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python-sql-2110511008/notebook/train_regression.ipynb
Jesselyn Mu a927f2d128 update model
2025-02-03 16:43:48 +07:00

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"status": "ok",
"timestamp": 1735311271540,
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>employee_id</th>\n",
" <th>domisili</th>\n",
" <th>jenis_kelamin</th>\n",
" <th>date_of_birth</th>\n",
" <th>join_date</th>\n",
" <th>resign_date</th>\n",
" <th>marriage_stat</th>\n",
" <th>dependant</th>\n",
" <th>education</th>\n",
" <th>absent_90D</th>\n",
" <th>...</th>\n",
" <th>active_work_category</th>\n",
" <th>work_stability_score</th>\n",
" <th>married_dependent_ratio</th>\n",
" <th>position_score</th>\n",
" <th>job_income_position_score</th>\n",
" <th>education_score</th>\n",
" <th>education_income_ratio</th>\n",
" <th>weighted_satisfaction_performance</th>\n",
" <th>resign_risk_indicator</th>\n",
" <th>adjusted_work_time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>EM10510</td>\n",
" <td>Kota Jakarta Utara</td>\n",
" <td>Laki-laki</td>\n",
" <td>1983-09-11</td>\n",
" <td>2021-02-09</td>\n",
" <td>2023-06-22</td>\n",
" <td>Married</td>\n",
" <td>1</td>\n",
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" <td>9.0</td>\n",
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" <td>2.800000</td>\n",
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" <td>1</td>\n",
" <td>1418218.0</td>\n",
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" <td>9.246870</td>\n",
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" <td>EM4322</td>\n",
" <td>Kabupaten Bekasi</td>\n",
" <td>Perempuan</td>\n",
" <td>1987-03-22</td>\n",
" <td>2022-02-28</td>\n",
" <td>2023-04-04</td>\n",
" <td>Married</td>\n",
" <td>1</td>\n",
" <td>SLTA</td>\n",
" <td>0.0</td>\n",
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" <td>9.650000</td>\n",
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" <td>EM1637</td>\n",
" <td>Kota Jakarta Barat</td>\n",
" <td>Laki-laki</td>\n",
" <td>1970-04-27</td>\n",
" <td>2020-12-23</td>\n",
" <td>2023-03-25</td>\n",
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" <td>4</td>\n",
" <td>D2</td>\n",
" <td>4.0</td>\n",
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" <td>Mid-term</td>\n",
" <td>5.400000</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>4885136.0</td>\n",
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" <td>1.628379e+06</td>\n",
" <td>1.0</td>\n",
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" <td>9.813826</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>EM14613</td>\n",
" <td>Kota Jakarta Pusat</td>\n",
" <td>Laki-laki</td>\n",
" <td>1988-06-10</td>\n",
" <td>2022-11-21</td>\n",
" <td>2024-03-23</td>\n",
" <td>Married</td>\n",
" <td>1</td>\n",
" <td>D3</td>\n",
" <td>2.0</td>\n",
" <td>...</td>\n",
" <td>Mid-term</td>\n",
" <td>5.333333</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4602479.0</td>\n",
" <td>4</td>\n",
" <td>1.150620e+06</td>\n",
" <td>2.4</td>\n",
" <td>Medium</td>\n",
" <td>9.756440</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>EM1084</td>\n",
" <td>Kabupaten Bogor</td>\n",
" <td>Perempuan</td>\n",
" <td>1977-05-25</td>\n",
" <td>2021-06-07</td>\n",
" <td>2023-07-21</td>\n",
" <td>Married</td>\n",
" <td>3</td>\n",
" <td>SLTA</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>Mid-term</td>\n",
" <td>25.000000</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1066966.0</td>\n",
" <td>1</td>\n",
" <td>1.066966e+06</td>\n",
" <td>2.6</td>\n",
" <td>Medium</td>\n",
" <td>9.080000</td>\n",
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"<p>5 rows × 37 columns</p>\n",
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"text/plain": [
" employee_id domisili jenis_kelamin date_of_birth join_date \\\n",
"0 EM10510 Kota Jakarta Utara Laki-laki 1983-09-11 2021-02-09 \n",
"1 EM4322 Kabupaten Bekasi Perempuan 1987-03-22 2022-02-28 \n",
"2 EM1637 Kota Jakarta Barat Laki-laki 1970-04-27 2020-12-23 \n",
"3 EM14613 Kota Jakarta Pusat Laki-laki 1988-06-10 2022-11-21 \n",
"4 EM1084 Kabupaten Bogor Perempuan 1977-05-25 2021-06-07 \n",
"\n",
" resign_date marriage_stat dependant education absent_90D ... \\\n",
"0 2023-06-22 Married 1 SLTA 9.0 ... \n",
"1 2023-04-04 Married 1 SLTA 0.0 ... \n",
"2 2023-03-25 Married 4 D2 4.0 ... \n",
"3 2024-03-23 Married 1 D3 2.0 ... \n",
"4 2023-07-21 Married 3 SLTA 0.0 ... \n",
"\n",
" active_work_category work_stability_score married_dependent_ratio \\\n",
"0 Mid-term 2.800000 2 \n",
"1 Mid-term 13.000000 2 \n",
"2 Mid-term 5.400000 5 \n",
"3 Mid-term 5.333333 2 \n",
"4 Mid-term 25.000000 4 \n",
"\n",
" position_score job_income_position_score education_score \\\n",
"0 1 1418218.0 1 \n",
"1 1 1060575.0 1 \n",
"2 1 4885136.0 3 \n",
"3 1 4602479.0 4 \n",
"4 1 1066966.0 1 \n",
"\n",
" education_income_ratio weighted_satisfaction_performance \\\n",
"0 1.418218e+06 2.2 \n",
"1 1.060575e+06 1.6 \n",
"2 1.628379e+06 1.0 \n",
"3 1.150620e+06 2.4 \n",
"4 1.066966e+06 2.6 \n",
"\n",
" resign_risk_indicator adjusted_work_time \n",
"0 Medium 9.246870 \n",
"1 Medium 9.650000 \n",
"2 Medium 9.813826 \n",
"3 Medium 9.756440 \n",
"4 Medium 9.080000 \n",
"\n",
"[5 rows x 37 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_csv('preprocessed_data_train_3.csv')\n",
"df.head()"
]
},
{
"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: 2024-10-30 00:00:00\n",
"Resign Date - Min: 2020-05-05 00:00:00\n",
"Resign Date - Max: 2024-10-31 00:00:00\n"
]
}
],
"source": [
"df['join_date'] = pd.to_datetime(df['join_date'])\n",
"df['resign_date'] = pd.to_datetime(df['resign_date'])\n",
"\n",
"min_join_date = df['join_date'].min()\n",
"max_join_date = df['join_date'].max()\n",
"\n",
"min_resign_date = df['resign_date'].min()\n",
"max_resign_date = df['resign_date'].max()\n",
"\n",
"print(\"Join Date - Min:\", min_join_date)\n",
"print(\"Join Date - Max:\", max_join_date)\n",
"print(\"Resign Date - Min:\", min_resign_date)\n",
"print(\"Resign Date - Max:\", max_resign_date)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "qT7W_7qbUmtZ"
},
"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'] < '2023-01-01']\n",
"valid_data = df[df['join_date'] >= '2023-01-01']\n",
