{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":84896,"databundleVersionId":10305135,"sourceType":"competition"}],"dockerImageVersionId":30822,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import os\nimport optuna\nimport random\nimport numpy as np\nimport pandas as pd \nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom xgboost import XGBRegressor\nfrom lightgbm import LGBMRegressor\nfrom catboost import CatBoostRegressor\nfrom sklearn.experimental import enable_iterative_imputer\nfrom sklearn.impute import IterativeImputer\nfrom sklearn.metrics import mean_squared_log_error\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import OneHotEncoder,StandardScaler\nfrom sklearn.ensemble import GradientBoostingRegressor, HistGradientBoostingRegressor, AdaBoostRegressor\n\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2025-01-06T11:59:17.942113Z","iopub.execute_input":"2025-01-06T11:59:17.942611Z","iopub.status.idle":"2025-01-06T11:59:24.083095Z","shell.execute_reply.started":"2025-01-06T11:59:17.94257Z","shell.execute_reply":"2025-01-06T11:59:24.081681Z"}},"outputs":[{"name":"stdout","text":"/kaggle/input/playground-series-s4e12/sample_submission.csv\n/kaggle/input/playground-series-s4e12/train.csv\n/kaggle/input/playground-series-s4e12/test.csv\n","output_type":"stream"}],"execution_count":1},{"cell_type":"code","source":"# SEED = 42\n# np.random.seed(SEED)\n# random.seed(SEED)\n# os.environ['PYTHONHASHSEED'] = str(SEED)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"orig_train = pd.read_csv(\"/kaggle/input/playground-series-s4e12/train.csv\")\norig_test = pd.read_csv(\"/kaggle/input/playground-series-s4e12/test.csv\")\ntrain = pd.read_csv(\"/kaggle/input/playground-series-s4e12/train.csv\")\ntest = pd.read_csv(\"/kaggle/input/playground-series-s4e12/test.csv\")\ntrain.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T11:59:24.084459Z","iopub.execute_input":"2025-01-06T11:59:24.085163Z","iopub.status.idle":"2025-01-06T11:59:46.024521Z","shell.execute_reply.started":"2025-01-06T11:59:24.085128Z","shell.execute_reply":"2025-01-06T11:59:46.023284Z"}},"outputs":[{"execution_count":2,"output_type":"execute_result","data":{"text/plain":"   id   Age  Gender  Annual Income Marital Status  Number of Dependents  \\\n0   0  19.0  Female        10049.0        Married                   1.0   \n1   1  39.0  Female        31678.0       Divorced                   3.0   \n2   2  23.0    Male        25602.0       Divorced                   3.0   \n3   3  21.0    Male       141855.0        Married                   2.0   \n4   4  21.0    Male        39651.0         Single                   1.0   \n\n  Education Level     Occupation  Health Score  Location  ... Previous Claims  \\\n0      Bachelor's  Self-Employed     22.598761     Urban  ...             2.0   \n1        Master's            NaN     15.569731     Rural  ...             1.0   \n2     High School  Self-Employed     47.177549  Suburban  ...             1.0   \n3      Bachelor's            NaN     10.938144     Rural  ...             1.0   \n4      Bachelor's  Self-Employed     20.376094     Rural  ...             0.0   \n\n   Vehicle Age  Credit Score  Insurance Duration           Policy Start Date  \\\n0         17.0         372.0                 5.0  2023-12-23 15:21:39.134960   \n1         12.0         694.0                 2.0  2023-06-12 15:21:39.111551   \n2         14.0           NaN                 3.0  2023-09-30 15:21:39.221386   \n3          0.0         367.0                 1.0  2024-06-12 15:21:39.226954   \n4          8.0         598.0                 4.0  2021-12-01 15:21:39.252145   \n\n  Customer Feedback Smoking Status Exercise Frequency Property Type  \\\n0              Poor             No             Weekly         House   \n1           Average            Yes            Monthly         House   \n2              Good            Yes             Weekly         House   \n3              Poor            Yes              Daily     Apartment   \n4              Poor            Yes             Weekly         House   \n\n  Premium Amount  \n0         2869.0  \n1         1483.0  \n2          567.0  \n3          765.0  \n4         2022.0  \n\n[5 rows x 21 columns]","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>id</th>\n      <th>Age</th>\n      <th>Gender</th>\n      <th>Annual Income</th>\n      <th>Marital Status</th>\n      <th>Number of Dependents</th>\n      <th>Education Level</th>\n      <th>Occupation</th>\n      <th>Health Score</th>\n      <th>Location</th>\n      <th>...</th>\n      <th>Previous Claims</th>\n      <th>Vehicle Age</th>\n      <th>Credit Score</th>\n      <th>Insurance Duration</th>\n      <th>Policy Start Date</th>\n      <th>Customer Feedback</th>\n      <th>Smoking Status</th>\n      <th>Exercise Frequency</th>\n      <th>Property Type</th>\n      <th>Premium Amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>19.0</td>\n      <td>Female</td>\n      <td>10049.0</td>\n      <td>Married</td>\n      <td>1.0</td>\n      <td>Bachelor's</td>\n      <td>Self-Employed</td>\n      <td>22.598761</td>\n      <td>Urban</td>\n      <td>...</td>\n      <td>2.0</td>\n      <td>17.0</td>\n      <td>372.0</td>\n      <td>5.0</td>\n      <td>2023-12-23 15:21:39.134960</td>\n      <td>Poor</td>\n      <td>No</td>\n      <td>Weekly</td>\n      <td>House</td>\n      <td>2869.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>39.0</td>\n      <td>Female</td>\n      <td>31678.0</td>\n      <td>Divorced</td>\n      <td>3.0</td>\n      <td>Master's</td>\n      <td>NaN</td>\n      <td>15.569731</td>\n      <td>Rural</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>12.0</td>\n      <td>694.0</td>\n      <td>2.0</td>\n      <td>2023-06-12 15:21:39.111551</td>\n      <td>Average</td>\n      <td>Yes</td>\n      <td>Monthly</td>\n      <td>House</td>\n      <td>1483.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>23.0</td>\n      <td>Male</td>\n      <td>25602.0</td>\n      <td>Divorced</td>\n      <td>3.0</td>\n      <td>High School</td>\n      <td>Self-Employed</td>\n      <td>47.177549</td>\n      <td>Suburban</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>14.0</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>2023-09-30 15:21:39.221386</td>\n      <td>Good</td>\n      <td>Yes</td>\n      <td>Weekly</td>\n      <td>House</td>\n      <td>567.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>21.0</td>\n      <td>Male</td>\n      <td>141855.0</td>\n      <td>Married</td>\n      <td>2.0</td>\n      <td>Bachelor's</td>\n      <td>NaN</td>\n      <td>10.938144</td>\n      <td>Rural</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>367.0</td>\n      <td>1.0</td>\n      <td>2024-06-12 15:21:39.226954</td>\n      <td>Poor</td>\n      <td>Yes</td>\n      <td>Daily</td>\n      <td>Apartment</td>\n      <td>765.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>21.0</td>\n      <td>Male</td>\n      <td>39651.0</td>\n      <td>Single</td>\n      <td>1.0</td>\n      <td>Bachelor's</td>\n      <td>Self-Employed</td>\n      <td>20.376094</td>\n      <td>Rural</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>598.0</td>\n      <td>4.0</td>\n      <td>2021-12-01 15:21:39.252145</td>\n      <td>Poor</td>\n      <td>Yes</td>\n      <td>Weekly</td>\n      <td>House</td>\n      <td>2022.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 21 columns</p>\n</div>"},"metadata":{}}],"execution_count":2},{"cell_type":"code","source":"class HelperMethods:\n    def __init__(self):\n        pass\n\n    def plot_null_values(self, data):\n        plt.figure(figsize=(10, 5)) \n\n        if isinstance(data, pd.DataFrame):\n            pass\n        else:\n            data = data.reset_index()\n\n        plt.xticks(rotation=45)\n        sns.barplot(x='index', y=0, data=data)\n        plt.title('Null values')\n        plt.xlabel('Index')\n        plt.ylabel('Count')\n        plt.show()\n\n\n    def one_hot_encoding(self, data):\n        ohe = OneHotEncoder(sparse=False, drop='first')\n        encoded_array  = ohe.fit_transform(data)\n        column_names = ohe.get_feature_names_out(data.columns)\n        encoded_df = pd.DataFrame(encoded_array, columns=column_names)\n        df_encoded = data.drop(data.columns, axis=1).join(encoded_df)\n        return df_encoded\n\n    def rmsle(self, y_true, y_pred):\n        return np.sqrt(mean_squared_log_error(y_true, np.maximum(y_pred, 0)))\n\n\n    def separate_numeric_and_categorical(self, data):\n        categorical = []\n        numerical = []\n        for col in data.columns:\n            if pd.api.types.is_numeric_dtype(data[col]):\n                numerical.append(col)\n            else:\n                categorical.append(col)\n        return numerical, categorical\n\n    def knn_imputer(self, data):\n        if not isinstance(data, pd.DataFrame):\n            raise TypeError(f'Expected data is type {type(data)}, not a Dataframe')\n            return\n        else:\n            imputer = IterativeImputer(max_iter=10, random_state=0)\n            imputed_array = imputer.fit_transform(data) \n            df_imputed = pd.DataFrame(imputed_array, columns=data.columns)\n\n        return df_imputed\n                     \nutils = HelperMethods()       ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T11:59:46.027056Z","iopub.execute_input":"2025-01-06T11:59:46.027469Z","iopub.status.idle":"2025-01-06T11:59:46.038597Z","shell.execute_reply.started":"2025-01-06T11:59:46.027434Z","shell.execute_reply":"2025-01-06T11:59:46.036982Z"}},"outputs":[],"execution_count":3},{"cell_type":"code","source":"test.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T11:59:46.040356Z","iopub.execute_input":"2025-01-06T11:59:46.04078Z","iopub.status.idle":"2025-01-06T11:59:46.087501Z","shell.execute_reply.started":"2025-01-06T11:59:46.040735Z","shell.execute_reply":"2025-01-06T11:59:46.085325Z"}},"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"        id   Age  Gender  Annual Income Marital Status  Number of Dependents  \\\n0  1200000  28.0  Female         2310.0            NaN                   4.0   \n1  1200001  31.0  Female       126031.0        Married                   2.0   \n2  1200002  47.0  Female        17092.0       Divorced                   0.0   \n3  1200003  28.0  Female        30424.0       Divorced                   3.0   \n4  1200004  24.0    Male        10863.0       Divorced                   2.0   \n\n  Education Level     Occupation  Health Score  Location    Policy Type  \\\n0      Bachelor's  Self-Employed      7.657981     Rural          Basic   \n1        Master's  Self-Employed     13.381379  Suburban        Premium   \n2             PhD     Unemployed     24.354527     Urban  Comprehensive   \n3             PhD  Self-Employed      5.136225  Suburban  Comprehensive   \n4     High School     Unemployed     11.844155  Suburban        Premium   \n\n   Previous Claims  Vehicle Age  Credit Score  Insurance Duration  \\\n0              NaN         19.0           NaN                 1.0   \n1              NaN         14.0         372.0                 8.0   \n2              NaN         16.0         819.0                 9.0   \n3              1.0          3.0         770.0                 5.0   \n4              NaN         14.0         755.0                 7.0   \n\n            Policy Start Date Customer