python-sql-2110511008/notebook/07_model_aug23.ipynb

{
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   "outputs": [
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     "data": {
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       "</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": [
    "X = df.drop(columns=['active_work_months','churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
    "y = df['active_work_months']\n",
    "\n",
    "df['join_date'] = pd.to_datetime(df['join_date'])\n",
    "\n",
    "train_data = df[df['join_date'] < '2023-08-01']\n",
    "valid_data = df[df['join_date'] >= '2023-08-01']\n",
    "\n",
    "X_train = train_data.drop(columns=['active_work_months', 'churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
    "y_train = train_data['active_work_months']\n",
    "\n",
    "X_valid = valid_data.drop(columns=['active_work_months', 'churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
    "y_valid = valid_data['active_work_months']\n",
    "\n",
    "cat_feature = ['departemen', 'position', 'domisili', 'marriage_stat', 'job_satisfaction', 'performance_rating',\n",
    "               'education', 'active_work_category', 'jenis_kelamin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Join Date - Min: 2020-01-02 00:00:00\n",
      "Join Date - Max: 2023-07-31 00:00:00\n",
      "Ukuran Data Train: (10078, 33)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jesselyn Mu\\AppData\\Local\\Temp\\ipykernel_11036\\1104601009.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_data['join_date'] = pd.to_datetime(train_data['join_date'])\n"
     ]
    }
   ],
   "source": [
    "train_data['join_date'] = pd.to_datetime(train_data['join_date'])\n",
    "\n",
    "min_join_date = train_data['join_date'].min()\n",
    "max_join_date = train_data['join_date'].max()\n",
    "\n",
    "print(\"Join Date - Min:\", min_join_date)\n",
    "print(\"Join Date - Max:\", max_join_date)\n",
    "print(\"Ukuran Data Train:\", train_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Join Date - Min: 2023-08-01 00:00:00\n",
      "Join Date - Max: 2024-08-02 00:00:00\n",
      "Ukuran Data Valid: (2210, 33)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jesselyn Mu\\AppData\\Local\\Temp\\ipykernel_11036\\2142914893.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  valid_data['join_date'] = pd.to_datetime(valid_data['join_date'])\n"
     ]
    }
   ],
   "source": [
    "valid_data['join_date'] = pd.to_datetime(valid_data['join_date'])\n",
    "\n",
    "min_join_date = valid_data['join_date'].min()\n",
    "max_join_date = valid_data['join_date'].max()\n",
    "\n",
    "print(\"Join Date - Min:\", min_join_date)\n",
    "print(\"Join Date - Max:\", max_join_date)\n",
    "print(\"Ukuran Data Valid:\", valid_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 14.5009742\ttest: 20.7509764\tbest: 20.7509764 (0)\ttotal: 299ms\tremaining: 4m 58s\n",
      "200:\tlearn: 2.3680725\ttest: 3.7582712\tbest: 3.7582712 (200)\ttotal: 23.4s\tremaining: 1m 33s\n",
      "400:\tlearn: 0.6074381\ttest: 1.2438678\tbest: 1.2438678 (400)\ttotal: 39s\tremaining: 58.2s\n",
      "600:\tlearn: 0.3585168\ttest: 0.6968303\tbest: 0.6968303 (600)\ttotal: 50.6s\tremaining: 33.6s\n",
      "800:\tlearn: 0.3105016\ttest: 0.5057286\tbest: 0.5057286 (800)\ttotal: 1m 7s\tremaining: 16.7s\n",
      "999:\tlearn: 0.2918692\ttest: 0.4375722\tbest: 0.4375722 (999)\ttotal: 1m 26s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.4375721622\n",
      "bestIteration = 999\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x2204d48bd60>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from catboost import CatBoostRegressor\n",
    "\n",
    "model = CatBoostRegressor(\n",
    "    iterations=1000,\n",
    "    learning_rate=0.01,\n",
    "    depth=6,\n",
    "    cat_features=cat_feature,\n",
    "    loss_function='RMSE',  # Fungsi kerugian regresi, seperti RMSE atau MAE\n",
    "    eval_metric='RMSE',    # Metrik evaluasi regresi\n",
    "    verbose=200\n",
    ")\n",
    "\n",
    "model.fit(X_train, y_train, eval_set=(X_valid, y_valid), use_best_model=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        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>MSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2 Score</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Train</th>\n",