"\n",
"X_train = train_data.drop(columns=['active_work_months', 'churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
"y_train = train_data['active_work_months']\n",
"\n",
"X_valid = valid_data.drop(columns=['active_work_months', 'churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
"y_valid = valid_data['active_work_months']\n",
"\n",
"cat_feature = ['departemen', 'position', 'domisili', 'marriage_stat', 'job_satisfaction', 'performance_rating',\n",
" 'education', 'active_work_category', 'resign_risk_indicator', 'jenis_kelamin']"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 29830,
"status": "ok",
"timestamp": 1735311423993,
"user": {
"displayName": "kelompok bersama",
"userId": "01911350349879401396"
},
"user_tz": -420
},
"id": "Q8deDWqJY1oC",
"outputId": "e3fd20f2-2385-4514-c1c6-058f047b7221"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0:\tlearn: 13.5942904\ttest: 23.3418347\tbest: 23.3418347 (0)\ttotal: 191ms\tremaining: 3m 10s\n",
"200:\tlearn: 2.2250306\ttest: 5.0068018\tbest: 5.0068018 (200)\ttotal: 8.01s\tremaining: 31.8s\n",
"400:\tlearn: 0.5799503\ttest: 2.2619679\tbest: 2.2619679 (400)\ttotal: 16.9s\tremaining: 25.2s\n",
"600:\tlearn: 0.3616911\ttest: 1.7906967\tbest: 1.7906967 (600)\ttotal: 25.9s\tremaining: 17.2s\n",
"800:\tlearn: 0.3091173\ttest: 1.6207404\tbest: 1.6207404 (800)\ttotal: 34.6s\tremaining: 8.6s\n",
"999:\tlearn: 0.2895548\ttest: 1.5407851\tbest: 1.5407851 (999)\ttotal: 43s\tremaining: 0us\n",
"\n",
"bestTest = 1.540785132\n",
"bestIteration = 999\n",
"\n"
]
},
{
"data": {
"text/plain": [
"<catboost.core.CatBoostRegressor at 0x1b653161810>"
]
},
"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": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 175
},
"executionInfo": {
"elapsed": 321,
"status": "ok",
"timestamp": 1735311559697,
"user": {
"displayName": "kelompok bersama",
"userId": "01911350349879401396"
},
"user_tz": -420
},
"id": "0AN-WzVOZrtG",
"outputId": "0aed7f15-c856-4524-c9ec-89119a498d39"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>MSE</th>\n",
" <td>2.374019</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MAE</th>\n",
" <td>0.902642</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RMSE</th>\n",
" <td>1.540785</td>\n",
" </tr>\n",
" <tr>\n",
" <th>R2 Score</th>\n",
" <td>0.925505</td>\n",
" </tr>\n",
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],
"text/plain": [
" Score\n",
"MSE 2.374019\n",
"MAE 0.902642\n",
"RMSE 1.540785\n",
"R2 Score 0.925505"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
"import numpy as np\n",
"\n",
"# Prediksi pada data valid\n",
"y_pred = model.predict(X_valid)\n",
"\n",
"# Menghitung metrik regresi\n",
"mse = mean_squared_error(y_valid, y_pred)\n",
"mae = mean_absolute_error(y_valid, y_pred)\n",
"rmse = np.sqrt(mse)\n",
"r2 = r2_score(y_valid, y_pred)\n",
"\n",
"# Membuat dataframe hasil metrik\n",
"metrics = {\n",
" \"MSE\": mse,\n",
" \"MAE\": mae,\n",
" \"RMSE\": rmse,\n",
" \"R2 Score\": r2\n",
"}\n",
"\n",
"metrics_df = pd.DataFrame.from_dict(metrics, orient='index', columns=['Score'])\n",
"metrics_df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# Membuat DataFrame untuk mempermudah visualisasi\n",
"comparison_df = pd.DataFrame({'Actual': y_valid, 'Predicted': y_pred})\n",
"\n",
"# Membatasi hanya pada 20 indeks pertama\n",
"comparison_df_subset = comparison_df.iloc[:20]\n",
"\n",
"# Scatter plot untuk membandingkan prediksi dan nilai asli\n",
"plt.figure(figsize=(10, 6))\n",
"plt.scatter(range(len(comparison_df_subset)), comparison_df_subset['Actual'], label='Actual Values', alpha=0.7, color='blue')\n",
"plt.scatter(range(len(comparison_df_subset)), comparison_df_subset['Predicted'], label='Predicted Values', alpha=0.7, color='orange')\n",
"plt.title('Comparison of Actual vs Predicted Active Work Months (First 20)')\n",
"plt.xlabel('Index')\n",
"plt.ylabel('Active Work Months')\n",
"plt.legend()\n",
"plt.grid(True)\n",
"plt.show()\n",
"\n",
"# Line plot untuk membandingkan prediksi dan nilai asli\n",
"plt.figure(figsize=(10, 6))\n",
"plt.plot(range(len(comparison_df_subset)), comparison_df_subset['Actual'], label='Actual Values', marker='o', linestyle='-', color='blue')\n",
"plt.plot(range(len(comparison_df_subset)), comparison_df_subset['Predicted'], label='Predicted Values', marker='x', linestyle='-', color='orange')\n",
"plt.title('Actual vs Predicted Active Work Months (First 20 - Line Plot)')\n",
"plt.xlabel('Index')\n",
"plt.ylabel('Active Work Months')\n",
"plt.legend()\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[I 2025-01-31 22:03:50,597] A new study created in memory with name: no-name-474216de-e104-4659-8f4c-89a9d348bbe2\n",
"[I 2025-01-31 22:04:09,058] Trial 0 finished with value: 11.852841151612708 and parameters: {'iterations': 759, 'learning_rate': 0.0014964661109911665, 'depth': 4, 'subsample': 0.5345403763007321, 'colsample_bylevel': 0.5557879487166669, 'l2_leaf_reg': 14.523593619112225, 'random_strength': 5.436136126384264}. Best is trial 0 with value: 11.852841151612708.\n",
"[I 2025-01-31 22:04:26,185] Trial 1 finished with value: 2.782326827024678 and parameters: {'iterations': 631, 'learning_rate': 0.0688926002637438, 'depth': 4, 'subsample': 0.5589286667267055, 'colsample_bylevel': 0.5102559732260701, 'l2_leaf_reg': 12.03344970062869, 'random_strength': 7.74554502548946}. Best is trial 1 with value: 2.782326827024678.\n",
"[I 2025-01-31 22:04:40,370] Trial 2 finished with value: 6.727048715765419 and parameters: {'iterations': 543, 'learning_rate': 0.004111528098062327, 'depth': 4, 'subsample': 0.7394617829547927, 'colsample_bylevel': 0.6796950909515516, 'l2_leaf_reg': 14.783270804681484, 'random_strength': 6.010320137030617}. Best is trial 1 with value: 2.782326827024678.\n",
"[I 2025-01-31 22:05:06,114] Trial 3 finished with value: 4.690053329511069 and parameters: {'iterations': 962, 'learning_rate': 0.004328088977730701, 'depth': 4, 'subsample': 0.613225875921462, 'colsample_bylevel': 0.7077028189537486, 'l2_leaf_reg': 5.0512639148135055, 'random_strength': 8.213889453763334}. Best is trial 1 with value: 2.782326827024678.\n",
