insurance_classification_ensemble_model_claim_YN.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyMSmf5Ng7WXIialP+sL+/Uv",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/fjadidi2001/Insurance/blob/main/insurance_classification_ensemble_model_claim_YN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iHm7dCnH1rDf",
        "outputId": "3e30f6f3-103d-47be-a83f-f28003601f03"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "First few rows of the dataset:\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",
            "\n",
            "Dataset information:\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",
            "\n",
            "Checking for missing values:\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",
            "\n",
            "Basic statistics:\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",
            "\n",
            "Distribution of ClaimYN:\n",
            "ClaimYN\n",
            "0    97.302\n",
            "1     2.698\n",
            "Name: proportion, dtype: float64\n",
            "\n",
            "Absolute counts:\n",
            "ClaimYN\n",
            "0    97302\n",
            "1     2698\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Step 1: Load the dataset\n",
        "from google.colab import drive\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Mount Google Drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Specify file path\n",
        "file_path = '/content/drive/My Drive/telematics_syn.csv'\n",
        "\n",
        "# Read the CSV file\n",
        "data = pd.read_csv(file_path)\n",
        "\n",
        "# Step 2: Explore the data\n",
        "print(\"First few rows of the dataset:\")\n",
        "print(data.head())\n",
        "print(\"\\nDataset information:\")\n",
        "print(data.info())\n",
        "print(\"\\nChecking for missing values:\")\n",
        "print(data.isnull().sum())\n",
        "print(\"\\nBasic statistics:\")\n",
        "print(data.describe())\n",
        "\n",
        "# Step 3: Create target variable ClaimYN\n",
        "data['ClaimYN'] = ((data['NB_Claim'] >= 1) & (data['AMT_Claim'] >= 1000)).astype(int)\n",
        "\n",
        "# Display distribution of ClaimYN\n",
        "print(\"\\nDistribution of ClaimYN:\")\n",
        "print(data['ClaimYN'].value_counts(normalize=True) * 100)\n",
        "print(\"\\nAbsolute counts:\")\n",
        "print(data['ClaimYN'].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import required libraries\n",
        "from sklearn.model_selection import train_test_split\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from collections import Counter\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Print initial column count\n",
        "print(\"Initial number of columns:\", len(data.columns))\n",
        "\n",
        "# Separate features and target\n",
        "# Exclude NB_Claim and AMT_Claim as they were used to create the target\n",
        "features = data.drop(['ClaimYN', 'NB_Claim', 'AMT_Claim'], axis=1)\n",
        "print(\"\\nNumber of columns after dropping target and claim columns:\", len(features.columns))\n",
        "\n",
        "# Print categorical columns unique values\n",
        "print(\"\\nUnique values in categorical columns:\")\n",
        "categorical_columns = ['Insured.sex', 'Marital', 'Car.use', 'Region']\n",
        "for col in categorical_columns:\n",
        "    print(f\"{col}: {features[col].unique()}\")\n",
        "\n",
        "# Convert categorical variables to numeric using one-hot encoding\n",
        "features = pd.get_dummies(features, columns=categorical_columns)\n",
        "print(\"\\nColumns after one-hot encoding:\")\n",
        "print(features.columns.tolist())\n",
        "print(\"\\nTotal number of columns after encoding:\", len(features.columns))\n",
        "\n",
        "X = features\n",
        "y = data['ClaimYN']\n",
        "\n",
        "# Proceed with train-test split and SMOTE\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
        "smote = SMOTE(random_state=42)\n",
        "X_train_balanced, y_train_balanced = smote.fit_resample(X_train, y_train)\n",
        "\n",
        "# Print final shapes\n",
        "print(\"\\nFinal dataset shapes:\")\n",
        "print(f\"Original features: {X.shape}\")\n",
        "print(f\"X_train_balanced: {X_train_balanced.shape}\")\n",
        "print(f\"X_test: {X_test.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5hFfY_du2wNB",
        "outputId": "05cce2ec-bc63-4191-fc6e-45beade8a1a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initial number of columns: 53\n",
            "\n",
            "Number of columns after dropping target and claim columns: 50\n",
            "\n",
            "Unique values in categorical columns:\n",
            "Insured.sex: ['Male' 'Female']\n",
            "Marital: ['Married' 'Single']\n",
            "Car.use: ['Commute' 'Private' 'Commercial' 'Farmer']\n",
            "Region: ['Urban' 'Rural']\n",
            "\n",
            "Columns after one-hot encoding:\n",
