s4e1.ipynb

{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Binary Classification with a Bank Churn Dataset\n","# https://www.kaggle.com/competitions/playground-series-s4e1/overview\n","\n","# LATEST SCORE 0.81478, ranking: 2800/3538\n","\n","# anthor: moonzfxs\n","# TIME: 2024/01/31"]},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2024-01-31T09:10:48.518871Z","iopub.status.busy":"2024-01-31T09:10:48.518487Z","iopub.status.idle":"2024-01-31T09:10:48.927150Z","shell.execute_reply":"2024-01-31T09:10:48.925285Z","shell.execute_reply.started":"2024-01-31T09:10:48.518838Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["/kaggle/input/playground-series-s4e1/sample_submission.csv\n","/kaggle/input/playground-series-s4e1/train.csv\n","/kaggle/input/playground-series-s4e1/test.csv\n"]}],"source":["# This Python 3 environment comes with many helpful analytics libraries installed\n","# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n","# For example, here's several helpful packages to load\n","\n","import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","\n","# Input data files are available in the read-only \"../input/\" directory\n","# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n","\n","import os\n","for dirname, _, filenames in os.walk('/kaggle/input'):\n","    for filename in filenames:\n","        print(os.path.join(dirname, filename))\n","\n","# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n","# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2024-01-31T09:10:48.929227Z","iopub.status.busy":"2024-01-31T09:10:48.928839Z","iopub.status.idle":"2024-01-31T09:10:49.675561Z","shell.execute_reply":"2024-01-31T09:10:49.674506Z","shell.execute_reply.started":"2024-01-31T09:10:48.929202Z"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>id</th>\n","      <th>CustomerId</th>\n","      <th>Surname</th>\n","      <th>CreditScore</th>\n","      <th>Geography</th>\n","      <th>Gender</th>\n","      <th>Age</th>\n","      <th>Tenure</th>\n","      <th>Balance</th>\n","      <th>NumOfProducts</th>\n","      <th>HasCrCard</th>\n","      <th>IsActiveMember</th>\n","      <th>EstimatedSalary</th>\n","      <th>Exited</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>15674932</td>\n","      <td>Okwudilichukwu</td>\n","      <td>668</td>\n","      <td>France</td>\n","      <td>Male</td>\n","      <td>33.0</td>\n","      <td>3</td>\n","      <td>0.00</td>\n","      <td>2</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>181449.97</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>15749177</td>\n","      <td>Okwudiliolisa</td>\n","      <td>627</td>\n","      <td>France</td>\n","      <td>Male</td>\n","      <td>33.0</td>\n","      <td>1</td>\n","      <td>0.00</td>\n","      <td>2</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>49503.50</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>15694510</td>\n","      <td>Hsueh</td>\n","      <td>678</td>\n","      <td>France</td>\n","      <td>Male</td>\n","      <td>40.0</td>\n","      <td>10</td>\n","      <td>0.00</td>\n","      <td>2</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>184866.69</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>15741417</td>\n","      <td>Kao</td>\n","      <td>581</td>\n","      <td>France</td>\n","      <td>Male</td>\n","      <td>34.0</td>\n","      <td>2</td>\n","      <td>148882.54</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>84560.88</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>15766172</td>\n","      <td>Chiemenam</td>\n","      <td>716</td>\n","      <td>Spain</td>\n","      <td>Male</td>\n","      <td>33.0</td>\n","      <td>5</td>\n","      <td>0.00</td>\n","      <td>2</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>15068.83</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   id  CustomerId         Surname  CreditScore Geography Gender   Age  Tenure  \\\n","0   