"[I 2025-01-31 22:05:37,726] Trial 4 finished with value: 3.7838313230344505 and parameters: {'iterations': 809, 'learning_rate': 0.011313129007769103, 'depth': 6, 'subsample': 0.5016261860337349, 'colsample_bylevel': 0.5766456136058663, 'l2_leaf_reg': 5.998060972309887, 'random_strength': 6.018890370319019}. Best is trial 1 with value: 2.782326827024678.\n",
"[I 2025-01-31 22:06:02,736] Trial 5 finished with value: 4.852253440694461 and parameters: {'iterations': 748, 'learning_rate': 0.00520517267258286, 'depth': 5, 'subsample': 0.5485566035315403, 'colsample_bylevel': 0.5337409295485034, 'l2_leaf_reg': 14.713809483161187, 'random_strength': 5.279249030163157}. Best is trial 1 with value: 2.782326827024678.\n",
"[I 2025-01-31 22:06:23,820] Trial 6 finished with value: 3.8733461927022397 and parameters: {'iterations': 694, 'learning_rate': 0.009239979129411872, 'depth': 4, 'subsample': 0.688277277261536, 'colsample_bylevel': 0.6620877160267076, 'l2_leaf_reg': 11.460620744541892, 'random_strength': 6.934032197250438}. Best is trial 1 with value: 2.782326827024678.\n",
"[I 2025-01-31 22:07:03,131] Trial 7 finished with value: 3.2239910016287734 and parameters: {'iterations': 918, 'learning_rate': 0.031176473967143774, 'depth': 6, 'subsample': 0.6860333334924864, 'colsample_bylevel': 0.7839197568184811, 'l2_leaf_reg': 14.496727744464716, 'random_strength': 7.6124177760405765}. Best is trial 1 with value: 2.782326827024678.\n",
"[I 2025-01-31 22:07:39,702] Trial 8 finished with value: 2.3342054540264265 and parameters: {'iterations': 831, 'learning_rate': 0.04518694466082468, 'depth': 6, 'subsample': 0.5547145239784439, 'colsample_bylevel': 0.6605692487332455, 'l2_leaf_reg': 5.931089190499905, 'random_strength': 9.381970152240088}. Best is trial 8 with value: 2.3342054540264265.\n",
"[I 2025-01-31 22:08:15,167] Trial 9 finished with value: 6.898961698774608 and parameters: {'iterations': 801, 'learning_rate': 0.0030466204092430872, 'depth': 6, 'subsample': 0.5885034372327637, 'colsample_bylevel': 0.7795385742828266, 'l2_leaf_reg': 8.36924405524966, 'random_strength': 7.924454636979776}. Best is trial 8 with value: 2.3342054540264265.\n",
"[I 2025-01-31 22:08:44,351] Trial 10 finished with value: 2.813059906292731 and parameters: {'iterations': 869, 'learning_rate': 0.09506915985958232, 'depth': 5, 'subsample': 0.773053102270278, 'colsample_bylevel': 0.6098195826696574, 'l2_leaf_reg': 19.907766421634943, 'random_strength': 9.961512860567842}. Best is trial 8 with value: 2.3342054540264265.\n",
"[I 2025-01-31 22:09:03,895] Trial 11 finished with value: 2.368947282806889 and parameters: {'iterations': 616, 'learning_rate': 0.09490011276218288, 'depth': 5, 'subsample': 0.5879129821636959, 'colsample_bylevel': 0.5114011291482327, 'l2_leaf_reg': 9.74540222842541, 'random_strength': 9.118958428692489}. Best is trial 8 with value: 2.3342054540264265.\n",
"[I 2025-01-31 22:09:20,976] Trial 12 finished with value: 2.9492933999301365 and parameters: {'iterations': 538, 'learning_rate': 0.03734917543038907, 'depth': 5, 'subsample': 0.634101535755758, 'colsample_bylevel': 0.6149087607795556, 'l2_leaf_reg': 8.754851646806793, 'random_strength': 9.52252352465122}. Best is trial 8 with value: 2.3342054540264265.\n",
"[I 2025-01-31 22:09:46,193] Trial 13 finished with value: 3.065532855429015 and parameters: {'iterations': 630, 'learning_rate': 0.038017280754407835, 'depth': 6, 'subsample': 0.5930689989662581, 'colsample_bylevel': 0.7302958917359129, 'l2_leaf_reg': 8.836162641729628, 'random_strength': 8.981981072468267}. Best is trial 8 with value: 2.3342054540264265.\n",
"[I 2025-01-31 22:10:11,617] Trial 14 finished with value: 3.3908649932569825 and parameters: {'iterations': 628, 'learning_rate': 0.019178482067712114, 'depth': 5, 'subsample': 0.668068547462943, 'colsample_bylevel': 0.6232205705636187, 'l2_leaf_reg': 7.173889636634161, 'random_strength': 8.820311100011175}. Best is trial 8 with value: 2.3342054540264265.\n",
"[I 2025-01-31 22:10:39,432] Trial 15 finished with value: 2.9044988796143985 and parameters: {'iterations': 869, 'learning_rate': 0.06260333828159473, 'depth': 5, 'subsample': 0.5010675524635919, 'colsample_bylevel': 0.5004056047202005, 'l2_leaf_reg': 10.229894565431731, 'random_strength': 8.816149945416772}. Best is trial 8 with value: 2.3342054540264265.\n",
"[I 2025-01-31 22:11:00,589] Trial 16 finished with value: 3.725099096600906 and parameters: {'iterations': 501, 'learning_rate': 0.09429316501260981, 'depth': 6, 'subsample': 0.5698961859044438, 'colsample_bylevel': 0.58173768320992, 'l2_leaf_reg': 6.5820739952329905, 'random_strength': 9.533926389710716}. Best is trial 8 with value: 2.3342054540264265.\n",
"[I 2025-01-31 22:11:23,980] Trial 17 finished with value: 2.722341758877473 and parameters: {'iterations': 686, 'learning_rate': 0.023070272594262208, 'depth': 5, 'subsample': 0.6317324050504781, 'colsample_bylevel': 0.72562237372333, 'l2_leaf_reg': 10.240074501827097, 'random_strength': 6.92600047428353}. Best is trial 8 with value: 2.3342054540264265.\n",
"[I 2025-01-31 22:11:56,050] Trial 18 finished with value: 2.6781381800641975 and parameters: {'iterations': 996, 'learning_rate': 0.05258897533675518, 'depth': 5, 'subsample': 0.5231598132903472, 'colsample_bylevel': 0.6477077001044423, 'l2_leaf_reg': 18.592000812751706, 'random_strength': 8.344932947613101}. Best is trial 8 with value: 2.3342054540264265.\n",
"[I 2025-01-31 22:12:18,385] Trial 19 finished with value: 3.4976580765293415 and parameters: {'iterations': 584, 'learning_rate': 0.019380508698435384, 'depth': 6, 'subsample': 0.5836852243872083, 'colsample_bylevel': 0.6877774946763483, 'l2_leaf_reg': 7.57681462211325, 'random_strength': 9.432363107129735}. Best is trial 8 with value: 2.3342054540264265.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best trial:\n",
" RMSE: 2.3342054540264265\n",
" Params: {'iterations': 831, 'learning_rate': 0.04518694466082468, 'depth': 6, 'subsample': 0.5547145239784439, 'colsample_bylevel': 0.6605692487332455, 'l2_leaf_reg': 5.931089190499905, 'random_strength': 9.381970152240088}\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": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0:\tlearn: 13.2553205\ttest: 22.9596935\tbest: 22.9596935 (0)\ttotal: 33.4ms\tremaining: 27.7s\n",