       "      <td>0.083453</td>\n",
       "      <td>0.234563</td>\n",
       "      <td>0.288883</td>\n",
       "      <td>0.999610</td>\n",
       "      <td>0.011642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Validation</th>\n",
       "      <td>0.191469</td>\n",
       "      <td>0.338036</td>\n",
       "      <td>0.437572</td>\n",
       "      <td>0.987453</td>\n",
       "      <td>0.049027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 MSE       MAE      RMSE  R2 Score      MAPE\n",
       "Train       0.083453  0.234563  0.288883  0.999610  0.011642\n",
       "Validation  0.191469  0.338036  0.437572  0.987453  0.049027"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Prediksi pada data training dan validasi\n",
    "y_pred_train = model.predict(X_train)\n",
    "y_pred_valid = model.predict(X_valid)\n",
    "\n",
    "# Menghitung metrik regresi untuk training\n",
    "mse_train = mean_squared_error(y_train, y_pred_train)\n",
    "mae_train = mean_absolute_error(y_train, y_pred_train)\n",
    "rmse_train = np.sqrt(mse_train)\n",
    "r2_train = r2_score(y_train, y_pred_train)\n",
    "mape_train = mean_absolute_percentage_error(y_train, y_pred_train)\n",
    "\n",
    "# Menghitung metrik regresi untuk validasi\n",
    "mse_valid = mean_squared_error(y_valid, y_pred_valid)\n",
    "mae_valid = mean_absolute_error(y_valid, y_pred_valid)\n",
    "rmse_valid = np.sqrt(mse_valid)\n",
    "r2_valid = r2_score(y_valid, y_pred_valid)\n",
    "mape_valid = mean_absolute_percentage_error(y_valid, y_pred_valid)\n",
    "\n",
    "# Membuat dataframe hasil metrik untuk training dan validation\n",
    "metrics = {\n",
    "    \"MSE\": [mse_train, mse_valid],\n",
    "    \"MAE\": [mae_train, mae_valid],\n",
    "    \"RMSE\": [rmse_train, rmse_valid],\n",
    "    \"R2 Score\": [r2_train, r2_valid],\n",
    "    \"MAPE\": [mape_train, mape_valid]\n",
    "}\n",
    "\n",
    "metrics_df = pd.DataFrame(metrics, index=[\"Train\", \"Validation\"])\n",
    "metrics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Membuat DataFrame untuk mempermudah visualisasi\n",
    "comparison_df = pd.DataFrame({'Actual': y_valid, 'Predicted': y_pred_valid})\n",
    "\n",
    "# Membatasi hanya pada 20 indeks pertama\n",
    "comparison_df_subset = comparison_df.iloc[:20]\n",
    "\n",
    "# Scatter plot untuk membandingkan prediksi dan nilai asli\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(range(len(comparison_df_subset)), comparison_df_subset['Actual'], label='Actual Values', alpha=0.7, color='blue')\n",
    "plt.scatter(range(len(comparison_df_subset)), comparison_df_subset['Predicted'], label='Predicted Values', alpha=0.7, color='orange')\n",
    "plt.title('Comparison of Actual vs Predicted Active Work Months (First 20)')\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Active Work Months')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Line plot untuk membandingkan prediksi dan nilai asli\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(range(len(comparison_df_subset)), comparison_df_subset['Actual'], label='Actual Values', marker='o', linestyle='-', color='blue')\n",
    "plt.plot(range(len(comparison_df_subset)), comparison_df_subset['Predicted'], label='Predicted Values', marker='x', linestyle='-', color='orange')\n",
    "plt.title('Actual vs Predicted Active Work Months (First 20 - Line Plot)')\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Active Work Months')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "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",
      "[I 2025-03-20 21:00:35,783] A new study created in memory with name: no-name-90c20206-17ac-4d18-b328-3e83b0b68b02\n",
      "[I 2025-03-20 21:01:21,239] Trial 0 finished with value: 8.419168492565904 and parameters: {'iterations': 829, 'learning_rate': 0.0015555213398614615, 'depth': 5, 'subsample': 0.7218038924225667, 'colsample_bylevel': 0.6317251084903674, 'l2_leaf_reg': 9.824596206043214, 'random_strength': 5.86501034445993}. Best is trial 0 with value: 8.419168492565904.\n",
      "[I 2025-03-20 21:02:07,180] Trial 1 finished with value: 2.733330716926934 and parameters: {'iterations': 884, 'learning_rate': 0.0053516318860709, 'depth': 5, 'subsample': 0.7097793314114498, 'colsample_bylevel': 0.6435980070119711, 'l2_leaf_reg': 13.121533573607655, 'random_strength': 9.324119789288343}. Best is trial 1 with value: 2.733330716926934.\n",