0    15674932  Okwudilichukwu          668    France   Male  33.0       3   \n","1   1    15749177   Okwudiliolisa          627    France   Male  33.0       1   \n","2   2    15694510           Hsueh          678    France   Male  40.0      10   \n","3   3    15741417             Kao          581    France   Male  34.0       2   \n","4   4    15766172       Chiemenam          716     Spain   Male  33.0       5   \n","\n","     Balance  NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  \\\n","0       0.00              2        1.0             0.0        181449.97   \n","1       0.00              2        1.0             1.0         49503.50   \n","2       0.00              2        1.0             0.0        184866.69   \n","3  148882.54              1        1.0             1.0         84560.88   \n","4       0.00              2        1.0             1.0         15068.83   \n","\n","   Exited  \n","0       0  \n","1       0  \n","2       0  \n","3       0  \n","4       0  "]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["df = pd.read_csv(\"/kaggle/input/playground-series-s4e1/train.csv\")  # (165034, 14)\n","data_test = pd.read_csv(\"/kaggle/input/playground-series-s4e1/test.csv\")\n","df.head()"]},{"cell_type":"code","execution_count":3,"metadata":{"execution":{"iopub.execute_input":"2024-01-31T09:10:49.678847Z","iopub.status.busy":"2024-01-31T09:10:49.676938Z","iopub.status.idle":"2024-01-31T09:10:49.718108Z","shell.execute_reply":"2024-01-31T09:10:49.717121Z","shell.execute_reply.started":"2024-01-31T09:10:49.678806Z"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>id</th>\n","      <th>CustomerId</th>\n","      <th>Surname</th>\n","      <th>CreditScore</th>\n","      <th>Geography</th>\n","      <th>Gender</th>\n","      <th>Age</th>\n","      <th>Tenure</th>\n","      <th>Balance</th>\n","      <th>NumOfProducts</th>\n","      <th>HasCrCard</th>\n","      <th>IsActiveMember</th>\n","      <th>EstimatedSalary</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>15674932</td>\n","      <td>Okwudilichukwu</td>\n","      <td>668</td>\n","      <td>France</td>\n","      <td>Male</td>\n","      <td>33.0</td>\n","      <td>3</td>\n","      <td>0.00</td>\n","      <td>2</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>181449.97</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>15749177</td>\n","      <td>Okwudiliolisa</td>\n","      <td>627</td>\n","      <td>France</td>\n","      <td>Male</td>\n","      <td>33.0</td>\n","      <td>1</td>\n","      <td>0.00</td>\n","      <td>2</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>49503.50</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>15694510</td>\n","      <td>Hsueh</td>\n","      <td>678</td>\n","      <td>France</td>\n","      <td>Male</td>\n","      <td>40.0</td>\n","      <td>10</td>\n","      <td>0.00</td>\n","      <td>2</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>184866.69</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>15741417</td>\n","      <td>Kao</td>\n","      <td>581</td>\n","      <td>France</td>\n","      <td>Male</td>\n","      <td>34.0</td>\n","      <td>2</td>\n","      <td>148882.54</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>84560.88</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>15766172</td>\n","      <td>Chiemenam</td>\n","      <td>716</td>\n","      <td>Spain</td>\n","      <td>Male</td>\n","      <td>33.0</td>\n","      <td>5</td>\n","      <td>0.00</td>\n","      <td>2</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>15068.83</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   id  CustomerId         Surname  CreditScore Geography Gender   Age  Tenure  \\\n","0   0    15674932  Okwudilichukwu          668    France   Male  33.0       3   \n","1   1    15749177   Okwudiliolisa          627    France   Male  33.0       1   \n","2   2    15694510           Hsueh          