Feedback Smoking Status  \\\n0  2023-06-04 15:21:39.245086              Poor            Yes   \n1  2024-04-22 15:21:39.224915              Good            Yes   \n2  2023-04-05 15:21:39.134960           Average            Yes   \n3  2023-10-25 15:21:39.134960              Poor            Yes   \n4  2021-11-26 15:21:39.259788           Average             No   \n\n  Exercise Frequency Property Type  \n0             Weekly         House  \n1             Rarely     Apartment  \n2            Monthly         Condo  \n3              Daily         House  \n4             Weekly         House  ","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>id</th>\n      <th>Age</th>\n      <th>Gender</th>\n      <th>Annual Income</th>\n      <th>Marital Status</th>\n      <th>Number of Dependents</th>\n      <th>Education Level</th>\n      <th>Occupation</th>\n      <th>Health Score</th>\n      <th>Location</th>\n      <th>Policy Type</th>\n      <th>Previous Claims</th>\n      <th>Vehicle Age</th>\n      <th>Credit Score</th>\n      <th>Insurance Duration</th>\n      <th>Policy Start Date</th>\n      <th>Customer Feedback</th>\n      <th>Smoking Status</th>\n      <th>Exercise Frequency</th>\n      <th>Property Type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1200000</td>\n      <td>28.0</td>\n      <td>Female</td>\n      <td>2310.0</td>\n      <td>NaN</td>\n      <td>4.0</td>\n      <td>Bachelor's</td>\n      <td>Self-Employed</td>\n      <td>7.657981</td>\n      <td>Rural</td>\n      <td>Basic</td>\n      <td>NaN</td>\n      <td>19.0</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>2023-06-04 15:21:39.245086</td>\n      <td>Poor</td>\n      <td>Yes</td>\n      <td>Weekly</td>\n      <td>House</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1200001</td>\n      <td>31.0</td>\n      <td>Female</td>\n      <td>126031.0</td>\n      <td>Married</td>\n      <td>2.0</td>\n      <td>Master's</td>\n      <td>Self-Employed</td>\n      <td>13.381379</td>\n      <td>Suburban</td>\n      <td>Premium</td>\n      <td>NaN</td>\n      <td>14.0</td>\n      <td>372.0</td>\n      <td>8.0</td>\n      <td>2024-04-22 15:21:39.224915</td>\n      <td>Good</td>\n      <td>Yes</td>\n      <td>Rarely</td>\n      <td>Apartment</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1200002</td>\n      <td>47.0</td>\n      <td>Female</td>\n      <td>17092.0</td>\n      <td>Divorced</td>\n      <td>0.0</td>\n      <td>PhD</td>\n      <td>Unemployed</td>\n      <td>24.354527</td>\n      <td>Urban</td>\n      <td>Comprehensive</td>\n      <td>NaN</td>\n      <td>16.0</td>\n      <td>819.0</td>\n      <td>9.0</td>\n      <td>2023-04-05 15:21:39.134960</td>\n      <td>Average</td>\n      <td>Yes</td>\n      <td>Monthly</td>\n      <td>Condo</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1200003</td>\n      <td>28.0</td>\n      <td>Female</td>\n      <td>30424.0</td>\n      <td>Divorced</td>\n      <td>3.0</td>\n      <td>PhD</td>\n      <td>Self-Employed</td>\n      <td>5.136225</td>\n      <td>Suburban</td>\n      <td>Comprehensive</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>770.0</td>\n      <td>5.0</td>\n      <td>2023-10-25 15:21:39.134960</td>\n      <td>Poor</td>\n      <td>Yes</td>\n      <td>Daily</td>\n      <td>House</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1200004</td>\n      <td>24.0</td>\n      <td>Male</td>\n      <td>10863.0</td>\n      <td>Divorced</td>\n      <td>2.0</td>\n      <td>High School</td>\n      <td>Unemployed</td>\n      <td>11.844155</td>\n      <td>Suburban</td>\n      <td>Premium</td>\n      <td>NaN</td>\n      <td>14.0</td>\n      <td>755.0</td>\n      <td>7.0</td>\n      <td>2021-11-26 15:21:39.259788</td>\n      <td>Average</td>\n      <td>No</td>\n      <td>Weekly</td>\n      <td>House</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":4},{"cell_type":"code","source":"print(f\"rows : {train.shape[0]}\\ncolumns : {train.shape[1]}\")\ny = train['Premium Amount']\ntrain = train.iloc[:, :-1]\n\ntrain.describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T11:59:46.089635Z","iopub.execute_input":"2025-01-06T11:59:46.090251Z","iopub.status.idle":"2025-01-06T11:59:46.986428Z","shell.execute_reply.started":"2025-01-06T11:59:46.090185Z","shell.execute_reply":"2025-01-06T11:59:46.985239Z"}},"outputs":[{"name":"stdout","text":"rows : 1200000\ncolumns : 21\n","output_type":"stream"},{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"                 id           Age  Annual Income  Number of Dependents  \\\ncount  1.200000e+06  1.181295e+06   1.155051e+06          1.090328e+06   \nmean   5.999995e+05  4.114556e+01   3.274522e+04          2.009934e+00   \nstd    3.464103e+05  1.353995e+01   3.217951e+04          1.417338e+00   \nmin    0.000000e+00  1.800000e+01   1.000000e+00          0.000000e+00   \n25%    2.999998e+05  3.000000e+01   8.001000e+03          1.000000e+00   \n50%    5.999995e+05  4.100000e+01   2.391100e+04          2.000000e+00   \n75%    8.999992e+05  5.300000e+01   4.463400e+04          3.000000e+00   \nmax    1.199999e+06  6.400000e+01   1.499970e+05          4.000000e+00   \n\n       Health Score  Previous Claims   Vehicle Age  Credit Score  \\\ncount  1.125924e+06    835971.000000  1.199994e+06  1.062118e+06   \nmean   2.561391e+01         1.002689  9.569889e+00  5.929244e+02   \nstd    1.220346e+01         0.982840  5.776189e+00  1.499819e+02   \nmin    2.012237e+00         0.000000  0.000000e+00  3.000000e+02   \n25%    1.591896e+01         0.000000  5.000000e+00  4.680000e+02   \n50%    2.457865e+01         1.000000  1.000000e+01  5.950000e+02   \n75%    3.452721e+01         2.000000  1.500000e+01  7.210000e+02   \nmax    5.897591e+01         9.000000  1.900000e+01  8.490000e+02   \n\n       Insurance Duration  \ncount        1.199999e+06  \nmean         5.018219e+00  \nstd          2.594331e+00  \nmin          1.000000e+00  \n25%          3.000000e+00  \n50%          5.000000e+00  \n75%          7.000000e+00  \nmax          9.000000e+00  ","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>id</th>\n      <th>Age</th>\n      <th>Annual Income</th>\n      <th>Number of Dependents</th>\n      <th>Health Score</th>\n      <th>Previous Claims</th>\n      <th>Vehicle Age</th>\n      <th>Credit Score</th>\n      <th>Insurance Duration</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1.200000e+06</td>\n      <td>1.181295e+06</td>\n      <td>1.155051e+06</td>\n      <td>1.090328e+06</td>\n      <td>1.125924e+06</td>\n      <td>835971.000000</td>\n      <td>1.199994e+06</td>\n      <td>1.062118e+06</td>\n      <td>1.199999e+06</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>5.999995e+05</td>\n      <td>4.114556e+01</td>\n      <td>3.274522e+04</td>\n      <td>2.009934e+00</td>\n      <td>2.561391e+01</td>\n      <td>1.002689</td>\n      <td>9.569889e+00</td>\n      <td>5.929244e+02</td>\n      <td>5.018219e+00</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>3.464103e+05</td>\n      <td>1.353995e+01</td>\n      <td>3.217951e+04</td>\n      <td>1.417338e+00</td>\n      <td>1.220346e+01</td>\n      <td>0.982840</td>\n      <td>5.776189e+00</td>\n      <td>1.499819e+02</td>\n      <td>2.594331e+00</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000e+00</td>\n      <td>1.800000e+01</td>\n      <td>1.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>2.012237e+00</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>3.000000e+02</td>\n      <td>1.000000e+00</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>2.999998e+05</td>\n      <td>3.000000e+01</td>\n      <td>8.001000e+03</td>\n      <td>1.000000e+00</td>\n      <td>1.591896e+01</td>\n      <td>0.000000</td>\n      <td>5.000000e+00</td>\n      <td>4.680000e+02</td>\n      <td>3.000000e+00</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>5.999995e+05</td>\n      <td>4.100000e+01</td>\n      <td>2.391100e+04</td>\n      <td>2.000000e+00</td>\n      <td>2.457865e+01</td>\n      <td>1.000000</td>\n      <td>1.000000e+01</td>\n      <td>5.950000e+02</td>\n      <td>5.000000e+00</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>8.999992e+05</td>\n      <td>5.300000e+01</td>\n      <td>4.463400e+04</td>\n      <td>3.000000e+00</td>\n      <td>3.452721e+01</td>\n      <td>2.000000</td>\n      <td>1.500000e+01</td>\n      <td>7.210000e+02</td>\n      <td>7.000000e+00</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.199999e+06</td>\n      <td>6.400000e+01</td>\n      <td>1.499970e+05</td>\n      <td>4.000000e+00</td>\n      <td>5.897591e+01</td>\n      <td>9.000000</td>\n      <td>1.900000e+01</td>\n      <td>8.490000e+02</td>\n      <td>9.000000e+00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":5},{"cell_type":"markdown","source":"Null values in the training data:","metadata":{}},{"cell_type":"code","source":"null_count = train.isnull().sum()\nutils.plot_null_values(data = null_count)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T11:59:46.987345Z","iopub.execute_input":"2025-01-06T11:59:46.987747Z","iopub.status.idle":"2025-01-06T11:59:48.102262Z","shell.execute_reply.started":"2025-01-06T11:59:46.987717Z","shell.execute_reply":"2025-01-06T11:59:48.10101Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x500 with 1 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\n"},"metadata":{}}],"execution_count":6},{"cell_type":"markdown","source":"Null values in test data","metadata":{}},{"cell_type":"code","source":"null_count = test.isnull().sum()\nutils.plot_null_values(data = null_count)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T11:59:48.103474Z","iopub.execute_input":"2025-01-06T11:59:48.10392Z","iopub.status.idle":"2025-01-06T11:59:49.106758Z","shell.execute_reply.started":"2025-01-06T11:59:48.103854Z","shell.execute_reply":"2025-01-06T11:59:49.105347Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x500 with 1 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\n"},"metadata":{}}],"execution_count":7},{"cell_type":"markdown","source":"### Preprocessing","metadata":{}},{"cell_type":"markdown","source":"Separating our categorical and numerical values","metadata":{}},{"cell_type":"code","source":"numerical_test, categorical_test =  utils.separate_numeric_and_categorical(test)      \nnumerical_test","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T11:59:49.109383Z","iopub.execute_input":"2025-01-06T11:59:49.109715Z","iopub.status.idle":"2025-01-06T11:59:49.117944Z","shell.execute_reply.started":"2025-01-06T11:59:49.109687Z","shell.execute_reply":"2025-01-06T11:59:49.116778Z"}},"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"['id',\n 'Age',\n 'Annual Income',\n 'Number of Dependents',\n 'Health Score',\n 'Previous Claims',\n 'Vehicle Age',\n 'Credit Score',\n 'Insurance Duration']"},"metadata":{}}],"execution_count":8},{"cell_type":"code","source":"numerical, categorical = utils.separate_numeric_and_categorical(train)                  \nnumerical","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T11:59:49.120002Z","iopub.execute_input":"2025-01-06T11:59:49.120322Z","iopub.status.idle":"2025-01-06T11:59:49.141144Z","shell.execute_reply.started":"2025-01-06T11:59:49.120295Z","shell.execute_reply":"2025-01-06T11:59:49.140018Z"}},"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"['id',\n 'Age',\n 'Annual Income',\n 'Number of Dependents',\n 'Health Score',\n 'Previous Claims',\n 