loan_risk_classification_v2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOmORmGyDVCyt52oOXr5bKP",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hemant-mogia/loan-risk-classification/blob/main/loan_risk_classification_v2.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": "jxolAS_7OH4m",
        "outputId": "569a2526-1407-4098-b485-e614dc5fd6e4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def load_data(train_path, test_path):\n",
        "    train = pd.read_csv(train_path)\n",
        "    test = pd.read_csv(test_path)\n",
        "    return train, test"
      ],
      "metadata": {
        "id": "GqSDjnKBOQLo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load data\n",
        "test, train  = load_data('/content/drive/MyDrive/bureau/Assignment_Test.csv', '/content/drive/MyDrive/bureau/Assignment_Train.csv')"
      ],
      "metadata": {
        "id": "nzPefwzXOSXP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "s_eASq6jOXB1",
        "outputId": "62c0792a-6358-4e19-f58e-e3c95292f91d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   DEALER ID APPLICATION LOGIN DATE HDB BRANCH NAME HDB BRANCH STATE  \\\n",
              "0     106989             07/20/2022        DELHI-SF            DELHI   \n",
              "1     108975             07/28/2022        PATNA-SF            BIHAR   \n",
              "2     111004             07/15/2022   DARJEELING-SF      WEST BENGAL   \n",
              "3     192020               07/04/22   SAHARANPUR-SF    UTTAR PRADESH   \n",
              "4      55095             07/15/2022       MODASA-SF          GUJARAT   \n",
              "\n",
              "  FIRST NAME MIDDLE NAME  LAST NAME      mobile AADHAR VERIFIED Cibil Score  \\\n",
              "0      SUNIL         NaN    CHANDER  9210574080              NO         726   \n",
              "1      AMRIT         NaN      KUMAR  8877987018              NO         NaN   \n",
              "2    ANIMESH         NaN      THAPA  8910862135              NO         737   \n",
              "3     ADITYA         NaN      SINGH  9758428017              NO         713   \n",
              "4     PARMAR  HARESHBHAI  AMRUTBHAI  9687028486              NO         669   \n",
              "\n",
              "   ...  Phone Social Premium.shaadi Phone Social Premium.skype  \\\n",
              "0  ...                          0.0                        0.0   \n",
              "1  ...                          0.0                        0.0   \n",
              "2  ...                          0.0                        0.0   \n",
              "3  ...                          0.0                        0.0   \n",
              "4  ...                          0.0                        0.0   \n",
              "\n",
              "   Phone Social Premium.toi Phone Social Premium.whatsapp  \\\n",
              "0                       1.0                           NaN   \n",
              "1                       0.0                           NaN   \n",
              "2                       0.0                           NaN   \n",
              "3                       1.0                           NaN   \n",
              "4                       1.0                           NaN   \n",
              "\n",
              "   Phone Social Premium.yatra  Phone Social Premium.zoho phone_digitalage  \\\n",
              "0                         NaN                        0.0           5324.0   \n",
              "1                         NaN                        0.0           1998.0   \n",
              "2                         NaN                        0.0             -1.0   \n",
              "3                         NaN                        0.0           1998.0   \n",
              "4                         NaN                        0.0           1998.0   \n",
              "\n",
              "  phone_nameMatchScore phone_phoneFootprintStrengthOverall Application Status  \n",
              "0            67.222222                                High           APPROVED  \n",
              "1           100.000000                                High           APPROVED  \n",
              "2            -1.000000                                 Low           APPROVED  \n",
              "3            72.777778                                High           APPROVED  \n",
              "4            68.095238                                High           DECLINED  \n",
              "\n",
              "[5 rows x 55 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6bb987df-70dc-4086-be68-a707ef1d97c7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DEALER ID</th>\n",
              "      <th>APPLICATION LOGIN DATE</th>\n",
              "      <th>HDB BRANCH NAME</th>\n",
              "      <th>HDB BRANCH STATE</th>\n",
              "      <th>FIRST NAME</th>\n",
              "      <th>MIDDLE NAME</th>\n",
