Insurance_MYV (1).ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.ensemble import StackingClassifier\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from imblearn.pipeline import Pipeline as ImbPipeline\n",
        "from sklearn.utils.class_weight import compute_class_weight\n"
      ],
      "metadata": {
        "id": "zyxePO1KjKly"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Load the dataset\n",
        "# Mount Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Specify file path\n",
        "file_path = '/content/drive/My Drive/telematics_syn.csv'\n",
        "\n",
        "# Import pandas (assuming you want to use it to read the CSV)\n",
        "import pandas as pd\n",
        "\n",
        "# Read the CSV file\n",
        "data = pd.read_csv(file_path)\n",
        "\n",
        "# Step 2: Explore the data\n",
        "print(data.head())\n",
        "print(data.info())\n",
        "\n",
        "# Check for missing values\n",
        "print(data.isnull().sum())\n",
        "\n",
        "# Display basic statistics\n",
        "print(data.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EozoDZ6xjI0g",
        "outputId": "397456e6-e343-4576-ce0e-d6ac9b2b0799"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "   Duration  Insured.age Insured.sex  Car.age  Marital  Car.use  Credit.score  \\\n",
            "0       366           45        Male       -1  Married  Commute         609.0   \n",
            "1       182           44      Female        3  Married  Commute         575.0   \n",
            "2       184           48      Female        6  Married  Commute         847.0   \n",
            "3       183           71        Male        6  Married  Private         842.0   \n",
            "4       183           84        Male       10  Married  Private         856.0   \n",
            "\n",
            "  Region  Annual.miles.drive  Years.noclaims  ...  Left.turn.intensity10  \\\n",
            "0  Urban             6213.71              25  ...                    1.0   \n",
            "1  Urban            12427.42              20  ...                   58.0   \n",
            "2  Urban            12427.42              14  ...                    0.0   \n",
            "3  Urban             6213.71              43  ...                    0.0   \n",
            "4  Urban             6213.71              65  ...                    2.0   \n",
            "\n",
            "   Left.turn.intensity11  Left.turn.intensity12  Right.turn.intensity08  \\\n",
            "0                    0.0                    0.0                     3.0   \n",
            "1                   24.0                   11.0                  1099.0   \n",
            "2                    0.0                    0.0                     0.0   \n",
            "3                    0.0                    0.0                     0.0   \n",
            "4                    0.0                    0.0                   325.0   \n",
            "\n",
            "   Right.turn.intensity09  Right.turn.intensity10  Right.turn.intensity11  \\\n",
            "0                     1.0                     0.0                     0.0   \n",
            "1                   615.0                   219.0                   101.0   \n",
            "2                     0.0                     0.0                     0.0   \n",
            "3                     0.0                     0.0                     0.0   \n",
            "4                   111.0                    18.0                     4.0   \n",
            "\n",
            "   Right.turn.intensity12  NB_Claim    AMT_Claim  \n",
            "0                     0.0         1  5100.171753  \n",
            "1                    40.0         1   883.554840  \n",
            "2                     0.0         0     0.000000  \n",
            "3                     0.0         0     0.000000  \n",
            "4                     2.0         0     0.000000  \n",
            "\n",
            "[5 rows x 52 columns]\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 100000 entries, 0 to 99999\n",
            "Data columns (total 52 columns):\n",
            " #   Column                  Non-Null Count   Dtype  \n",
            "---  ------                  --------------   -----  \n",
            " 0   Duration                100000 non-null  int64  \n",
            " 1   Insured.age             100000 non-null  int64  \n",
            " 2   Insured.sex             100000 non-null  object \n",
            " 3   Car.age                 100000 non-null  int64  \n",
            " 4   Marital                 100000 non-null  object \n",
            " 5   Car.use                 100000 non-null  object \n",
            " 6   Credit.score            100000 non-null  float64\n",
            " 7   Region                  100000 non-null  object \n",
            " 8   Annual.miles.drive      100000 non-null  float64\n",
            " 9   Years.noclaims          100000 non-null  int64  \n",
            " 10  Territory               100000 non-null  int64  \n",
            " 11  Annual.pct.driven       100000 non-null  float64\n",
            " 12  Total.miles.driven      100000 non-null  float64\n",
            " 13  Pct.drive.mon           100000 non-null  float64\n",
            " 14  Pct.drive.tue           100000 non-null  float64\n",
            " 15  Pct.drive.wed           100000 non-null  float64\n",
            " 16  Pct.drive.thr           100000 non-null  float64\n",
            " 17  Pct.drive.fri           100000 non-null  float64\n",
            " 18  Pct.drive.sat           100000 non-null  float64\n",