'Vehicle Age',\n 'Credit Score',\n 'Insurance Duration']"},"metadata":{}}],"execution_count":9},{"cell_type":"code","source":"train[categorical].head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T11:59:49.142345Z","iopub.execute_input":"2025-01-06T11:59:49.142746Z","iopub.status.idle":"2025-01-06T11:59:49.334735Z","shell.execute_reply.started":"2025-01-06T11:59:49.142706Z","shell.execute_reply":"2025-01-06T11:59:49.333361Z"}},"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"   Gender Marital Status Education Level     Occupation  Location  \\\n0  Female        Married      Bachelor's  Self-Employed     Urban   \n1  Female       Divorced        Master's            NaN     Rural   \n2    Male       Divorced     High School  Self-Employed  Suburban   \n3    Male        Married      Bachelor's            NaN     Rural   \n4    Male         Single      Bachelor's  Self-Employed     Rural   \n\n     Policy Type           Policy Start Date Customer Feedback Smoking Status  \\\n0        Premium  2023-12-23 15:21:39.134960              Poor             No   \n1  Comprehensive  2023-06-12 15:21:39.111551           Average            Yes   \n2        Premium  2023-09-30 15:21:39.221386              Good            Yes   \n3          Basic  2024-06-12 15:21:39.226954              Poor            Yes   \n4        Premium  2021-12-01 15:21:39.252145              Poor            Yes   \n\n  Exercise Frequency Property Type  \n0             Weekly         House  \n1            Monthly         House  \n2             Weekly         House  \n3              Daily     Apartment  \n4             Weekly         House  ","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>Gender</th>\n      <th>Marital Status</th>\n      <th>Education Level</th>\n      <th>Occupation</th>\n      <th>Location</th>\n      <th>Policy Type</th>\n      <th>Policy Start Date</th>\n      <th>Customer Feedback</th>\n      <th>Smoking Status</th>\n      <th>Exercise Frequency</th>\n      <th>Property Type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Female</td>\n      <td>Married</td>\n      <td>Bachelor's</td>\n      <td>Self-Employed</td>\n      <td>Urban</td>\n      <td>Premium</td>\n      <td>2023-12-23 15:21:39.134960</td>\n      <td>Poor</td>\n      <td>No</td>\n      <td>Weekly</td>\n      <td>House</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Female</td>\n      <td>Divorced</td>\n      <td>Master's</td>\n      <td>NaN</td>\n      <td>Rural</td>\n      <td>Comprehensive</td>\n      <td>2023-06-12 15:21:39.111551</td>\n      <td>Average</td>\n      <td>Yes</td>\n      <td>Monthly</td>\n      <td>House</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Male</td>\n      <td>Divorced</td>\n      <td>High School</td>\n      <td>Self-Employed</td>\n      <td>Suburban</td>\n      <td>Premium</td>\n      <td>2023-09-30 15:21:39.221386</td>\n      <td>Good</td>\n      <td>Yes</td>\n      <td>Weekly</td>\n      <td>House</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Male</td>\n      <td>Married</td>\n      <td>Bachelor's</td>\n      <td>NaN</td>\n      <td>Rural</td>\n      <td>Basic</td>\n      <td>2024-06-12 15:21:39.226954</td>\n      <td>Poor</td>\n      <td>Yes</td>\n      <td>Daily</td>\n      <td>Apartment</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Male</td>\n      <td>Single</td>\n      <td>Bachelor's</td>\n      <td>Self-Employed</td>\n      <td>Rural</td>\n      <td>Premium</td>\n      <td>2021-12-01 15:21:39.252145</td>\n      <td>Poor</td>\n      <td>Yes</td>\n      <td>Weekly</td>\n      <td>House</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":10},{"cell_type":"markdown","source":"### Cyclical encoding for policy start date\n","metadata":{}},{"cell_type":"code","source":"def date(df):\n\n    df['Policy Start Date'] = pd.to_datetime(df['Policy Start Date'])\n    df['Year'] = df['Policy Start Date'].dt.year\n    df['Day'] = df['Policy Start Date'].dt.day\n    df['Month'] = df['Policy Start Date'].dt.month\n    df['Month_name'] = df['Policy Start Date'].dt.month_name()\n    df['Day_of_week'] = df['Policy Start Date'].dt.day_name()\n    df['Week'] = df['Policy Start Date'].dt.isocalendar().week\n    df['Year_sin'] = np.sin(2 * np.pi * df['Year'])\n    df['Year_cos'] = np.cos(2 * np.pi * df['Year'])\n    min_year = df['Year'].min()\n    max_year = df['Year'].max()\n    df['Year_sin'] = np.sin(2 * np.pi * (df['Year'] - min_year) / (max_year - min_year))\n    df['Year_cos'] = np.cos(2 * np.pi * (df['Year'] - min_year) / (max_year - min_year))\n    df['Month_sin'] = np.sin(2 * np.pi * df['Month'] / 12) \n    df['Month_cos'] = np.cos(2 * np.pi * df['Month'] / 12)\n    df['Day_sin'] = np.sin(2 * np.pi * df['Day'] / 31)  \n    df['Day_cos'] = np.cos(2 * np.pi * df['Day'] / 31)\n    df['Group']=(df['Year']-2020)*48+df['Month']*4+df['Day']//7\n    \n    df.drop('Policy Start Date', axis=1, inplace=True)\n\n    return df\n\n\ntrain = date(train)\ntrain","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T11:59:49.336469Z","iopub.execute_input":"2025-01-06T11:59:49.336851Z","iopub.status.idle":"2025-01-06T11:59:52.5377Z","shell.execute_reply.started":"2025-01-06T11:59:49.336819Z","shell.execute_reply":"2025-01-06T11:59:52.536336Z"}},"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"              id   Age  Gender  Annual Income Marital Status  \\\n0              0  19.0  Female        10049.0        Married   \n1              1  39.0  Female        31678.0       Divorced   \n2              2  23.0    Male        25602.0       Divorced   \n3              3  21.0    Male       141855.0        Married   \n4              4  21.0    Male        39651.0         Single   \n...          ...   ...     ...            ...            ...   \n1199995  1199995  36.0  Female        27316.0        Married   \n1199996  1199996  54.0    Male        35786.0       Divorced   \n1199997  1199997  19.0    Male        51884.0       Divorced   \n1199998  1199998  55.0    Male            NaN         Single   \n1199999  1199999  21.0  Female            NaN       Divorced   \n\n         Number of Dependents Education Level     Occupation  Health Score  \\\n0                         1.0      Bachelor's  Self-Employed     22.598761   \n1                         3.0        Master's            NaN     15.569731   \n2                         3.0     High School  Self-Employed     47.177549   \n3                         2.0      Bachelor's            NaN     10.938144   \n4                         1.0      Bachelor's  Self-Employed     20.376094   \n...                       ...             ...            ...           ...   \n1199995                   0.0        Master's     Unemployed     13.772907   \n1199996                   NaN        Master's  Self-Employed     11.483482   \n1199997                   0.0        Master's            NaN     14.724469   \n1199998                   1.0             PhD            NaN     18.547381   \n1199999                   0.0             PhD            NaN     10.125323   \n\n         Location  ... Month_name  Day_of_week  Week      Year_sin  Year_cos  \\\n0           Urban  ...   December     Saturday    51 -9.510565e-01  0.309017   \n1           Rural  ...       June       Monday    24 -9.510565e-01  0.309017   \n2        Suburban  ...  September     Saturday    39 -9.510565e-01  0.309017   \n3           Rural  ...       June    Wednesday    24 -2.449294e-16  1.000000   \n4           Rural  ...   December    Wednesday    48  5.877853e-01 -0.809017   \n...           ...  ...        ...          ...   ...           ...       ...   \n1199995     Urban  ...        May    Wednesday    18 -9.510565e-01  0.309017   \n1199996     Rural  ...  September     Saturday    36 -5.877853e-01 -0.809017   \n1199997  Suburban  ...        May      Tuesday    21  5.877853e-01 -0.809017   \n1199998  Suburban  ...  September       Sunday    37  5.877853e-01 -0.809017   \n1199999     Rural  ...     August    Wednesday    35  9.510565e-01  0.309017   \n\n            Month_sin     Month_cos   Day_sin   Day_cos  Group  \n0       -2.449294e-16  1.000000e+00 -0.998717 -0.050649    195  \n1        1.224647e-16 -1.000000e+00  0.651372 -0.758758    169  \n2       -1.000000e+00 -1.836970e-16 -0.201299  0.979530    184  \n3        1.224647e-16 -1.000000e+00  0.651372 -0.758758    217  \n4       -2.449294e-16  1.000000e+00  0.201299  0.979530     96  \n...               ...           ...       ...       ...    ...  \n1199995  5.000000e-01 -8.660254e-01  0.571268  0.820763    164  \n1199996 -1.000000e+00 -1.836970e-16  0.897805 -0.440394    133  \n1199997  5.000000e-01 -8.660254e-01 -0.937752  0.347305     71  \n1199998 -1.000000e+00 -1.836970e-16 -0.651372 -0.758758     86  \n1199999 -8.660254e-01 -5.000000e-01 -0.848644  0.528964     35  \n\n[1200000 rows x 32 columns]","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>id</th>\n      <th>Age</th>\n      <th>Gender</th>\n      <th>Annual Income</th>\n      <th>Marital Status</th>\n      <th>Number of Dependents</th>\n      <th>Education Level</th>\n      <th>Occupation</th>\n      <th>Health Score</th>\n      <th>Location</th>\n      <th>...</th>\n      <th>Month_name</th>\n      <th>Day_of_week</th>\n      <th>Week</th>\n      <th>Year_sin</th>\n      <th>Year_cos</th>\n      <th>Month_sin</th>\n      <th>Month_cos</th>\n      <th>Day_sin</th>\n      <th>Day_cos</th>\n      <th>Group</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>19.0</td>\n      <td>Female</td>\n      <td>10049.0</td>\n      <td>Married</td>\n      <td>1.0</td>\n      <td>Bachelor's</td>\n      <td>Self-Employed</td>\n      <td>22.598761</td>\n      <td>Urban</td>\n      <td>...</td>\n      <td>December</td>\n      <td>Saturday</td>\n      <td>51</td>\n      <td>-9.510565e-01</td>\n      <td>0.309017</td>\n      <td>-2.449294e-16</td>\n      <td>1.000000e+00</td>\n      <td>-0.998717</td>\n      <td>-0.050649</td>\n      <td>195</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>39.0</td>\n      <td>Female</td>\n      <td>31678.0</td>\n      <td>Divorced</td>\n      <td>3.0</td>\n      <td>Master's</td>\n      <td>NaN</td>\n      <td>15.569731</td>\n      <td>Rural</td>\n      <td>...</td>\n      <td>June</td>\n      <td>Monday</td>\n      <td>24</td>\n      <td>-9.510565e-01</td>\n      <td>0.309017</td>\n      <td>1.224647e-16</td>\n      <td>-1.000000e+00</td>\n      <td>0.651372</td>\n      <td>-0.758758</td>\n      <td>169</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>23.0</td>\n      <td>Male</td>\n      <td>25602.0</td>\n      <td>Divorced</td>\n      <td>3.0</td>\n      <td>High School</td>\n      <td>Self-Employed</td>\n      <td>47.177549</td>\n      <td>Suburban</td>\n      <td>...</td>\n      <td>September</td>\n      <td>Saturday</td>\n      <td>39</td>\n      <td>-9.510565e-01</td>\n      <td>0.309017</td>\n      <td>-1.000000e+00</td>\n      <td>-1.836970e-16</td>\n      <td>-0.201299</td>\n      <td>0.979530</td>\n      <td>184</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>21.0</td>\n      <td>Male</td>\n      <td>141855.0</td>\n      <td>Married</td>\n      <td>2.0</td>\n      <td>Bachelor's</td>\n      <td>NaN</td>\n      <td>10.938144</td>\n      <td>Rural</td>\n      <td>...