              "      <th>LAST NAME</th>\n",
              "      <th>mobile</th>\n",
              "      <th>AADHAR VERIFIED</th>\n",
              "      <th>Cibil Score</th>\n",
              "      <th>...</th>\n",
              "      <th>Phone Social Premium.shaadi</th>\n",
              "      <th>Phone Social Premium.skype</th>\n",
              "      <th>Phone Social Premium.toi</th>\n",
              "      <th>Phone Social Premium.whatsapp</th>\n",
              "      <th>Phone Social Premium.yatra</th>\n",
              "      <th>Phone Social Premium.zoho</th>\n",
              "      <th>phone_digitalage</th>\n",
              "      <th>phone_nameMatchScore</th>\n",
              "      <th>phone_phoneFootprintStrengthOverall</th>\n",
              "      <th>Application Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>106989</td>\n",
              "      <td>07/20/2022</td>\n",
              "      <td>DELHI-SF</td>\n",
              "      <td>DELHI</td>\n",
              "      <td>SUNIL</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CHANDER</td>\n",
              "      <td>9210574080</td>\n",
              "      <td>NO</td>\n",
              "      <td>726</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5324.0</td>\n",
              "      <td>67.222222</td>\n",
              "      <td>High</td>\n",
              "      <td>APPROVED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>108975</td>\n",
              "      <td>07/28/2022</td>\n",
              "      <td>PATNA-SF</td>\n",
              "      <td>BIHAR</td>\n",
              "      <td>AMRIT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>KUMAR</td>\n",
              "      <td>8877987018</td>\n",
              "      <td>NO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>High</td>\n",
              "      <td>APPROVED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>111004</td>\n",
              "      <td>07/15/2022</td>\n",
              "      <td>DARJEELING-SF</td>\n",
              "      <td>WEST BENGAL</td>\n",
              "      <td>ANIMESH</td>\n",
              "      <td>NaN</td>\n",
              "      <td>THAPA</td>\n",
              "      <td>8910862135</td>\n",
              "      <td>NO</td>\n",
              "      <td>737</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>Low</td>\n",
              "      <td>APPROVED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>192020</td>\n",
              "      <td>07/04/22</td>\n",
              "      <td>SAHARANPUR-SF</td>\n",
              "      <td>UTTAR PRADESH</td>\n",
              "      <td>ADITYA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SINGH</td>\n",
              "      <td>9758428017</td>\n",
              "      <td>NO</td>\n",
              "      <td>713</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>72.777778</td>\n",
              "      <td>High</td>\n",
              "      <td>APPROVED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>55095</td>\n",
              "      <td>07/15/2022</td>\n",
              "      <td>MODASA-SF</td>\n",
              "      <td>GUJARAT</td>\n",
              "      <td>PARMAR</td>\n",
              "      <td>HARESHBHAI</td>\n",
              "      <td>AMRUTBHAI</td>\n",
              "      <td>9687028486</td>\n",
              "      <td>NO</td>\n",
              "      <td>669</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>68.095238</td>\n",
              "      <td>High</td>\n",
              "      <td>DECLINED</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 55 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6bb987df-70dc-4086-be68-a707ef1d97c7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6bb987df-70dc-4086-be68-a707ef1d97c7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6bb987df-70dc-4086-be68-a707ef1d97c7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0778b344-c236-412a-b94b-401b39564a9e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0778b344-c236-412a-b94b-401b39564a9e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0778b344-c236-412a-b94b-401b39564a9e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# List of columns to consider as features for training the model\n",
        "train_cols_to_consider = [\n",
        "    # Application Details\n",
        "    # 'APPLICATION LOGIN DATE',\n",
        "    'AADHAR VERIFIED',\n",
        "\n",
        "    # Financial Information\n",
        "    'Cibil Score',\n",
        "    'TOTAL ASSET COST',\n",
        "    'APPLIED AMOUNT',\n",
        "\n",
        "    # Demographics\n",
        "    'MARITAL STATUS',\n",
        "    'GENDER',\n",
        "    'DOB',\n",
        "    'AGE',\n",
        "\n",
        "    # Phone Related Features\n",
        "    'phone_digitalage',\n",
        "    'phone_nameMatchScore',\n",
