOqYm1a9fC398fzZo1E29wtGjRInG5paUl9u7dCz8/P3h4eKBkyZKYPHlyri5VBACVIAhCPo/3vWPacp7cJRDp3fUN/nKXQKR3jpbG7+6UD21+PJ7ndXd+/XEBVvL+4KkEIiIiEvFUAhERKZYKfJjf6xgMiIhIsXI7iVAJGAyIiEixCmvy4YeEwYCIiBSLuUCKwYCIiBRLzWQgwasSiIiISMQRAyIiUiwOGEgxGBARkWJx8qEUgwERESkWc4EUgwERESkWJx9KMRgQEZFiMRZI5SgYbNu2LccbbN++fZ6LISIiInnlKBj4+PjkaGMqlQoZGRn5qYeIiKjQcPKhVI6CQWZmpr7rICIiKnR8VoIU5xgQEZFiccRAKk/B4OnTp9i/fz9u376N1NRUnWXffvttgRRGRESkb8wFUrkOBqdPn0br1q3x7NkzPH36FDY2Nnjw4AGKFSsGOzs7BgMiIvpgcMRAKtfPShg+fDjatWuHx48fw9TUFEePHsWtW7fg4eGBOXPm6KNGIiIiKiS5DgYxMTEYOXIk1Go1DAwMoNVqUaZMGQQHB2PChAn6qJGIiEgv1Kq8v4qqXAcDIyMjqNUvV7Ozs8Pt27cBAJaWlvj3338LtjoiIiI9UqlUeX4VVbmeY1CrVi2cOHECFStWROPGjTF58mQ8ePAAq1evRrVq1fRRIxERkV4U3R/veZfrEYMZM2bA0dERADB9+nRYW1tj8ODBuH//Pn766acCL5CIiEhf1CpVnl9FVa5HDOrUqSP+2c7ODrt37y7QgoiIiEg+vMEREREpVhH+xT/Pch0MPvroo7dOurh+/Xq+CiIiIiosRXkSYV7lOhgMGzZM531aWhpOnz6N3bt3Y/To0QVVFxERkd4xF0jlOhgMHTo02/YlS5bg5MmT+S6IiIiosBTlSYR5leurEt6kVatW+OOPPwpqc0RERHqnUuX9VVQVWDDYtGkTbGxsCmpzREREJIM83eDo1ckagiAgPj4e9+/fx9KlSwu0OCIiIn3i5EOpXAeDDh066HyQarUatra2aNKkCSpXrlygxeXV4x0j5C6BSO+s6/rLXQKR3j0//YNet19gw+ZFSK6DQUBAgB7KICIiKnwcMZDKdVgyMDBAQkKCpP3hw4cwMDAokKKIiIgKA5+uKJXrEQNBELJt12q1MDY2zndBREREhaUo/4DPqxwHg0WLFgF4Oezyyy+/wNzcXFyWkZGBqKio92aOAREREeVNjoPB/PnzAbwcMVi+fLnOaQNjY2OUK1cOy5cvL/gKiYiI9IRzDKRyHAxu3LgBAPDy8sLmzZthbW2tt6KIiIgKA08lSOV6jsG+ffv0UQcREVGh44CBVK6vSujcuTNmzZolaQ8ODkbXrl0LpCgiIqLCoFap8vwqqnIdDKKiotC6dWtJe6tWrRAVFVUgRRERERUGdT5eRVWujy0lJSXbyxKNjIyQnJxcIEURERGRPHIdDNzd3bFhwwZJ+2+//QY3N7cCKYqIiKgw8OmKUrmefDhp0iR06tQJ165dQ9OmTQEA4eHhWLduHTZt2lTgBRIREelLUZ4rkFe5Dgbt2rXD1q1bMWPGDGzatAmmpqaoUaMGIiIi+NhlIiL6oDAXSOU6GABAmzZt0KZNGwBAcnIy1q9fj1GjRiE6OhoZGRkFWiAREZG+8D4GUnmeWBkVFQVfX184OTlh7ty5aNq0KY4ePVqQtREREekVL1eUytWIQXx8PEJCQrBixQokJyejW7du0Gq12Lp1KyceEhERFQE5HjFo164dXF1dcebMGSxYsAB3797F4sWL9VkbERGRXvGqBKkcjxj89ddf+PbbbzF48GBUrFhRnzUREREVCs4xkMrxiMHBgwfx5MkTeHh4oF69evjhhx/w4MEDfdZGRESkV6p8/FdU5TgY1K9fHz///DPi4uLw9ddf47fffoOTkxMyMzMRFhaGJ0+e6LNOIiKiAqdW5f1VVOX6qgQzMzP07dsXBw8exNmzZzFy5EjMnDkTdnZ2aN++vT5qJCIi0gsGA6l8PQfC1dUVwcHBuHPnDtavX19QNREREZFMCuQBUQYGBvDx8cG2bdsKYnNERESFQqVS5fmVG1FRUWjXrh2cnJygUqmwdetWneWCIGDy5MlwdHSEqakpmjdvjqtXr+r0efToEXr27AkLCwtYWVmhX79+SElJ0elz5swZNGzYECYmJihTpgyCg4Nz/ZkU5SdHEhERvVVhnUp4+vQpatSogSVLlmS7PDg4GIsWLcLy5ctx7NgxmJmZwdvbGy9evBD79OzZE+fPn0dYWBh27NiBqKgoDBw4UFyenJyMFi1awNnZGdHR0Zg9ezYCAgLw008/5apWlSAIQu4O7/33Il3uCoj0z7quv9wlEOnd89M/6HX786Ku53ndEY3K52k9lUqFLVu2wMfHB8DL0QInJyeMHDkSo0aNAgAkJSXB3t4eISEh6N69Oy5evAg3NzecOHECderUAQDs3r0brVu3xp07d+Dk5IRly5Zh4sSJiI+Ph7GxMQBg3Lhx2Lp1Ky5dupTj+jhiQEREipWfWyJrtVokJyfrvLRaba5ruHHjBuLj49G8eXOxzdLSEvXq1cORI0cAAEeOHIGVlZUYCgCgefPmUKvVOHbsmNinUaNGYigAAG9vb1y+fBmPHz/O+WeS6yMgIiIqIvJzKiEoKAiWlpY6r6CgoFzXEB8fDwCwt7fXabe3txeXxcfHw87OTme5oaEhbGxsdPpkt41X95ETeXq6IhERkdKNHz8eI0aM0GnTaDQyVVNwGAyIiEix8vPMA41GUyBBwMHBAQBw7949ODo6iu337t1DzZo1xT4JCQk666Wnp+PRo0fi+g4ODrh3755On6z3WX1ygqcSiIhIsdRQ5flVUD766CM4ODggPDxcbEtOTsaxY8fg6ekJAPD09ERiYiKio6PFPhEREcjMzES9evXEPlFRUUhLSxP7hIWFwdXVFdbW1jmuh8GAiIgUq7CerpiSkoKYmBjExMQAeDnhMCYmBrdv34ZKpcKwYcPw/fffY9u2bTh79ix69eoFJycn8cqFKlWqoGXLlhgwYACOHz+OQ4cOwd/fH927d4eTkxMAoEePHjA2Nka/fv1w/vx5bNiwAQsXLpSc7ngXnkogIiLFKqxbG588eRJeXl7i+6wf1r6+vggJCcGYMWPw9OlTDBw4EImJifj000+xe/dumJiYiOusXbsW/v7+aNasGdRqNTp37oxFixaJyy0tLbF37174+fnBw8MDJUuWxOTJk3XudZATvI8B0QeK9zEgJdD3fQx+Onorz+sOrO9cgJW8P3gqgYiIiEQ8lUBERIqVn6sSiioGAyIiUiw1k4EEgwERESkWc4EUgwERESkWJ9pJMRgQEZFiqThkIMGwRERERCKOGBARkWJxvECKwYCIiBSLVyVIMRgQEZFiMRZIMRgQEZFiccBAisGAiIgUi1clSPGqBCIiIhJxxICIiBSLvx1LMRgQEZFi8VSCFIMBEREpFmOBFIMBEREpFkcMpBgMiIhIsTjHQIqfCREREYk4YkBERIrFUwlSDAZERKRYjAVSDAZERKRYHDCQYjAgIiLFUnPMQILBgIiIFIsjBlK8KoGIiIhE78WIQWJiIo4fP46EhARkZmbqLOvVq5dMVRERUVGn4qkECdmDwfbt29GzZ0+kpKTAwsJC59IRlUrFYEBERHrDUwlSsp9KGDlyJPr27YuUlBQkJibi8ePH4uvRo0dyl0dEREWYGqo8v4oq2UcM/vvvP3z77bcoVqyY3KUQEZHCcMRASvYRA29vb5w8eVLuMoiISIFUqry/iirZRwzatGmD0aNH48KFC3B3d4eRkZHO8vbt28tUGRERkfKoBEEQ5CxArX7zoIVKpUJGRkaut/kiPT8VEX0YrOv6y10Ckd49P/2DXrcfdvFBntf9rErJAqzk/SH7iMHrlycSEREVFnURPiWQV7LOMUhLS4OhoSHOnTsnZxlERKRQqnz8V1TJOmJgZGSEsmXL5ul0ARERUX4V5UmEeSX7VQkTJ07EhAkTeM8CIiKi94Dscwx++OEHxMbGwsnJCc7OzjAzM9NZfurUKZkqIyKioq4onxLIK9mDgY+Pj9wlUA6t+PlHhIftxY0b16ExMUHNmrUwbMQolPuovKSvIAjwGzQAhw4ewPxFS9C0WXMZKibSNapvC/g0rYFK5ezxXJuGY/9cx8SFf+LqrQSxz+KJ3dG0niscbS2R8lyLo//cwHcL/8SVm/fEPk0+roQp37RFVRcnPH2eirXbj2HKku3IyJBOpi5fpiSOrh+HjMxMODYaUyjHSTnHyYdSsgeDKVOmyF0C5dDJE8fx+Rc9UdXdHRnpGVi8cB4GDeiHzdt2Su5cuWZVqM5zL4jeBw1ru2D5hihEn78FQ0MDTPVvhx3L/FGr0/d49iIVAHD64r/47a8T+DfuMWwsi2HioDbYsdQPldtOQWamAPdKpbB18WDMWrEH/SatgpOdFRZP6A4DAzXGz9+isz9DQzVWBfXBodPXUL/GR3IcMr0DRwykZJ9jQB+OZT+tQIeOneDiUhGulSsjcPpMxMXdxcUL53X6Xbp4EatCf8XUaTNkqpQoex38l2LN9mO4eD0eZ6/8h4FT1qCsow1quZUR+/y6+RAOnbqG23GPEHPpDqYu2Y4yjjZwdioBAOjSojbOXb2LoJ924/q/D3AwOhYTF27F190awryYRmd/Ad+0w+Ub9/DHXp4SfV/xzodSsgcDtVoNAwODN77o/ZXy5AkAwMLSUmx7/vw5xo8ZiQnfTUZJW1u5SiPKEQtzEwDA46Rn2S4vZmKMXu3r48adB7gT/xgAoDE2xAttmk6/59o0mJoYo1aVsmJb47qV0OmzWhg2c6OeqqeCoMrHq6iS/VTCli26Q29paWk4ffo0QkNDMXXqVJmqonfJzMxE8KwZqFmrNipWrCS2z54VhBq1asGrKecU0PtNpVJh9qguOHz6Gi5ci9NZNrBrQ0wf5gPzYhpcvhGPNoN/QFr6y8uqww5fhH8PL3Rr6YFNe0/BoYQFJgxsBQBwtLUAANhYmuHnqV+iz3ehePL0ReEeGFE+yR4MOnToIGnr0qULqlatig0bNqBfv35vXV+r1UKr1eq0CQYaaDSaN6xBBWHG91Nx7epVhKxeJ7ZFRoTjxLGj2LBpy1vWJHo/LBjfDVVdHNGsz3zJst/+OoHwY5fgUNICw3o1x5pZfdG0zzxoU9MRfvQSJizYikUTumPFtF7QpqVj5s+78WltF2RmvrzD/NJJX2DD7pM4dOpaYR8W5ZK6KJ8TyCPZn5XwJtevX0f16tWRkpLy1n4BAQGSkYWJk6bgu8kBeqxO2WZ8H4jIfeH4NXQNSpf+37nZ4KDpWLd2tc7zLzIyMqBWq1Hbow5WhKyWo9wii89KyLv5Y7uibZPqaN5vAW7dffjWvkaGBoiLCsY3geuwcXe0zjJHW0s8Tn4GZycbxGyehE97BiP6wm3ERQXD3PR/v5yoVCoYGKiRnp4Bv+/XY9WfR/VyXEWRvp+VcDQ2Mc/r1nexKrA63ieyjxhk5/nz51i0aBFKlSr1zr7jx4/HiBEjdNoEA44W6IMgCAiaPg0R4WFYEbJaJxQAQN/+A9GxS1edti4+7TBq7Hg0buJVmKUSvdH8sV3RvmkNtBiw8J2hAHj5Q10FFYyNpP9cxt1PAgB0a1kH/8Y9wulL/wIAmvjOhcErAbltk+oY2bs5vHrPw92ExII5ECoYHDCQkD0YWFtb61zWJggCnjx5gmLFimHNmjXvXF+jkZ424NMV9WPGtKn4a9cOLFi8FGbFzPDg/n0AgHnx4jAxMUFJW9tsJxw6OjpJQgSRHBaM74bPW9VB1+E/IeXpC9iXKA4ASEp5gRfaNJQrVQJdvD0QfuQiHjxOQSl7K4zs0wLPtWnYc/B/V98M79UMew9fRGZmJjo0q4lRfT7Dl2N+FU8lXL5xT2e/td3KIlMQJHMZSH68XFFK9mCwYMECnfdqtRq2traoV68erK2t5SmKsrVxw3oAQL/eX+m0B34fhA4dO8lRElGufN2tEQAg7JdhOu0DJq/Gmu3HoE1NR4NaFeDfowmsLYoh4eETHDwVC6/ec3H/8