      "[I 2025-03-20 21:02:32,264] Trial 2 finished with value: 1.1897665017142585 and parameters: {'iterations': 635, 'learning_rate': 0.023704951537819915, 'depth': 4, 'subsample': 0.7686389413016427, 'colsample_bylevel': 0.5266671315496564, 'l2_leaf_reg': 15.175290458163712, 'random_strength': 7.30745741132724}. Best is trial 2 with value: 1.1897665017142585.\n",
      "[I 2025-03-20 21:03:16,738] Trial 3 finished with value: 1.7154507371626437 and parameters: {'iterations': 782, 'learning_rate': 0.012722641891947339, 'depth': 5, 'subsample': 0.6239927785252992, 'colsample_bylevel': 0.551697952957819, 'l2_leaf_reg': 8.011605642650462, 'random_strength': 7.351190134542403}. Best is trial 2 with value: 1.1897665017142585.\n",
      "[I 2025-03-20 21:03:56,278] Trial 4 finished with value: 1.0342169780611064 and parameters: {'iterations': 590, 'learning_rate': 0.026972969679793426, 'depth': 5, 'subsample': 0.7586099689360802, 'colsample_bylevel': 0.6775428452906951, 'l2_leaf_reg': 8.716335425228655, 'random_strength': 8.009471482166916}. Best is trial 4 with value: 1.0342169780611064.\n",
      "[I 2025-03-20 21:04:38,974] Trial 5 finished with value: 0.9367588642619448 and parameters: {'iterations': 806, 'learning_rate': 0.025597264277354852, 'depth': 5, 'subsample': 0.6503807167080523, 'colsample_bylevel': 0.6858283170142669, 'l2_leaf_reg': 15.122433690868604, 'random_strength': 9.26015271022034}. Best is trial 5 with value: 0.9367588642619448.\n",
      "[I 2025-03-20 21:05:17,061] Trial 6 finished with value: 3.3099767746788875 and parameters: {'iterations': 789, 'learning_rate': 0.005334295156738072, 'depth': 4, 'subsample': 0.6150191027518872, 'colsample_bylevel': 0.5100653940971351, 'l2_leaf_reg': 16.26273857150823, 'random_strength': 9.363327197652792}. Best is trial 5 with value: 0.9367588642619448.\n",
      "[I 2025-03-20 21:06:11,133] Trial 7 finished with value: 2.430297164863871 and parameters: {'iterations': 837, 'learning_rate': 0.006452050997341309, 'depth': 5, 'subsample': 0.5569957978664557, 'colsample_bylevel': 0.7974026616845746, 'l2_leaf_reg': 17.640163162948227, 'random_strength': 8.93735519303648}. Best is trial 5 with value: 0.9367588642619448.\n",
      "[I 2025-03-20 21:06:40,233] Trial 8 finished with value: 3.059791812580938 and parameters: {'iterations': 590, 'learning_rate': 0.006568464895546362, 'depth': 5, 'subsample': 0.5764687596920144, 'colsample_bylevel': 0.5755754855362132, 'l2_leaf_reg': 8.522648009171846, 'random_strength': 9.538382092752938}. Best is trial 5 with value: 0.9367588642619448.\n",
      "[I 2025-03-20 21:07:13,639] Trial 9 finished with value: 7.76817781695066 and parameters: {'iterations': 617, 'learning_rate': 0.002539968080520203, 'depth': 4, 'subsample': 0.6372121192487497, 'colsample_bylevel': 0.6082864452448382, 'l2_leaf_reg': 17.654618698706546, 'random_strength': 9.459556159452063}. Best is trial 5 with value: 0.9367588642619448.\n",
      "[I 2025-03-20 21:08:20,357] Trial 10 finished with value: 0.4103763995321103 and parameters: {'iterations': 979, 'learning_rate': 0.09529892832775133, 'depth': 6, 'subsample': 0.5209516193113186, 'colsample_bylevel': 0.7296584784356337, 'l2_leaf_reg': 5.36555480808817, 'random_strength': 5.055795530221549}. Best is trial 10 with value: 0.4103763995321103.\n",
      "[I 2025-03-20 21:09:34,792] Trial 11 finished with value: 0.33873805409112234 and parameters: {'iterations': 989, 'learning_rate': 0.07851963900488154, 'depth': 6, 'subsample': 0.5200611359234908, 'colsample_bylevel': 0.7185209948848563, 'l2_leaf_reg': 5.259953652103869, 'random_strength': 5.611812114220319}. Best is trial 11 with value: 0.33873805409112234.\n",
      "[I 2025-03-20 21:10:53,294] Trial 12 finished with value: 0.33747220251551263 and parameters: {'iterations': 1000, 'learning_rate': 0.09306028108980487, 'depth': 6, 'subsample': 0.5034416033403175, 'colsample_bylevel': 0.7452683981181829, 'l2_leaf_reg': 5.279315402542746, 'random_strength': 5.294692039250562}. Best is trial 12 with value: 0.33747220251551263.\n",
      "[I 2025-03-20 21:11:51,728] Trial 13 finished with value: 1.6267506473342959 and parameters: {'iterations': 996, 'learning_rate': 0.09325178395683269, 'depth': 6, 'subsample': 0.5125699226468535, 'colsample_bylevel': 0.756377904971589, 'l2_leaf_reg': 5.519312813157406, 'random_strength': 6.120809817278766}. Best is trial 12 with value: 0.33747220251551263.\n",