        "    'phone_phoneFootprintStrengthOverall'\n",
        "\n",
        "    # Target Variable\n",
        "    'Application Status'\n",
        "]"
      ],
      "metadata": {
        "id": "W0WYBDKpROUc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train.columns.tolist())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iAJrM9-ve6zH",
        "outputId": "7ae06dee-f8ee-45d1-f34a-6efe0a09a141"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['DEALER ID', 'APPLICATION LOGIN DATE', 'HDB BRANCH NAME', 'HDB BRANCH STATE', 'FIRST NAME', 'MIDDLE NAME', 'LAST NAME', 'mobile', 'AADHAR VERIFIED', 'Cibil Score', 'MOBILE VERIFICATION', 'DEALER NAME', 'TOTAL ASSET COST', 'ASSET CTG', 'ASSET MODEL NO', 'APPLIED AMOUNT', 'PRIMARY ASSET MAKE', 'Primary Asset Model No', 'Personal Email Address', 'MARITAL STATUS', 'GENDER', 'DOB', 'AGE', 'ADDRESS TYPE', 'EMPLOY CONSTITUTION', 'EMPLOYER NAME', 'EMPLOYER TYPE', 'Pan Name', 'name', 'vpa', 'upi_name', 'Phone Social Premium.a23games', 'Phone Social Premium.amazon', 'Phone Social Premium.byjus', 'Phone Social Premium.flipkart', 'Phone Social Premium.housing', 'Phone Social Premium.indiamart', 'Phone Social Premium.instagram', 'Phone Social Premium.isWABusiness', 'Phone Social Premium.jeevansaathi', 'Phone Social Premium.jiomart', 'Phone Social Premium.microsoft', 'Phone Social Premium.my11', 'Phone Social Premium.paytm', 'Phone Social Premium.rummycircle', 'Phone Social Premium.shaadi', 'Phone Social Premium.skype', 'Phone Social Premium.toi', 'Phone Social Premium.whatsapp', 'Phone Social Premium.yatra', 'Phone Social Premium.zoho', 'phone_digitalage', 'phone_nameMatchScore', 'phone_phoneFootprintStrengthOverall', 'Application Status']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# List of columns to consider as features for training the model\n",
        "train_cols_to_consider = [\n",
        "    # Application Details\n",
        "    # 'APPLICATION LOGIN DATE',\n",
        "    'AADHAR VERIFIED',\n",
        "\n",
        "    # Financial Information\n",
        "    'Cibil Score',\n",
        "    'TOTAL ASSET COST',\n",
        "    'APPLIED AMOUNT',\n",
        "\n",
        "    # Demographics\n",
        "    'MARITAL STATUS',\n",
        "    'GENDER',\n",
        "    'AGE',\n",
        "\n",
        "    # Phone Related Features\n",
        "    'phone_digitalage',\n",
        "    'phone_nameMatchScore',\n",
        "    'phone_phoneFootprintStrengthOverall',\n",
        "\n",
        "    # Target Variable\n",
        "    'Application Status'\n",
        "]\n",
        "\n",
        "# Check the existing columns\n",
        "existing_columns = train.columns.tolist()\n",
        "missing_columns = [col for col in train_cols_to_consider if col not in existing_columns]\n",
        "\n",
        "if missing_columns:\n",
        "    print(f\"Columns missing from DataFrame: {missing_columns}\")\n",
        "else:\n",
        "    # If all columns exist, create the DataFrame with selected columns\n",
        "    train_df = train[train_cols_to_consider]\n",
        "    print(\"Columns selected successfully.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xbbKa4V-fOb8",
        "outputId": "fcb303fc-f67e-4d19-ae63-a2fcee74f7ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Columns selected successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "PGi1wSibfRsw",
        "outputId": "47d8ef59-20cd-47cd-99af-5c30e3dbe2e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  AADHAR VERIFIED Cibil Score  TOTAL ASSET COST  APPLIED AMOUNT  \\\n",
              "0              NO         726           95041.0           85000   \n",
              "1              NO         NaN               NaN           90000   \n",
              "2              NO         737          119436.0           75000   \n",
              "3              NO         713           87000.0           78500   \n",
              "4              NO         669               NaN           70000   \n",
              "\n",
              "  MARITAL STATUS GENDER  AGE  phone_digitalage  phone_nameMatchScore  \\\n",
              "0        Married   Male   44            5324.0             67.222222   \n",
              "1            NaN   Male   25            1998.0            100.000000   \n",
              "2         Single   Male   22              -1.0             -1.000000   \n",
              "3         Single   Male   24            1998.0             72.777778   \n",
              "4            NaN   Male   33            1998.0             68.095238   \n",
              "\n",
              "  phone_phoneFootprintStrengthOverall Application Status  \n",
              "0                                High           APPROVED  \n",
              "1                                High           APPROVED  \n",
              "2                                 Low           APPROVED  \n",