            "['Duration', 'Insured.age', 'Car.age', 'Credit.score', 'Annual.miles.drive', 'Years.noclaims', 'Territory', 'Annual.pct.driven', 'Total.miles.driven', 'Pct.drive.mon', 'Pct.drive.tue', 'Pct.drive.wed', 'Pct.drive.thr', 'Pct.drive.fri', 'Pct.drive.sat', 'Pct.drive.sun', 'Pct.drive.2hrs', 'Pct.drive.3hrs', 'Pct.drive.4hrs', 'Pct.drive.wkday', 'Pct.drive.wkend', 'Pct.drive.rush am', 'Pct.drive.rush pm', 'Avgdays.week', 'Accel.06miles', 'Accel.08miles', 'Accel.09miles', 'Accel.11miles', 'Accel.12miles', 'Accel.14miles', 'Brake.06miles', 'Brake.08miles', 'Brake.09miles', 'Brake.11miles', 'Brake.12miles', 'Brake.14miles', 'Left.turn.intensity08', 'Left.turn.intensity09', 'Left.turn.intensity10', 'Left.turn.intensity11', 'Left.turn.intensity12', 'Right.turn.intensity08', 'Right.turn.intensity09', 'Right.turn.intensity10', 'Right.turn.intensity11', 'Right.turn.intensity12', 'Insured.sex_Female', 'Insured.sex_Male', 'Marital_Married', 'Marital_Single', 'Car.use_Commercial', 'Car.use_Commute', 'Car.use_Farmer', 'Car.use_Private', 'Region_Rural', 'Region_Urban']\n",
            "\n",
            "Total number of columns after encoding: 56\n",
            "\n",
            "Final dataset shapes:\n",
            "Original features: (100000, 56)\n",
            "X_train_balanced: (155684, 56)\n",
            "X_test: (20000, 56)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import required libraries\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Feature Engineering\n",
        "def create_aggregated_features(df):\n",
        "    \"\"\"Create aggregated features from existing ones\"\"\"\n",
        "\n",
        "    # Aggregate acceleration features\n",
        "    df['Agg_Accel'] = df[[\n",
        "        'Accel.06miles', 'Accel.08miles', 'Accel.09miles',\n",
        "        'Accel.11miles', 'Accel.12miles', 'Accel.14miles'\n",
        "    ]].mean(axis=1)\n",
        "\n",
        "    # Aggregate braking features\n",
        "    df['Agg_Brake'] = df[[\n",
        "        'Brake.06miles', 'Brake.08miles', 'Brake.09miles',\n",
        "        'Brake.11miles', 'Brake.12miles', 'Brake.14miles'\n",
        "    ]].mean(axis=1)\n",
        "\n",
        "    # Aggregate left turn features\n",
        "    df['Agg_Left_Turn'] = df[[\n",
        "        'Left.turn.intensity08', 'Left.turn.intensity09', 'Left.turn.intensity10',\n",
        "        'Left.turn.intensity11', 'Left.turn.intensity12'\n",
        "    ]].mean(axis=1)\n",
        "\n",
        "    # Aggregate right turn features\n",
        "    df['Agg_Right_Turn'] = df[[\n",
        "        'Right.turn.intensity08', 'Right.turn.intensity09', 'Right.turn.intensity10',\n",
        "        'Right.turn.intensity11', 'Right.turn.intensity12'\n",
        "    ]].mean(axis=1)\n",
        "\n",
        "    # Create overall harsh driving score\n",
        "    df['Harsh_Driving_Score'] = (\n",
        "        df['Agg_Accel'] + df['Agg_Brake'] +\n",
        "        df['Agg_Left_Turn'] + df['Agg_Right_Turn']\n",
        "    ) / 4\n",
        "\n",
        "    # Create rush hour driving ratio\n",
        "    df['Rush_Hour_Ratio'] = (\n",
        "        df['Pct.drive.rush am'] + df['Pct.drive.rush pm']\n",
        "    ) / df['Total.miles.driven']\n",
        "\n",
        "    return df\n",
        "\n",
        "# Standardization function\n",
        "def standardize_features(X_train, X_test):\n",
        "    \"\"\"Standardize numerical features\"\"\"\n",
        "    scaler = StandardScaler()\n",
        "\n",
        "    # Get numerical columns (exclude dummy variables)\n",
        "    numerical_cols = X_train.select_dtypes(include=['float64', 'int64']).columns\n",
        "\n",
        "    # Fit and transform training data\n",
        "    X_train_scaled = X_train.copy()\n",
        "    X_train_scaled[numerical_cols] = scaler.fit_transform(X_train[numerical_cols])\n",
        "\n",
        "    # Transform test data\n",
        "    X_test_scaled = X_test.copy()\n",
        "    X_test_scaled[numerical_cols] = scaler.transform(X_test[numerical_cols])\n",
        "\n",
        "    return X_train_scaled, X_test_scaled, scaler\n",
        "\n",
        "# Apply preprocessing pipeline\n",
        "def preprocess_data(X_train, X_test):\n",
        "    \"\"\"Complete preprocessing pipeline\"\"\"\n",
        "\n",
        "    # 1. Feature Engineering\n",
        "    print(\"Applying feature engineering...\")\n",
        "    X_train = create_aggregated_features(X_train)\n",
        "    X_test = create_aggregated_features(X_test)\n",
        "\n",
        "    # 2. Standardization\n",
        "    print(\"Standardizing features...\")\n",
        "    X_train_scaled, X_test_scaled, scaler = standardize_features(X_train, X_test)\n",
        "\n",
        "    # Print feature names and their shapes\n",
        "    print(\"\\nFinal feature set:\")\n",
        "    print(f\"Training set shape: {X_train_scaled.shape}\")\n",
        "    print(f\"Test set shape: {X_test_scaled.shape}\")\n",
        "    print(\"\\nFeatures included:\")\n",
        "    print(X_train_scaled.columns.tolist())\n",
        "\n",
        "    return X_train_scaled, X_test_scaled, scaler\n",
        "\n",
        "# Apply preprocessing to our balanced dataset\n",