678    France   Male  40.0      10   \n","3   3    15741417             Kao          581    France   Male  34.0       2   \n","4   4    15766172       Chiemenam          716     Spain   Male  33.0       5   \n","\n","     Balance  NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  \n","0       0.00              2        1.0             0.0        181449.97  \n","1       0.00              2        1.0             1.0         49503.50  \n","2       0.00              2        1.0             0.0        184866.69  \n","3  148882.54              1        1.0             1.0         84560.88  \n","4       0.00              2        1.0             1.0         15068.83  "]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["train_target = df['Exited']\n","\n","df = df.drop(columns=['Exited'])\n","df.head()"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2024-01-31T09:10:49.720616Z","iopub.status.busy":"2024-01-31T09:10:49.720246Z","iopub.status.idle":"2024-01-31T09:10:50.203767Z","shell.execute_reply":"2024-01-31T09:10:50.202519Z","shell.execute_reply.started":"2024-01-31T09:10:49.720587Z"},"trusted":true},"outputs":[],"source":["from sklearn.preprocessing import StandardScaler\n","\n","def feature_processing(df):\n","    df = pd.get_dummies(df, columns=['Geography'], prefix = ['Geo'])\n","    df = df.drop(['id', 'CustomerId', 'Surname'], axis = 1)\n","    df['Gender'].replace({'Male': 0, 'Female': 1}, inplace=True)\n","    \n","    scaler = StandardScaler()\n","    df_scaled = scaler.fit_transform(df)\n","    df_scaled = pd.DataFrame(df_scaled, columns = df.columns)\n","    return df_scaled"]},{"cell_type":"code","execution_count":5,"metadata":{"execution":{"iopub.execute_input":"2024-01-31T09:10:50.205391Z","iopub.status.busy":"2024-01-31T09:10:50.205051Z","iopub.status.idle":"2024-01-31T09:10:50.587607Z","shell.execute_reply":"2024-01-31T09:10:50.586609Z","shell.execute_reply.started":"2024-01-31T09:10:50.205364Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["/tmp/ipykernel_34/2264549894.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n","The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n","\n","For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n","\n","\n","  df['Gender'].replace({'Male': 0, 'Female': 1}, inplace=True)\n","/tmp/ipykernel_34/2264549894.py:6: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n","  df['Gender'].replace({'Male': 0, 'Female': 1}, inplace=True)\n"]}],"source":["from sklearn.model_selection import train_test_split\n","\n","df_scaled = feature_processing(df)  # (165034, 12)\n","\n","X_train, X_test, y_train, y_test = train_test_split(df_scaled, train_target, test_size=0.3, random_state=42)"]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2024-01-31T09:10:50.589117Z","iopub.status.busy":"2024-01-31T09:10:50.588801Z","iopub.status.idle":"2024-01-31T09:10:50.607107Z","shell.execute_reply":"2024-01-31T09:10:50.605804Z","shell.execute_reply.started":"2024-01-31T09:10:50.589092Z"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>CreditScore</th>\n","      <th>Gender</th>\n","      <th>Age</th>\n","      <th>Tenure</th>\n","      <th>Balance</th>\n","      <th>NumOfProducts</th>\n","      <th>HasCrCard</th>\n","      <th>IsActiveMember</th>\n","      <th>EstimatedSalary</th>\n","      <th>Geo_France</th>\n","      <th>Geo_Germany</th>\n","      <th>Geo_Spain</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.144135</td>\n","      <td>-0.878465</td>\n","      <td>-0.578074</td>\n","      <td>-0.719973</td>\n","      <td>-0.883163</td>\n","      <td>0.814298</td>\n","      <td>0.571263</td>\n","      <td>-0.99555</td>\n","      <td>1.369486</td>\n","      <td>0.866992</td>\n","      <td>-0.515098</td>\n","      <td>-0.530199</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>-0.367706</td>\n","      <td>-0.878465</td>\n","      <td>-0.578074</td>\n","      <td>-1.432694</td>\n","      <td>-0.883163</td>\n","      <td>0.814298</td>\n","      <td>0.571263</td>\n","      <td>1.00447</td>\n","      <td>-1.254085</td>\n","      <td>0.866992</td>\n","      <td>-0.515098</td>\n","      <td>-0.530199</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.268974</td>\n","      <td>-0.878465</td>\n","      <td>0.211354</td>\n","      <td>1.774548</td>\n","      <td>-0.883163</td>\n","      <td>0.814298</td>\n","      <td>0.571263</td>\n","      <td>-0.99555</td>\n","      <td>1.437422</td>\n","      <td>0.866992</td>\n","      <td>-0.515098</td>\n","      <td>-0.530199</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>-0.941966</td>\n","      <td>-0.878465</td>\n","      <td>-0.465299</td>\n","      <td>-1.076334</td>\n","      <td>1.486918</td>\n","      <td>-1.013348</td>\n","      <td>0.571263</td>\n","      <td>1.00447</td>\n","      <td>-0.557018</td>\n","      <td>0.866992</td>\n","      <td>-0.515098</td>\n","      <td>-0.530199</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.743362</td>\n","      <td>-0.878465</td>\n","      <td>-0.578074</td>\n","      <td>-0.007253</td>\n","      <td>-0.883163</td>\n","      <td>0.814298</td>\n","      <td>0.571263</td>\n","      <td>1.00447</td>\n","      <td>-1.938770</td>\n","      <td>-1.153414</td>\n","      <td>-0.515098</td>\n","      <td>1.886084</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   CreditScore    Gender       Age    Tenure   Balance  NumOfProducts  \\\n","0     0.144135 -0.878465 -0.578074 -0.719973 -0.883163       0.814298   \n","1    -0.367706 -0.878465 -0.578074 -1.432694 -0.883163       0.814298   \n","2     0.268974 -0.878465  0.211354  1.774548 -0.883163       0.814298   \n","3    -0.941966 -0.878465 -0.465299 -1.076334  1.486918      -1.013348   \n","4     0.743362 -0.878465 -0.578074 -0.007253 -0.883163       0.814298   \n","\n","   HasCrCard  IsActiveMember  EstimatedSalary  Geo_France  Geo_Germany  \\\n","0   0.571263        -0.99555         1.369486    0.866992    -0.515098   \n","1   0.571263         1.00447        -1.254085    0.866992    -0.515098   \n","2   0.571263        -0.99555         1.437422    0.866992    -0.515098   \n","3   0.571263         1.00447        -0.557018    0.866992    -0.515098   \n","4   0.571263         1.00447        -1.938770   -1.153414    -0.515098   \n","\n","   Geo_Spain  \n","0  -0.530199  \n","1  -0.530199  \n","2  -0.530199  \n","3  -0.530199  \n","4   1.886084  "]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["df_scaled.head()"]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2024-01-31T09:10:50.609918Z","iopub.status.busy":"2024-01-31T09:10:50.609076Z","iopub.status.idle":"2024-01-31T09:10:50.778847Z","shell.execute_reply":"2024-01-31T09:10:50.778121Z","shell.execute_reply.started":"2024-01-31T09:10:50.609880Z"},"trusted":true},"outputs":[{"data":{"text/html":["<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"],"text/plain":["LinearRegression()"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["from sklearn.linear_model import LinearRegression \n","\n","model = LinearRegression()\n","model.fit(X_train, y_train)"]},{"cell_type":"code","execution_count":8,"metadata":{"execution":{"iopub.execute_input":"2024-01-31T09:10:50.781242Z","iopub.status.busy":"2024-01-31T09:10:50.780442Z","iopub.status.idle":"2024-01-31T09:10:50.798637Z","shell.execute_reply":"2024-01-31T09:10:50.797456Z","shell.execute_reply.started":"2024-01-31T09:10:50.781202Z"},"trusted":true},"outputs":[{"data":{"text/plain":["0.1278821420249966"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["from sklearn.metrics import mean_squared_error\n","\n","mean_squared_error(y_test, model.predict(X_test), squared=True)  # 0.3580"]},{"cell_type":"code","execution_count":9,"metadata":{"execution":{"iopub.execute_input":"2024-01-31T09:10:50.801864Z","iopub.status.busy":"2024-01-31T09:10:50.800474Z","iopub.status.idle":"2024-01-31T09:10:50.830411Z","shell.execute_reply":"2024-01-31T09:10:50.829168Z","shell.execute_reply.started":"2024-01-31T09:10:50.801824Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["[-0.00780625  0.0440436   0.12451881 -0.00644826 -0.02306092 -0.07103512\n"," -0.00934359 -0.08019787  0.00577427 -0.02600885  0.05345113 -0.02147157]\n","0.21217495976326559\n","{'copy_X': True, 'fit_intercept': True, 'n_jobs': None, 'positive': False}\n","[ 0.22849833 -0.00658316  0.01134928 ...  