              "3                                High           APPROVED  \n",
              "4                                High           DECLINED  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-edd6b3f7-fff5-4650-882e-9ac4bf194cff\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AADHAR VERIFIED</th>\n",
              "      <th>Cibil Score</th>\n",
              "      <th>TOTAL ASSET COST</th>\n",
              "      <th>APPLIED AMOUNT</th>\n",
              "      <th>MARITAL STATUS</th>\n",
              "      <th>GENDER</th>\n",
              "      <th>AGE</th>\n",
              "      <th>phone_digitalage</th>\n",
              "      <th>phone_nameMatchScore</th>\n",
              "      <th>phone_phoneFootprintStrengthOverall</th>\n",
              "      <th>Application Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NO</td>\n",
              "      <td>726</td>\n",
              "      <td>95041.0</td>\n",
              "      <td>85000</td>\n",
              "      <td>Married</td>\n",
              "      <td>Male</td>\n",
              "      <td>44</td>\n",
              "      <td>5324.0</td>\n",
              "      <td>67.222222</td>\n",
              "      <td>High</td>\n",
              "      <td>APPROVED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>90000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Male</td>\n",
              "      <td>25</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>High</td>\n",
              "      <td>APPROVED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NO</td>\n",
              "      <td>737</td>\n",
              "      <td>119436.0</td>\n",
              "      <td>75000</td>\n",
              "      <td>Single</td>\n",
              "      <td>Male</td>\n",
              "      <td>22</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>Low</td>\n",
              "      <td>APPROVED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NO</td>\n",
              "      <td>713</td>\n",
              "      <td>87000.0</td>\n",
              "      <td>78500</td>\n",
              "      <td>Single</td>\n",
              "      <td>Male</td>\n",
              "      <td>24</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>72.777778</td>\n",
              "      <td>High</td>\n",
              "      <td>APPROVED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NO</td>\n",
              "      <td>669</td>\n",
              "      <td>NaN</td>\n",
              "      <td>70000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Male</td>\n",
              "      <td>33</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>68.095238</td>\n",
              "      <td>High</td>\n",
              "      <td>DECLINED</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-edd6b3f7-fff5-4650-882e-9ac4bf194cff')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-edd6b3f7-fff5-4650-882e-9ac4bf194cff button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-edd6b3f7-fff5-4650-882e-9ac4bf194cff');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f9813701-e1e7-4eec-a751-b8cb146b5dc5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f9813701-e1e7-4eec-a751-b8cb146b5dc5')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f9813701-e1e7-4eec-a751-b8cb146b5dc5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_df",
              "summary": "{\n  \"name\": \"train_df\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"AADHAR VERIFIED\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"NO\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cibil Score\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 267,\n        \"samples\": [\n          \"791\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TOTAL ASSET COST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16745.027040975914,\n        \"min\": 51873.0,\n        \"max\": 241116.0,\n        \"num_unique_values\": 3692,\n        \"samples\": [\n          98054.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"APPLIED AMOUNT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27000,\n        \"min\": 10400,\n        \"max\": 1420000,\n        \"num_unique_values\": 1230,\n        \"samples\": [\n          150600\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MARITAL STATUS\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Single\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GENDER\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 0,\n        \"max\": 79,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"phone_digitalage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 984.1142481344085,\n        \"min\": -1.0,\n        \"max\": 6311.0,\n        \"num_unique_values\": 1781,\n        \"samples\": [\n          2849.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"phone_nameMatchScore\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42.100616429132614,\n        \"min\": -1.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 1007,\n        \"samples\": [\n          96.66666667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"phone_phoneFootprintStrengthOverall\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Low\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Application Status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"DECLINED\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.set_option('display.max_columns', None)\n",