f9Oa7Zo4IYx/b2hMTLE2Sv/oevwn7D30IXCPBQqIJxiIPXezjHID44YkBJwjgEpgb7nGBy/npTndT8ub/nuTh8g2UcMACAxMRHHjx9HQkICMjN1bynaq1cvmaoiIqKijgMGUrIHg+3bt6Nnz55ISUmBhYWFznwDlUrFYEBERPrDZCAh+50PR44cib59+yIlJQWJiYl4/Pix+OKjmImISJ9U+fivqJJ9xOC///7Dt99+K3kIDxERkb5x8qGU7CMG3t7eOHnypNxlEBGRAvFZCVKyjBhs27ZN/HObNm0wevRoXLhwAe7u7jAyMtLp2759+8Iuj4iISLFkuVzx1Vvmvo1KpUJGRkaut8/LFUkJeLkiKYG+L1c8dSs5z+vWdrbIcd/sbt/v6uqKS5cuAQBevHiBkSNH4rfffoNWq4W3tzeWLl0Ke3t7sf/t27cxePBg7Nu3D+bm5vD19UVQUBAMDQv2d3xZRgxevySRiIhIDoU5ibBq1ar4+++/xfev/kAfPnw4du7cid9//x2Wlpbw9/dHp06dcOjQIQAvnzvTpk0bODg44PDhw4iLi0OvXr1gZGSEGTNmFGidss0xiIiIgJubG5KTpWktKSkJVatWxYEDB2SojIiIlEKlyvsrtwwNDeHg4CC+SpYsCeDlz7wVK1Zg3rx5aNq0KTw8PLBy5UocPnwYR4++fODW3r17ceHCBaxZswY1a9ZEq1atMG3aNCxZsgSpqakF+ZHIFwwWLFiAAQMGwMJCOhRjaWmJr7/+GvPmzZOhMiIiUor8TD7UarVITk7WeWm12jfu6+rVq3ByckL58uXRs2dP3L59GwAQHR2NtLQ0NG/eXOxbuXJllC1bFkeOHAEAHDlyBO7u7jqnFry9vZGcnIzz58+jIMkWDP755x+0bNnyjctbtGiB6OjoNy4nIiLKt3wkg6CgIFhaWuq8goKCst1NvXr1EBISgt27d2PZsmW4ceMGGjZsiCdPniA+Ph7GxsawsrLSWcfe3h7x8fEAgPj4eJ1QkLU8a1lBku0+Bvfu3ZNcgfAqQ0ND3P//p/cRERG9b8aPH48RI0botL3+tN8srVq1Ev9cvXp11KtXD87Ozti4cSNMTU31WmduyTZiUKpUKZw7d+6Ny8+cOQNHR8dCrIiIiJQmP3c+1Gg0sLCw0Hm9KRi8zsrKCpUqVUJsbCwcHByQmpqKxMREnT737t2Dg4MDAMDBwQH37t2TLM9aVpBkCwatW7fGpEmT8OLFC8my58+fY8qUKWjbtq0MlRERkVIU5uTDV6WkpODatWtwdHSEh4cHjIyMEB4eLi6/fPkybt++DU9PTwCAp6cnzp49i4SEBLFPWFgYLCws4Obmlr9iXiPbY5fv3buH2rVrw8DAAP7+/nB1dQUAXLp0CUuWLEFGRgZOnTolOaeSE7yPASkB72NASqDv+xicu5OS53WrlTbPcd9Ro0ahXbt2cHZ2xt27dzFlyhTExMTgwoULsLW1xeDBg7Fr1y6EhITAwsICQ4YMAQAcPnwYwMvLFWvWrAknJycEBwcjPj4eX331Ffr371/glyvKNsfA3t4ehw8fxuDBgzF+/Hhk5ROVSgVvb28sWbIkT6GAiIgoxwrpNgZ37tzBF198gYcPH8LW1haffvopjh49CltbWwDA/PnzoVar0blzZ50bHGUxMDDAjh07MHjwYHh6esLMzAy+vr4IDAws8FplGzF41ePHjxEbGwtBEFCxYkVYW1vna3scMSAl4IgBKYG+RwzO//c0z+tWLWVWgJW8P2R/uiIAWFtbo27dunKXQUREpHjvRTAgIiKSAx+7LMVgQEREisVcIMVgQEREysVkIMFgQEREilWYT1f8UDAYEBGRYnGOgZRsdz4kIiKi9w9HDIiISLE4YCDFYEBERMrFZCDBYEBERIrFyYdSDAZERKRYnHwoxWBARESKxVwgxasSiIiISMQRAyIiUi4OGUgwGBARkWJx8qEUgwERESkWJx9KMRgQEZFiMRdIMRgQEZFyMRlI8KoEIiIiEnHEgIiIFIuTD6