      "[I 2025-03-20 21:12:42,167] Trial 14 finished with value: 0.4564504106167195 and parameters: {'iterations': 919, 'learning_rate': 0.052020741738658456, 'depth': 6, 'subsample': 0.5007317413228829, 'colsample_bylevel': 0.727489010605425, 'l2_leaf_reg': 11.083178624062024, 'random_strength': 5.261787256931901}. Best is trial 12 with value: 0.33747220251551263.\n",
      "[I 2025-03-20 21:13:32,464] Trial 15 finished with value: 0.6231066699606179 and parameters: {'iterations': 696, 'learning_rate': 0.04926421412297825, 'depth': 6, 'subsample': 0.5528336472517523, 'colsample_bylevel': 0.7710394282985423, 'l2_leaf_reg': 6.189751630816335, 'random_strength': 6.502158303659374}. Best is trial 12 with value: 0.33747220251551263.\n",
      "[I 2025-03-20 21:14:53,119] Trial 16 finished with value: 0.4792492732081974 and parameters: {'iterations': 930, 'learning_rate': 0.04640033657256652, 'depth': 6, 'subsample': 0.5859719980036684, 'colsample_bylevel': 0.700113722637731, 'l2_leaf_reg': 7.099786946755341, 'random_strength': 6.688129574746723}. Best is trial 12 with value: 0.33747220251551263.\n",
      "[I 2025-03-20 21:15:49,049] Trial 17 finished with value: 1.61841931396652 and parameters: {'iterations': 518, 'learning_rate': 0.015200955659638398, 'depth': 6, 'subsample': 0.534977261704631, 'colsample_bylevel': 0.7246250429139481, 'l2_leaf_reg': 19.80600077744929, 'random_strength': 5.673411846650424}. Best is trial 12 with value: 0.33747220251551263.\n",
      "[I 2025-03-20 21:17:36,811] Trial 18 finished with value: 0.5548288731588591 and parameters: {'iterations': 938, 'learning_rate': 0.07194118095780964, 'depth': 6, 'subsample': 0.6665507151821397, 'colsample_bylevel': 0.790138958827564, 'l2_leaf_reg': 12.301321434830928, 'random_strength': 6.796168129707886}. Best is trial 12 with value: 0.33747220251551263.\n",
      "[I 2025-03-20 21:18:43,121] Trial 19 finished with value: 0.6231757616191587 and parameters: {'iterations': 719, 'learning_rate': 0.03682071614516647, 'depth': 6, 'subsample': 0.5866796565002883, 'colsample_bylevel': 0.7504213556109071, 'l2_leaf_reg': 10.442003723130012, 'random_strength': 8.36360703538023}. Best is trial 12 with value: 0.33747220251551263.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best trial:\n",
      "  RMSE: 0.33747220251551263\n",
      "  Params: {'iterations': 1000, 'learning_rate': 0.09306028108980487, 'depth': 6, 'subsample': 0.5034416033403175, 'colsample_bylevel': 0.7452683981181829, 'l2_leaf_reg': 5.279315402542746, 'random_strength': 5.294692039250562}\n"
     ]
    }
   ],
   "source": [
    "import optuna\n",
    "from catboost import CatBoostRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Fungsi objective untuk Optuna\n",
    "def objective(trial):\n",
    "    # Definisikan parameter yang akan dioptimasi\n",
    "    params = {\n",
    "        'iterations': trial.suggest_int('iterations', 500, 1000),\n",
    "        'learning_rate': trial.suggest_float('learning_rate', 0.001, 0.1, log=True),\n",
    "        'depth': trial.suggest_int('depth', 4, 6),\n",
    "        'subsample': trial.suggest_float('subsample', 0.5, 0.8),\n",
    "        'colsample_bylevel': trial.suggest_float('colsample_bylevel', 0.5, 0.8),\n",
    "        'l2_leaf_reg': trial.suggest_float('l2_leaf_reg', 5, 20),\n",
    "        'random_strength': trial.suggest_float('random_strength', 5, 10),\n",
    "        'cat_features': cat_feature,\n",
    "        'loss_function': 'RMSE',  # Fungsi kerugian untuk regresi\n",
    "        'random_state': 42,\n",
    "        'verbose': 0\n",
    "    }\n",
    "\n",
    "    # Inisialisasi model dengan parameter yang dioptimasi\n",
    "    model = CatBoostRegressor(**params)\n",
    "\n",
    "    # Melatih model dengan validasi\n",
    "    model.fit(X_train, y_train, eval_set=(X_valid, y_valid), use_best_model=True)\n",
    "\n",
    "    # Prediksi nilai target\n",
    "    y_pred = model.predict(X_valid)\n",
    "\n",
    "    # Hitung RMSE\n",
    "    rmse = np.sqrt(mean_squared_error(y_valid, y_pred))\n",
    "\n",
    "    return rmse  # Mengembalikan RMSE sebagai skor yang ingin diminimalkan\n",
    "\n",
    "# Membuat studi Optuna\n",
    "study = optuna.create_study(direction=\"minimize\")  # Minimalkan RMSE\n",
    "study.optimize(objective, n_trials=20)\n",