            " 19  Pct.drive.sun           100000 non-null  float64\n",
            " 20  Pct.drive.2hrs          100000 non-null  float64\n",
            " 21  Pct.drive.3hrs          100000 non-null  float64\n",
            " 22  Pct.drive.4hrs          100000 non-null  float64\n",
            " 23  Pct.drive.wkday         100000 non-null  float64\n",
            " 24  Pct.drive.wkend         100000 non-null  float64\n",
            " 25  Pct.drive.rush am       100000 non-null  float64\n",
            " 26  Pct.drive.rush pm       100000 non-null  float64\n",
            " 27  Avgdays.week            100000 non-null  float64\n",
            " 28  Accel.06miles           100000 non-null  float64\n",
            " 29  Accel.08miles           100000 non-null  float64\n",
            " 30  Accel.09miles           100000 non-null  float64\n",
            " 31  Accel.11miles           100000 non-null  float64\n",
            " 32  Accel.12miles           100000 non-null  float64\n",
            " 33  Accel.14miles           100000 non-null  float64\n",
            " 34  Brake.06miles           100000 non-null  float64\n",
            " 35  Brake.08miles           100000 non-null  float64\n",
            " 36  Brake.09miles           100000 non-null  float64\n",
            " 37  Brake.11miles           100000 non-null  float64\n",
            " 38  Brake.12miles           100000 non-null  float64\n",
            " 39  Brake.14miles           100000 non-null  float64\n",
            " 40  Left.turn.intensity08   100000 non-null  float64\n",
            " 41  Left.turn.intensity09   100000 non-null  float64\n",
            " 42  Left.turn.intensity10   100000 non-null  float64\n",
            " 43  Left.turn.intensity11   100000 non-null  float64\n",
            " 44  Left.turn.intensity12   100000 non-null  float64\n",
            " 45  Right.turn.intensity08  100000 non-null  float64\n",
            " 46  Right.turn.intensity09  100000 non-null  float64\n",
            " 47  Right.turn.intensity10  100000 non-null  float64\n",
            " 48  Right.turn.intensity11  100000 non-null  float64\n",
            " 49  Right.turn.intensity12  100000 non-null  float64\n",
            " 50  NB_Claim                100000 non-null  int64  \n",
            " 51  AMT_Claim               100000 non-null  float64\n",
            "dtypes: float64(42), int64(6), object(4)\n",
            "memory usage: 39.7+ MB\n",
            "None\n",
            "Duration                  0\n",
            "Insured.age               0\n",
            "Insured.sex               0\n",
            "Car.age                   0\n",
            "Marital                   0\n",
            "Car.use                   0\n",
            "Credit.score              0\n",
            "Region                    0\n",
            "Annual.miles.drive        0\n",
            "Years.noclaims            0\n",
            "Territory                 0\n",
            "Annual.pct.driven         0\n",
            "Total.miles.driven        0\n",
            "Pct.drive.mon             0\n",
            "Pct.drive.tue             0\n",
            "Pct.drive.wed             0\n",
            "Pct.drive.thr             0\n",
            "Pct.drive.fri             0\n",
            "Pct.drive.sat             0\n",
            "Pct.drive.sun             0\n",
            "Pct.drive.2hrs            0\n",
            "Pct.drive.3hrs            0\n",
            "Pct.drive.4hrs            0\n",
            "Pct.drive.wkday           0\n",
            "Pct.drive.wkend           0\n",
            "Pct.drive.rush am         0\n",
            "Pct.drive.rush pm         0\n",
            "Avgdays.week              0\n",
            "Accel.06miles             0\n",
            "Accel.08miles             0\n",
            "Accel.09miles             0\n",
            "Accel.11miles             0\n",
            "Accel.12miles             0\n",
            "Accel.14miles             0\n",
            "Brake.06miles             0\n",
            "Brake.08miles             0\n",
            "Brake.09miles             0\n",
            "Brake.11miles             0\n",
            "Brake.12miles             0\n",
            "Brake.14miles             0\n",
            "Left.turn.intensity08     0\n",
            "Left.turn.intensity09     0\n",
            "Left.turn.intensity10     0\n",
            "Left.turn.intensity11     0\n",
            "Left.turn.intensity12     0\n",
            "Right.turn.intensity08    0\n",
            "Right.turn.intensity09    0\n",
            "Right.turn.intensity10    0\n",
            "Right.turn.intensity11    0\n",
            "Right.turn.intensity12    0\n",
            "NB_Claim                  0\n",
            "AMT_Claim                 0\n",
            "dtype: int64\n",
            "            Duration    Insured.age        Car.age   Credit.score  \\\n",
            "count  100000.000000  100000.000000  100000.000000  100000.000000   \n",
            "mean      314.204060      51.378950       5.639720     800.888870   \n",
            "std        79.746222      15.467075       4.062135      83.382316   \n",
            "min        27.000000      16.000000      -2.000000     422.000000   \n",
            "25%       200.000000      39.000000       2.000000     766.000000   \n",
            "50%       365.000000      51.000000       5.000000     825.000000   \n",
            "75%       366.000000      63.000000       8.000000     856.000000   \n",
            "max       366.000000     103.000000      20.000000     900.000000   \n",
            "\n",
            "       Annual.miles.drive  Years.noclaims      Territory  Annual.pct.driven  \\\n",