</td>\n      <td>June</td>\n      <td>Wednesday</td>\n      <td>24</td>\n      <td>-2.449294e-16</td>\n      <td>1.000000</td>\n      <td>1.224647e-16</td>\n      <td>-1.000000e+00</td>\n      <td>0.651372</td>\n      <td>-0.758758</td>\n      <td>217</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>21.0</td>\n      <td>Male</td>\n      <td>39651.0</td>\n      <td>Single</td>\n      <td>1.0</td>\n      <td>Bachelor's</td>\n      <td>Self-Employed</td>\n      <td>20.376094</td>\n      <td>Rural</td>\n      <td>...</td>\n      <td>December</td>\n      <td>Wednesday</td>\n      <td>48</td>\n      <td>5.877853e-01</td>\n      <td>-0.809017</td>\n      <td>-2.449294e-16</td>\n      <td>1.000000e+00</td>\n      <td>0.201299</td>\n      <td>0.979530</td>\n      <td>96</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1199995</th>\n      <td>1199995</td>\n      <td>36.0</td>\n      <td>Female</td>\n      <td>27316.0</td>\n      <td>Married</td>\n      <td>0.0</td>\n      <td>Master's</td>\n      <td>Unemployed</td>\n      <td>13.772907</td>\n      <td>Urban</td>\n      <td>...</td>\n      <td>May</td>\n      <td>Wednesday</td>\n      <td>18</td>\n      <td>-9.510565e-01</td>\n      <td>0.309017</td>\n      <td>5.000000e-01</td>\n      <td>-8.660254e-01</td>\n      <td>0.571268</td>\n      <td>0.820763</td>\n      <td>164</td>\n    </tr>\n    <tr>\n      <th>1199996</th>\n      <td>1199996</td>\n      <td>54.0</td>\n      <td>Male</td>\n      <td>35786.0</td>\n      <td>Divorced</td>\n      <td>NaN</td>\n      <td>Master's</td>\n      <td>Self-Employed</td>\n      <td>11.483482</td>\n      <td>Rural</td>\n      <td>...</td>\n      <td>September</td>\n      <td>Saturday</td>\n      <td>36</td>\n      <td>-5.877853e-01</td>\n      <td>-0.809017</td>\n      <td>-1.000000e+00</td>\n      <td>-1.836970e-16</td>\n      <td>0.897805</td>\n      <td>-0.440394</td>\n      <td>133</td>\n    </tr>\n    <tr>\n      <th>1199997</th>\n      <td>1199997</td>\n      <td>19.0</td>\n      <td>Male</td>\n      <td>51884.0</td>\n      <td>Divorced</td>\n      <td>0.0</td>\n      <td>Master's</td>\n      <td>NaN</td>\n      <td>14.724469</td>\n      <td>Suburban</td>\n      <td>...</td>\n      <td>May</td>\n      <td>Tuesday</td>\n      <td>21</td>\n      <td>5.877853e-01</td>\n      <td>-0.809017</td>\n      <td>5.000000e-01</td>\n      <td>-8.660254e-01</td>\n      <td>-0.937752</td>\n      <td>0.347305</td>\n      <td>71</td>\n    </tr>\n    <tr>\n      <th>1199998</th>\n      <td>1199998</td>\n      <td>55.0</td>\n      <td>Male</td>\n      <td>NaN</td>\n      <td>Single</td>\n      <td>1.0</td>\n      <td>PhD</td>\n      <td>NaN</td>\n      <td>18.547381</td>\n      <td>Suburban</td>\n      <td>...</td>\n      <td>September</td>\n      <td>Sunday</td>\n      <td>37</td>\n      <td>5.877853e-01</td>\n      <td>-0.809017</td>\n      <td>-1.000000e+00</td>\n      <td>-1.836970e-16</td>\n      <td>-0.651372</td>\n      <td>-0.758758</td>\n      <td>86</td>\n    </tr>\n    <tr>\n      <th>1199999</th>\n      <td>1199999</td>\n      <td>21.0</td>\n      <td>Female</td>\n      <td>NaN</td>\n      <td>Divorced</td>\n      <td>0.0</td>\n      <td>PhD</td>\n      <td>NaN</td>\n      <td>10.125323</td>\n      <td>Rural</td>\n      <td>...</td>\n      <td>August</td>\n      <td>Wednesday</td>\n      <td>35</td>\n      <td>9.510565e-01</td>\n      <td>0.309017</td>\n      <td>-8.660254e-01</td>\n      <td>-5.000000e-01</td>\n      <td>-0.848644</td>\n      <td>0.528964</td>\n      <td>35</td>\n    </tr>\n  </tbody>\n</table>\n<p>1200000 rows × 32 columns</p>\n</div>"},"metadata":{}}],"execution_count":11},{"cell_type":"code","source":"test = date(test)\ntest","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T11:59:52.538744Z","iopub.execute_input":"2025-01-06T11:59:52.539128Z","iopub.status.idle":"2025-01-06T11:59:54.681547Z","shell.execute_reply.started":"2025-01-06T11:59:52.539089Z","shell.execute_reply":"2025-01-06T11:59:54.68037Z"}},"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"             id   Age  Gender  Annual Income Marital Status  \\\n0       1200000  28.0  Female         2310.0            NaN   \n1       1200001  31.0  Female       126031.0        Married   \n2       1200002  47.0  Female        17092.0       Divorced   \n3       1200003  28.0  Female        30424.0       Divorced   \n4       1200004  24.0    Male        10863.0       Divorced   \n...         ...   ...     ...            ...            ...   \n799995  1999995  50.0  Female        38782.0        Married   \n799996  1999996   NaN  Female        73462.0         Single   \n799997  1999997  26.0  Female        35178.0         Single   \n799998  1999998  34.0  Female        45661.0         Single   \n799999  1999999  25.0    Male        24843.0       Divorced   \n\n        Number of Dependents Education Level     Occupation  Health Score  \\\n0                        4.0      Bachelor's  Self-Employed      7.657981   \n1                        2.0        Master's  Self-Employed     13.381379   \n2                        0.0             PhD     Unemployed     24.354527   \n3                        3.0             PhD  Self-Employed      5.136225   \n4                        2.0     High School     Unemployed     11.844155   \n...                      ...             ...            ...           ...   \n799995                   1.0      Bachelor's            NaN     14.498639   \n799996                   0.0        Master's            NaN      8.145748   \n799997                   0.0        Master's       Employed      6.636583   \n799998                   3.0        Master's            NaN     15.937248   \n799999                   3.0     High School            NaN     24.893939   \n\n        Location  ... Month_name  Day_of_week  Week      Year_sin  Year_cos  \\\n0          Rural  ...       June       Sunday    22 -9.510565e-01  0.309017   \n1       Suburban  ...      April       Monday    17 -2.449294e-16  1.000000   \n2          Urban  ...      April    Wednesday    14 -9.510565e-01  0.309017   \n3       Suburban  ...    October    Wednesday    43 -9.510565e-01  0.309017   \n4       Suburban  ...   November       Friday    47  5.877853e-01 -0.809017   \n...          ...  ...        ...          ...   ...           ...       ...   \n799995     Rural  ...       July       Friday    27  5.877853e-01 -0.809017   \n799996     Rural  ...      March      Tuesday    13 -9.510565e-01  0.309017   \n799997     Urban  ...  September       Monday    40  0.000000e+00  1.000000   \n799998     Urban  ...        May       Monday    19 -5.877853e-01 -0.809017   \n799999  Suburban  ...        May      Tuesday    20  5.877853e-01 -0.809017   \n\n           Month_sin     Month_cos   Day_sin   Day_cos  Group  \n0       1.224647e-16 -1.000000e+00  0.724793  0.688967    168  \n1       8.660254e-01 -5.000000e-01 -0.968077 -0.250653    211  \n2       8.660254e-01 -5.000000e-01  0.848644  0.528964    160  \n3      -8.660254e-01  5.000000e-01 -0.937752  0.347305    187  \n4      -5.000000e-01  8.660254e-01 -0.848644  0.528964     95  \n...              ...           ...       ...       ...    ...  \n799995 -5.000000e-01 -8.660254e-01  0.968077 -0.250653     77  \n799996  1.000000e+00  6.123234e-17 -0.571268  0.820763    160  \n799997 -1.000000e+00 -1.836970e-16 -0.201299  0.979530     -8  \n799998  5.000000e-01 -8.660254e-01  0.968077 -0.250653    117  \n799999  5.000000e-01 -8.660254e-01 -0.485302 -0.874347     70  \n\n[800000 rows x 32 columns]","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>id</th>\n      <th>Age</th>\n      <th>Gender</th>\n      <th>Annual Income</th>\n      <th>Marital Status</th>\n      <th>Number of Dependents</th>\n      <th>Education Level</th>\n      <th>Occupation</th>\n      <th>Health Score</th>\n      <th>Location</th>\n      <th>...</th>\n      <th>Month_name</th>\n      <th>Day_of_week</th>\n      <th>Week</th>\n      <th>Year_sin</th>\n      <th>Year_cos</th>\n      <th>Month_sin</th>\n      <th>Month_cos</th>\n      <th>Day_sin</th>\n      <th>Day_cos</th>\n      <th>Group</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1200000</td>\n      <td>28.0</td>\n      <td>Female</td>\n      <td>2310.0</td>\n      <td>NaN</td>\n      <td>4.0</td>\n      <td>Bachelor's</td>\n      <td>Self-Employed</td>\n      <td>7.657981</td>\n      <td>Rural</td>\n      <td>...</td>\n      <td>June</td>\n      <td>Sunday</td>\n      <td>22</td>\n      <td>-9.510565e-01</td>\n      <td>0.309017</td>\n      <td>1.224647e-16</td>\n      <td>-1.000000e+00</td>\n      <td>0.724793</td>\n      <td>0.688967</td>\n      <td>168</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1200001</td>\n      <td>31.0</td>\n      <td>Female</td>\n      <td>126031.0</td>\n      <td>Married</td>\n      <td>2.0</td>\n      <td>Master's</td>\n      <td>Self-Employed</td>\n      <td>13.381379</td>\n      <td>Suburban</td>\n      <td>...</td>\n      <td>April</td>\n      <td>Monday</td>\n      <td>17</td>\n      <td>-2.449294e-16</td>\n      <td>1.000000</td>\n      <td>8.660254e-01</td>\n      <td>-5.000000e-01</td>\n      <td>-0.968077</td>\n      <td>-0.250653</td>\n      <td>211</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1200002</td>\n      <td>47.0</td>\n      <td>Female</td>\n      <td>17092.0</td>\n      <td>Divorced</td>\n      <td>0.0</td>\n      <td>PhD</td>\n      <td>Unemployed</td>\n      <td>24.354527</td>\n      <td>Urban</td>\n      <td>...</td>\n      <td>April</td>\n      <td>Wednesday</td>\n      <td>14</td>\n      <td>-9.510565e-01</td>\n      <td>0.309017</td>\n      <td>8.660254e-01</td>\n      <td>-5.000000e-01</td>\n      <td>0.848644</td>\n      <td>0.528964</td>\n      <td>160</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1200003</td>\n      <td>28.0</td>\n      <td>Female</td>\n      <td>30424.0</td>\n      <td>Divorced</td>\n      <td>3.0</td>\n      <td>PhD</td>\n      <td>Self-Employed</td>\n      <td>5.136225</td>\n      <td>Suburban</td>\n      <td>...</td>\n      <td>October</td>\n      <td>Wednesday</td>\n      <td>43</td>\n      <td>-9.510565e-01</td>\n      <td>0.309017</td>\n      <td>-8.660254e-01</td>\n      <td>5.000000e-01</td>\n      <td>-0.937752</td>\n      <td>0.347305</td>\n      <td>187</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1200004</td>\n      <td>24.0</td>\n      <td>Male</td>\n      <td>10863.0</td>\n      <td>Divorced</td>\n      <td>2.0</td>\n      <td>High School</td>\n      <td>Unemployed</td>\n      <td>11.844155</td>\n      <td>Suburban</td>\n      <td>...</td>\n      <td>November</td>\n      <td>Friday</td>\n      <td>47</td>\n      <td>5.877853e-01</td>\n      <td>-0.809017</td>\n      <td>-5.000000e-01</td>\n      <td>8.660254e-01</td>\n      <td>-0.848644</td>\n      <td>0.528964</td>\n      <td>95</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>799995</th>\n      <td>1999995</td>\n      <td>50.0</td>\n      <td>Female</td>\n      <td>38782.0</td>\n      <td>Married</td>\n      <td>1.0</td>\n      <td>Bachelor's</td>\n      <td>NaN</td>\n      <td>14.498639</td>\n      <td>Rural</td>\n      <td>...</td>\n      <td>July</td>\n      <td>Friday</td>\n      <td>27</td>\n      <td>5.877853e-01</td>\n      <td>-0.809017</td>\n      <td>-5.000000e-01</td>\n      <td>-8.660254e-01</td>\n      <td>0.968077</td>\n      <td>-0.250653</td>\n      <td>77</td>\n    </tr>\n    <tr>\n      <th>799996</th>\n      <td>1999996</td>\n      <td>NaN</td>\n      <td>Female</td>\n      <td>73462.0</td>\n      <td>Single</td>\n      <td>0.0</td>\n      <td>Master's</td>\n      <td>NaN</td>\n      <td>8.145748</td>\n      <td>Rural</td>\n      <td>...