        "X_train_processed, X_test_processed, scaler = preprocess_data(X_train_balanced, X_test)\n",
        "\n",
        "# Print sample statistics to verify preprocessing\n",
        "print(\"\\nSample statistics after preprocessing:\")\n",
        "print(X_train_processed.describe().round(2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oq_dWqZy3zHr",
        "outputId": "acf4d4b7-34e4-40e6-c104-cf229e0e6216"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Applying feature engineering...\n",
            "Standardizing features...\n",
            "\n",
            "Final feature set:\n",
            "Training set shape: (155684, 62)\n",
            "Test set shape: (20000, 62)\n",
            "\n",
            "Features included:\n",
            "['Duration', 'Insured.age', 'Car.age', 'Credit.score', 'Annual.miles.drive', 'Years.noclaims', 'Territory', 'Annual.pct.driven', 'Total.miles.driven', 'Pct.drive.mon', 'Pct.drive.tue', 'Pct.drive.wed', 'Pct.drive.thr', 'Pct.drive.fri', 'Pct.drive.sat', 'Pct.drive.sun', 'Pct.drive.2hrs', 'Pct.drive.3hrs', 'Pct.drive.4hrs', 'Pct.drive.wkday', 'Pct.drive.wkend', 'Pct.drive.rush am', 'Pct.drive.rush pm', 'Avgdays.week', 'Accel.06miles', 'Accel.08miles', 'Accel.09miles', 'Accel.11miles', 'Accel.12miles', 'Accel.14miles', 'Brake.06miles', 'Brake.08miles', 'Brake.09miles', 'Brake.11miles', 'Brake.12miles', 'Brake.14miles', 'Left.turn.intensity08', 'Left.turn.intensity09', 'Left.turn.intensity10', 'Left.turn.intensity11', 'Left.turn.intensity12', 'Right.turn.intensity08', 'Right.turn.intensity09', 'Right.turn.intensity10', 'Right.turn.intensity11', 'Right.turn.intensity12', 'Insured.sex_Female', 'Insured.sex_Male', 'Marital_Married', 'Marital_Single', 'Car.use_Commercial', 'Car.use_Commute', 'Car.use_Farmer', 'Car.use_Private', 'Region_Rural', 'Region_Urban', 'Agg_Accel', 'Agg_Brake', 'Agg_Left_Turn', 'Agg_Right_Turn', 'Harsh_Driving_Score', 'Rush_Hour_Ratio']\n",
            "\n",
            "Sample statistics after preprocessing:\n",
            "        Duration  Insured.age    Car.age  Credit.score  Annual.miles.drive  \\\n",
            "count  155684.00    155684.00  155684.00     155684.00           155684.00   \n",
            "mean       -0.00         0.00       0.00          0.00               -0.00   \n",
            "std         1.00         1.00       1.00          1.00                1.00   \n",
            "min        -4.70        -2.19      -1.85         -4.21               -2.44   \n",
            "25%         0.40        -0.82      -0.78         -0.57               -0.84   \n",
            "50%         0.48        -0.06      -0.25          0.23               -0.04   \n",
            "75%         0.50         0.70       0.55          0.73                0.76   \n",
            "max         0.50         3.72       4.02          1.41               12.17   \n",
            "\n",
            "       Years.noclaims  Territory  Annual.pct.driven  Total.miles.driven  \\\n",
            "count       155684.00  155684.00          155684.00           155684.00   \n",
            "mean             0.00       0.00               0.00               -0.00   \n",
            "std              1.00       1.00               1.00                1.00   \n",
            "min             -1.65      -2.07              -2.09               -1.29   \n",
            "25%             -0.86      -0.89              -0.76               -0.77   \n",
            "50%             -0.12       0.25               0.09               -0.21   \n",
            "75%              0.74       0.84               0.98                0.55   \n",
            "max              3.54       1.56               1.25                7.87   \n",
            "\n",
            "       Pct.drive.mon  ...  Right.turn.intensity09  Right.turn.intensity10  \\\n",
            "count      155684.00  ...               155684.00               155684.00   \n",
            "mean            0.00  ...                    0.00                    0.00   \n",
            "std             1.00  ...                    1.00                    1.00   \n",
            "min            -4.12  ...                   -0.07                   -0.05   \n",
            "25%            -0.44  ...                   -0.06                   -0.05   \n",
            "50%            -0.03  ...                   -0.06                   -0.05   \n",
            "75%             0.39  ...                   -0.03                   -0.04   \n",
            "max            25.26  ...                   53.94                   56.68   \n",
            "\n",
            "       Right.turn.intensity11  Right.turn.intensity12  Agg_Accel  Agg_Brake  \\\n",
            "count               155684.00               155684.00  155684.00  155684.00   \n",
            "mean                    -0.00                    0.00      -0.00       0.00   \n",
            "std                      1.00                    1.00       1.00       1.00   \n",
            "min                     -0.04                   -0.04      -0.65      -1.10   \n",
            "25%                     -0.04                   -0.04      -0.49      -0.62   \n",
            "50%                     -0.04                   -0.04      -0.26      -0.25   \n",