UYDIiISLE4+VCKwYCIiBSLuUCKwYCIiBSLIwZSDAZERKRgTAav41UJREREJOKIARERKRZPJUgxGBARkWIxF0gxGBARkWJxxECKwYCIiBSLNziSYjAgIiLlYi6Q4FUJREREJOKIARERKRYHDKQYDIiISLE4+VCKwYCIiBSLkw+lGAyIiEi5mAskGAyIiEixmAukeFUCERERiThiQEREisXJh1IMBkREpFicfCjFYEBERIrFEQMpzjEgIiIiEUcMiIhIsThiIMURAyIiIhJxxICIiBSLkw+lGAyIiEixeCpBisGAiIgUi7lAisGAiIiUi8lAgpMPiYiISMQRAyIiUixOPpRiMCAiIsXi5EMpBgMiIlIs5gIpBgMiIlIuJgMJBgMiIlIszjGQ4lUJREREJOKIARERKRYnH0qpBEEQ5C6CPmxarRZBQUEYP348NBqN3OUQ6QW/56QUDAaUb8nJybC0tERSUhIsLCzkLodIL/g9J6XgHAMiIiISMRgQERGRiMGAiIiIRAwGlG8ajQZTpkzhhCwq0vg9J6Xg5EMiIiISccSAiIiIRAwGREREJGIwICIiIhGDAelFZGQkVCoVEhMT5S6F6J1UKhW2bt0qdxlE7wUGgw9c7969oVKpMHPmTJ32rVu3QpXLm4CXK1cOCxYsyFHf06dPo2vXrrC3t4eJiQkqVqyIAQMG4MqVK7naJ1FhiI+Px5AhQ1C+fHloNBqUKVMG7dq1Q3h4uNylEb13GAyKABMTE8yaNQuPHz8ulP3t2LED9evXh1arxdq1a3Hx4kWsWbMGlpaWmDRpkl73nZqaqtftU9Fz8+ZNeHh4ICIiArNnz8bZs2exe/dueHl5wc/PT2/75XeVPlgCfdB8fX2Ftm3bCpUrVxZGjx4ttm/ZskV4/a9306ZNgpubm2BsbCw4OzsLc+bMEZc1btxYAKDzys7Tp0+FkiVLCj4+Ptkuf/z4sSAIgrBv3z4BgPD3338LHh4egqmpqeDp6SlcunRJp/YOHTrorD906FChcePGOnX5+fkJQ4cOFUqUKCE0adIkR9smytKqVSuhVKlSQkpKimRZ1vcVgPDzzz8LPj4+gqmpqeDi4iL8+eefYr+VK1cKlpaWOuu+/v/YlClThBo1agg///yzUK5cOUGlUuVo20TvG44YFAEGBgaYMWMGFi9ejDt37mTbJzo6Gt26dUP37t1x9uxZBAQEYNKkSQgJCQEAbN68GaVLl0ZgYCDi4uIQFxeX7Xb27NmDBw8eYMyYMdkut7Ky0nk/ceJEzJ07FydPnoShoSH69u2b6+MLDQ2FsbExDh06hOXLlxfotqloe/ToEXbv3g0/Pz+YmZlJlr/6fZ06dSq6deuGM2fOoHXr1ujZsycePXqUq/3Fxsbijz/+wObNmxETE1Og2yYqLAwGRUTHjh1Rs2ZNTJkyJdvl8+bNQ7NmzTBp0iRUqlQJvXv3hr+/P2bPng0AsLGxgYGBAYoXLw4HBwc4ODhku52rV68CACpXrpyjuqZPn47GjRvDzc0N48aNw+HDh/HixYtcHVvFihURHBwMV1dXuLq6Fui2qWiLjY2FIAg5+r727t0bX3zxBVxcXDBjxgykpKTg+PHjudpfamoqVq1ahVq1aqF69eoFum2iwsJgUITMmjULoaGhuHjxomTZxYsX0aBBA522Bg0a4OrVq8jIyMjxPoRc3ijz1X8cHR0dAQAJCQm52oaHh4fetk1FW26+r69+n8zMzGBhYZHr75OzszNsbW31sm2iwsJgUIQ0atQI3t7eGD9+vN72UalSJQDApUuXctTfyMhI/HPWVRKZmZkAALVaLfmHOy0tTbKN7IaA37VtIuDlaJNKpcrR9/XV7xPw8julj+/q69smet8wGBQxM2fOxPbt23HkyBGd9ipVquDQoUM6bYcOHUKlSpVgYGAAADA2Nn7n6EGLFi1QsmRJBAcHZ7s8N/ctsLW1lcxlePW8LFF+2djYwNvbG0uWLMHTp08ly3P6fbW1tcWTJ090tsHvKhVVDAZFjLu7O3r27IlFixbptI8cORLh4eGYNm0arly5gtDQUPzwww8YNWqU2KdcuXKIiorCf//9hwcPHmS7fTMzM/zyyy/YuXMn2rdvj7///hs3b97EyZMnMWbMGAwaNCjHtTZt2hQnT57EqlWrcPXqVUyZMgXnzp3L24ETvcGSJUuQkZGBjz/+GH/88QeuXr2KixcvYtGiRfD09MzRNurVq4dixYphwoQJuHbtGtatWydO3CUqahgMiqDAwEDJMGXt2rWxceNG/Pbbb6hWrRomT56MwMBA9O7dW2e9mzdvokKFCtmeJ83SoUMHHD58GEZGRujRowcqV66ML774AklJSfj+++9zXKe3tzcmTZqEMWPGoG7dunjy5Al69eqV6+Mlepvy5cvj1KlT8PLywsiRI1GtWjV89tlnCA8Px7Jly3K0DRsbG6xZswa7du2Cu7s71q9fj4CAAP0WTiQTPnaZiIiIRBwxICIiIhGDAREREYkYDIiIiEjEYEBEREQiBgMiIiISMRgQERGRiMGAiIiIRAwGREREJGIwIPoA9O7dGz4+PuL7Jk2aYNiwYYVeR2RkJFQqVa6eiUFEHxYGA6J86N27N1QqFVQqFYyNjeHi4oLAwECkp6frdb+bN2/GtGnTctSXP8yJKDcM5S6A6EPXsmVLrFy5ElqtFrt27YKfnx+MjIwkj79OTU2FsbFxgezTxsamQLZDRPQ6jhgQ5ZNGo4GDgwOcnZ0xePBgNG/eHNu2bROH/6dPnw4nJye4uroCAP79919069YNVlZWsLGxQYcOHXDz5k1xexkZGRgxYgSsrKxQokQJjBkzBq8/0uT1UwlarRZjx45FmTJloNFo4OLighUrVuDmzZvw8vICAFhbW0OlUokPzsrMzERQUBA++ugjmJqaokaNGti0aZPOfnbt2oVKlSrB1NQUXl5eOnUSUdHEYEBUwExNTZGamgoACA8Px+XLlxEWFoYdO3YgLS0N3t7eKF68OA4cOIBDhw7B3NwcLVu2FNeZO3cuQkJC8Ouvv+LgwYN49OgRtmzZ8tZ99urVC+vXr8eiRYtw8eJF/PjjjzA3N0eZMmXwxx9/AAAuX76MuLg4LFy4EAAQFBSEVatWYfny5Th//jyGDx+OL7/8Evv37wfwMsB06tQJ7dq1Q0xMDPr3749x48bp62MjoveFQER55uvrK3To0EEQBEHIzMwUwsLCBI1GI4waNUrw9fUV7O3tBa1WK/ZfvXq14OrqKmRmZoptWq1WMDU1Ffbs2SMIgiA4OjoKwcHB4vK0tDShdOnS4n4EQRAaN24sDB06VBAEQbh8+bIAQAgLC8u2xn379gkAhMePH4ttL168EIoVKyYcPnxYp2+/fv2EL774QhAEQRg/frzg5uams3zs2LGSbRFR0cI5BkT5tGPHDpibmyMtLQ2ZmZno0aMHAgIC4OfnB3d3d515Bf/88w9iY2NRvHhxnW28ePEC165dQ1JSEuLi4lCvXj1xmaGhIerUqSM5nZAlJiYGBgYGaNy4cY5rjo2NxbNnz/DZZ5/ptKempqJWrVoAgIsXL+rUAQCenp453gcRfZgYDIjyycvLC8uWLYOxsTGcnJxgaPi//63MzMx0+qakpMDDwwNr166VbMfW1jZP+zc1Nc31OikpKQCAnTt3olSpUjrLNBpNnuogoqKBwYAon8zMzODi4pKjvrVr18aGDRtgZ2cHCwuLbPs4Ojri2LFjaNSoEQAgPT0d0dHRqF27drb93d3dkZmZif3796N58+aS5VkjFhkZGWKbm5sbNBoNbt++/caRhipVqmDbtm06bUePHn33QRLRB42TD4kKUc+ePVGyZEl06NABBw4cwI0bNxAZGYlvv/0Wd+7cAQAMHToUM2fOxNatW3Hp0iV88803b70HQbly5eDr64u+ffti69at4jY3btwIAHB2doZKpcKOHTtw//59pKSkoHjx4hg1ahSGDx+O0NBQXLt2DadOncLixYsRGhoKABg0aBCuXr2K0aNH4/Lly1i3bh1CQkL0/RERkcwYDIgKUbFixRAVFYWyZcuiU6dOqFKlCvr164cXL16IIwgjR47EV199BV9fX3h6eqJ48eLo2LHjW7e7bNkydOnSBd988w0qV66MAQMG4OnTpwCAUqVKYerUqRg3bhzs7e3h7+8PAJg2bRomTZqEoKAgVKlSBS1btsTOnTvx0UcfAQDKli2LP/74A1u3bkWNGjWwfPlyzJgxQ4+fDhG9D1TCm2Y0ERERkeJwxICIiIhEDAZEREQkYjAgIiIiEYMBERERiRgMiIiISMRgQERERCIGAyIiIhIxGBAREZGIwYCIiIhEDAZEREQkYjAgIiIi0f8BQ/RpBDcJhFsAAAAASUVORK5CYII=",
      "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: 0.91\n",
      "Recall: 0.99\n",
      "F1 Score: 0.95\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: 0.90\n",
      "Precision: 0.72\n",
      "Recall: 0.98\n",
      "F1 Score: 0.83\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = model.predict(X_train_res)\n",
    "y_test_pred = model.predict(X_test)\n",
    "\n",
    "# Evaluasi untuk data training\n",
    "evaluate_model(y_train_res, 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": [
    {
     "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: 0.91\n",
      "Recall: 0.99\n",
      "F1 Score: 0.95\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: 0.90\n",
      "Precision: 0.72\n",
      "Recall: 0.98\n",
      "F1 Score: 0.83\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = model.predict(X_train_res)\n",
    "y_test_pred = model.predict(X_test)\n",
    "\n",
    "# Evaluasi untuk data training\n",
    "evaluate_model(y_train_res, 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": [
    {
     "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": 20,
     "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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9791044776119403\n",
      "Precision: 1.0\n",
      "Recall: 0.9791044776119403\n",
      "F1 Score: 0.9894419306184012\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": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9850746268656716\n",
      "Precision: 1.0\n",
      "Recall: 0.9850746268656716\n",
      "F1 Score: 0.9924812030075187\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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