    "\n",
    "# Menampilkan hasil terbaik\n",
    "print(\"Best trial:\")\n",
    "print(f\"  RMSE: {study.best_value}\")\n",
    "print(f\"  Params: {study.best_params}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 13.4115622\ttest: 19.0660791\tbest: 19.0660791 (0)\ttotal: 193ms\tremaining: 3m 13s\n",
      "200:\tlearn: 0.3985082\ttest: 0.5382811\tbest: 0.5382373 (199)\ttotal: 21.7s\tremaining: 1m 26s\n",
      "400:\tlearn: 0.2915505\ttest: 0.4082137\tbest: 0.4080268 (398)\ttotal: 38.8s\tremaining: 57.9s\n",
      "600:\tlearn: 0.2452564\ttest: 0.3555501\tbest: 0.3555501 (600)\ttotal: 55.4s\tremaining: 36.8s\n",
      "800:\tlearn: 0.2201339\ttest: 0.3435762\tbest: 0.3435629 (786)\ttotal: 1m 16s\tremaining: 18.9s\n",
      "999:\tlearn: 0.2028099\ttest: 0.3375486\tbest: 0.3374722 (998)\ttotal: 1m 40s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.3374722025\n",
      "bestIteration = 998\n",
      "\n",
      "Shrink model to first 999 iterations.\n",
      "Final RMSE: 0.33747220251551263\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Ambil parameter terbaik dari Optuna\n",
    "best_params = study.best_trial.params\n",
    "\n",
    "# Tambahkan parameter tetap (yang tidak dioptimasi)\n",
    "best_params.update({\n",
    "    'loss_function': 'RMSE',  # Gunakan RMSE sebagai loss function\n",
    "    'cat_features': cat_feature,\n",
    "    'random_state': 42,\n",
    "    'verbose': 200,  # Aktifkan output verbose\n",
    "    'od_type': 'Iter',\n",
    "    'od_wait': 50\n",
    "})\n",
    "\n",
    "# Latih model dengan parameter terbaik\n",
    "final_model = CatBoostRegressor(**best_params)\n",
    "final_model.fit(X_train, y_train, eval_set=(X_valid, y_valid), use_best_model=True)\n",
    "\n",
    "# Evaluasi model final\n",
    "y_pred = final_model.predict(X_valid)\n",
    "final_rmse = np.sqrt(mean_squared_error(y_valid, y_pred))  # Hitung RMSE\n",
    "print(f\"Final RMSE: {final_rmse}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>MSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2 Score</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Train</th>\n",
       "      <td>0.042248</td>\n",
       "      <td>0.146864</td>\n",
       "      <td>0.205544</td>\n",
       "      <td>0.999803</td>\n",
       "      <td>0.007405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Validation</th>\n",
       "      <td>0.113887</td>\n",
       "      <td>0.243289</td>\n",
       "      <td>0.337472</td>\n",
       "      <td>0.992537</td>\n",
       "      <td>0.036795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 MSE       MAE      RMSE  R2 Score      MAPE\n",
       "Train       0.042248  0.146864  0.205544  0.999803  0.007405\n",
       "Validation  0.113887  0.243289  0.337472  0.992537  0.036795"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Prediksi pada data training dan validasi\n",
    "y_pred_train = final_model.predict(X_train)\n",
    "y_pred_valid = final_model.predict(X_valid)\n",
    "\n",
    "# Menghitung metrik regresi untuk training\n",
    "mse_train = mean_squared_error(y_train, y_pred_train)\n",
    "mae_train = mean_absolute_error(y_train, y_pred_train)\n",
    "rmse_train = np.sqrt(mse_train)\n",
    "r2_train = r2_score(y_train, y_pred_train)\n",
    "mape_train = mean_absolute_percentage_error(y_train, y_pred_train)\n",
    "\n",
    "# Menghitung metrik regresi untuk validasi\n",
    "mse_valid = mean_squared_error(y_valid, y_pred_valid)\n",
    "mae_valid = mean_absolute_error(y_valid, y_pred_valid)\n",
    "rmse_valid = np.sqrt(mse_valid)\n",
    "r2_valid = r2_score(y_valid, y_pred_valid)\n",
    "mape_valid = mean_absolute_percentage_error(y_valid, y_pred_valid)\n",
    "\n",
    "# Membuat dataframe hasil metrik untuk training dan validation\n",
    "metrics = {\n",
    "    \"MSE\": [mse_train, mse_valid],\n",
    "    \"MAE\": [mae_train, mae_valid],\n",
    "    \"RMSE\": [rmse_train, rmse_valid],\n",
    "    \"R2 Score\": [r2_train, r2_valid],\n",
    "    \"MAPE\": [mape_train, mape_valid]\n",
    "}\n",
    "\n",
    "metrics_df = pd.DataFrame(metrics, index=[\"Train\", \"Validation\"])\n",
    "metrics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Membuat DataFrame untuk mempermudah visualisasi\n",
    "comparison_df = pd.DataFrame({'Actual': y_valid, 'Predicted': y_pred_valid})\n",
    "\n",
    "# Membatasi hanya pada 20 indeks pertama\n",