            "75%                     -0.04                   -0.04       0.13       0.32   \n",
            "max                     60.77                   71.58      40.62      34.67   \n",
            "\n",
            "       Agg_Left_Turn  Agg_Right_Turn  Harsh_Driving_Score  Rush_Hour_Ratio  \n",
            "count      155684.00       155684.00            155684.00        155684.00  \n",
            "mean            0.00           -0.00                 0.00            -0.00  \n",
            "std             1.00            1.00                 1.00             1.00  \n",
            "min            -0.05           -0.06                -0.08            -0.02  \n",
            "25%            -0.05           -0.06                -0.08            -0.02  \n",
            "50%            -0.05           -0.05                -0.07            -0.02  \n",
            "75%            -0.04           -0.03                -0.05            -0.01  \n",
            "max            51.07           59.07                38.06           349.51  \n",
            "\n",
            "[8 rows x 52 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pytorch_tabnet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q_P-eyX943Uc",
        "outputId": "f764826e-8151-4bac-9887-90f2d34f6abe"
      },
      "execution_count": null,
      "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[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.5/44.5 kB\u001b[0m \u001b[31m2.5 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 numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, matthews_corrcoef, roc_curve\n",
        "\n",
        "# Deep Neural Network\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "# XGBoost\n",
        "import xgboost as xgb\n",
        "\n",
        "# TabNet\n",
        "from pytorch_tabnet.tab_model import TabNetClassifier\n",
        "import torch\n",
        "\n",
        "# Ensure reproducibility\n",
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)\n",
        "torch.manual_seed(42)\n",
        "\n",
        "# Assuming X and y are your features and target variables\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Standardize the features\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# 1. Deep Neural Network\n",
        "def create_dnn_model(input_dim):\n",
        "    model = Sequential([\n",
        "        Dense(64, activation='relu', input_dim=input_dim),\n",
        "        Dropout(0.3),\n",
        "        Dense(32, activation='relu'),\n",
        "        Dropout(0.2),\n",
        "        Dense(16, activation='relu'),\n",
        "        Dense(1, activation='sigmoid')\n",
        "    ])\n",
        "    model.compile(optimizer=Adam(learning_rate=0.001), loss='binary_crossentropy', metrics=['accuracy'])\n",
        "    return model\n",
        "\n",
        "dnn_model = create_dnn_model(X_train.shape[1])\n",
        "dnn_history = dnn_model.fit(X_train_scaled, y_train, epochs=50, batch_size=32, validation_split=0.2, verbose=0)\n",
        "\n",
        "# 2. XGBoost\n",
        "xgb_model = xgb.XGBClassifier(\n",
        "    n_estimators=100,\n",
        "    learning_rate=0.1,\n",
        "    max_depth=5,\n",
        "    min_child_weight=1,\n",
        "    subsample=0.8,\n",
        "    colsample_bytree=0.8,\n",
        "    random_state=42\n",
        ")\n",
        "xgb_model.fit(X_train, y_train)\n",
        "\n",
        "# 3. TabNet\n",
        "tabnet_model = TabNetClassifier(\n",
        "    n_d=16, n_a=16, n_steps=5,\n",
        "    optimizer_fn=torch.optim.Adam,\n",
        "    optimizer_params=dict(lr=2e-2),\n",
        "    scheduler_params={\"step_size\":10, \"gamma\":0.9},\n",
        "    scheduler_fn=torch.optim.lr_scheduler.StepLR,\n",
        "    mask_type='entmax'\n",
        ")\n",
        "\n",
        "X_train_tab = X_train.astype(np.float32)\n",
        "y_train_tab = y_train.astype(np.int64)\n",
        "X_test_tab = X_test.astype(np.float32)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t3hekMtJ4tIG",
        "outputId": "529bce1d-ee9c-47fa-e522-a1da6d0e2c6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
            "  warnings.warn(f\"Device used : {self.device}\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, matthews_corrcoef, roc_curve\n",
        "\n",
        "# Ensure TabNet is properly fitted\n",
        "# Convert X_train_tab, X_test_tab to NumPy arrays before fitting\n",
        "tabnet_model.fit(\n",
        "    X_train_tab.values, y_train_tab,  # Use .values to get the NumPy array\n",
        "    eval_set=[(X_test_tab.values, y_test.astype(np.int64))],  # Use .values here as well\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": {
        "id": "3U2l4oufiWkv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dea21c77-2508-4055-81dd-4b54ab8325f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 0  | loss: 0.14825 | val_0_auc: 0.54718 |  0:00:08s\n",
            "epoch 1  | loss: 0.1147  | val_0_auc: 0.75003 |  0:00:13s\n",
            "epoch 2  | loss: 0.11168 | val_0_auc: 0.79227 |  0:00:19s\n",
            "epoch 3  | loss: 0.10895 | val_0_auc: 0.79463 |  0:00:25s\n",