</td>\n      <td>March</td>\n      <td>Tuesday</td>\n      <td>13</td>\n      <td>-9.510565e-01</td>\n      <td>0.309017</td>\n      <td>1.000000e+00</td>\n      <td>6.123234e-17</td>\n      <td>-0.571268</td>\n      <td>0.820763</td>\n      <td>160</td>\n    </tr>\n    <tr>\n      <th>799997</th>\n      <td>1999997</td>\n      <td>26.0</td>\n      <td>Female</td>\n      <td>35178.0</td>\n      <td>Single</td>\n      <td>0.0</td>\n      <td>Master's</td>\n      <td>Employed</td>\n      <td>6.636583</td>\n      <td>Urban</td>\n      <td>...</td>\n      <td>September</td>\n      <td>Monday</td>\n      <td>40</td>\n      <td>0.000000e+00</td>\n      <td>1.000000</td>\n      <td>-1.000000e+00</td>\n      <td>-1.836970e-16</td>\n      <td>-0.201299</td>\n      <td>0.979530</td>\n      <td>-8</td>\n    </tr>\n    <tr>\n      <th>799998</th>\n      <td>1999998</td>\n      <td>34.0</td>\n      <td>Female</td>\n      <td>45661.0</td>\n      <td>Single</td>\n      <td>3.0</td>\n      <td>Master's</td>\n      <td>NaN</td>\n      <td>15.937248</td>\n      <td>Urban</td>\n      <td>...</td>\n      <td>May</td>\n      <td>Monday</td>\n      <td>19</td>\n      <td>-5.877853e-01</td>\n      <td>-0.809017</td>\n      <td>5.000000e-01</td>\n      <td>-8.660254e-01</td>\n      <td>0.968077</td>\n      <td>-0.250653</td>\n      <td>117</td>\n    </tr>\n    <tr>\n      <th>799999</th>\n      <td>1999999</td>\n      <td>25.0</td>\n      <td>Male</td>\n      <td>24843.0</td>\n      <td>Divorced</td>\n      <td>3.0</td>\n      <td>High School</td>\n      <td>NaN</td>\n      <td>24.893939</td>\n      <td>Suburban</td>\n      <td>...</td>\n      <td>May</td>\n      <td>Tuesday</td>\n      <td>20</td>\n      <td>5.877853e-01</td>\n      <td>-0.809017</td>\n      <td>5.000000e-01</td>\n      <td>-8.660254e-01</td>\n      <td>-0.485302</td>\n      <td>-0.874347</td>\n      <td>70</td>\n    </tr>\n  </tbody>\n</table>\n<p>800000 rows × 32 columns</p>\n</div>"},"metadata":{}}],"execution_count":12},{"cell_type":"code","source":"train['Gender']  = train['Gender'].replace({\"Female\": 1, \"Male\":0})\ntrain['Gender']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T11:59:54.682679Z","iopub.execute_input":"2025-01-06T11:59:54.683036Z","iopub.status.idle":"2025-01-06T11:59:55.119075Z","shell.execute_reply.started":"2025-01-06T11:59:54.683006Z","shell.execute_reply":"2025-01-06T11:59:55.118126Z"}},"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"0          1\n1          1\n2          0\n3          0\n4          0\n          ..\n1199995    1\n1199996    0\n1199997    0\n1199998    0\n1199999    1\nName: Gender, Length: 1200000, dtype: int64"},"metadata":{}}],"execution_count":13},{"cell_type":"code","source":"test['Gender']  = test['Gender'].replace({\"Female\": 1, \"Male\":0})\ntest['Gender']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T11:59:55.119947Z","iopub.execute_input":"2025-01-06T11:59:55.120207Z","iopub.status.idle":"2025-01-06T11:59:55.416049Z","shell.execute_reply.started":"2025-01-06T11:59:55.120185Z","shell.execute_reply":"2025-01-06T11:59:55.414989Z"}},"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"0         1\n1         1\n2         1\n3         1\n4         0\n         ..\n799995    1\n799996    1\n799997    1\n799998    1\n799999    0\nName: Gender, Length: 800000, dtype: int64"},"metadata":{}}],"execution_count":14},{"cell_type":"code","source":"# removing target variable form train and test sets\ncategorical.remove( 'Policy Start Date')\ncategorical_test.remove( 'Policy Start Date')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T11:59:55.417293Z","iopub.execute_input":"2025-01-06T11:59:55.417783Z","iopub.status.idle":"2025-01-06T11:59:55.42332Z","shell.execute_reply.started":"2025-01-06T11:59:55.417739Z","shell.execute_reply":"2025-01-06T11:59:55.421529Z"}},"outputs":[],"execution_count":15},{"cell_type":"code","source":"categorical = train[categorical].fillna(\"Unknown\")\ncategorical = utils.one_hot_encoding(categorical)\n\ncategorical_test = test[categorical_test].fillna(\"Unknown\")\ncategorical_test = utils.one_hot_encoding(categorical_test)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T11:59:55.424446Z","iopub.execute_input":"2025-01-06T11:59:55.424875Z","iopub.status.idle":"2025-01-06T12:00:05.00842Z","shell.execute_reply.started":"2025-01-06T11:59:55.424835Z","shell.execute_reply":"2025-01-06T12:00:05.006865Z"}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n  warnings.warn(\n/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n  warnings.warn(\n","output_type":"stream"}],"execution_count":16},{"cell_type":"code","source":"test = test.drop('id', axis=1)\ntrain = train.drop('id', axis=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T12:00:05.010068Z","iopub.execute_input":"2025-01-06T12:00:05.010529Z","iopub.status.idle":"2025-01-06T12:00:05.431483Z","shell.execute_reply.started":"2025-01-06T12:00:05.010481Z","shell.execute_reply":"2025-01-06T12:00:05.429797Z"}},"outputs":[],"execution_count":17},{"cell_type":"code","source":"numerical_test.remove('id')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T12:00:05.43236Z","iopub.execute_input":"2025-01-06T12:00:05.432654Z","iopub.status.idle":"2025-01-06T12:00:05.438605Z","shell.execute_reply.started":"2025-01-06T12:00:05.432629Z","shell.execute_reply":"2025-01-06T12:00:05.436953Z"}},"outputs":[],"execution_count":18},{"cell_type":"code","source":"numerical.remove('id')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T12:00:05.43972Z","iopub.execute_input":"2025-01-06T12:00:05.440139Z","iopub.status.idle":"2025-01-06T12:00:05.467333Z","shell.execute_reply.started":"2025-01-06T12:00:05.440105Z","shell.execute_reply":"2025-01-06T12:00:05.46568Z"}},"outputs":[],"execution_count":19},{"cell_type":"code","source":"train = pd.concat([train[numerical], categorical], axis=1)\ntrain","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T12:00:05.469235Z","iopub.execute_input":"2025-01-06T12:00:05.469778Z","iopub.status.idle":"2025-01-06T12:00:06.231601Z","shell.execute_reply.started":"2025-01-06T12:00:05.46972Z","shell.execute_reply":"2025-01-06T12:00:06.230507Z"}},"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"          Age  Annual Income  Number of Dependents  Health Score  \\\n0        19.0        10049.0                   1.0     22.598761   \n1        39.0        31678.0                   3.0     15.569731   \n2        23.0        25602.0                   3.0     47.177549   \n3        21.0       141855.0                   2.0     10.938144   \n4        21.0        39651.0                   1.0     20.376094   \n...       ...            ...                   ...           ...   \n1199995  36.0        27316.0                   0.0     13.772907   \n1199996  54.0        35786.0                   NaN     11.483482   \n1199997  19.0        51884.0                   0.0     14.724469   \n1199998  55.0            NaN                   1.0     18.547381   \n1199999  21.0            NaN                   0.0     10.125323   \n\n         Previous Claims  Vehicle Age  Credit Score  Insurance Duration  \\\n0                    2.0         17.0         372.0                 5.0   \n1                    1.0         12.0         694.0                 2.0   \n2                    1.0         14.0           NaN                 3.0   \n3                    1.0          0.0         367.0                 1.0   \n4                    0.0          8.0         598.0                 4.0   \n...                  ...          ...           ...                 ...   \n1199995              NaN          5.0         372.0                 3.0   \n1199996              NaN         10.0         597.0                 4.0   \n1199997              0.0         19.0           NaN                 6.0   \n1199998              1.0          7.0         407.0                 4.0   \n1199999              0.0         18.0         502.0                 6.0   \n\n         Gender_1  Marital Status_Married  ...  Policy Type_Premium  \\\n0             1.0                     1.0  ...                  1.0   \n1             1.0                     0.0  ...                  0.0   \n2             0.0                     0.0  ...                  1.0   \n3             0.0                     1.0  ...                  0.0   \n4             0.0                     0.0  ...                  1.0   \n...           ...                     ...  ...                  ...   \n1199995       1.0                     1.0  ...                  1.0   \n1199996       0.0                     0.0  ...                  0.0   \n1199997       0.0                     0.0  ...                  0.0   \n1199998       0.0                     0.0  ...                  1.0   \n1199999       1.0                     0.0  ...                  1.0   \n\n         Customer Feedback_Good  Customer Feedback_Poor  \\\n0                           0.0                     1.0   \n1                           0.0                     0.0   \n2                           1.0                     0.0   \n3                           0.0                     1.0   \n4                           0.0                     1.0   \n...                         ...                     ...   \n1199995                     0.0                     1.0   \n1199996                     0.0                     1.0   \n1199997                     1.0                     0.0   \n1199998                     0.0                     1.0   \n1199999                     1.0                     0.0   \n\n         Customer Feedback_Unknown  Smoking Status_Yes  \\\n0                              0.0                 0.0   \n1                              0.0                 1.0   \n2                              0.0                 1.0   \n3                              0.0                 1.0   \n4                              0.0                 1.0   \n...                            ...                 ...   \n1199995                        0.0                 0.0   \n1199996                        0.0                 0.0   \n1199997                        0.0                 0.0   \n1199998                        0.0                 0.0   \n1199999                        0.0                 1.0   \n\n         Exercise Frequency_Monthly  Exercise Frequency_Rarely  \\\n0                               0.0                        0.0   \n1                               1.0                        0.0   \n2                               0.0                        0.0   \n3                               0.0                        0.0   \n4                               0.0                        0.0   \n...                             ...                        ...   \n1199995                         0.0                        0.0   \n1199996                         0.0                        0.0   \n1199997                         1.0                        0.0   \n1199998                         0.0                        0.0   \n1199999                         1.0                        0.0   \n\n         Exercise Frequency_Weekly  Property Type_Condo  Property Type_House  \n0                              1.0                  0.0                  1.0  \n1                              0.0                  0.0                  1.0  \n2                              1.0                  0.0                  1.0  \n3                              0.0                  0.0                  0.0  \n4                              1.0                  0.0                  1.0  \n...                            ...                  ...                  ...  \n1199995                        0.0                  0.0                  0.0  \n1199996                        1.0                  0.0                  0.0  \n1199997                        0.0                  1.0                  0.0  \n1199998                        0.0                  0.0                  0.0  \n1199999                        0.0                  0.0                  1.0  \n\n[1200000 rows x 31 columns]","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>Age</th>\n      <th>Annual Income</th>\n      <th>Number of Dependents</th>\n      <th>Health Score</th>\n      <th>Previous Claims</th>\n      <th>Vehicle Age</th>\n      <th>Credit Score</th>\n      <th>Insurance Duration</th>\n      <th>Gender_1</th>\n      <th>Marital Status_Married</th>\n      <th>...</th>\n      <th>Policy Type_Premium</th>\n      <th>Customer Feedback_Good</th>\n      <th>Customer Feedback_Poor</th>\n      <th>Customer Feedback_Unknown</th>\n      <th>Smoking Status_Yes</th>\n      <th>Exercise Frequency_Monthly</th>\n      <th>Exercise Frequency_Rarely</th>\n      <th>Exercise Frequency_Weekly</th>\n      <th>Property Type_Condo</th>\n      <th>Property Type_House</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>19.0</td>\n      <td>10049.0</td>\n      <td>1.0</td>\n      <td>22.598761</td>\n      <td>2.0</td>\n      <td>17.0</td>\n      <td>372.0</td>\n      <td>5.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>39.0</td>\n      <td>31678.0</td>\n      <td>3.0</td>\n      <td>15.569731</td>\n      <td>1.0</td>\n      <td>12.0</td>\n      <td>694.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>23.0</td>\n      <td>25602.0</td>\n      <td>3.0</td>\n      <td>47.177549</td>\n      <td>1.0</td>\n      <td>14.0</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>21.0</td>\n      <td>141855.0</td>\n      <td>2.0</td>\n      <td>10.938144</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>367.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>21.0</td>\n      <td>39651.0</td>\n      <td>1.0</td>\n      <td>20.376094</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>598.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1199995</th>\n      <td>36.0</td>\n      <td>27316.0</td>\n      <td>0.0</td>\n      <td>13.772907</td>\n      <td>NaN</td>\n      <td>5.0</td>\n      <td>372.0</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1199996</th>\n      <td>54.0</td>\n      <td>35786.0</td>\n      <td>NaN</td>\n      <td>11.483482</td>\n      <td>NaN</td>\n      <td>10.0</td>\n      <td>597.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1199997</th>\n      <td>19.0</td>\n      <td>51884.0</td>\n      <td>0.0</td>\n      <td>14.724469</td>\n      <td>0.0</td>\n      <td>19.0</td>\n      <td>NaN</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1199998</th>\n      <td>55.0</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>18.547381</td>\n      <td>1.0</td>\n      <td>7.0</td>\n      <td>407.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1199999</th>\n      <td>21.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>10.125323</td>\n      <td>0.0</td>\n      <td>18.0</td>\n      <td>502.0</td>\n      <td>6.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1200000 rows × 31 columns</p>\n</div>"},"metadata":{}}],"execution_count":20},{"cell_type":"code","source":"test = pd.concat([test[numerical_test], categorical_test], axis=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T12:00:06.232565Z","iopub.execute_input":"2025-01-06T12:00:06.233017Z","iopub.status.idle":"2025-01-06T12:00:06.376434Z","shell.execute_reply.started":"2025-01-06T12:00:06.232977Z","shell.execute_reply":"2025-01-06T12:00:06.374872Z"}},"outputs":[],"execution_count":21},{"cell_type":"code","source":"train = utils.knn_imputer(train)\ntest = utils.knn_imputer(test)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T12:00:06.377995Z","iopub.execute_input":"2025-01-06T12:00:06.378323Z","iopub.status.idle":"2025-01-06T12:10:35.009151Z","shell.execute_reply.started":"2025-01-06T12:00:06.378297Z","shell.execute_reply":"2025-01-06T12:10:35.008039Z"}},"outputs":[],"execution_count":22},{"cell_type":"code","source":"corr_matrix = train.corr()\n\nplt.figure(figsize=(20, 20))\nsns.heatmap(corr_matrix, annot=True, cmap='coolwarm', linewidths=0.5, fmt='.2f')\nplt.title(\"Correlation Matrix\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T12:10:35.01477Z","iopub.execute_input":"2025-01-06T12:10:35.015125Z","iopub.status.idle":"2025-01-06T12:10:40.99059Z","shell.execute_reply.started":"2025-01-06T12:10:35.0151Z","shell.execute_reply":"2025-01-06T12:10:40.989318Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x2000 with 2 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\n"},"metadata":{}}],"execution_count":23},{"cell_type":"code","source":"scaler = StandardScaler()\ntrain = scaler.fit_transform(train)\ntest = scaler.fit_transform(test)\ntrain","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T12:10:40.992421Z","iopub.execute_input":"2025-01-06T12:10:40.992846Z","iopub.status.idle":"2025-01-06T12:10:41.878339Z","shell.execute_reply.started":"2025-01-06T12:10:40.992803Z","shell.execute_reply":"2025-01-06T12:10:41.877326Z"}},"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"array([[-1.64846982, -0.72273326, -0.74753701, ...,  1.70858889,\n        -0.70667328,  1.41328864],\n       [-0.15971038, -0.03861317,  0.73282344, ..., -0.5852783 ,\n        -0.70667328,  1.41328864],\n       [-1.35071793, -0.2307956 ,  0.73282344, ...,  1.70858889,\n        -0.70667328,  1.41328864],\n       ...,\n       [-1.64846982,  0.60049776, -1.48771724, ..., -0.5852783 ,\n         1.4150811 , -0.70756954],\n       [ 1.03129718,  0.24447279, -0.74753701, ..., -0.5852783 ,\n        -0.70667328, -0.70756954],\n       [-1.49959387,  0.00455751, -1.48771724, ..., -0.5852783 ,\n        -0.70667328,  1.41328864]])"},"metadata":{}}],"execution_count":24},{"cell_type":"code","source":"test","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T12:10:41.879282Z","iopub.execute_input":"2025-01-06T12:10:41.879551Z","iopub.status.idle":"2025-01-06T12:10:41.886606Z","shell.execute_reply.started":"2025-01-06T12:10:41.879527Z","shell.execute_reply":"2025-01-06T12:10:41.885186Z"}},"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"array([[-0.9780147 , -0.96852319,  1.47565488, ...,  1.70637412,\n        -0.70738191,  1.41228932],\n       [-0.75466305,  2.94179439, -0.00691452, ..., -0.58603795,\n        -0.70738191, -0.70807021],\n       [ 0.43654576, -0.5013243 , -1.48948392, ..., -0.58603795,\n         1.41366352, -0.70807021],\n       ...,\n       [-1.1269158 ,  0.0703006 , -1.48948392, ..., -0.58603795,\n        -0.70738191, -0.70807021],\n       [-0.5313114 ,  0.40162559,  0.73437018, ...,  1.70637412,\n         1.41366352, -0.70807021],\n       [-1.20136635, -0.25634671,  0.73437018, ..., -0.58603795,\n        -0.70738191,  1.41228932]])"},"metadata":{}}],"execution_count":25},{"cell_type":"markdown","source":"Evaluating models to get the best rmsle score","metadata":{}},{"cell_type":"code","source":"models = {\n    \"XGBoost\": XGBRegressor(random_state=42),\n    \"LightGBM\": LGBMRegressor(random_state=42),\n    \"CatBoost\": CatBoostRegressor(verbose=0, random_state=42),\n    \"Gradient Boosting\": GradientBoostingRegressor(random_state=42),\n    \"Histogram Gradient Boosting\": HistGradientBoostingRegressor(random_state=42),\n    \"AdaBoost\": AdaBoostRegressor(random_state=42),\n}\ndef evaluate_models(models, X, y):\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n    results = {}\n\n    for name, model in models.items():\n        model.fit(X_train, y_train)\n        predictions = model.predict(X_test)\n        score = utils.rmsle(y_test, predictions)\n        results[name] = score\n        print(f\"{name} RMSLE: {score:.4f}\")\n    \n    return results\n\nresults = evaluate_models(models, train, y)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T12:10:41.887792Z","iopub.execute_input":"2025-01-06T12:10:41.888255Z","iopub.status.idle":"2025-01-06T12:21:06.460516Z","shell.execute_reply.started":"2025-01-06T12:10:41.888215Z","shell.execute_reply":"2025-01-06T12:21:06.459247Z"}},"outputs":[{"name":"stdout","text":"XGBoost RMSLE: 1.1512\n[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.156231 seconds.\nYou can set `force_row_wise=true` to remove the overhead.\nAnd if memory is not enough, you can set `force_col_wise=true`.\n[LightGBM] [Info] Total Bins 1628\n[LightGBM] [Info] Number of data points in the train set: 960000, number of used features: 31\n[LightGBM] [Info] Start training from score 1102.505529\nLightGBM RMSLE: 1.1506\nCatBoost RMSLE: 1.1512\nGradient Boosting RMSLE: 1.1624\nHistogram Gradient Boosting RMSLE: 1.1512\nAdaBoost RMSLE: 1.2830\n","output_type":"stream"}],"execution_count":26},{"cell_type":"markdown","source":"#### Using optuna to optimize hyperparameters","metadata":{}},{"cell_type":"code","source":"max_key = max(results, key=results.get)\nmax_value = results[max_key]\n\n\nmin_key = min(results, key=results.get)\nmin_value = results[min_key]\n\nprint(f\"{min_key}:{min_value}\")\nprint(f\"{max_key}:{max_value}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T12:21:06.461864Z","iopub.execute_input":"2025-01-06T12:21:06.46228Z","iopub.status.idle":"2025-01-06T12:21:06.469937Z","shell.execute_reply.started":"2025-01-06T12:21:06.462242Z","shell.execute_reply":"2025-01-06T12:21:06.468662Z"}},"outputs":[{"name":"stdout","text":"LightGBM:1.1505637007673575\nAdaBoost:1.28301736126282\n","output_type":"stream"}],"execution_count":27},{"cell_type":"code","source":"X = train\ny = orig_train['Premium Amount']\nX_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=42)\n    \n\ndef objective(trial):\n     param = {\n        'objective': 'reg:squarederror',   # Regression problem, using squared error loss function\n        'booster': 'gbtree',               # Type of booster (can be 'gbtree', 'gblinear', 'dart')\n        'eval_metric': 'rmse',             # Evaluation metric, here it's RMSE\n        'verbosity': 0,                    # Suppress warnings\n        'n_estimators': trial.suggest_int('n_estimators', 50, 1000),   # Number of trees (iterations)\n        'max_depth': trial.suggest_int('max_depth', 3, 15),             # Maximum depth of a tree\n        'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.3),  # Learning rate\n        'subsample': trial.suggest_float('subsample', 0.6, 1.0),      # Fraction of samples used for training each tree\n        'colsample_bytree': trial.suggest_float('colsample_bytree', 0.6, 1.0),  # Fraction of features used for training each tree\n        'lambda': trial.suggest_float('lambda', 0.01, 10.0),          # L2 regularization term\n        'alpha': trial.suggest_float('alpha', 0.01, 10.0),            # L1 regularization term\n        'min_child_weight': trial.suggest_int('min_child_weight', 