    "comparison_df_subset = comparison_df.iloc[:20]\n",
    "\n",
    "# Scatter plot untuk membandingkan prediksi dan nilai asli\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(range(len(comparison_df_subset)), comparison_df_subset['Actual'], label='Actual Values', alpha=0.7, color='blue')\n",
    "plt.scatter(range(len(comparison_df_subset)), comparison_df_subset['Predicted'], label='Predicted Values', alpha=0.7, color='orange')\n",
    "plt.title('Comparison of Actual vs Predicted Active Work Months (First 20)')\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Active Work Months')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Line plot untuk membandingkan prediksi dan nilai asli\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(range(len(comparison_df_subset)), comparison_df_subset['Actual'], label='Actual Values', marker='o', linestyle='-', color='blue')\n",
    "plt.plot(range(len(comparison_df_subset)), comparison_df_subset['Predicted'], label='Predicted Values', marker='x', linestyle='-', color='orange')\n",
    "plt.title('Actual vs Predicted Active Work Months (First 20 - Line Plot)')\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Active Work Months')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Training RMSE: 0.2918692068345175\n",
      "Final Validation RMSE: 0.4375721621551542\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ambil hasil evaluasi dari model\n",
    "evals_result = model.get_evals_result()\n",
    "\n",
    "# Menampilkan skor terakhir\n",
    "train_score = evals_result['learn']['RMSE'][-1]\n",
    "val_score = evals_result['validation']['RMSE'][-1]\n",
    "\n",
    "print(f\"Final Training RMSE: {train_score}\")\n",
    "print(f\"Final Validation RMSE: {val_score}\")\n",
    "\n",
    "# Import library untuk visualisasi\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Ambil skor training dan validation dari evals_result\n",
    "train_rmse = evals_result['learn']['RMSE']\n",
    "val_rmse = evals_result['validation']['RMSE']\n",
    "\n",
    "# Plot learning curve\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train_rmse, label='Training RMSE', color='blue')\n",
    "plt.plot(val_rmse, label='Validation RMSE', color='orange')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('RMSE')\n",
    "plt.title('Learning Curve')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Training RMSE: 0.20280993448644016\n",
      "Final Validation RMSE: 0.33754861114711854\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ambil hasil evaluasi dari model\n",
    "evals_result = final_model.get_evals_result()\n",
    "\n",
    "# Menampilkan skor terakhir\n",
    "train_score = evals_result['learn']['RMSE'][-1]\n",
    "val_score = evals_result['validation']['RMSE'][-1]\n",
    "\n",
    "print(f\"Final Training RMSE: {train_score}\")\n",
    "print(f\"Final Validation RMSE: {val_score}\")\n",
    "\n",
    "# Import library untuk visualisasi\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Ambil skor training dan validation dari evals_result\n",
    "train_rmse = evals_result['learn']['RMSE']\n",
    "val_rmse = evals_result['validation']['RMSE']\n",
    "\n",
    "# Plot learning curve\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train_rmse, label='Training RMSE', color='blue')\n",
    "plt.plot(val_rmse, label='Validation RMSE', color='orange')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('RMSE')\n",
    "plt.title('Learning Curve')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CatBoost Regression model saved to 'regression_model.sav'\n",
      "CatBoost Regression model saved to 'regression_model.sav'\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "with open('D:/Tugas Akhir/Codingan/Development/App/model/regression_model_1year.sav', 'wb') as f:\n",
    "    pickle.dump(model, f)\n",
    "print(\"CatBoost Regression model saved to 'regression_model.sav'\")\n",
    "\n",
    "with open('D:/Tugas Akhir/Codingan/Development/App/model/regression_model_final_1year.sav', 'wb') as f:\n",
    "    pickle.dump(final_model, f)\n",
    "print(\"CatBoost Regression model saved to 'regression_model.sav'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mengurutkan data berdasarkan waktu (join_date)\n",
    "df = df.sort_values('join_date')\n",
    "X = df.drop(columns=['active_work_months', 'churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