            "epoch 4  | loss: 0.10858 | val_0_auc: 0.80322 |  0:00:30s\n",
            "epoch 5  | loss: 0.108   | val_0_auc: 0.80129 |  0:00:37s\n",
            "epoch 6  | loss: 0.10857 | val_0_auc: 0.80574 |  0:00:42s\n",
            "epoch 7  | loss: 0.10631 | val_0_auc: 0.80857 |  0:00:48s\n",
            "epoch 8  | loss: 0.106   | val_0_auc: 0.80653 |  0:00:55s\n",
            "epoch 9  | loss: 0.10564 | val_0_auc: 0.80469 |  0:01:00s\n",
            "epoch 10 | loss: 0.10528 | val_0_auc: 0.81359 |  0:01:06s\n",
            "epoch 11 | loss: 0.10495 | val_0_auc: 0.80905 |  0:01:12s\n",
            "epoch 12 | loss: 0.10565 | val_0_auc: 0.80377 |  0:01:18s\n",
            "epoch 13 | loss: 0.1055  | val_0_auc: 0.80445 |  0:01:23s\n",
            "epoch 14 | loss: 0.10512 | val_0_auc: 0.81124 |  0:01:29s\n",
            "epoch 15 | loss: 0.10532 | val_0_auc: 0.81088 |  0:01:35s\n",
            "epoch 16 | loss: 0.10438 | val_0_auc: 0.80543 |  0:01:40s\n",
            "epoch 17 | loss: 0.10473 | val_0_auc: 0.80634 |  0:01:46s\n",
            "epoch 18 | loss: 0.1044  | val_0_auc: 0.80932 |  0:01:52s\n",
            "epoch 19 | loss: 0.10394 | val_0_auc: 0.80913 |  0:01:57s\n",
            "epoch 20 | loss: 0.10527 | val_0_auc: 0.80783 |  0:02:03s\n",
            "\n",
            "Early stopping occurred at epoch 20 with best_epoch = 10 and best_val_0_auc = 0.81359\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": [
        "# Make predictions\n",
        "y_pred_dnn = dnn_model.predict(X_test_scaled).ravel()\n",
        "y_pred_xgb = xgb_model.predict_proba(X_test)[:, 1]\n",
        "\n",
        "# Convert X_test_tab columns to strings\n",
        "# This ensures TabNet does not misinterpret column names as indices\n",
        "X_test_tab.columns = X_test_tab.columns.astype(str)\n",
        "\n",
        "# Now predict using TabNet\n",
        "y_pred_tabnet = tabnet_model.predict_proba(X_test_tab.values)[:, 1]\n",
        "\n",
        "# Ensemble predictions (simple average)\n",
        "y_pred_ensemble = (y_pred_dnn + y_pred_xgb + y_pred_tabnet) / 3\n",
        "\n",
        "# Calculate metrics for ensemble\n",
        "y_pred_ensemble_class = (y_pred_ensemble > 0.5).astype(int)\n"
      ],
      "metadata": {
        "id": "DuihmNQL0WOn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "afcf2eef-e43a-4c9a-ad7d-7e383d2cddf3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "metrics_ensemble = {\n",
        "    'accuracy': accuracy_score(y_test, y_pred_ensemble_class),\n",
        "    'precision': precision_score(y_test, y_pred_ensemble_class),\n",
        "    'recall': recall_score(y_test, y_pred_ensemble_class),\n",
        "    'f1': f1_score(y_test, y_pred_ensemble_class),\n",
        "    'auc': roc_auc_score(y_test, y_pred_ensemble),\n",
        "    'matthews_corr': matthews_corrcoef(y_test, y_pred_ensemble_class)\n",
        "}\n",
        "\n",
        "# Print ensemble metrics\n",
        "print(\"\\nEnsemble Model Performance Metrics:\")\n",
        "for metric, value in metrics_ensemble.items():\n",
        "    print(f\"{metric}: {value:.4f}\")\n",
        "\n",
        "# Plot ROC curves\n",
        "plt.figure(figsize=(10, 8))\n",
        "for name, y_pred in [('DNN', y_pred_dnn), ('XGBoost', y_pred_xgb), ('TabNet', y_pred_tabnet), ('Ensemble', y_pred_ensemble)]:\n",
        "    fpr, tpr, _ = roc_curve(y_test, y_pred)\n",
        "    auc = roc_auc_score(y_test, y_pred)\n",
        "    plt.plot(fpr, tpr, label=f'{name} (AUC = {auc:.3f})')\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curves Comparison')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        },
        "id": "yAnh8_lf1r4M",
        "outputId": "5b073724-335d-4977-be8f-96448dc022b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Ensemble Model Performance Metrics:\n",
            "accuracy: 0.9737\n",
            "precision: 0.9524\n",
            "recall: 0.0367\n",
            "f1: 0.0707\n",
            "auc: 0.8671\n",
            "matthews_corr: 0.1842\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.metrics import make_scorer, accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, matthews_corrcoef\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "\n",
        "# Define a smaller parameter grid\n",
        "param_grid = {\n",
        "    'n_estimators': [100, 200],\n",
        "    'max_depth': [5, 10, None],\n",
        "    'min_samples_split': [2, 5],\n",
        "    'min_samples_leaf': [1, 2],\n",
        "    'max_features': ['sqrt', 'log2'],\n",
        "    'class_weight': [None, 'balanced']\n",
        "}\n",
        "\n",
        "# Define scoring metrics\n",
        "scoring = {\n",
        "    'accuracy': make_scorer(accuracy_score),\n",
        "    'precision': make_scorer(precision_score),\n",
        "    'recall': make_scorer(recall_score),\n",
        "    'f1': make_scorer(f1_score),\n",
        "    'auc': make_scorer(roc_auc_score),\n",
        "    'matthews_corr': make_scorer(matthews_corrcoef)\n",
        "}\n"
      ],
      "metadata": {
        "id": "8HtjAuCk4ac2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize Random Forest Classifier\n",