            "count       100000.000000   100000.000000  100000.000000      100000.000000   \n",
            "mean          9124.122908       28.839960      56.531390           0.502294   \n",
            "std           3826.144730       16.123717      24.036518           0.299189   \n",
            "min              0.000000        0.000000      11.000000           0.002740   \n",
            "25%           6213.710000       15.000000      35.000000           0.249315   \n",
            "50%           7456.452000       29.000000      62.000000           0.490411   \n",
            "75%          12427.420000       41.000000      78.000000           0.753425   \n",
            "max          56731.172300       79.000000      91.000000           1.000000   \n",
            "\n",
            "       Total.miles.driven  Pct.drive.mon  ...  Left.turn.intensity10  \\\n",
            "count       100000.000000  100000.000000  ...          100000.000000   \n",
            "mean          4833.575303       0.139365  ...             551.574010   \n",
            "std           4545.943016       0.042807  ...           14687.929802   \n",
            "min              0.095298       0.000000  ...               0.000000   \n",
            "25%           1529.897500       0.120894  ...               0.000000   \n",
            "50%           3468.287765       0.137909  ...               3.000000   \n",
            "75%           6779.876842       0.155203  ...              30.000000   \n",
            "max          47282.603936       0.998172  ...          794380.000000   \n",
            "\n",
            "       Left.turn.intensity11  Left.turn.intensity12  Right.turn.intensity08  \\\n",
            "count          100000.000000          100000.000000           100000.000000   \n",
            "mean              487.340690             447.758420              843.461830   \n",
            "std             14198.331308           13719.790281            11630.185503   \n",
            "min                 0.000000               0.000000                0.000000   \n",
            "25%                 0.000000               0.000000               11.000000   \n",
            "50%                 1.000000               0.000000              122.000000   \n",
            "75%                 9.000000               2.000000              680.000000   \n",
            "max            793926.000000          793170.000000           841210.000000   \n",
            "\n",
            "       Right.turn.intensity09  Right.turn.intensity10  Right.turn.intensity11  \\\n",
            "count           100000.000000           100000.000000           100000.000000   \n",
            "mean               565.056100              326.654840              246.713120   \n",
            "std              10657.402935             9460.244357             8977.569994   \n",
            "min                  0.000000                0.000000                0.000000   \n",
            "25%                  3.000000                0.000000                0.000000   \n",
            "50%                 43.000000                7.000000                2.000000   \n",
            "75%                321.000000               81.000000               27.000000   \n",
            "max             841207.000000           841200.000000           841176.000000   \n",
            "\n",
            "       Right.turn.intensity12      NB_Claim      AMT_Claim  \n",
            "count           100000.000000  100000.00000  100000.000000  \n",
            "mean               198.753690       0.04494     137.602253  \n",
            "std               8585.177049       0.21813    1264.320056  \n",
            "min                  0.000000       0.00000       0.000000  \n",
            "25%                  0.000000       0.00000       0.000000  \n",
            "50%                  0.000000       0.00000       0.000000  \n",
            "75%                  9.000000       0.00000       0.000000  \n",
            "max             841144.000000       3.00000  104074.886700  \n",
            "\n",
            "[8 rows x 48 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Separate features and target\n",
        "X = data.drop(['NB_Claim', 'AMT_Claim'], axis=1)\n",
        "y = (data['NB_Claim'] > 0).astype(int)  # Convert to binary (0 for no claim, 1 for claim)"
      ],
      "metadata": {
        "id": "BAW6-84xjgXz"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check class balance\n",
        "class_counts = y.value_counts()\n",
        "print(\"Class distribution:\")\n",
        "print(class_counts)\n",
        "print(f\"Class balance ratio: {class_counts[0] / class_counts[1]:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eX-VMoCloHAF",
        "outputId": "f9fd5961-5404-40db-e102-7a6b54c4ea65"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class distribution:\n",
            "NB_Claim\n",
            "0    95728\n",
            "1     4272\n",
            "Name: count, dtype: int64\n",
            "Class balance ratio: 22.41\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "cc7Rq-zSoEaY"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define numeric and categorical columns\n",
        "numeric_features = X.select_dtypes(include=['int64', 'float64']).columns\n",
        "categorical_features = ['Insured.sex', 'Marital', 'Car.use', 'Region']\n",
        "\n",
        "# Create preprocessor\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', StandardScaler(), numeric_features),\n",
        "        ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features)\n",