"200:\tlearn: 0.9396532\ttest: 3.4434437\tbest: 3.4434437 (200)\ttotal: 7.79s\tremaining: 24.4s\n",
"400:\tlearn: 0.3768311\ttest: 2.5350476\tbest: 2.5350476 (400)\ttotal: 18.5s\tremaining: 19.8s\n",
"600:\tlearn: 0.3062632\ttest: 2.4067533\tbest: 2.4067533 (599)\ttotal: 28s\tremaining: 10.7s\n",
"800:\tlearn: 0.2733607\ttest: 2.3423564\tbest: 2.3423564 (800)\ttotal: 37s\tremaining: 1.39s\n",
"830:\tlearn: 0.2690607\ttest: 2.3342055\tbest: 2.3342055 (830)\ttotal: 38.8s\tremaining: 0us\n",
"\n",
"bestTest = 2.334205454\n",
"bestIteration = 830\n",
"\n",
"Final RMSE: 2.3342054540264265\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": 12,
"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})\n",
"\n",
"# Membatasi hanya pada 20 indeks pertama\n",
"comparison_df_subset = comparison_df.iloc[:20]\n",
"\n",
"# Scatter plot untuk membandingkan prediksi dan nilai asli\n",
"plt.figure(figsize=(10, 6))\n",
"plt.scatter(range(len(comparison_df_subset)), comparison_df_subset['Actual'], label='Actual Values', alpha=0.7, color='blue')\n",
"plt.scatter(range(len(comparison_df_subset)), comparison_df_subset['Predicted'], label='Predicted Values', alpha=0.7, color='orange')\n",
"plt.title('Comparison of Actual vs Predicted Active Work Months (First 20)')\n",
"plt.xlabel('Index')\n",
"plt.ylabel('Active Work Months')\n",
"plt.legend()\n",
"plt.grid(True)\n",
"plt.show()\n",
"\n",
"# Line plot untuk membandingkan prediksi dan nilai asli\n",
"plt.figure(figsize=(10, 6))\n",
"plt.plot(range(len(comparison_df_subset)), comparison_df_subset['Actual'], label='Actual Values', marker='o', linestyle='-', color='blue')\n",
"plt.plot(range(len(comparison_df_subset)), comparison_df_subset['Predicted'], label='Predicted Values', marker='x', linestyle='-', color='orange')\n",
"plt.title('Actual vs Predicted Active Work Months (First 20 - Line Plot)')\n",
"plt.xlabel('Index')\n",
"plt.ylabel('Active Work Months')\n",
"plt.legend()\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final Training RMSE: 0.28955484233140216\n",
"Final Validation RMSE: 1.5407851315191226\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": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final Training RMSE: 0.2690606507832273\n",
"Final Validation RMSE: 2.334205454026432\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": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"\n",
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"\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>MSE</th>\n",
" <td>5.448515</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MAE</th>\n",
" <td>1.116134</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RMSE</th>\n",
" <td>2.334205</td>\n",
" </tr>\n",
" <tr>\n",
" <th>R2 Score</th>\n",
" <td>0.829029</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Score\n",
"MSE 5.448515\n",
"MAE 1.116134\n",
"RMSE 2.334205\n",
"R2 Score 0.829029"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
"import numpy as np\n",
"\n",
"# Prediksi pada data valid\n",
"y_pred = final_model.predict(X_valid)\n",
"\n",
"# Menghitung metrik regresi\n",
"mse = mean_squared_error(y_valid, y_pred)\n",
"mae = mean_absolute_error(y_valid, y_pred)\n",
"rmse = np.sqrt(mse)\n",
"r2 = r2_score(y_valid, y_pred)\n",
"\n",
"# Membuat dataframe hasil metrik\n",
"metrics = {\n",
" \"MSE\": mse,\n",
" \"MAE\": mae,\n",
" \"RMSE\": rmse,\n",
" \"R2 Score\": r2\n",
"}\n",
"\n",
"metrics_df = pd.DataFrame.from_dict(metrics, orient='index', columns=['Score'])\n",
"metrics_df"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CatBoost Regression model saved to 'regression_model.sav'\n"
]
}
],
"source": [
"import pickle\n",
"\n",
"with open('regression_model.sav', 'wb') as f:\n",
" pickle.dump(model, f)\n",
"print(\"CatBoost Regression model saved to 'regression_model.sav'\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CatBoost Regression model saved to 'regression_model.sav'\n"
]
}
],
"source": [
"import pickle\n",
"\n",
"with open('regression_model_final.sav', 'wb') as f:\n",
" pickle.dump(final_model, f)\n",
"print(\"CatBoost Regression model saved to 'regression_model.sav'\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"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": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0:\tlearn: 9.1646718\ttotal: 29.5ms\tremaining: 29.4s\n",
"200:\tlearn: 1.8381351\ttotal: 6.45s\tremaining: 25.7s\n",
"400:\tlearn: 0.6192440\ttotal: 12.9s\tremaining: 19.3s\n",
"600:\tlearn: 0.4141800\ttotal: 19.9s\tremaining: 13.2s\n",
"800:\tlearn: 0.3521180\ttotal: 27.1s\tremaining: 6.74s\n",
"999:\tlearn: 0.3155382\ttotal: 35.7s\tremaining: 0us\n",
"0:\tlearn: 9.8938338\ttotal: 41.5ms\tremaining: 41.4s\n",
"200:\tlearn: 1.8182014\ttotal: 7.76s\tremaining: 30.8s\n",
"400:\tlearn: 0.5997486\ttotal: 15s\tremaining: 22.4s\n",
"600:\tlearn: 0.4063431\ttotal: 22.2s\tremaining: 14.7s\n",
"800:\tlearn: 0.3487810\ttotal: 30.2s\tremaining: 7.51s\n",
"999:\tlearn: 0.3168919\ttotal: 38s\tremaining: 0us\n",
"0:\tlearn: 11.9438216\ttotal: 34.6ms\tremaining: 34.6s\n",
"200:\tlearn: 2.0373408\ttotal: 8.97s\tremaining: 35.7s\n",
"400:\tlearn: 0.5734042\ttotal: 18.7s\tremaining: 27.9s\n",
"600:\tlearn: 0.3694254\ttotal: 28.3s\tremaining: 18.8s\n",
"800:\tlearn: 0.3167774\ttotal: 37.7s\tremaining: 9.38s\n",
"999:\tlearn: 0.2925512\ttotal: 47.9s\tremaining: 0us\n",
"0:\tlearn: 13.7420148\ttotal: 47.3ms\tremaining: 47.2s\n",
"200:\tlearn: 2.2489494\ttotal: 11.4s\tremaining: 45.2s\n",
"400:\tlearn: 0.5929080\ttotal: 23s\tremaining: 34.3s\n",
"600:\tlearn: 0.3608850\ttotal: 33.3s\tremaining: 22.1s\n",
"800:\tlearn: 0.3051274\ttotal: 44s\tremaining: 10.9s\n",
"999:\tlearn: 0.2856118\ttotal: 55.1s\tremaining: 0us\n",
"0:\tlearn: 14.7940073\ttotal: 55.6ms\tremaining: 55.6s\n",
"200:\tlearn: 2.3902581\ttotal: 11s\tremaining: 43.8s\n",
"400:\tlearn: 0.6209255\ttotal: 22s\tremaining: 32.9s\n",
"600:\tlearn: 0.3622676\ttotal: 33.4s\tremaining: 22.2s\n",
"800:\tlearn: 0.3017520\ttotal: 46s\tremaining: 11.4s\n",
"999:\tlearn: 0.2791322\ttotal: 58.3s\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": [
"R² Scores for each fold: [0.98369199 0.9882848 0.99649707 0.99211623 0.80882796]\n",