        "rf = RandomForestClassifier(random_state=42)\n",
        "\n",
        "# Set up RandomizedSearchCV with StratifiedKFold\n",
        "n_iter_search = 2  # Number of parameter settings that are sampled\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
        "\n",
        "random_search = RandomizedSearchCV(\n",
        "    estimator=rf,\n",
        "    param_distributions=param_grid,\n",
        "    n_iter=n_iter_search,\n",
        "    scoring=scoring,\n",
        "    refit='matthews_corr',  # Optimize for Matthews Correlation Coefficient\n",
        "    cv=cv,\n",
        "    n_jobs=-1,\n",
        "    verbose=2,\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "# Fit the random search\n",
        "print(\"Starting RandomizedSearchCV...\")\n",
        "random_search.fit(X_train, y_train)\n",
        "print(\"RandomizedSearchCV completed.\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S3P4biC96s4d",
        "outputId": "95a38f36-8917-4a0a-bf5f-1c5f97c6234c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting RandomizedSearchCV...\n",
            "Fitting 5 folds for each of 2 candidates, totalling 10 fits\n",
            "RandomizedSearchCV completed.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the best model\n",
        "best_rf = random_search.best_estimator_\n",
        "\n",
        "# Make predictions with the best model\n",
        "y_pred_rf = best_rf.predict(X_test)\n",
        "y_pred_rf_proba = best_rf.predict_proba(X_test)[:, 1]\n",
        "\n",
        "# Calculate metrics\n",
        "metrics_rf = {\n",
        "    'accuracy': accuracy_score(y_test, y_pred_rf),\n",
        "    'precision': precision_score(y_test, y_pred_rf),\n",
        "    'recall': recall_score(y_test, y_pred_rf),\n",
        "    'f1': f1_score(y_test, y_pred_rf),\n",
        "    'auc': roc_auc_score(y_test, y_pred_rf_proba),\n",
        "    'matthews_corr': matthews_corrcoef(y_test, y_pred_rf)\n",
        "}\n",
        "\n",
        "# Print Random Forest metrics\n",
        "print(\"\\nOptimized Random Forest Model Performance Metrics:\")\n",
        "for metric, value in metrics_rf.items():\n",
        "    print(f\"{metric}: {value:.4f}\")\n",
        "\n",
        "# Print best parameters\n",
        "print(\"\\nBest Parameters:\")\n",
        "print(random_search.best_params_)\n",
        "\n",
        "# Create new ensemble including Random Forest\n",
        "y_pred_new_ensemble = (y_pred_dnn + y_pred_xgb + y_pred_tabnet + y_pred_rf_proba) / 4\n",
        "y_pred_new_ensemble_class = (y_pred_new_ensemble > 0.5).astype(int)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BgLbu3E96y61",
        "outputId": "d5e0bb0b-8963-4981-fa01-5c32cc500832"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Optimized Random Forest Model Performance Metrics:\n",
            "accuracy: 0.9787\n",
            "precision: 0.9338\n",
            "recall: 0.2330\n",
            "f1: 0.3730\n",
            "auc: 0.9158\n",
            "matthews_corr: 0.4607\n",
            "\n",
            "Best Parameters:\n",
            "{'n_estimators': 100, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': None, 'class_weight': 'balanced'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate metrics for new ensemble\n",
        "metrics_new_ensemble = {\n",
        "    'accuracy': accuracy_score(y_test, y_pred_new_ensemble_class),\n",
        "    'precision': precision_score(y_test, y_pred_new_ensemble_class),\n",
        "    'recall': recall_score(y_test, y_pred_new_ensemble_class),\n",
        "    'f1': f1_score(y_test, y_pred_new_ensemble_class),\n",
        "    'auc': roc_auc_score(y_test, y_pred_new_ensemble),\n",
        "    'matthews_corr': matthews_corrcoef(y_test, y_pred_new_ensemble_class)\n",
        "}\n",
        "\n",
        "# Print new ensemble metrics\n",
        "print(\"\\nNew Ensemble Model (including RF) Performance Metrics:\")\n",
        "for metric, value in metrics_new_ensemble.items():\n",
        "    print(f\"{metric}: {value:.4f}\")\n",
        "\n",
        "# Plot updated ROC curves\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import roc_curve\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "for name, y_pred in [('DNN', y_pred_dnn), ('XGBoost', y_pred_xgb),\n",
        "                     ('TabNet', y_pred_tabnet), ('Random Forest', y_pred_rf_proba),\n",
        "                     ('New Ensemble', y_pred_new_ensemble)]:\n",
        "    fpr, tpr, _ = roc_curve(y_test, y_pred)\n",
        "    auc = roc_auc_score(y_test, y_pred)\n",
        "    plt.plot(fpr, tpr, label=f'{name} (AUC = {auc:.3f})')\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Updated ROC Curves Comparison')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        },
        "id": "H3c4jqJDALDI",
        "outputId": "f8e022ba-ec3a-407f-f441-d1648d63638e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "New Ensemble Model (including RF) Performance Metrics:\n",
            "accuracy: 0.9745\n",
            "precision: 0.9722\n",
            "recall: 0.0642\n",
            "f1: 0.1205\n",
            "auc: 0.9163\n",