        "    ])"
      ],
      "metadata": {
        "id": "-aAec1jJjjkA"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute class weights\n",
        "class_weights = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)\n",
        "class_weight_dict = dict(zip(np.unique(y_train), class_weights))"
      ],
      "metadata": {
        "id": "Xy8BhtjmDv9h"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Random Forest classifier with balanced class weights\n",
        "rf_classifier = RandomForestClassifier(n_estimators=100, class_weight='balanced', random_state=42, n_jobs=-1)\n"
      ],
      "metadata": {
        "id": "_9jK2mk9lAn7"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the full pipeline\n",
        "pipeline = Pipeline([\n",
        "    ('preprocessor', preprocessor),\n",
        "    ('classifier', rf_classifier)\n",
        "])"
      ],
      "metadata": {
        "id": "WyJgyO49lFBb"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "n_AMdYd4hunO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "c1f47c60-28af-43ef-8fdb-6d228da0f27f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('preprocessor',\n",
              "                 ColumnTransformer(transformers=[('num', StandardScaler(),\n",
              "                                                  Index(['Duration', 'Insured.age', 'Car.age', 'Credit.score',\n",
              "       'Annual.miles.drive', 'Years.noclaims', 'Territory',\n",
              "       'Annual.pct.driven', 'Total.miles.driven', 'Pct.drive.mon',\n",
              "       'Pct.drive.tue', 'Pct.drive.wed', 'Pct.drive.thr', 'Pct.drive.fri',\n",
              "       'Pct.drive.sat', 'Pct.drive.sun', 'P...\n",
              "       'Left.turn.intensity12', 'Right.turn.intensity08',\n",
              "       'Right.turn.intensity09', 'Right.turn.intensity10',\n",
              "       'Right.turn.intensity11', 'Right.turn.intensity12'],\n",
              "      dtype='object')),\n",
              "                                                 ('cat',\n",
              "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
              "                                                  ['Insured.sex', 'Marital',\n",
              "                                                   'Car.use', 'Region'])])),\n",
              "                ('classifier',\n",
              "                 RandomForestClassifier(class_weight='balanced', n_jobs=-1,\n",
              "                                        random_state=42))])"
            ],
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              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
              "                                                  Index([&#x27;Duration&#x27;, &#x27;Insured.age&#x27;, &#x27;Car.age&#x27;, &#x27;Credit.score&#x27;,\n",
              "       &#x27;Annual.miles.drive&#x27;, &#x27;Years.noclaims&#x27;, &#x27;Territory&#x27;,\n",
              "       &#x27;Annual.pct.driven&#x27;, &#x27;Total.miles.driven&#x27;, &#x27;Pct.drive.mon&#x27;,\n",
              "       &#x27;Pct.drive.tue&#x27;, &#x27;Pct.drive.wed&#x27;, &#x27;Pct.drive.thr&#x27;, &#x27;Pct.drive.fri&#x27;,\n",
              "       &#x27;Pct.drive.sat&#x27;, &#x27;Pct.drive.sun&#x27;, &#x27;P...\n",
              "       &#x27;Left.turn.intensity12&#x27;, &#x27;Right.turn.intensity08&#x27;,\n",
              "       &#x27;Right.turn.intensity09&#x27;, &#x27;Right.turn.intensity10&#x27;,\n",
              "       &#x27;Right.turn.intensity11&#x27;, &#x27;Right.turn.intensity12&#x27;],\n",
              "      dtype=&#x27;object&#x27;)),\n",
              "                                                 (&#x27;cat&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
              "                                                  [&#x27;Insured.sex&#x27;, &#x27;Marital&#x27;,\n",
              "                                                   &#x27;Car.use&#x27;, &#x27;Region&#x27;])])),\n",
              "                (&#x27;classifier&#x27;,\n",
              "                 RandomForestClassifier(class_weight=&#x27;balanced&#x27;, n_jobs=-1,\n",
              "                                        random_state=42))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
              "                                                  Index([&#x27;Duration&#x27;, &#x27;Insured.age&#x27;, &#x27;Car.age&#x27;, &#x27;Credit.score&#x27;,\n",
              "       &#x27;Annual.miles.drive&#x27;, &#x27;Years.noclaims&#x27;, &#x27;Territory&#x27;,\n",
              "       &#x27;Annual.pct.driven&#x27;, &#x27;Total.miles.driven&#x27;, &#x27;Pct.drive.mon&#x27;,\n",
              "       &#x27;Pct.drive.tue&#x27;, &#x27;Pct.drive.wed&#x27;, &#x27;Pct.drive.thr&#x27;, &#x27;Pct.drive.fri&#x27;,\n",
              "       &#x27;Pct.drive.sat&#x27;, &#x27;Pct.drive.sun&#x27;, &#x27;P...\n",
              "       &#x27;Left.turn.intensity12&#x27;, &#x27;Right.turn.intensity08&#x27;,\n",
              "       &#x27;Right.turn.intensity09&#x27;, &#x27;Right.turn.intensity10&#x27;,\n",
              "       &#x27;Right.turn.intensity11&#x27;, &#x27;Right.turn.intensity12&#x27;],\n",
              "      dtype=&#x27;object&#x27;)),\n",
              "                                                 (&#x27;cat&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
              "                                                  [&#x27;Insured.sex&#x27;, &#x27;Marital&#x27;,\n",
              "                                                   &#x27;Car.use&#x27;, &#x27;Region&#x27;])])),\n",
              "                (&#x27;classifier&#x27;,\n",