"Mean R²: 0.95\n",
"Standard Deviation: 0.07\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": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
},
{
"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: [0.95016289 0.96477909 0.99462171 0.97371542 0.38662643]\n",
"Mean R²: 0.85\n",
"Standard Deviation: 0.23\n"
]
}
],
"source": [
"time_series_cross_validate_and_visualize_r2(final_model, X, y, n_splits=5)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YHxMZUUgarGa"
},
"source": [
"# Testing"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 348
},
"executionInfo": {
"elapsed": 772,
"status": "ok",
"timestamp": 1735311729780,
"user": {
"displayName": "kelompok bersama",
"userId": "01911350349879401396"
},
"user_tz": -420
},
"id": "hwyHyRX_ap0R",
"outputId": "f98c2683-1c1e-437b-fc49-9f61f5dbba4f"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>employee_id</th>\n",
" <th>domisili</th>\n",
" <th>jenis_kelamin</th>\n",
" <th>date_of_birth</th>\n",
" <th>join_date</th>\n",
" <th>resign_date</th>\n",
" <th>marriage_stat</th>\n",
" <th>dependant</th>\n",
" <th>education</th>\n",
" <th>absent_90D</th>\n",
" <th>...</th>\n",
" <th>active_work_category</th>\n",
" <th>work_stability_score</th>\n",
" <th>married_dependent_ratio</th>\n",
" <th>position_score</th>\n",
" <th>job_income_position_score</th>\n",
" <th>education_score</th>\n",
" <th>education_income_ratio</th>\n",
" <th>weighted_satisfaction_performance</th>\n",
" <th>resign_risk_indicator</th>\n",
" <th>adjusted_work_time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>EM0406</td>\n",
" <td>Kota Jakarta Utara</td>\n",
" <td>Laki-laki</td>\n",
" <td>1975-01-07</td>\n",
" <td>2021-09-25</td>\n",
" <td>2023-12-07</td>\n",
" <td>Married</td>\n",
" <td>3</td>\n",
" <td>SLTA</td>\n",
" <td>3.0</td>\n",
" <td>...</td>\n",
" <td>Mid-term</td>\n",
" <td>6.500000</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1578410.0</td>\n",
" <td>1</td>\n",
" <td>1578410.00</td>\n",
" <td>1.0</td>\n",
" <td>Medium</td>\n",
" <td>9.797428</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>EM1772</td>\n",
" <td>Kabupaten Bogor</td>\n",
" <td>Perempuan</td>\n",
" <td>1993-04-18</td>\n",
" <td>2021-02-23</td>\n",
" <td>2023-06-24</td>\n",
" <td>Married</td>\n",
" <td>1</td>\n",
" <td>SLTA</td>\n",
" <td>2.0</td>\n",
" <td>...</td>\n",
" <td>Mid-term</td>\n",
" <td>9.333333</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2003154.0</td>\n",
" <td>1</td>\n",
" <td>2003154.00</td>\n",
" <td>2.2</td>\n",
" <td>Medium</td>\n",
" <td>9.342582</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>EM7996</td>\n",
" <td>Tangerang</td>\n",
" <td>Laki-laki</td>\n",
" <td>1998-02-12</td>\n",
" <td>2023-05-04</td>\n",
" <td>2024-06-29</td>\n",
" <td>Single</td>\n",
" <td>0</td>\n",
" <td>SLTA</td>\n",
" <td>5.0</td>\n",
" <td>...</td>\n",
" <td>Mid-term</td>\n",
" <td>2.333333</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1394384.0</td>\n",
" <td>1</td>\n",
" <td>1394384.00</td>\n",
" <td>3.0</td>\n",
" <td>Medium</td>\n",
" <td>9.551975</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>EM13978</td>\n",
" <td>Kota Jakarta Barat</td>\n",
" <td>Perempuan</td>\n",
" <td>1982-12-26</td>\n",
" <td>2021-09-11</td>\n",
" <td>2023-04-03</td>\n",
" <td>Married</td>\n",
" <td>0</td>\n",
" <td>D3</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>Mid-term</td>\n",
" <td>18.000000</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4151999.0</td>\n",
" <td>4</td>\n",
" <td>1037999.75</td>\n",
" <td>2.6</td>\n",
" <td>Medium</td>\n",
" <td>9.180000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>EM9860</td>\n",
" <td>Tangerang</td>\n",
" <td>Perempuan</td>\n",
" <td>1997-03-26</td>\n",
" <td>2023-06-20</td>\n",
" <td>2024-10-02</td>\n",
" <td>Single</td>\n",
" <td>0</td>\n",
" <td>SLTA</td>\n",
" <td>15.0</td>\n",
" <td>...</td>\n",
" <td>Mid-term</td>\n",
" <td>0.937500</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1560817.0</td>\n",
" <td>1</td>\n",
" <td>1560817.00</td>\n",
" <td>2.6</td>\n",
" <td>Medium</td>\n",
" <td>9.414301</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 37 columns</p>\n",
"</div>"
],
"text/plain": [
" employee_id domisili jenis_kelamin date_of_birth join_date \\\n",
"0 EM0406 Kota Jakarta Utara Laki-laki 1975-01-07 2021-09-25 \n",
"1 EM1772 Kabupaten Bogor Perempuan 1993-04-18 2021-02-23 \n",
"2 EM7996 Tangerang Laki-laki 1998-02-12 2023-05-04 \n",
"3 EM13978 Kota Jakarta Barat Perempuan 1982-12-26 2021-09-11 \n",
"4 EM9860 Tangerang Perempuan 1997-03-26 2023-06-20 \n",
"\n",
" resign_date marriage_stat dependant education absent_90D ... \\\n",
"0 2023-12-07 Married 3 SLTA 3.0 ... \n",
"1 2023-06-24 Married 1 SLTA 2.0 ... \n",
"2 2024-06-29 Single 0 SLTA 5.0 ... \n",
"3 2023-04-03 Married 0 D3 0.0 ... \n",
"4 2024-10-02 Single 0 SLTA 15.0 ... \n",
"\n",
" active_work_category work_stability_score married_dependent_ratio \\\n",
"0 Mid-term 6.500000 4 \n",
"1 Mid-term 9.333333 2 \n",
"2 Mid-term 2.333333 1 \n",
"3 Mid-term 18.000000 1 \n",
"4 Mid-term 0.937500 1 \n",
"\n",
" position_score job_income_position_score education_score \\\n",
"0 1 1578410.0 1 \n",
"1 1 2003154.0 1 \n",
"2 1 1394384.0 1 \n",
"3 1 4151999.0 4 \n",
"4 1 1560817.0 1 \n",
"\n",
" education_income_ratio weighted_satisfaction_performance \\\n",
"0 1578410.00 1.0 \n",
"1 2003154.00 2.2 \n",
"2 1394384.00 3.0 \n",
"3 1037999.75 2.6 \n",
"4 1560817.00 2.6 \n",
"\n",
" resign_risk_indicator adjusted_work_time \n",
"0 Medium 9.797428 \n",
"1 Medium 9.342582 \n",
"2 Medium 9.551975 \n",
"3 Medium 9.180000 \n",
"4 Medium 9.414301 \n",
"\n",
"[5 rows x 37 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_test = pd.read_csv('D:/Tugas Akhir/Codingan/Notebook - Playground/preprocessed_data_test_7.csv')\n",
"df_test.head()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 561,
"status": "ok",
"timestamp": 1735309156053,
"user": {
"displayName": "kelompok bersama",
"userId": "01911350349879401396"
},
"user_tz": -420
},
"id": "VX58wjd8bSfB",
"outputId": "6d7dd536-8981-4528-8fb5-4fb3e0fec7f4"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R² Score: 0.9965371265802584\n",