        "train_df.head(10)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "VSjvJ3VGQ5Mv",
        "outputId": "69315fc2-bf77-4dcb-a3ba-fe7adc358c47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  AADHAR VERIFIED Cibil Score  TOTAL ASSET COST  APPLIED AMOUNT  \\\n",
              "0              NO         726           95041.0           85000   \n",
              "1              NO         NaN               NaN           90000   \n",
              "2              NO         737          119436.0           75000   \n",
              "3              NO         713           87000.0           78500   \n",
              "4              NO         669               NaN           70000   \n",
              "5              NO         NaN               NaN          107000   \n",
              "6              NO         NaN          112562.0           99999   \n",
              "7              NO         762               NaN          102000   \n",
              "8              NO         NaN           78120.0           72500   \n",
              "9              NO         NaN          109500.0           90000   \n",
              "\n",
              "  MARITAL STATUS  GENDER  AGE  phone_digitalage  phone_nameMatchScore  \\\n",
              "0        Married    Male   44            5324.0             67.222222   \n",
              "1            NaN    Male   25            1998.0            100.000000   \n",
              "2         Single    Male   22              -1.0             -1.000000   \n",
              "3         Single    Male   24            1998.0             72.777778   \n",
              "4            NaN    Male   33            1998.0             68.095238   \n",
              "5            NaN  Female   32            1998.0             73.015873   \n",
              "6        Married    Male   30             750.0            100.000000   \n",
              "7            NaN    Male    0            1998.0            100.000000   \n",
              "8        Married    Male   24            1998.0             79.411765   \n",
              "9         Single    Male   25            1988.0             71.759259   \n",
              "\n",
              "  phone_phoneFootprintStrengthOverall Application Status  \n",
              "0                                High           APPROVED  \n",
              "1                                High           APPROVED  \n",
              "2                                 Low           APPROVED  \n",
              "3                                High           APPROVED  \n",
              "4                                High           DECLINED  \n",
              "5                                High           APPROVED  \n",
              "6                              Medium           APPROVED  \n",
              "7                                High           DECLINED  \n",
              "8                                High           APPROVED  \n",
              "9                                High           APPROVED  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-85559247-c560-49fc-b5ab-0e94d170094d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AADHAR VERIFIED</th>\n",
              "      <th>Cibil Score</th>\n",
              "      <th>TOTAL ASSET COST</th>\n",
              "      <th>APPLIED AMOUNT</th>\n",
              "      <th>MARITAL STATUS</th>\n",
              "      <th>GENDER</th>\n",
              "      <th>AGE</th>\n",
              "      <th>phone_digitalage</th>\n",
              "      <th>phone_nameMatchScore</th>\n",
              "      <th>phone_phoneFootprintStrengthOverall</th>\n",
              "      <th>Application Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NO</td>\n",
              "      <td>726</td>\n",
              "      <td>95041.0</td>\n",
              "      <td>85000</td>\n",
              "      <td>Married</td>\n",
              "      <td>Male</td>\n",
              "      <td>44</td>\n",
              "      <td>5324.0</td>\n",
              "      <td>67.222222</td>\n",
              "      <td>High</td>\n",
              "      <td>APPROVED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>90000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Male</td>\n",
              "      <td>25</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>High</td>\n",