    "y = df['active_work_months']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 8.8750523\ttotal: 69.8ms\tremaining: 1m 9s\n",
      "200:\tlearn: 1.8266624\ttotal: 19.6s\tremaining: 1m 18s\n",
      "400:\tlearn: 0.6002046\ttotal: 34.2s\tremaining: 51.1s\n",
      "600:\tlearn: 0.4057507\ttotal: 47.4s\tremaining: 31.5s\n",
      "800:\tlearn: 0.3438624\ttotal: 56.4s\tremaining: 14s\n",
      "999:\tlearn: 0.3090144\ttotal: 1m 5s\tremaining: 0us\n",
      "0:\tlearn: 10.0220337\ttotal: 96.6ms\tremaining: 1m 36s\n",
      "200:\tlearn: 1.8546127\ttotal: 13.1s\tremaining: 52.3s\n",
      "400:\tlearn: 0.5945915\ttotal: 25.6s\tremaining: 38.3s\n",
      "600:\tlearn: 0.4005480\ttotal: 37.2s\tremaining: 24.7s\n",
      "800:\tlearn: 0.3411443\ttotal: 47.3s\tremaining: 11.7s\n",
      "999:\tlearn: 0.3122553\ttotal: 56s\tremaining: 0us\n",
      "0:\tlearn: 12.0372673\ttotal: 40.6ms\tremaining: 40.6s\n",
      "200:\tlearn: 2.0552654\ttotal: 13.1s\tremaining: 52.1s\n",
      "400:\tlearn: 0.5435496\ttotal: 27.6s\tremaining: 41.2s\n",
      "600:\tlearn: 0.3529030\ttotal: 42.9s\tremaining: 28.5s\n",
      "800:\tlearn: 0.3119731\ttotal: 58.8s\tremaining: 14.6s\n",
      "999:\tlearn: 0.2901350\ttotal: 1m 11s\tremaining: 0us\n",
      "0:\tlearn: 13.6068194\ttotal: 39.4ms\tremaining: 39.3s\n",
      "200:\tlearn: 2.2563070\ttotal: 10.5s\tremaining: 41.8s\n",
      "400:\tlearn: 0.6022229\ttotal: 25.5s\tremaining: 38.1s\n",
      "600:\tlearn: 0.3770988\ttotal: 37.2s\tremaining: 24.7s\n",
      "800:\tlearn: 0.3261190\ttotal: 48.1s\tremaining: 11.9s\n",
      "999:\tlearn: 0.3030996\ttotal: 59s\tremaining: 0us\n",
      "0:\tlearn: 14.5501971\ttotal: 43.8ms\tremaining: 43.8s\n",
      "200:\tlearn: 2.3530705\ttotal: 14.4s\tremaining: 57.3s\n",
      "400:\tlearn: 0.5963977\ttotal: 25.1s\tremaining: 37.5s\n",
      "600:\tlearn: 0.3533729\ttotal: 40.3s\tremaining: 26.7s\n",
      "800:\tlearn: 0.3052574\ttotal: 58.8s\tremaining: 14.6s\n",
      "999:\tlearn: 0.2890980\ttotal: 1m 16s\tremaining: 0us\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R² Scores for each fold: [0.99295281 0.98555108 0.99626666 0.99311907 0.99219813]\n",
      "Mean R²: 0.99\n",
      "Standard Deviation: 0.00\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import TimeSeriesSplit, cross_val_score\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Fungsi untuk menghitung skor cross-validation dengan TimeSeriesSplit\n",
    "def time_series_cross_validate_and_visualize_r2(model, X, y, n_splits=5):\n",
    "    # TimeSeriesSplit untuk data terkait waktu\n",
    "    tscv = TimeSeriesSplit(n_splits=n_splits)\n",
    "\n",
    "    # Hitung skor cross-validation dengan metrik R²\n",
    "    scores = cross_val_score(model, X, y, scoring='r2', cv=tscv)\n",
    "\n",
    "    # Rata-rata dan standar deviasi\n",
    "    mean_score = np.mean(scores)\n",
    "    std_score = np.std(scores)\n",
    "\n",
    "    # Visualisasi hasil cross-validation\n",
    "    plt.figure(figsize=(8, 5))\n",
    "    plt.plot(range(1, n_splits + 1), scores, marker='o', linestyle='-', color='b', label='Fold Score')\n",
    "    plt.axhline(y=mean_score, color='r', linestyle='--', label=f'Mean R²: {mean_score:.2f}')\n",
    "    plt.fill_between(range(1, n_splits + 1), mean_score - std_score, mean_score + std_score, color='r', alpha=0.2, label='±1 Std Dev')\n",
    "    plt.title('Time-Based Cross-Validation Scores (R²)')\n",
    "    plt.xlabel('Fold')\n",
    "    plt.ylabel('R² Score')\n",
    "    plt.legend()\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "\n",
    "    # Cetak hasil skor\n",
    "    print(f'R² Scores for each fold: {scores}')\n",
    "    print(f'Mean R²: {mean_score:.2f}')\n",
    "    print(f'Standard Deviation: {std_score:.2f}')\n",
    "\n",
    "# Contoh penggunaan\n",
    "time_series_cross_validate_and_visualize_r2(model, X, y, n_splits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 8.3318339\ttotal: 60.4ms\tremaining: 1m\n",
      "200:\tlearn: 0.3772212\ttotal: 9.2s\tremaining: 36.6s\n",
      "400:\tlearn: 0.2385344\ttotal: 18.5s\tremaining: 27.6s\n",
      "600:\tlearn: 0.1793524\ttotal: 30s\tremaining: 19.9s\n",
      "800:\tlearn: 0.1419319\ttotal: 44.5s\tremaining: 11.1s\n",
      "999:\tlearn: 0.1195831\ttotal: 1m 1s\tremaining: 0us\n",