"Mean Absolute Error (MAE): 0.3994272346412294\n",
"Mean Squared Error (MSE): 0.6346462447604223\n",
"Root Mean Squared Error (RMSE): 0.7966468758241774\n"
]
}
],
"source": [
"from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
"import numpy as np\n",
"\n",
"# Menghapus kolom yang tidak diperlukan untuk prediksi\n",
"X_test = df_test.drop(columns=['active_work_months','churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
"\n",
"# Lakukan prediksi menggunakan model final\n",
"y_pred = model.predict(X_test)\n",
"\n",
"# Tambahkan prediksi ke DataFrame\n",
"X_test['predicted_active_work'] = y_pred\n",
"\n",
"# Hitung metrik evaluasi\n",
"r2 = r2_score(df_test['active_work_months'], y_pred)\n",
"mae = mean_absolute_error(df_test['active_work_months'], y_pred)\n",
"mse = mean_squared_error(df_test['active_work_months'], y_pred)\n",
"rmse = np.sqrt(mse)\n",
"\n",
"# Cetak hasil\n",
"print(\"R² Score:\", r2)\n",
"print(\"Mean Absolute Error (MAE):\", mae)\n",
"print(\"Mean Squared Error (MSE):\", mse)\n",
"print(\"Root Mean Squared Error (RMSE):\", rmse)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>employee_id</th>\n",
" <th>domisili</th>\n",
" <th>jenis_kelamin</th>\n",
" <th>date_of_birth</th>\n",
" <th>join_date</th>\n",
" <th>resign_date</th>\n",
" <th>marriage_stat</th>\n",
" <th>dependant</th>\n",
" <th>education</th>\n",
" <th>absent_90D</th>\n",
" <th>...</th>\n",
" <th>active_work_category</th>\n",
" <th>work_stability_score</th>\n",
" <th>married_dependent_ratio</th>\n",
" <th>position_score</th>\n",
" <th>job_income_position_score</th>\n",
" <th>education_score</th>\n",
" <th>education_income_ratio</th>\n",
" <th>weighted_satisfaction_performance</th>\n",
" <th>resign_risk_indicator</th>\n",
" <th>adjusted_work_time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>EM0120</td>\n",
" <td>Tangerang</td>\n",
" <td>Laki-laki</td>\n",
" <td>1990-02-18</td>\n",
" <td>2023-01-11</td>\n",
" <td>2024-01-30</td>\n",
" <td>Married</td>\n",
" <td>1</td>\n",
" <td>SLTA</td>\n",
" <td>11.0</td>\n",
" <td>...</td>\n",
" <td>Mid-term</td>\n",
" <td>1.000000</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1140915.0</td>\n",
" <td>1</td>\n",
" <td>1140915.0</td>\n",
" <td>1.4</td>\n",
" <td>Medium</td>\n",
" <td>9.393432</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>EM13985</td>\n",
" <td>Kepulauan Seribu</td>\n",
" <td>Perempuan</td>\n",
" <td>1987-02-01</td>\n",
" <td>2022-09-26</td>\n",
" <td>2023-11-08</td>\n",
" <td>Married</td>\n",
" <td>1</td>\n",
" <td>SLTA</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>Mid-term</td>\n",
" <td>13.000000</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2103348.0</td>\n",
" <td>1</td>\n",
" <td>2103348.0</td>\n",
" <td>1.8</td>\n",
" <td>Medium</td>\n",
" <td>9.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>EM2560</td>\n",
" <td>Tangerang</td>\n",
" <td>Perempuan</td>\n",
" <td>1999-11-01</td>\n",
" <td>2023-01-05</td>\n",
" <td>2024-05-04</td>\n",
" <td>Single</td>\n",
" <td>0</td>\n",
" <td>SLTA</td>\n",
" <td>10.0</td>\n",
" <td>...</td>\n",
" <td>Mid-term</td>\n",
" <td>1.454545</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2145814.0</td>\n",
" <td>1</td>\n",
" <td>2145814.0</td>\n",
" <td>1.6</td>\n",
" <td>Medium</td>\n",
" <td>9.205670</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>EM0343</td>\n",
" <td>Kabupaten Bekasi</td>\n",
" <td>Laki-laki</td>\n",
" <td>1990-10-12</td>\n",
" <td>2022-02-01</td>\n",
" <td>2023-07-17</td>\n",
" <td>Single</td>\n",
" <td>0</td>\n",
" <td>SLTA</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>Mid-term</td>\n",
" <td>8.500000</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2331081.0</td>\n",
" <td>1</td>\n",
" <td>2331081.0</td>\n",
" <td>2.6</td>\n",
" <td>Medium</td>\n",
" <td>9.154017</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>EM14458</td>\n",
" <td>Kabupaten Bogor</td>\n",
" <td>Perempuan</td>\n",
" <td>1996-04-24</td>\n",
" <td>2022-10-23</td>\n",
" <td>2023-12-30</td>\n",
" <td>Married</td>\n",
" <td>1</td>\n",
" <td>SLTA</td>\n",
" <td>12.0</td>\n",
" <td>...</td>\n",
" <td>Mid-term</td>\n",
" <td>1.076923</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1798725.0</td>\n",
" <td>1</td>\n",
" <td>1798725.0</td>\n",
" <td>2.6</td>\n",
" <td>Medium</td>\n",
" <td>9.706741</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 37 columns</p>\n",
"</div>"
],
"text/plain": [
" employee_id domisili jenis_kelamin date_of_birth join_date \\\n",
"0 EM0120 Tangerang Laki-laki 1990-02-18 2023-01-11 \n",
"1 EM13985 Kepulauan Seribu Perempuan 1987-02-01 2022-09-26 \n",
"2 EM2560 Tangerang Perempuan 1999-11-01 2023-01-05 \n",
"3 EM0343 Kabupaten Bekasi Laki-laki 1990-10-12 2022-02-01 \n",
"4 EM14458 Kabupaten Bogor Perempuan 1996-04-24 2022-10-23 \n",
"\n",
" resign_date marriage_stat dependant education absent_90D ... \\\n",
"0 2024-01-30 Married 1 SLTA 11.0 ... \n",
"1 2023-11-08 Married 1 SLTA 0.0 ... \n",
"2 2024-05-04 Single 0 SLTA 10.0 ... \n",
"3 2023-07-17 Single 0 SLTA 1.0 ... \n",
"4 2023-12-30 Married 1 SLTA 12.0 ... \n",
"\n",
" active_work_category work_stability_score married_dependent_ratio \\\n",
"0 Mid-term 1.000000 2 \n",
"1 Mid-term 13.000000 2 \n",
"2 Mid-term 1.454545 1 \n",
"3 Mid-term 8.500000 1 \n",
"4 Mid-term 1.076923 2 \n",
"\n",
" position_score job_income_position_score education_score \\\n",
"0 1 1140915.0 1 \n",
"1 1 2103348.0 1 \n",
"2 1 2145814.0 1 \n",
"3 1 2331081.0 1 \n",
"4 1 1798725.0 1 \n",
"\n",
" education_income_ratio weighted_satisfaction_performance \\\n",
"0 1140915.0 1.4 \n",
"1 2103348.0 1.8 \n",
"2 2145814.0 1.6 \n",
"3 2331081.0 2.6 \n",
"4 1798725.0 2.6 \n",
"\n",
" resign_risk_indicator adjusted_work_time \n",
"0 Medium 9.393432 \n",
"1 Medium 9.300000 \n",
"2 Medium 9.205670 \n",
"3 Medium 9.154017 \n",
"4 Medium 9.706741 \n",
"\n",
"[5 rows x 37 columns]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_test = pd.read_csv('D:/Tugas Akhir/Codingan/Notebook - Playground/preprocessed_data_test_5.csv')\n",
"df_test.head()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R² Score: 0.9965371265802584\n",
"Mean Absolute Error (MAE): 0.3994272346412294\n",
"Mean Squared Error (MSE): 0.6346462447604223\n",