            "matthews_corr: 0.2465\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import cross_val_predict\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, matthews_corrcoef\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Function to get out-of-fold predictions\n",
        "def get_oof_predictions(clf, X, y, cv=5):\n",
        "    predictions = cross_val_predict(clf, X, y, cv=cv, method='predict_proba')\n",
        "    return predictions[:, 1]\n",
        "\n",
        "# Create base models\n",
        "rf = RandomForestClassifier(n_estimators=2, random_state=42)\n",
        "gb = GradientBoostingClassifier(n_estimators=2, random_state=42)\n",
        "\n",
        "# Get out-of-fold predictions for each base model\n",
        "print(\"Generating out-of-fold predictions...\")\n",
        "oof_rf = get_oof_predictions(rf, X_train, y_train)\n",
        "oof_gb = get_oof_predictions(gb, X_train, y_train)\n",
        "\n",
        "# For DNN and XGBoost, we need to generate predictions for the training set\n",
        "# Assuming these models are already trained\n",
        "oof_dnn = dnn_model.predict(X_train_scaled).ravel()\n",
        "oof_xgb = xgb_model.predict_proba(X_train)[:, 1]\n",
        "\n",
        "# For TabNet, we'll use a separate approach\n",
        "try:\n",
        "    oof_tabnet = tabnet_model.predict_proba(X_train_tab)[:, 1]\n",
        "except KeyError:\n",
        "    print(\"Warning: TabNet prediction failed. Using zeros as placeholder.\")\n",
        "    oof_tabnet = np.zeros(len(X_train))\n",
        "\n",
        "# Create a new feature matrix using out-of-fold predictions\n",
        "X_train_meta = np.column_stack((oof_rf, oof_gb, oof_dnn, oof_xgb, oof_tabnet))\n",
        "\n",
        "# Scale the meta-features\n",
        "scaler = StandardScaler()\n",
        "X_train_meta_scaled = scaler.fit_transform(X_train_meta)\n",
        "\n",
        "# Train a meta-model\n",
        "meta_model = LogisticRegression(random_state=42)\n",
        "meta_model.fit(X_train_meta_scaled, y_train)\n",
        "\n",
        "# Train base models on the full training data\n",
        "print(\"Training base models...\")\n",
        "rf.fit(X_train, y_train)\n",
        "gb.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "print(\"Making predictions...\")\n",
        "test_rf = rf.predict_proba(X_test)[:, 1]\n",
        "test_gb = gb.predict_proba(X_test)[:, 1]\n",
        "test_dnn = y_pred_dnn  # Assuming this is already computed for the test set\n",
        "test_xgb = y_pred_xgb  # Assuming this is already computed for the test set\n",
        "\n",
        "# For TabNet, use the same approach as for training\n",
        "try:\n",
        "    test_tabnet = tabnet_model.predict_proba(X_test_tab)[:, 1]\n",
        "except KeyError:\n",
        "    print(\"Warning: TabNet prediction failed for test set. Using zeros as placeholder.\")\n",
        "    test_tabnet = np.zeros(len(X_test))\n",
        "\n",
        "# Create test meta-features\n",
        "X_test_meta = np.column_stack((test_rf, test_gb, test_dnn, test_xgb, test_tabnet))\n",
        "X_test_meta_scaled = scaler.transform(X_test_meta)\n",
        "\n",
        "# Make final predictions\n",
        "y_pred_stacking = meta_model.predict(X_test_meta_scaled)\n",
        "y_pred_stacking_proba = meta_model.predict_proba(X_test_meta_scaled)[:, 1]\n",
        "\n",
        "# Calculate metrics for stacking ensemble\n",
        "metrics_stacking = {\n",
        "    'accuracy': accuracy_score(y_test, y_pred_stacking),\n",
        "    'precision': precision_score(y_test, y_pred_stacking),\n",
        "    'recall': recall_score(y_test, y_pred_stacking),\n",
        "    'f1': f1_score(y_test, y_pred_stacking),\n",
        "    'auc': roc_auc_score(y_test, y_pred_stacking_proba),\n",
        "    'matthews_corr': matthews_corrcoef(y_test, y_pred_stacking)\n",
        "}\n",
        "\n",
        "# Print stacking ensemble metrics\n",
        "print(\"\\nStacking Ensemble Model Performance Metrics:\")\n",
        "for metric, value in metrics_stacking.items():\n",
        "    print(f\"{metric}: {value:.4f}\")\n",
        "\n",
        "# Plot updated ROC curves\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import roc_curve\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "for name, y_pred in [('DNN', y_pred_dnn), ('XGBoost', y_pred_xgb),\n",
        "                     ('TabNet', test_tabnet), ('Random Forest', test_rf),\n",
        "                     ('Gradient Boosting', test_gb),\n",
        "                     ('Stacking Ensemble', y_pred_stacking_proba)]:\n",
        "    fpr, tpr, _ = roc_curve(y_test, y_pred)\n",
        "    auc = roc_auc_score(y_test, y_pred)\n",
        "    plt.plot(fpr, tpr, label=f'{name} (AUC = {auc:.3f})')\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Updated ROC Curves Comparison (with Stacking)')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "\n",
        "# Feature importance of the meta-model\n",
        "feature_importance = pd.DataFrame({\n",
        "    'feature': ['Random Forest', 'Gradient Boosting', 'DNN', 'XGBoost', 'TabNet'],\n",
        "    'importance': np.abs(meta_model.coef_[0])  # Use absolute values for importance\n",