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      "800:\tlearn: 0.1868132\ttotal: 46.3s\tremaining: 11.5s\n",
      "999:\tlearn: 0.1623341\ttotal: 1m 7s\tremaining: 0us\n",
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      "200:\tlearn: 0.4018675\ttotal: 20.5s\tremaining: 1m 21s\n",
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      "800:\tlearn: 0.1981024\ttotal: 51.6s\tremaining: 12.8s\n",
      "999:\tlearn: 0.1763203\ttotal: 1m 5s\tremaining: 0us\n",
      "0:\tlearn: 12.7721933\ttotal: 81.5ms\tremaining: 1m 21s\n",
      "200:\tlearn: 0.4260864\ttotal: 18.3s\tremaining: 1m 12s\n",
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      "999:\tlearn: 0.2060358\ttotal: 1m 9s\tremaining: 0us\n",
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      "200:\tlearn: 0.3635484\ttotal: 18.6s\tremaining: 1m 13s\n",
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      "800:\tlearn: 0.2072017\ttotal: 1m 6s\tremaining: 16.4s\n",
      "999:\tlearn: 0.1894711\ttotal: 1m 15s\tremaining: 0us\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R² Scores for each fold: [0.98823121 0.97435808 0.99617635 0.99508864 0.99193755]\n",
      "Mean R²: 0.99\n",
      "Standard Deviation: 0.01\n"
     ]
    }
   ],
   "source": [
    "time_series_cross_validate_and_visualize_r2(final_model, X, y, n_splits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 18,
     "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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R² Score: 0.9981849949227025\n",
      "Mean Absolute Error (MAE): 0.22961074811195165\n",
      "Mean Squared Error (MSE): 0.08608200743545322\n",
      "Root Mean Squared Error (RMSE): 0.2933973541725508\n",
      "Mean Absolute Percentage Error (MAPE): 0.012638509013727962\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "import numpy as np\n",
    "\n",
    "# Menghapus kolom yang tidak diperlukan untuk prediksi\n",
    "X_test = df_test.drop(columns=['active_work_months','churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
    "\n",
    "# Lakukan prediksi menggunakan model final\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Tambahkan prediksi ke DataFrame\n",
    "X_test['predicted_active_work'] = y_pred\n",
    "\n",
    "# Hitung metrik evaluasi\n",
    "r2 = r2_score(df_test['active_work_months'], y_pred)\n",
    "mae = mean_absolute_error(df_test['active_work_months'], y_pred)\n",
    "mse = mean_squared_error(df_test['active_work_months'], y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "mape = mean_absolute_percentage_error(df_test['active_work_months'], y_pred)\n",
    "\n",
    "# Cetak hasil\n",
    "print(\"R² Score:\", r2)\n",
    "print(\"Mean Absolute Error (MAE):\", mae)\n",
    "print(\"Mean Squared Error (MSE):\", mse)\n",
    "print(\"Root Mean Squared Error (RMSE):\", rmse)\n",
    "print(\"Mean Absolute Percentage Error (MAPE):\", mape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R² Score: 0.9988471099705245\n",
      "Mean Absolute Error (MAE): 0.16198294161847646\n",
      "Mean Squared Error (MSE): 0.054679234416987466\n",
      "Root Mean Squared Error (RMSE): 0.23383591344570548\n",
      "Mean Absolute Percentage Error (MAPE): 0.009256325676074095\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "import numpy as np\n",
    "\n",
    "# Menghapus kolom yang tidak diperlukan untuk prediksi\n",
    "X_test = df_test.drop(columns=['active_work_months','churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
    "\n",
    "# Lakukan prediksi menggunakan model final\n",
    "y_pred = final_model.predict(X_test)\n",
    "\n",
    "# Tambahkan prediksi ke DataFrame\n",
    "X_test['predicted_active_work'] = y_pred\n",
    "\n",
    "# Hitung metrik evaluasi\n",
    "r2 = r2_score(df_test['active_work_months'], y_pred)\n",
    "mae = mean_absolute_error(df_test['active_work_months'], y_pred)\n",
    "mse = mean_squared_error(df_test['active_work_months'], y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "mape = mean_absolute_percentage_error(df_test['active_work_months'], y_pred)\n",
    "\n",
    "# Cetak hasil\n",
    "print(\"R² Score:\", r2)\n",
    "print(\"Mean Absolute Error (MAE):\", mae)\n",
    "print(\"Mean Squared Error (MSE):\", mse)\n",
    "print(\"Root Mean Squared Error (RMSE):\", rmse)\n",
    "print(\"Mean Absolute Percentage Error (MAPE):\", mape)"
   ]
  }
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