1, 10),  # Minimum sum of instance weight (hessian) needed in a child\n        'gamma': trial.suggest_float('gamma', 0, 1.0)                  # Minimum loss reduction required to make a further partition\n        }\n    \n     model = XGBRegressor(**param)\n     model.fit(X_train, y_train)\n    \n     rmsle = utils.rmsle(y_valid, y_valid)\n     return rmsle\n\nstudy = optuna.create_study(direction=\"minimize\")\nstudy.optimize(objective, n_trials=50)\n\nbest_params = study.best_params\n\nprint(\"Best RMSLE:\", study.best_value)\nprint(\"Best Parameters:\", best_params)\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T12:21:06.471035Z","iopub.execute_input":"2025-01-06T12:21:06.471367Z","iopub.status.idle":"2025-01-06T13:23:32.194899Z","shell.execute_reply.started":"2025-01-06T12:21:06.471326Z","shell.execute_reply":"2025-01-06T13:23:32.193056Z"}},"outputs":[{"name":"stderr","text":"[I 2025-01-06 12:21:07,109] A new study created in memory with name: no-name-74ff32a4-5b8a-41ca-a6cb-f184aa055315\n[I 2025-01-06 12:24:05,957] Trial 0 finished with value: 0.0 and parameters: {'n_estimators': 508, 'max_depth': 15, 'learning_rate': 0.2574876700361473, 'subsample': 0.6609562967363541, 'colsample_bytree': 0.7179444146604738, 'lambda': 7.058617016816236, 'alpha': 8.198815469032201, 'min_child_weight': 2, 'gamma': 0.46448361463346166}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:24:55,072] Trial 1 finished with value: 0.0 and parameters: {'n_estimators': 648, 'max_depth': 8, 'learning_rate': 0.25515645034795953, 'subsample': 0.7455943682005913, 'colsample_bytree': 0.7737550223087823, 'lambda': 8.50925135932519, 'alpha': 1.158521200997155, 'min_child_weight': 9, 'gamma': 0.007970839701606725}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:25:33,718] Trial 2 finished with value: 0.0 and parameters: {'n_estimators': 445, 'max_depth': 9, 'learning_rate': 0.132253461604859, 'subsample': 0.612567392374427, 'colsample_bytree': 0.9188144511190427, 'lambda': 9.843641337687734, 'alpha': 3.5928957034148308, 'min_child_weight': 9, 'gamma': 0.5007358216032508}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:26:14,987] Trial 3 finished with value: 0.0 and parameters: {'n_estimators': 738, 'max_depth': 6, 'learning_rate': 0.09605975042587996, 'subsample': 0.6273943414286255, 'colsample_bytree': 0.6651078407949355, 'lambda': 9.750187322626056, 'alpha': 1.6694644378478554, 'min_child_weight': 4, 'gamma': 0.8087452555019065}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:30:25,770] Trial 4 finished with value: 0.0 and parameters: {'n_estimators': 894, 'max_depth': 15, 'learning_rate': 0.2983046780858939, 'subsample': 0.9091705252964143, 'colsample_bytree': 0.9260349306966984, 'lambda': 9.578864418097002, 'alpha': 1.1638839314001772, 'min_child_weight': 8, 'gamma': 0.5077497938165865}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:32:58,543] Trial 5 finished with value: 0.0 and parameters: {'n_estimators': 662, 'max_depth': 15, 'learning_rate': 0.013273721883218554, 'subsample': 0.8300532023116182, 'colsample_bytree': 0.904601990349333, 'lambda': 4.69462101802443, 'alpha': 7.626201628963118, 'min_child_weight': 10, 'gamma': 0.3296990892667174}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:33:21,151] Trial 6 finished with value: 0.0 and parameters: {'n_estimators': 429, 'max_depth': 6, 'learning_rate': 0.25236349244224987, 'subsample': 0.8598652659924706, 'colsample_bytree': 0.884301250540293, 'lambda': 6.1920963735055645, 'alpha': 9.059142928390925, 'min_child_weight': 7, 'gamma': 0.1940997955470135}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:33:32,212] Trial 7 finished with value: 0.0 and parameters: {'n_estimators': 78, 'max_depth': 11, 'learning_rate': 0.18201819411010553, 'subsample': 0.7091489074052748, 'colsample_bytree': 0.780881727543571, 'lambda': 4.25213987439557, 'alpha': 1.1662210958355583, 'min_child_weight': 6, 'gamma': 0.42752252966930815}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:33:59,226] Trial 8 finished with value: 0.0 and parameters: {'n_estimators': 229, 'max_depth': 11, 'learning_rate': 0.11943785248375649, 'subsample': 0.67270610792872, 'colsample_bytree': 0.6873330686684652, 'lambda': 1.3633303836145314, 'alpha': 9.028359700028103, 'min_child_weight': 4, 'gamma': 0.20313813463910524}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:34:55,591] Trial 9 finished with value: 0.0 and parameters: {'n_estimators': 235, 'max_depth': 15, 'learning_rate': 0.22796783794156777, 'subsample': 0.9846052384892147, 'colsample_bytree': 0.6554683277454432, 'lambda': 7.155118056485246, 'alpha': 1.96797508840143, 'min_child_weight': 3, 'gamma': 0.10826632051638763}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:35:29,520] Trial 10 finished with value: 0.0 and parameters: {'n_estimators': 973, 'max_depth': 3, 'learning_rate': 0.20345555766208423, 'subsample': 0.7726584451814943, 'colsample_bytree': 0.7336673316973288, 'lambda': 2.501626998510256, 'alpha': 6.486388993710469, 'min_child_weight': 1, 'gamma': 0.7225310078968405}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:36:23,595] Trial 11 finished with value: 0.0 and parameters: {'n_estimators': 600, 'max_depth': 9, 'learning_rate': 0.2790039226452751, 'subsample': 0.7385956208745388, 'colsample_bytree': 0.8133203369269615, 'lambda': 7.581549959363085, 'alpha': 4.45704232033406, 'min_child_weight': 1, 'gamma': 0.6506969300644578}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:38:15,837] Trial 12 finished with value: 0.0 and parameters: {'n_estimators': 730, 'max_depth': 12, 'learning_rate': 0.24431486760977772, 'subsample': 0.6793690128514076, 'colsample_bytree': 0.8131022644961233, 'lambda': 7.83880437192938, 'alpha': 5.973652115387121, 'min_child_weight': 5, 'gamma': 0.9840141005820406}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:38:47,547] Trial 13 finished with value: 0.0 and parameters: {'n_estimators': 499, 'max_depth': 7, 'learning_rate': 0.1769343311117721, 'subsample': 0.786469220465628, 'colsample_bytree': 0.6063709773408594, 'lambda': 6.087006843461474, 'alpha': 2.9410178249413175, 'min_child_weight': 2, 'gamma': 0.020909243839417002}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:39:44,747] Trial 14 finished with value: 0.0 and parameters: {'n_estimators': 348, 'max_depth': 13, 'learning_rate': 0.27468269641326326, 'subsample': 0.7316804650204616, 'colsample_bytree': 0.7576765321822801, 'lambda': 8.31785038281242, 'alpha': 9.98845680334064, 'min_child_weight': 10, 'gamma': 0.34476863329578267}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:40:13,149] Trial 15 finished with value: 0.0 and parameters: {'n_estimators': 787, 'max_depth': 3, 'learning_rate': 0.21171627998714362, 'subsample': 0.664108514390962, 'colsample_bytree': 0.9933951765392968, 'lambda': 6.223858043460607, 'alpha': 0.37225091658238973, 'min_child_weight': 7, 'gamma': 0.020875319924876012}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:40:56,571] Trial 16 finished with value: 0.0 and parameters: {'n_estimators': 576, 'max_depth': 7, 'learning_rate': 0.06488832371825184, 'subsample': 0.7639756538302561, 'colsample_bytree': 0.7211035227989507, 'lambda': 3.7862473780896426, 'alpha': 6.956935046138737, 'min_child_weight': 6, 'gamma': 0.6191360399500434}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:41:53,176] Trial 17 finished with value: 0.0 and parameters: {'n_estimators': 323, 'max_depth': 13, 'learning_rate': 0.16572924004528258, 'subsample': 0.8498084168787697, 'colsample_bytree': 0.8240737647064013, 'lambda': 8.83671490791336, 'alpha': 5.324027921736757, 'min_child_weight': 3, 'gamma': 0.9037249755315642}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:43:00,541] Trial 18 finished with value: 0.0 and parameters: {'n_estimators': 633, 'max_depth': 10, 'learning_rate': 0.2578936270078429, 'subsample': 0.702056812819127, 'colsample_bytree': 0.8539613279212422, 'lambda': 6.720344850232736, 'alpha': 7.709135237827418, 'min_child_weight': 8, 'gamma': 0.26710449146044546}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:44:01,327] Trial 19 finished with value: 0.0 and parameters: {'n_estimators': 812, 'max_depth': 8, 'learning_rate': 0.20494382096164515, 'subsample': 0.6389598514195576, 'colsample_bytree': 0.6040132942972367, 'lambda': 5.377634652645494, 'alpha': 4.239909519802047, 'min_child_weight': 5, 'gamma': 0.12292655354872428}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:44:28,422] Trial 20 finished with value: 0.0 and parameters: {'n_estimators': 531, 'max_depth': 5, 'learning_rate': 0.28836672488370907, 'subsample': 0.601769525926617, 'colsample_bytree': 0.7126819040191882, 'lambda': 8.536633143163547, 'alpha': 2.8487666392572883, 'min_child_weight': 9, 'gamma': 0.5844803633633978}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:45:18,206] Trial 21 finished with value: 0.0 and parameters: {'n_estimators': 469, 'max_depth': 10, 'learning_rate': 0.13398673061590602, 'subsample': 0.6017438289862593, 'colsample_bytree': 0.9612987260036725, 'lambda': 9.908360856173669, 'alpha': 3.0410228499153975, 'min_child_weight': 9, 'gamma': 0.5136684799918574}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:45:49,937] Trial 22 finished with value: 0.0 and parameters: {'n_estimators': 361, 'max_depth': 9, 'learning_rate': 0.13817675036065452, 'subsample': 0.6441607281105765, 'colsample_bytree': 0.7630869046168475, 'lambda': 8.90614792842878, 'alpha': 4.338104280009498, 'min_child_weight': 9, 'gamma': 0.40154948593658485}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:46:22,923] Trial 23 finished with value: 0.0 and parameters: {'n_estimators': 439, 'max_depth': 8, 'learning_rate': 0.0708739066924901, 'subsample': 0.7051222372498374, 'colsample_bytree': 0.858629643548352, 'lambda': 7.690383167725644, 'alpha': 0.19847587791321342, 'min_child_weight': 7, 'gamma': 0.7887594624646583}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:48:21,826] Trial 24 finished with value: 0.0 and parameters: {'n_estimators': 668, 'max_depth': 13, 'learning_rate': 0.22693343729791524, 'subsample': 0.7481429682807385, 'colsample_bytree': 0.7857458036458494, 'lambda': 9.099349740443444, 'alpha': 3.4499960090183666, 'min_child_weight': 8, 'gamma': 0.4584444619643817}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:49:09,324] Trial 25 finished with value: 0.0 and parameters: {'n_estimators': 570, 'max_depth': 9, 'learning_rate': 0.14894628994262576, 'subsample': 0.8068910538949552, 'colsample_bytree': 0.8389292193667042, 'lambda': 0.12464711926874639, 'alpha': 5.502850768376402, 'min_child_weight': 10, 'gamma': 0.560932996257923}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:49:23,612] Trial 26 finished with value: 0.0 and parameters: {'n_estimators': 254, 'max_depth': 5, 'learning_rate': 0.1901762109412734, 'subsample': 0.6354150455650093, 'colsample_bytree': 0.7448585773896629, 'lambda': 8.106154361133845, 'alpha': 2.484127137671666, 'min_child_weight': 9, 'gamma': 0.33644543636019986}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:49:52,900] Trial 27 finished with value: 0.0 and parameters: {'n_estimators': 409, 'max_depth': 8, 'learning_rate': 0.10124160277855337, 'subsample': 0.6728094538825015, 'colsample_bytree': 0.6852670899247173, 'lambda': 6.897772417076637, 'alpha': 4.175315346836093, 'min_child_weight': 2, 'gamma': 