              "      <td>APPROVED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NO</td>\n",
              "      <td>737</td>\n",
              "      <td>119436.0</td>\n",
              "      <td>75000</td>\n",
              "      <td>Single</td>\n",
              "      <td>Male</td>\n",
              "      <td>22</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>Low</td>\n",
              "      <td>APPROVED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NO</td>\n",
              "      <td>713</td>\n",
              "      <td>87000.0</td>\n",
              "      <td>78500</td>\n",
              "      <td>Single</td>\n",
              "      <td>Male</td>\n",
              "      <td>24</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>72.777778</td>\n",
              "      <td>High</td>\n",
              "      <td>APPROVED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NO</td>\n",
              "      <td>669</td>\n",
              "      <td>NaN</td>\n",
              "      <td>70000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Male</td>\n",
              "      <td>33</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>68.095238</td>\n",
              "      <td>High</td>\n",
              "      <td>DECLINED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>107000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Female</td>\n",
              "      <td>32</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>73.015873</td>\n",
              "      <td>High</td>\n",
              "      <td>APPROVED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>NO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112562.0</td>\n",
              "      <td>99999</td>\n",
              "      <td>Married</td>\n",
              "      <td>Male</td>\n",
              "      <td>30</td>\n",
              "      <td>750.0</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>Medium</td>\n",
              "      <td>APPROVED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>NO</td>\n",
              "      <td>762</td>\n",
              "      <td>NaN</td>\n",
              "      <td>102000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>High</td>\n",
              "      <td>DECLINED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>NO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>78120.0</td>\n",
              "      <td>72500</td>\n",
              "      <td>Married</td>\n",
              "      <td>Male</td>\n",
              "      <td>24</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>79.411765</td>\n",
              "      <td>High</td>\n",
              "      <td>APPROVED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>NO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>109500.0</td>\n",
              "      <td>90000</td>\n",
              "      <td>Single</td>\n",
              "      <td>Male</td>\n",
              "      <td>25</td>\n",
              "      <td>1988.0</td>\n",
              "      <td>71.759259</td>\n",
              "      <td>High</td>\n",
              "      <td>APPROVED</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85559247-c560-49fc-b5ab-0e94d170094d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-85559247-c560-49fc-b5ab-0e94d170094d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-85559247-c560-49fc-b5ab-0e94d170094d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-02e8a48a-f01d-496d-81f2-c393fd714ce0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-02e8a48a-f01d-496d-81f2-c393fd714ce0')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-02e8a48a-f01d-496d-81f2-c393fd714ce0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_df",
              "summary": "{\n  \"name\": \"train_df\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"AADHAR VERIFIED\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"NO\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cibil Score\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 267,\n        \"samples\": [\n          \"791\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TOTAL ASSET COST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16745.027040975914,\n        \"min\": 51873.0,\n        \"max\": 241116.0,\n        \"num_unique_values\": 3692,\n        \"samples\": [\n          98054.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"APPLIED AMOUNT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27000,\n        \"min\": 10400,\n        \"max\": 1420000,\n        \"num_unique_values\": 1230,\n        \"samples\": [\n          150600\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MARITAL STATUS\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Single\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GENDER\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 0,\n        \"max\": 79,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"phone_digitalage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 984.1142481344085,\n        \"min\": -1.0,\n        \"max\": 6311.0,\n        \"num_unique_values\": 1781,\n        \"samples\": [\n          2849.