              "                 RandomForestClassifier(class_weight=&#x27;balanced&#x27;, n_jobs=-1,\n",
              "                                        random_state=42))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
              "                                 Index([&#x27;Duration&#x27;, &#x27;Insured.age&#x27;, &#x27;Car.age&#x27;, &#x27;Credit.score&#x27;,\n",
              "       &#x27;Annual.miles.drive&#x27;, &#x27;Years.noclaims&#x27;, &#x27;Territory&#x27;,\n",
              "       &#x27;Annual.pct.driven&#x27;, &#x27;Total.miles.driven&#x27;, &#x27;Pct.drive.mon&#x27;,\n",
              "       &#x27;Pct.drive.tue&#x27;, &#x27;Pct.drive.wed&#x27;, &#x27;Pct.drive.thr&#x27;, &#x27;Pct.drive.fri&#x27;,\n",
              "       &#x27;Pct.drive.sat&#x27;, &#x27;Pct.drive.sun&#x27;, &#x27;Pct.drive.2hrs&#x27;, &#x27;Pct.drive.3hrs&#x27;,...\n",
              "       &#x27;Left.turn.intensity08&#x27;, &#x27;Left.turn.intensity09&#x27;,\n",
              "       &#x27;Left.turn.intensity10&#x27;, &#x27;Left.turn.intensity11&#x27;,\n",
              "       &#x27;Left.turn.intensity12&#x27;, &#x27;Right.turn.intensity08&#x27;,\n",
              "       &#x27;Right.turn.intensity09&#x27;, &#x27;Right.turn.intensity10&#x27;,\n",
              "       &#x27;Right.turn.intensity11&#x27;, &#x27;Right.turn.intensity12&#x27;],\n",
              "      dtype=&#x27;object&#x27;)),\n",
              "                                (&#x27;cat&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
              "                                 [&#x27;Insured.sex&#x27;, &#x27;Marital&#x27;, &#x27;Car.use&#x27;,\n",
              "                                  &#x27;Region&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>Index([&#x27;Duration&#x27;, &#x27;Insured.age&#x27;, &#x27;Car.age&#x27;, &#x27;Credit.score&#x27;,\n",
              "       &#x27;Annual.miles.drive&#x27;, &#x27;Years.noclaims&#x27;, &#x27;Territory&#x27;,\n",
              "       &#x27;Annual.pct.driven&#x27;, &#x27;Total.miles.driven&#x27;, &#x27;Pct.drive.mon&#x27;,\n",
              "       &#x27;Pct.drive.tue&#x27;, &#x27;Pct.drive.wed&#x27;, &#x27;Pct.drive.thr&#x27;, &#x27;Pct.drive.fri&#x27;,\n",
              "       &#x27;Pct.drive.sat&#x27;, &#x27;Pct.drive.sun&#x27;, &#x27;Pct.drive.2hrs&#x27;, &#x27;Pct.drive.3hrs&#x27;,\n",
              "       &#x27;Pct.drive.4hrs&#x27;, &#x27;Pct.drive.wkday&#x27;, &#x27;Pct.drive.wkend&#x27;,\n",
              "       &#x27;Pct.drive.rush am&#x27;, &#x27;Pct.drive.rush pm&#x27;, &#x27;Avgdays.week&#x27;,\n",
              "       &#x27;Accel.06miles&#x27;, &#x27;Accel.08miles&#x27;, &#x27;Accel.09miles&#x27;, &#x27;Accel.11miles&#x27;,\n",
              "       &#x27;Accel.12miles&#x27;, &#x27;Accel.14miles&#x27;, &#x27;Brake.06miles&#x27;, &#x27;Brake.08miles&#x27;,\n",
              "       &#x27;Brake.09miles&#x27;, &#x27;Brake.11miles&#x27;, &#x27;Brake.12miles&#x27;, &#x27;Brake.14miles&#x27;,\n",
              "       &#x27;Left.turn.intensity08&#x27;, &#x27;Left.turn.intensity09&#x27;,\n",
              "       &#x27;Left.turn.intensity10&#x27;, &#x27;Left.turn.intensity11&#x27;,\n",
              "       &#x27;Left.turn.intensity12&#x27;, &#x27;Right.turn.intensity08&#x27;,\n",
              "       &#x27;Right.turn.intensity09&#x27;, &#x27;Right.turn.intensity10&#x27;,\n",
              "       &#x27;Right.turn.intensity11&#x27;, &#x27;Right.turn.intensity12&#x27;],\n",
              "      dtype=&#x27;object&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;Insured.sex&#x27;, &#x27;Marital&#x27;, &#x27;Car.use&#x27;, &#x27;Region&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, n_jobs=-1, random_state=42)</pre></div> </div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# Fit the pipeline\n",
        "pipeline.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions\n",
        "y_pred = pipeline.predict(X_test)\n",
        "\n",
        "# Print the classification report\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QEqXr63alJas",
        "outputId": "5967bd9d-ee3c-4c8a-8aef-6523d8b3dd3b"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      1.00      0.98     19130\n",
            "           1       0.97      0.26      0.41       870\n",
            "\n",
            "    accuracy                           0.97     20000\n",
            "   macro avg       0.97      0.63      0.70     20000\n",
            "weighted avg       0.97      0.97      0.96     20000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the confusion matrix\n",
        "print(confusion_matrix(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "weoVfrhhlM22",
        "outputId": "aac9ca80-3943-4dcd-be85-05674ba46a79"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[19122     8]\n",
            " [  641   229]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Perform cross-validation\n",
        "cv_scores = cross_val_score(pipeline, X, y, cv=5)\n",
        "print(f\"Cross-validation scores: {cv_scores}\")\n",
        "print(f\"Mean CV score: {cv_scores.mean():.3f} (+/- {cv_scores.std() * 2:.3f})\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fpGnUNrQlO0P",
        "outputId": "58594f50-34c5-4d41-d5bb-8c55b3832cd5"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-validation scores: [0.968   0.9677  0.96725 0.96695 0.9692 ]\n",
            "Mean CV score: 0.968 (+/- 0.002)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature importance\n",
        "feature_importance = pipeline.named_steps['classifier'].feature_importances_\n",
        "feature_names = (preprocessor.named_transformers_['num'].get_feature_names_out().tolist() +\n",