0.2222625   0.18305285\n","  0.46871022]\n","0.22810809036504032\n"]}],"source":["# model detail\n","print(model.coef_) # Partial Regression Coefficients\n","print(model.intercept_) # Intercept of the regression line\n","print(model.get_params()) # Get parameters\n","print(model.predict(X_test)) # Predicted values\n","print(model.score(X_test, y_test)) # coefficient of determination"]},{"cell_type":"code","execution_count":10,"metadata":{"execution":{"iopub.execute_input":"2024-01-31T09:10:50.837572Z","iopub.status.busy":"2024-01-31T09:10:50.834019Z","iopub.status.idle":"2024-01-31T09:10:51.064926Z","shell.execute_reply":"2024-01-31T09:10:51.063853Z","shell.execute_reply.started":"2024-01-31T09:10:50.837515Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["/tmp/ipykernel_34/2264549894.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n","The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n","\n","For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n","\n","\n","  df['Gender'].replace({'Male': 0, 'Female': 1}, inplace=True)\n","/tmp/ipykernel_34/2264549894.py:6: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n","  df['Gender'].replace({'Male': 0, 'Female': 1}, inplace=True)\n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>CreditScore</th>\n","      <th>Gender</th>\n","      <th>Age</th>\n","      <th>Tenure</th>\n","      <th>Balance</th>\n","      <th>NumOfProducts</th>\n","      <th>HasCrCard</th>\n","      <th>IsActiveMember</th>\n","      <th>EstimatedSalary</th>\n","      <th>Geo_France</th>\n","      <th>Geo_Germany</th>\n","      <th>Geo_Spain</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>-0.878176</td>\n","      <td>1.135026</td>\n","      <td>-1.706504</td>\n","      <td>-1.067887</td>\n","      <td>-0.881274</td>\n","      <td>0.820030</td>\n","      <td>-1.746219</td>\n","      <td>1.009580</td>\n","      <td>0.967874</td>\n","      <td>0.861202</td>\n","      <td>-0.513237</td>\n","      <td>-0.526975</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.329567</td>\n","      <td>1.135026</td>\n","      <td>0.888990</td>\n","      <td>-1.067887</td>\n","      <td>-0.881274</td>\n","      <td>-1.015806</td>\n","      <td>0.572666</td>\n","      <td>-0.990511</td>\n","      <td>-0.790939</td>\n","      <td>0.861202</td>\n","      <td>-0.513237</td>\n","      <td>-0.526975</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>-0.006609</td>\n","      <td>1.135026</td>\n","      <td>-0.465181</td>\n","      <td>0.713922</td>\n","      <td>-0.881274</td>\n","      <td>0.820030</td>\n","      <td>0.572666</td>\n","      <td>-0.990511</td>\n","      <td>0.528413</td>\n","      <td>0.861202</td>\n","      <td>-0.513237</td>\n","      <td>-0.526975</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.304665</td>\n","      <td>-0.881037</td>\n","      <td>-0.239486</td>\n","      <td>1.070284</td>\n","      <td>-0.881274</td>\n","      <td>-1.015806</td>\n","      <td>0.572666</td>\n","      <td>-0.990511</td>\n","      <td>0.032150</td>\n","      <td>0.861202</td>\n","      <td>-0.513237</td>\n","      <td>-0.526975</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1.188684</td>\n","      <td>-0.881037</td>\n","      <td>-0.013791</td>\n","      <td>1.783008</td>\n","      <td>1.050038</td>\n","      <td>-1.015806</td>\n","      <td>0.572666</td>\n","      <td>-0.990511</td>\n","      <td>0.539331</td>\n","      <td>-1.161167</td>\n","      <td>1.948417</td>\n","      <td>-0.526975</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   CreditScore    Gender       Age    Tenure   Balance  NumOfProducts  \\\n","0    -0.878176  1.135026 -1.706504 -1.067887 -0.881274       0.820030   \n","1     0.329567  1.135026  0.888990 -1.067887 -0.881274      -1.015806   \n","2    -0.006609  1.135026 -0.465181  0.713922 -0.881274       0.820030   \n","3     0.304665 -0.881037 -0.239486  1.070284 -0.881274      -1.015806   \n","4     1.188684 -0.881037 -0.013791  1.783008  1.050038      -1.015806   \n","\n","   HasCrCard  IsActiveMember  EstimatedSalary  Geo_France  Geo_Germany  \\\n","0  -1.746219        1.009580         0.967874    0.861202    -0.513237   \n","1   0.572666       -0.990511        -0.790939    0.861202    -0.513237   \n","2   0.572666       -0.990511         0.528413    0.861202    -0.513237   \n","3   0.572666       -0.990511         0.032150    0.861202    -0.513237   \n","4   0.572666       -0.990511         0.539331   -1.161167     1.948417   \n","\n","   Geo_Spain  \n","0  -0.526975  \n","1  -0.526975  \n","2  -0.526975  \n","3  -0.526975  \n","4  -0.526975  "]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["test_ids = data_test['id']\n","\n","data_test_scaled = feature_processing(data_test)  # (110023, 12)\n","\n","data_test_scaled.head()"]},{"cell_type":"code","execution_count":11,"metadata":{"execution":{"iopub.execute_input":"2024-01-31T09:10:51.066242Z","iopub.status.busy":"2024-01-31T09:10:51.065964Z","iopub.status.idle":"2024-01-31T09:10:51.071862Z","shell.execute_reply":"2024-01-31T09:10:51.070891Z","shell.execute_reply.started":"2024-01-31T09:10:51.066219Z"},"trusted":true},"outputs":[{"data":{"text/plain":["(110023, 12)"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["data_test_scaled.shape"]},{"cell_type":"code","execution_count":12,"metadata":{"execution":{"iopub.execute_input":"2024-01-31T09:10:51.073407Z","iopub.status.busy":"2024-01-31T09:10:51.073014Z","iopub.status.idle":"2024-01-31T09:10:51.088140Z","shell.execute_reply":"2024-01-31T09:10:51.087107Z","shell.execute_reply.started":"2024-01-31T09:10:51.073369Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["-0.07209143618662081\n"]}],"source":["sub_target = model.predict(data_test_scaled)\n","print(sub_target[0])"]},{"cell_type":"code","execution_count":13,"metadata":{"execution":{"iopub.execute_input":"2024-01-31T09:10:51.090256Z","iopub.status.busy":"2024-01-31T09:10:51.089776Z","iopub.status.idle":"2024-01-31T09:10:51.392054Z","shell.execute_reply":"2024-01-31T09:10:51.391052Z","shell.execute_reply.started":"2024-01-31T09:10:51.090229Z"},"trusted":true},"outputs":[],"source":["sub = pd.DataFrame({'id': test_ids, 'Exited': sub_target})\n","\n","sub.to_csv('submission.csv', index=False)"]},{"cell_type":"code","execution_count":14,"metadata":{"execution":{"iopub.execute_input":"2024-01-31T09:10:51.393691Z","iopub.status.busy":"2024-01-31T09:10:51.393263Z","iopub.status.idle":"2024-01-31T09:10:51.402070Z","shell.execute_reply":"2024-01-31T09:10:51.400618Z","shell.execute_reply.started":"2024-01-31T09:10:51.393657Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["            id    Exited\n","0       165034 -0.072091\n","1       165035  0.500658\n","2       165036  0.200382\n","3       165037  0.262506\n","4       165038  0.421681\n","...        ...       ...\n","110018  275052 -0.021351\n","110019  275053  0.136764\n","110020  275054  0.061399\n","110021  275055  0.148614\n","110022  275056  0.330913\n","\n","[110023 rows x 2 columns]\n"]}],"source":["print(sub)"]}],"metadata":{"kaggle":{"accelerator":"none","dataSources":[{"databundleVersionId":7405009,"sourceId":65711,"sourceType":"competition"}],"dockerImageVersionId":30646,"isGpuEnabled":false,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.13"}},"nbformat":4,"nbformat_minor":4}