"Root Mean Squared Error (RMSE): 0.7966468758241774\n"
]
}
],
"source": [
"from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
"import numpy as np\n",
"\n",
"# Menghapus kolom yang tidak diperlukan untuk prediksi\n",
"X_test = df_test.drop(columns=['active_work_months','churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
"\n",
"# Lakukan prediksi menggunakan model final\n",
"y_pred = model.predict(X_test)\n",
"\n",
"# Tambahkan prediksi ke DataFrame\n",
"X_test['predicted_active_work'] = y_pred\n",
"\n",
"# Hitung metrik evaluasi\n",
"r2 = r2_score(df_test['active_work_months'], y_pred)\n",
"mae = mean_absolute_error(df_test['active_work_months'], y_pred)\n",
"mse = mean_squared_error(df_test['active_work_months'], y_pred)\n",
"rmse = np.sqrt(mse)\n",
"\n",
"# Cetak hasil\n",
"print(\"R² Score:\", r2)\n",
"print(\"Mean Absolute Error (MAE):\", mae)\n",
"print(\"Mean Squared Error (MSE):\", mse)\n",
"print(\"Root Mean Squared Error (RMSE):\", rmse)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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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>active_work_category</th>\n",
" <th>work_stability_score</th>\n",
" <th>married_dependent_ratio</th>\n",
" <th>position_score</th>\n",
" <th>job_income_position_score</th>\n",
" <th>education_score</th>\n",
" <th>education_income_ratio</th>\n",
" <th>weighted_satisfaction_performance</th>\n",
" <th>resign_risk_indicator</th>\n",
" <th>adjusted_work_time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>EM0012</td>\n",
" <td>Tangerang</td>\n",
" <td>Laki-laki</td>\n",
" <td>1970-12-21</td>\n",
" <td>2023-02-23</td>\n",
" <td>2024-08-07</td>\n",
" <td>Married</td>\n",
" <td>3</td>\n",
" <td>D3</td>\n",
" <td>2.0</td>\n",
" <td>...</td>\n",
" <td>Mid-term</td>\n",
" <td>5.666667</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>4708861.0</td>\n",
" <td>4</td>\n",
" <td>1.177215e+06</td>\n",
" <td>1.4</td>\n",
" <td>Medium</td>\n",
" <td>9.857106</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>EM0026</td>\n",
" <td>Kota Depok</td>\n",
" <td>Laki-laki</td>\n",
" <td>1986-11-14</td>\n",
" <td>2022-04-17</td>\n",
" <td>2024-08-04</td>\n",
" <td>Married</td>\n",
" <td>2</td>\n",
" <td>SLTA</td>\n",
" <td>4.0</td>\n",
" <td>...</td>\n",
" <td>Mid-term</td>\n",
" <td>5.600000</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1430853.0</td>\n",
" <td>1</td>\n",
" <td>1.430853e+06</td>\n",
" <td>1.0</td>\n",
" <td>Medium</td>\n",
" <td>9.694593</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>EM0041</td>\n",
" <td>Kota Jakarta Barat</td>\n",
" <td>Laki-laki</td>\n",
" <td>1983-03-16</td>\n",
" <td>2023-06-15</td>\n",
" <td>2024-09-06</td>\n",
" <td>Divorce</td>\n",
" <td>3</td>\n",
" <td>SLTA</td>\n",
" <td>7.0</td>\n",
" <td>...</td>\n",
" <td>Mid-term</td>\n",
" <td>1.750000</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1379381.0</td>\n",
" <td>1</td>\n",
" <td>1.379381e+06</td>\n",
" <td>2.4</td>\n",
" <td>Medium</td>\n",
" <td>9.059429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>EM0053</td>\n",
" <td>Kota Jakarta Timur</td>\n",
" <td>Laki-laki</td>\n",
" <td>1979-07-13</td>\n",
" <td>2023-07-11</td>\n",
" <td>2024-09-21</td>\n",
" <td>Single</td>\n",
" <td>0</td>\n",
" <td>SLTA</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>Mid-term</td>\n",
" <td>7.000000</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1911583.0</td>\n",
" <td>1</td>\n",
" <td>1.911583e+06</td>\n",
" <td>1.0</td>\n",
" <td>Medium</td>\n",
" <td>9.842189</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>EM0057</td>\n",
" <td>Kota Jakarta Barat</td>\n",
" <td>Perempuan</td>\n",
" <td>2000-03-13</td>\n",
" <td>2022-07-14</td>\n",
" <td>2024-08-29</td>\n",
" <td>Single</td>\n",
" <td>0</td>\n",
" <td>D2</td>\n",
" <td>8.0</td>\n",
" <td>...</td>\n",
" <td>Mid-term</td>\n",
" <td>2.777778</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3724157.0</td>\n",
" <td>3</td>\n",
" <td>1.241386e+06</td>\n",
" <td>2.0</td>\n",
" <td>Medium</td>\n",
" <td>9.047730</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 37 columns</p>\n",
"</div>"
],
"text/plain": [
" employee_id domisili jenis_kelamin date_of_birth join_date \\\n",
"0 EM0012 Tangerang Laki-laki 1970-12-21 2023-02-23 \n",
"1 EM0026 Kota Depok Laki-laki 1986-11-14 2022-04-17 \n",
"2 EM0041 Kota Jakarta Barat Laki-laki 1983-03-16 2023-06-15 \n",
"3 EM0053 Kota Jakarta Timur Laki-laki 1979-07-13 2023-07-11 \n",
"4 EM0057 Kota Jakarta Barat Perempuan 2000-03-13 2022-07-14 \n",
"\n",
" resign_date marriage_stat dependant education absent_90D ... \\\n",
"0 2024-08-07 Married 3 D3 2.0 ... \n",
"1 2024-08-04 Married 2 SLTA 4.0 ... \n",
"2 2024-09-06 Divorce 3 SLTA 7.0 ... \n",
"3 2024-09-21 Single 0 SLTA 1.0 ... \n",
"4 2024-08-29 Single 0 D2 8.0 ... \n",
"\n",
" active_work_category work_stability_score married_dependent_ratio \\\n",
"0 Mid-term 5.666667 4 \n",
"1 Mid-term 5.600000 3 \n",
"2 Mid-term 1.750000 1 \n",
"3 Mid-term 7.000000 1 \n",
"4 Mid-term 2.777778 1 \n",
"\n",
" position_score job_income_position_score education_score \\\n",
"0 1 4708861.0 4 \n",
"1 1 1430853.0 1 \n",
"2 1 1379381.0 1 \n",
"3 1 1911583.0 1 \n",
"4 1 3724157.0 3 \n",
"\n",
" education_income_ratio weighted_satisfaction_performance \\\n",
"0 1.177215e+06 1.4 \n",
"1 1.430853e+06 1.0 \n",
"2 1.379381e+06 2.4 \n",
"3 1.911583e+06 1.0 \n",
"4 1.241386e+06 2.0 \n",
"\n",
" resign_risk_indicator adjusted_work_time \n",
"0 Medium 9.857106 \n",
"1 Medium 9.694593 \n",
"2 Medium 9.059429 \n",
"3 Medium 9.842189 \n",
"4 Medium 9.047730 \n",
"\n",
"[5 rows x 37 columns]"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_test = pd.read_csv('D:\\Tugas Akhir\\Codingan\\Development\\Data\\data_testing_resign_6.csv')\n",
"df_test.head()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R² Score: 0.9983869290912244\n",
"Mean Absolute Error (MAE): 0.18480799751480997\n",
"Mean Squared Error (MSE): 0.06445121600267545\n",
"Root Mean Squared Error (RMSE): 0.25387244041580304\n"
]
}
],
"source": [
"from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
"import numpy as np\n",
"\n",
"# Menghapus kolom yang tidak diperlukan untuk prediksi\n",