0.7027104003193976}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:50:12,025] Trial 28 finished with value: 0.0 and parameters: {'n_estimators': 150, 'max_depth': 11, 'learning_rate': 0.26634349962533105, 'subsample': 0.7098920082226595, 'colsample_bytree': 0.9458490045035978, 'lambda': 5.381811305624881, 'alpha': 3.558534776457112, 'min_child_weight': 8, 'gamma': 0.2535957889000002}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:50:44,078] Trial 29 finished with value: 0.0 and parameters: {'n_estimators': 524, 'max_depth': 6, 'learning_rate': 0.2316963005750283, 'subsample': 0.6226558477804707, 'colsample_bytree': 0.8816799091044689, 'lambda': 9.84333039421111, 'alpha': 1.903816205660362, 'min_child_weight': 4, 'gamma': 0.803538892855068}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:51:53,950] Trial 30 finished with value: 0.0 and parameters: {'n_estimators': 712, 'max_depth': 10, 'learning_rate': 0.07391247346022864, 'subsample': 0.6586541579621726, 'colsample_bytree': 0.6567866455444358, 'lambda': 9.34836735248708, 'alpha': 0.9310515224221776, 'min_child_weight': 6, 'gamma': 0.08446447809979915}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:52:39,528] Trial 31 finished with value: 0.0 and parameters: {'n_estimators': 893, 'max_depth': 5, 'learning_rate': 0.10432146125195296, 'subsample': 0.616257784526759, 'colsample_bytree': 0.6373722509088389, 'lambda': 9.979903656799035, 'alpha': 1.5741814625451718, 'min_child_weight': 3, 'gamma': 0.8417029880579068}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:53:26,912] Trial 32 finished with value: 0.0 and parameters: {'n_estimators': 803, 'max_depth': 7, 'learning_rate': 0.03856291038127168, 'subsample': 0.930078565026574, 'colsample_bytree': 0.7012165342134391, 'lambda': 9.333240634929664, 'alpha': 1.032637544991863, 'min_child_weight': 4, 'gamma': 0.971467346329711}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:54:08,602] Trial 33 finished with value: 0.0 and parameters: {'n_estimators': 717, 'max_depth': 6, 'learning_rate': 0.2974911833555503, 'subsample': 0.6355548792413505, 'colsample_bytree': 0.6580203601774313, 'lambda': 8.53746786568206, 'alpha': 2.4321700112691387, 'min_child_weight': 2, 'gamma': 0.6965842037572787}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:54:36,001] Trial 34 finished with value: 0.0 and parameters: {'n_estimators': 661, 'max_depth': 4, 'learning_rate': 0.09678829150694014, 'subsample': 0.6928612105546583, 'colsample_bytree': 0.9090014039135705, 'lambda': 9.328021156634984, 'alpha': 8.064465073853249, 'min_child_weight': 5, 'gamma': 0.5267632867686075}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:57:29,118] Trial 35 finished with value: 0.0 and parameters: {'n_estimators': 882, 'max_depth': 14, 'learning_rate': 0.12286100309278097, 'subsample': 0.8789598424189984, 'colsample_bytree': 0.7844589826325205, 'lambda': 7.4680153112177035, 'alpha': 0.6340451234643909, 'min_child_weight': 10, 'gamma': 0.39251101405081346}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 12:58:13,870] Trial 36 finished with value: 0.0 and parameters: {'n_estimators': 608, 'max_depth': 8, 'learning_rate': 0.03067808137058145, 'subsample': 0.6580907086033456, 'colsample_bytree': 0.6844984947620711, 'lambda': 8.394000769678453, 'alpha': 1.6802548321176158, 'min_child_weight': 3, 'gamma': 0.4561080924720893}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 13:02:33,986] Trial 37 finished with value: 0.0 and parameters: {'n_estimators': 982, 'max_depth': 15, 'learning_rate': 0.15784646482046893, 'subsample': 0.7189781198995249, 'colsample_bytree': 0.6311113334183946, 'lambda': 3.3280712892452597, 'alpha': 8.57969776161645, 'min_child_weight': 7, 'gamma': 0.20019815830729168}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 13:02:58,650] Trial 38 finished with value: 0.0 and parameters: {'n_estimators': 416, 'max_depth': 6, 'learning_rate': 0.11253534915698718, 'subsample': 0.8066756286353866, 'colsample_bytree': 0.7326696177821534, 'lambda': 6.493637465024522, 'alpha': 9.997156396854013, 'min_child_weight': 2, 'gamma': 0.9084644542185377}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 13:03:35,988] Trial 39 finished with value: 0.0 and parameters: {'n_estimators': 544, 'max_depth': 7, 'learning_rate': 0.2430301433405265, 'subsample': 0.6862720507765951, 'colsample_bytree': 0.770590372064648, 'lambda': 7.340588438545441, 'alpha': 6.912083906139204, 'min_child_weight': 4, 'gamma': 0.6596584640975875}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 13:05:36,624] Trial 40 finished with value: 0.0 and parameters: {'n_estimators': 753, 'max_depth': 12, 'learning_rate': 0.0883221423821479, 'subsample': 0.6151138172390684, 'colsample_bytree': 0.6993815180986678, 'lambda': 8.020553162437375, 'alpha': 1.3207190487360168, 'min_child_weight': 1, 'gamma': 0.7734789086431741}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 13:09:03,794] Trial 41 finished with value: 0.0 and parameters: {'n_estimators': 897, 'max_depth': 14, 'learning_rate': 0.2969757861468299, 'subsample': 0.910711664716811, 'colsample_bytree': 0.91707823826453, 'lambda': 9.623409215765545, 'alpha': 2.184095212723345, 'min_child_weight': 8, 'gamma': 0.4798204658733476}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 13:12:11,757] Trial 42 finished with value: 0.0 and parameters: {'n_estimators': 857, 'max_depth': 14, 'learning_rate': 0.2659210649512394, 'subsample': 0.9364461873071375, 'colsample_bytree': 0.9381665255814735, 'lambda': 8.88074128652434, 'alpha': 0.01964718520607689, 'min_child_weight': 9, 'gamma': 0.3055782868621818}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 13:14:09,415] Trial 43 finished with value: 0.0 and parameters: {'n_estimators': 483, 'max_depth': 15, 'learning_rate': 0.2501617490027984, 'subsample': 0.986067899162625, 'colsample_bytree': 0.9941581259287515, 'lambda': 9.633623369589333, 'alpha': 0.7111716074693055, 'min_child_weight': 10, 'gamma': 0.554121266168811}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 13:16:07,225] Trial 44 finished with value: 0.0 and parameters: {'n_estimators': 939, 'max_depth': 11, 'learning_rate': 0.2837597722593177, 'subsample': 0.8807436677184467, 'colsample_bytree': 0.8919804667888086, 'lambda': 8.622439964320783, 'alpha': 1.3786269516652028, 'min_child_weight': 7, 'gamma': 0.162438502728187}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 13:17:07,919] Trial 45 finished with value: 0.0 and parameters: {'n_estimators': 678, 'max_depth': 9, 'learning_rate': 0.27519208331669365, 'subsample': 0.8276076928632754, 'colsample_bytree': 0.9772482800466407, 'lambda': 9.257320350867255, 'alpha': 4.877578277823356, 'min_child_weight': 8, 'gamma': 0.6145715181311058}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 13:19:14,229] Trial 46 finished with value: 0.0 and parameters: {'n_estimators': 835, 'max_depth': 12, 'learning_rate': 0.21794866560691223, 'subsample': 0.7483334875051276, 'colsample_bytree': 0.7964640412762256, 'lambda': 4.567791493208604, 'alpha': 3.6402385416621708, 'min_child_weight': 6, 'gamma': 0.3938821264339206}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 13:22:37,942] Trial 47 finished with value: 0.0 and parameters: {'n_estimators': 775, 'max_depth': 15, 'learning_rate': 0.18136035950157348, 'subsample': 0.7784477662155117, 'colsample_bytree': 0.8704943779225445, 'lambda': 8.037233532258716, 'alpha': 6.283055872832347, 'min_child_weight': 10, 'gamma': 0.4248392872026358}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 13:23:02,676] Trial 48 finished with value: 0.0 and parameters: {'n_estimators': 615, 'max_depth': 4, 'learning_rate': 0.19443158818396789, 'subsample': 0.9561296103482627, 'colsample_bytree': 0.8245951928543712, 'lambda': 5.95107518560944, 'alpha': 9.204872632931641, 'min_child_weight': 9, 'gamma': 0.07053131370163872}. Best is trial 0 with value: 0.0.\n[I 2025-01-06 13:23:32,185] Trial 49 finished with value: 0.0 and parameters: {'n_estimators': 289, 'max_depth': 10, 'learning_rate': 0.055128026813867276, 'subsample': 0.7248821635176861, 'colsample_bytree': 0.6710078210279682, 'lambda': 8.97186857333436, 'alpha': 2.058910720598596, 'min_child_weight': 1, 'gamma': 0.24020596124270588}. Best is trial 0 with value: 0.0.\n","output_type":"stream"},{"name":"stdout","text":"Best RMSLE: 0.0\nBest Parameters: {'n_estimators': 508, 'max_depth': 15, 'learning_rate': 0.2574876700361473, 'subsample': 0.6609562967363541, 'colsample_bytree': 0.7179444146604738, 'lambda': 7.058617016816236, 'alpha': 8.198815469032201, 'min_child_weight': 2, 'gamma': 0.46448361463346166}\n","output_type":"stream"}],"execution_count":28},{"cell_type":"markdown","source":"Best RMSLE: 1.1392509523625756\nBest Parameters: {'max_depth': 11, 'num_leaves': 145, 'learning_rate': 0.02602483311401839, 'n_estimators': 953, 'min_child_samples': 49, 'subsample': 0.780223559316748, 'colsample_bytree': 0.9630826496236902, 'reg_alpha': 0.06682492360581052, 'reg_lambda': 0.3185191981340434}","metadata":{}},{"cell_type":"code","source":"#Visualize the optimization history\noptuna.visualization.plot_optimization_history(study)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T13:23:32.196793Z","iopub.execute_input":"2025-01-06T13:23:32.197245Z","iopub.status.idle":"2025-01-06T13:23:32.807726Z","shell.execute_reply.started":"2025-01-06T13:23:32.197205Z","shell.execute_reply":"2025-01-06T13:23:32.806242Z"}},"outputs":[{"output_type":"display_data","data":{"text/html":"<html>\n<head><meta charset=\"utf-8\" /></head>\n<body>\n    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.35.2.min.js\"></script>          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</div>\n</body>\n</html>"},"metadata":{}}],"execution_count":29},{"cell_type":"code","source":"#Visualize hyperparameter importance\noptuna.visualization.plot_param_importances(study)\n\n\n# Something is up, will take a look later","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"model_opt = XGBRegressor(best_params)\nmodel_opt.fit(X_train, y_train)\n\nrmsle = utils.rmsle(y_valid, preds)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T13:23:33.158501Z","iopub.status.idle":"2025-01-06T13:23:33.15883Z","shell.execute_reply":"2025-01-06T13:23:33.158701Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"preds_test = model_opt.predict(test)\npreds_test = np.clip(preds, 0, None)  # Ensure no negative predictions","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T13:23:33.159836Z","iopub.status.idle":"2025-01-06T13:23:33.160305Z","shell.execute_reply":"2025-01-06T13:23:33.160109Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"submission = pd.DataFrame({\n    'Id': orig_test['id'],  \n    'Premium Amount': preds_test  # Your predictions\n})\n\n# # Save submission to a CSV file\n# submission.to_csv('submission.csv', index=False)\nsubmission.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T13:23:33.16118Z","iopub.status.idle":"2025-01-06T13:23:33.161643Z","shell.execute_reply":"2025-01-06T13:23:33.161432Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"submission.to_csv('submission.csv', index=False)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-06T13:23:33.162607Z","iopub.status.idle":"2025-01-06T13:23:33.162936Z","shell.execute_reply":"2025-01-06T13:23:33.162779Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}