        "                 preprocessor.named_transformers_['cat'].get_feature_names_out().tolist())"
      ],
      "metadata": {
        "id": "ZGIeezWvlPtE"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Sort feature importances in descending order\n",
        "indices = np.argsort(feature_importance)[::-1]\n",
        "\n",
        "# Print the top 20 feature ranking\n",
        "print(\"\\nTop 20 Feature ranking:\")\n",
        "for f in range(min(20, len(feature_names))):\n",
        "    print(\"%d. %s (%f)\" % (f + 1, feature_names[indices[f]], feature_importance[indices[f]]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1S24ZW-ylW6D",
        "outputId": "c318d5f3-7da9-4d0a-c584-dcb891ef617c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Top 20 Feature ranking:\n",
            "1. Total.miles.driven (0.103526)\n",
            "2. Annual.pct.driven (0.092099)\n",
            "3. Duration (0.033678)\n",
            "4. Credit.score (0.032826)\n",
            "5. Pct.drive.2hrs (0.030017)\n",
            "6. Avgdays.week (0.030016)\n",
            "7. Pct.drive.thr (0.028129)\n",
            "8. Pct.drive.rush am (0.027172)\n",
            "9. Pct.drive.rush pm (0.024612)\n",
            "10. Pct.drive.wed (0.024558)\n",
            "11. Pct.drive.mon (0.024377)\n",
            "12. Pct.drive.tue (0.023702)\n",
            "13. Pct.drive.fri (0.023537)\n",
            "14. Brake.06miles (0.022965)\n",
            "15. Pct.drive.sat (0.022950)\n",
            "16. Pct.drive.sun (0.022392)\n",
            "17. Car.age (0.021313)\n",
            "18. Accel.06miles (0.020572)\n",
            "19. Years.noclaims (0.020419)\n",
            "20. Left.turn.intensity09 (0.020307)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pytorch_tabnet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9T_v7lk2IUB3",
        "outputId": "b77199db-b090-42ef-dad1-cfd1fe2e05ba"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pytorch_tabnet\n",
            "  Downloading pytorch_tabnet-4.1.0-py3-none-any.whl.metadata (15 kB)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pytorch_tabnet) (1.26.4)\n",
            "Requirement already satisfied: scikit_learn>0.21 in /usr/local/lib/python3.10/dist-packages (from pytorch_tabnet) (1.5.2)\n",
            "Requirement already satisfied: scipy>1.4 in /usr/local/lib/python3.10/dist-packages (from pytorch_tabnet) (1.13.1)\n",
            "Requirement already satisfied: torch>=1.3 in /usr/local/lib/python3.10/dist-packages (from pytorch_tabnet) (2.4.1+cu121)\n",
            "Requirement already satisfied: tqdm>=4.36 in /usr/local/lib/python3.10/dist-packages (from pytorch_tabnet) (4.66.5)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit_learn>0.21->pytorch_tabnet) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit_learn>0.21->pytorch_tabnet) (3.5.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.3->pytorch_tabnet) (3.16.1)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.3->pytorch_tabnet) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.3->pytorch_tabnet) (1.13.3)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.3->pytorch_tabnet) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.3->pytorch_tabnet) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.3->pytorch_tabnet) (2024.6.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.3->pytorch_tabnet) (2.1.5)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.3->pytorch_tabnet) (1.3.0)\n",
            "Downloading pytorch_tabnet-4.1.0-py3-none-any.whl (44 kB)\n",
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/44.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.5/44.5 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pytorch_tabnet\n",
            "Successfully installed pytorch_tabnet-4.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score\n",
        "from pytorch_tabnet.tab_model import TabNetClassifier\n",
        "import torch"
      ],
      "metadata": {
        "id": "J3vBsDChIfRB"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Select top 20 features based on the provided ranking\n",
        "top_features = [\n",
        "    'Total.miles.driven', 'Annual.pct.driven', 'Duration', 'Credit.score',\n",
        "    'Pct.drive.2hrs', 'Avgdays.week', 'Pct.drive.thr', 'Pct.drive.rush am',\n",
        "    'Pct.drive.rush pm', 'Pct.drive.wed', 'Pct.drive.mon', 'Pct.drive.tue',\n",
        "    'Pct.drive.fri', 'Brake.06miles', 'Pct.drive.sat', 'Pct.drive.sun',\n",
        "    'Car.age', 'Accel.06miles', 'Years.noclaims', 'Left.turn.intensity09'\n",
        "]"
      ],
      "metadata": {
        "id": "Js4C4zjbIiaq"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare the data\n",
        "X = data[top_features]\n",
        "y = (data['NB_Claim'] > 0).astype(int)\n",
        "\n",
        "# Split the data\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n"
      ],
      "metadata": {
        "id": "UQOt6WJxInVX"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Scale the features\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n"
      ],
      "metadata": {
        "id": "3sQKq2sfIqDz"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert to PyTorch tensors\n",
        "X_train_tensor = torch.FloatTensor(X_train_scaled)\n",
        "y_train_tensor = torch.LongTensor(y_train.values)\n",
        "X_test_tensor = torch.FloatTensor(X_test_scaled)\n",
        "y_test_tensor = torch.LongTensor(y_test.values)"
      ],
      "metadata": {
        "id": "cprfpj6wIsfd"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize and train TabNet\n",