"X_test = df_test.drop(columns=['active_work_months','churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
"\n",
"# Lakukan prediksi menggunakan model final\n",
"y_pred = final_model.predict(X_test)\n",
"\n",
"# Tambahkan prediksi ke DataFrame\n",
"X_test['predicted_active_work'] = y_pred\n",
"\n",
"# Hitung metrik evaluasi\n",
"r2 = r2_score(df_test['active_work_months'], y_pred)\n",
"mae = mean_absolute_error(df_test['active_work_months'], y_pred)\n",
"mse = mean_squared_error(df_test['active_work_months'], y_pred)\n",
"rmse = np.sqrt(mse)\n",
"\n",
"# Cetak hasil\n",
"print(\"R² Score:\", r2)\n",
"print(\"Mean Absolute Error (MAE):\", mae)\n",
"print(\"Mean Squared Error (MSE):\", mse)\n",
"print(\"Root Mean Squared Error (RMSE):\", rmse)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"\n",
"# Nilai aktual dan prediksi\n",
"y_actual = df_test['active_work_months'] # Nilai aktual dari active_work_months\n",
"y_pred = X_test['predicted_active_work'] # Nilai prediksi dari model regresi\n",
"\n",
"# Membuat DataFrame untuk mempermudah visualisasi\n",
"comparison_df = pd.DataFrame({'Actual': y_actual, 'Predicted': y_pred})\n",
"\n",
"# Scatter plot untuk membandingkan prediksi dan nilai asli\n",
"plt.figure(figsize=(10, 6))\n",
"plt.scatter(range(len(y_actual)), y_actual, label='Actual Values', alpha=0.7, color='blue')\n",
"plt.scatter(range(len(y_pred)), y_pred, label='Predicted Values', alpha=0.7, color='orange')\n",
"plt.title('Comparison of Actual vs Predicted Active Work Months')\n",
"plt.xlabel('Index')\n",
"plt.ylabel('Active Work Months')\n",
"plt.legend()\n",
"plt.grid(True)\n",
"plt.show()\n",
"\n",
"# Line plot untuk membandingkan prediksi dan nilai asli\n",
"plt.figure(figsize=(10, 6))\n",
"plt.plot(range(len(y_actual)), y_actual, label='Actual Values', marker='o', linestyle='-', color='blue')\n",
"plt.plot(range(len(y_pred)), y_pred, label='Predicted Values', marker='x', linestyle='-', color='orange')\n",
"plt.title('Actual vs Predicted Active Work Months (Line Plot)')\n",
"plt.xlabel('Index')\n",
"plt.ylabel('Active Work Months')\n",
"plt.legend()\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CatBoost Regression model saved to 'regression_model.sav'\n"
]
}
],
"source": [
"import pickle\n",
"\n",
"with open('regression_model.sav', 'wb') as f:\n",
" pickle.dump(final_model, f)\n",
"print(\"CatBoost Regression model saved to 'regression_model.sav'\")"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting shap\n",
" Obtaining dependency information for shap from https://files.pythonhosted.org/packages/02/48/033ab9a2dee26d3de7e57cf532ab1d8408a608544c85ff98e6ea65775bdf/shap-0.46.0-cp39-cp39-win_amd64.whl.metadata\n",
" Downloading shap-0.46.0-cp39-cp39-win_amd64.whl.metadata (25 kB)\n",
"Requirement already satisfied: numpy in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from shap) (1.22.4)\n",
"Requirement already satisfied: scipy in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from shap) (1.9.3)\n",
"Requirement already satisfied: scikit-learn in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from shap) (1.0.2)\n",
"Requirement already satisfied: pandas in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from shap) (1.4.2)\n",
"Requirement already satisfied: tqdm>=4.27.0 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from shap) (4.65.0)\n",
"Requirement already satisfied: packaging>20.9 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from shap) (23.1)\n",
"Collecting slicer==0.0.8 (from shap)\n",
" Obtaining dependency information for slicer==0.0.8 from https://files.pythonhosted.org/packages/63/81/9ef641ff4e12cbcca30e54e72fb0951a2ba195d0cda0ba4100e532d929db/slicer-0.0.8-py3-none-any.whl.metadata\n",
" Downloading slicer-0.0.8-py3-none-any.whl.metadata (4.0 kB)\n",
"Requirement already satisfied: numba in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from shap) (0.58.1)\n",
"Requirement already satisfied: cloudpickle in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from shap) (1.6.0)\n",
"Requirement already satisfied: colorama in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from tqdm>=4.27.0->shap) (0.4.6)\n",
"Requirement already satisfied: llvmlite<0.42,>=0.41.0dev0 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from numba->shap) (0.41.0)\n",
"Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from pandas->shap) (2.8.2)\n",
"Requirement already satisfied: pytz>=2020.1 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from pandas->shap) (2023.3.post1)\n",
"Requirement already satisfied: joblib>=0.11 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from scikit-learn->shap) (1.2.0)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from scikit-learn->shap) (2.2.0)\n",
"Requirement already satisfied: six>=1.5 in c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.1->pandas->shap) (1.16.0)\n",
"Downloading shap-0.46.0-cp39-cp39-win_amd64.whl (456 kB)\n",
" ---------------------------------------- 0.0/456.1 kB ? eta -:--:--\n",
" --------------------------------------- 10.2/456.1 kB ? eta -:--:--\n",
" --------- ------------------------------ 112.6/456.1 kB 1.6 MB/s eta 0:00:01\n",
" ------------------------------------ --- 419.8/456.1 kB 3.8 MB/s eta 0:00:01\n",
" ---------------------------------------- 456.1/456.1 kB 3.6 MB/s eta 0:00:00\n",
"Downloading slicer-0.0.8-py3-none-any.whl (15 kB)\n",
"Installing collected packages: slicer, shap\n",
"Successfully installed shap-0.46.0 slicer-0.0.8\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: Ignoring invalid distribution -pencv-python (c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages)\n",
"WARNING: Ignoring invalid distribution -treamlit (c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages)\n",
"WARNING: Ignoring invalid distribution -pencv-python (c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages)\n",
"WARNING: Ignoring invalid distribution -treamlit (c:\\users\\jesselyn mu\\anaconda3\\lib\\site-packages)\n"
]
}
],
"source": [
"%pip install shap"
]
}
],
"metadata": {
"colab": {
"authorship_tag": "ABX9TyNQ761UqVawGErIP7JLgmhK",
"provenance": []
},
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}