        "})\n",
        "feature_importance = feature_importance.sort_values('importance', ascending=False)\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(feature_importance['feature'], feature_importance['importance'])\n",
        "plt.title('Feature Importance in Stacking Ensemble')\n",
        "plt.xlabel('Base Models')\n",
        "plt.ylabel('Importance')\n",
        "plt.xticks(rotation=45)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "P2Jq-YCiGXvA",
        "outputId": "9d5271fa-4832-4625-c23a-c8dd87eafdaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generating out-of-fold predictions...\n",
            "\u001b[1m2500/2500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step\n",
            "Warning: TabNet prediction failed. Using zeros as placeholder.\n",
            "Training base models...\n",
            "Making predictions...\n",
            "Warning: TabNet prediction failed for test set. Using zeros as placeholder.\n",
            "\n",
            "Stacking Ensemble Model Performance Metrics:\n",
            "accuracy: 0.9747\n",
            "precision: 0.5841\n",
            "recall: 0.2422\n",
            "f1: 0.3424\n",
            "auc: 0.8564\n",
            "matthews_corr: 0.3656\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 4: Ensemble Method with GBM, RF, and XGBoost\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "\n",
        "# Define parameter grids for each model\n",
        "gb_param_grid = {\n",
        "    'n_estimators': [100, 200, 300],\n",
        "    'learning_rate': [0.01, 0.1, 0.2],\n",
        "    'max_depth': [3, 4, 5],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4]\n",
        "}\n",
        "\n",
        "rf_param_grid = {\n",
        "    'n_estimators': [100, 200, 300],\n",
        "    'max_depth': [None, 10, 20, 30],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4],\n",
        "    'max_features': ['sqrt', 'log2']\n",
        "}\n",
        "\n",
        "xgb_param_grid = {\n",
        "    'n_estimators': [100, 200, 300],\n",
        "    'learning_rate': [0.01, 0.1, 0.2],\n",
        "    'max_depth': [3, 4, 5],\n",
        "    'min_child_weight': [1, 5, 10],\n",
        "    'subsample': [0.7, 0.8, 0.9]\n",
        "}\n",
        "\n",
        "# Function to perform RandomizedSearchCV\n",
        "def perform_random_search(model, param_grid, X, y):\n",
        "    random_search = RandomizedSearchCV(model, param_distributions=param_grid,\n",
        "                                       n_iter=10, cv=3, random_state=42, n_jobs=-1)\n",
        "    random_search.fit(X, y)\n",
        "    return random_search.best_estimator_\n",
        "\n",
        "# Perform RandomizedSearchCV for each model\n",
        "print(\"Tuning Gradient Boosting Machine...\")\n",
        "gb_best = perform_random_search(GradientBoostingClassifier(random_state=42), gb_param_grid, X_train_processed, y_train_balanced)\n",
        "\n",
        "print(\"Tuning Random Forest...\")\n",
        "rf_best = perform_random_search(RandomForestClassifier(random_state=42), rf_param_grid, X_train_processed, y_train_balanced)\n",
        "\n",
        "print(\"Tuning XGBoost...\")\n",
        "xgb_best = perform_random_search(XGBClassifier(random_state=42), xgb_param_grid, X_train_processed, y_train_balanced)\n",
        "\n",
        "# Create the ensemble model\n",
        "ensemble = VotingClassifier(\n",
        "    estimators=[('gb', gb_best), ('rf', rf_best), ('xgb', xgb_best)],\n",
        "    voting='soft'\n",
        ")\n",
        "\n",
        "# Fit the ensemble model\n",
        "print(\"Fitting the ensemble model...\")\n",
        "ensemble.fit(X_train_processed, y_train_balanced)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = ensemble.predict(X_test_processed)\n",
        "y_pred_proba = ensemble.predict_proba(X_test_processed)[:, 1]\n",
        "\n",
        "# Calculate metrics\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "precision = precision_score(y_test, y_pred)\n",
        "recall = recall_score(y_test, y_pred)\n",
        "f1 = f1_score(y_test, y_pred)\n",
        "auc_roc = roc_auc_score(y_test, y_pred_proba)\n",
        "\n",
        "# Print results\n",
        "print(\"\\nEnsemble Model Performance:\")\n",
        "print(f\"Accuracy: {accuracy:.4f}\")\n",
        "print(f\"Precision: {precision:.4f}\")\n",
        "print(f\"Recall: {recall:.4f}\")\n",
        "print(f\"F1-score: {f1:.4f}\")\n",
        "print(f\"AUC-ROC: {auc_roc:.4f}\")\n",
        "\n",
        "# Print best parameters for each model\n",
        "print(\"\\nBest parameters for Gradient Boosting Machine:\")\n",
        "print(gb_best.get_params())\n",
        "print(\"\\nBest parameters for Random Forest:\")\n",
        "print(rf_best.get_params())\n",
        "print(\"\\nBest parameters for XGBoost:\")\n",
        "print(xgb_best.get_params())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5yv4yhpBL1D6",
        "outputId": "f2b8acbe-07fb-4b4a-862e-05baf9abbf3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tuning Gradient Boosting Machine...\n"
          ]
        }
      ]
    }
  ]
}