        "tabnet_model = TabNetClassifier(\n",
        "    n_d=8, n_a=8, n_steps=3,\n",
        "    gamma=1.3, n_independent=2, n_shared=2,\n",
        "    cat_idxs=[], cat_dims=[],\n",
        "    cat_emb_dim=1, optimizer_fn=torch.optim.Adam,\n",
        "    optimizer_params=dict(lr=2e-2),\n",
        "    mask_type='entmax',\n",
        "    lambda_sparse=1e-3, momentum=0.3, clip_value=2.,\n",
        "    device_name='auto'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0wBkbtIZIvIo",
        "outputId": "b602b632-8a3f-46c8-8425-ce39978818f5"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cpu\n",
            "  warnings.warn(f\"Device used : {self.device}\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tabnet_model.fit(\n",
        "    X_train_tensor.numpy(), y_train_tensor.numpy(),\n",
        "    eval_set=[(X_test_tensor.numpy(), y_test_tensor.numpy())],\n",
        "    max_epochs=100,\n",
        "    patience=10,\n",
        "    batch_size=1024,\n",
        "    virtual_batch_size=128,\n",
        "    num_workers=0,\n",
        "    drop_last=False\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bmVNCFkGISKq",
        "outputId": "aae41129-30b4-4467-b83f-1b287fc68fc0"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 0  | loss: 0.18404 | val_0_auc: 0.77508 |  0:00:04s\n",
            "epoch 1  | loss: 0.15517 | val_0_auc: 0.78477 |  0:00:08s\n",
            "epoch 2  | loss: 0.15385 | val_0_auc: 0.78575 |  0:00:13s\n",
            "epoch 3  | loss: 0.15333 | val_0_auc: 0.78692 |  0:00:17s\n",
            "epoch 4  | loss: 0.15285 | val_0_auc: 0.7868  |  0:00:21s\n",
            "epoch 5  | loss: 0.15216 | val_0_auc: 0.79419 |  0:00:26s\n",
            "epoch 6  | loss: 0.15212 | val_0_auc: 0.79033 |  0:00:31s\n",
            "epoch 7  | loss: 0.15163 | val_0_auc: 0.7905  |  0:00:35s\n",
            "epoch 8  | loss: 0.15166 | val_0_auc: 0.79377 |  0:00:40s\n",
            "epoch 9  | loss: 0.15105 | val_0_auc: 0.79186 |  0:00:44s\n",
            "epoch 10 | loss: 0.15111 | val_0_auc: 0.78904 |  0:00:48s\n",
            "epoch 11 | loss: 0.15066 | val_0_auc: 0.79639 |  0:00:53s\n",
            "epoch 12 | loss: 0.1495  | val_0_auc: 0.79603 |  0:00:58s\n",
            "epoch 13 | loss: 0.14988 | val_0_auc: 0.79625 |  0:01:02s\n",
            "epoch 14 | loss: 0.14894 | val_0_auc: 0.80203 |  0:01:06s\n",
            "epoch 15 | loss: 0.14905 | val_0_auc: 0.79634 |  0:01:11s\n",
            "epoch 16 | loss: 0.15076 | val_0_auc: 0.79501 |  0:01:15s\n",
            "epoch 17 | loss: 0.15039 | val_0_auc: 0.80315 |  0:01:19s\n",
            "epoch 18 | loss: 0.14906 | val_0_auc: 0.79557 |  0:01:25s\n",
            "epoch 19 | loss: 0.14989 | val_0_auc: 0.80049 |  0:01:29s\n",
            "epoch 20 | loss: 0.14938 | val_0_auc: 0.80112 |  0:01:33s\n",
            "epoch 21 | loss: 0.14871 | val_0_auc: 0.80179 |  0:01:38s\n",
            "epoch 22 | loss: 0.14895 | val_0_auc: 0.80186 |  0:01:42s\n",
            "epoch 23 | loss: 0.14834 | val_0_auc: 0.80219 |  0:01:46s\n",
            "epoch 24 | loss: 0.14776 | val_0_auc: 0.80607 |  0:01:51s\n",
            "epoch 25 | loss: 0.14853 | val_0_auc: 0.80202 |  0:01:56s\n",
            "epoch 26 | loss: 0.14918 | val_0_auc: 0.80674 |  0:01:59s\n",
            "epoch 27 | loss: 0.14822 | val_0_auc: 0.80534 |  0:02:04s\n",
            "epoch 28 | loss: 0.14765 | val_0_auc: 0.80744 |  0:02:09s\n",
            "epoch 29 | loss: 0.14756 | val_0_auc: 0.80602 |  0:02:13s\n",
            "epoch 30 | loss: 0.14696 | val_0_auc: 0.81095 |  0:02:17s\n",
            "epoch 31 | loss: 0.14726 | val_0_auc: 0.80709 |  0:02:22s\n",
            "epoch 32 | loss: 0.14677 | val_0_auc: 0.80191 |  0:02:26s\n",
            "epoch 33 | loss: 0.14656 | val_0_auc: 0.81354 |  0:02:30s\n",
            "epoch 34 | loss: 0.14665 | val_0_auc: 0.80683 |  0:02:35s\n",
            "epoch 35 | loss: 0.14652 | val_0_auc: 0.802   |  0:02:39s\n",
            "epoch 36 | loss: 0.14592 | val_0_auc: 0.80794 |  0:02:43s\n",
            "epoch 37 | loss: 0.14581 | val_0_auc: 0.81074 |  0:02:49s\n",
            "epoch 38 | loss: 0.14536 | val_0_auc: 0.81088 |  0:02:55s\n",
            "epoch 39 | loss: 0.14523 | val_0_auc: 0.81108 |  0:02:59s\n",
            "epoch 40 | loss: 0.14395 | val_0_auc: 0.80816 |  0:03:04s\n",
            "epoch 41 | loss: 0.14492 | val_0_auc: 0.80988 |  0:03:09s\n",
            "epoch 42 | loss: 0.14421 | val_0_auc: 0.81728 |  0:03:13s\n",
            "epoch 43 | loss: 0.14436 | val_0_auc: 0.81086 |  0:03:17s\n",
            "epoch 44 | loss: 0.1446  | val_0_auc: 0.81402 |  0:03:22s\n",
            "epoch 45 | loss: 0.14337 | val_0_auc: 0.81194 |  0:03:26s\n",
            "epoch 46 | loss: 0.14387 | val_0_auc: 0.81098 |  0:03:30s\n",
            "epoch 47 | loss: 0.14404 | val_0_auc: 0.80299 |  0:03:36s\n",
            "epoch 48 | loss: 0.14298 | val_0_auc: 0.80729 |  0:03:40s\n",
            "epoch 49 | loss: 0.14348 | val_0_auc: 0.81703 |  0:03:44s\n",
            "epoch 50 | loss: 0.14331 | val_0_auc: 0.81571 |  0:03:49s\n",
            "epoch 51 | loss: 0.14256 | val_0_auc: 0.81519 |  0:03:53s\n",
            "epoch 52 | loss: 0.14232 | val_0_auc: 0.81894 |  0:03:57s\n",
            "epoch 53 | loss: 0.14192 | val_0_auc: 0.81396 |  0:04:02s\n",
            "epoch 54 | loss: 0.14182 | val_0_auc: 0.81692 |  0:04:07s\n",
            "epoch 55 | loss: 0.14186 | val_0_auc: 0.81204 |  0:04:11s\n",
            "epoch 56 | loss: 0.14284 | val_0_auc: 0.81685 |  0:04:15s\n",
            "epoch 57 | loss: 0.14119 | val_0_auc: 0.81759 |  0:04:20s\n",
            "epoch 58 | loss: 0.14108 | val_0_auc: 0.81026 |  0:04:24s\n",
            "epoch 59 | loss: 0.14016 | val_0_auc: 0.81358 |  0:04:28s\n",
            "epoch 60 | loss: 0.1405  | val_0_auc: 0.82241 |  0:04:34s\n",
            "epoch 61 | loss: 0.14084 | val_0_auc: 0.80871 |  0:04:38s\n",
            "epoch 62 | loss: 0.14194 | val_0_auc: 0.8135  |  0:04:42s\n",
            "epoch 63 | loss: 0.14054 | val_0_auc: 0.8048  |  0:04:47s\n",
            "epoch 64 | loss: 0.14326 | val_0_auc: 0.81531 |  0:04:51s\n",
            "epoch 65 | loss: 0.14136 | val_0_auc: 0.81723 |  0:04:55s\n",
            "epoch 66 | loss: 0.14162 | val_0_auc: 0.81651 |  0:05:00s\n",
            "epoch 67 | loss: 0.14147 | val_0_auc: 0.80962 |  0:05:04s\n",
            "epoch 68 | loss: 0.14195 | val_0_auc: 0.81566 |  0:05:08s\n",
            "epoch 69 | loss: 0.14129 | val_0_auc: 0.81572 |  0:05:13s\n",
            "epoch 70 | loss: 0.13983 | val_0_auc: 0.81819 |  0:05:18s\n",
            "\n",
            "Early stopping occurred at epoch 70 with best_epoch = 60 and best_val_0_auc = 0.82241\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
            "  warnings.warn(wrn_msg)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Automatically detect device (GPU or CPU)\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "#The TabNetClassifier does not have a to() method, instead it sets the device during initialization\n",
        "#Remove or comment out the following line:\n",
        "#tabnet_model.to(device)\n",
        "\n",
        "# Instead, ensure you specified the correct device when you initialized the model\n",
        "#If you are still encountering issues and your device is set to 'auto', you can set it using the device param\n",
        "#tabnet_model = TabNetClassifier(device_name=device) # Replace device with 'cuda' or 'cpu' as needed\n",
        "\n",
        "# Convert data to PyTorch tensors and move to the same device as the model\n",
        "X_train_tensor = torch.tensor(X_train_scaled, dtype=torch.float32).to(device)\n",
        "X_test_tensor = torch.tensor(X_test_scaled, dtype=torch.float32).to(device)\n",
        "\n",
        "# Extract learned features using the model's forward function to get embeddings\n",
        "with torch.no_grad():\n",
        "    # Make sure network method takes device into account\n",
        "    # Assuming the first element of the tuple is the desired output\n",
        "    output = tabnet_model.network(X_train_tensor.to(tabnet_model.device))\n",
        "    train_features = output[0].cpu().numpy()  # Access the first element and move to CPU for numpy\n",
        "\n",
        "    output = tabnet_model.network(X_test_tensor.to(tabnet_model.device))\n",
        "    test_features = output[0].cpu().numpy() # Access the first element and move to CPU for numpy\n",
        "\n",
        "\n",
        "# Train SVM on the extracted features\n",
        "svm_model = SVC(kernel='rbf', class_weight='balanced', probability=True, random_state=42)\n",
        "svm_model.fit(train_features, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = svm_model.predict(test_features)\n",
        "y_pred_proba = svm_model.predict_proba(test_features)[:, 1]\n",
        "\n",
        "# Print the classification report\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "# Print the confusion matrix\n",
        "print(\"\\nConfusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "\n",
        "# Calculate ROC AUC score\n",
        "roc_auc = roc_auc_score(y_test, y_pred_proba)\n",
        "print(f\"\\nROC AUC Score: {roc_auc:.3f}\")\n",
        "\n",
        "# Feature importance from TabNet\n",
        "feat_importances = tabnet_model.feature_importances_\n",
        "feat_importance_dict = dict(zip(top_features, feat_importances))\n",
        "sorted_feat_importances = sorted(feat_importance_dict.items(), key=lambda x: x[1], reverse=True)\n",
        "\n",
        "print(\"\\nTabNet Feature Importances:\")\n",
        "for feat, importance in sorted_feat_importances:\n",
        "    print(f\"{feat}: {importance:.6f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "djDbzv6_Ll86",
        "outputId": "5e17361c-7503-4a58-e58b-83eb5c2300fc"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.70      0.82     19146\n",
            "           1       0.10      0.78      0.18       854\n",
            "\n",
            "    accuracy                           0.70     20000\n",
            "   macro avg       0.54      0.74      0.50     20000\n",
            "weighted avg       0.95      0.70      0.79     20000\n",
            "\n",
            "\n",
            "Confusion Matrix:\n",
            "[[13362  5784]\n",
            " [  187   667]]\n",
            "\n",
            "ROC AUC Score: 0.808\n",
            "\n",
            "TabNet Feature Importances:\n",
            "Duration: 0.285232\n",
            "Total.miles.driven: 0.184323\n",
            "Avgdays.week: 0.164837\n",
            "Annual.pct.driven: 0.113711\n",
            "Brake.06miles: 0.065162\n",
            "Pct.drive.sat: 0.038803\n",
            "Years.noclaims: 0.033412\n",
            "Credit.score: 0.031820\n",
            "Pct.drive.2hrs: 0.023103\n",
            "Pct.drive.sun: 0.021995\n",
            "Pct.drive.tue: 0.014535\n",
            "Pct.drive.rush am: 0.013778\n",
            "Car.age: 0.006937\n",
            "Pct.drive.mon: 0.002280\n",
            "Accel.06miles: 0.000035\n",
            "Pct.drive.rush pm: 0.000033\n",
            "Pct.drive.thr: 0.000003\n",
            "Pct.drive.fri: 0.000001\n",
            "Pct.drive.wed: 0.000001\n",
            "Left.turn.intensity09: 0.000000\n"
          ]
        }
      ]
    }
  ]
}