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"phone_nameMatchScore\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42.100616429132614,\n        \"min\": -1.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 1007,\n        \"samples\": [\n          96.66666667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"phone_phoneFootprintStrengthOverall\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Low\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Application Status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"DECLINED\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target = train_df['Application Status']\n",
        "\n",
        "train_df = train_df.drop('Application Status', axis=1)"
      ],
      "metadata": {
        "id": "NuGlqoIOiwAE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "WO9kHw8lisBc",
        "outputId": "36c61a6c-748a-457c-a710-4eb919bf072f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Application Status\n",
              "APPROVED    6677\n",
              "DECLINED    3323\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Application Status</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>APPROVED</th>\n",
              "      <td>6677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DECLINED</th>\n",
              "      <td>3323</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target = target.map({'APPROVED': 1, 'DECLINED': 0})\n"
      ],
      "metadata": {
        "id": "Nfoj4lQmjVPl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "mQWKDdvijjVS",
        "outputId": "a501e100-d393-474d-b50e-78c303507394"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Application Status\n",
              "1    6677\n",
              "0    3323\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Application Status</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3323</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_df.isnull().sum()[train_df.isnull().sum() > 0])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UPzhqLFpV6es",
        "outputId": "d2eb6f83-714f-4ab7-9826-3509b7e515d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cibil Score                            4297\n",
            "TOTAL ASSET COST                       5108\n",
            "MARITAL STATUS                         4894\n",
            "phone_digitalage                          4\n",
            "phone_nameMatchScore                      4\n",
            "phone_phoneFootprintStrengthOverall       6\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_df[['Cibil Score', 'TOTAL ASSET COST']].dtypes)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ms_IKH3faCTb",
        "outputId": "bc902890-59a4-4f27-f94c-2b8f57c2e098"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cibil Score          object\n",
            "TOTAL ASSET COST    float64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.impute import KNNImputer\n",
        "\n",
        "# Impute 'Single' for missing MARITAL_STATUS\n",
        "train_df.loc[:, 'MARITAL STATUS'] = train_df['MARITAL STATUS'].fillna('Single')\n",
        "\n",
        "# Convert columns to numeric\n",
        "train_df.loc[:, 'Cibil Score'] = pd.to_numeric(train_df['Cibil Score'], errors='coerce')\n",
        "train_df.loc[:, 'TOTAL ASSET COST'] = pd.to_numeric(train_df['TOTAL ASSET COST'], errors='coerce')\n",
        "\n",
        "# Create a new DataFrame for the columns to be imputed\n",
        "impute_df = train_df[['Cibil Score', 'TOTAL ASSET COST']].copy()\n",
        "\n",
        "# KNN imputer for Cibil Score and TOTAL ASSET COST\n",
        "imputer = KNNImputer(n_neighbors=5)\n",
        "imputed_values = imputer.fit_transform(impute_df)\n",
        "\n",
        "# Assign the imputed values back to the original DataFrame\n",
        "train_df.loc[:, ['Cibil Score', 'TOTAL ASSET COST']] = imputed_values\n"
      ],
      "metadata": {
        "id": "l7t76fSAaqF3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_df.isnull().sum()[train_df.isnull().sum() > 0])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LRdShKJybH11",
        "outputId": "06e88ae9-4d90-469f-c1ff-dc1f41b6a85c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "phone_digitalage                       4\n",
            "phone_nameMatchScore                   4\n",
            "phone_phoneFootprintStrengthOverall    6\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert columns to numeric\n",
        "train_df.loc[:, 'phone_digitalage'] = pd.to_numeric(train_df['phone_digitalage'], errors='coerce')\n",
        "train_df.loc[:, 'phone_nameMatchScore'] = pd.to_numeric(train_df['phone_nameMatchScore'], errors='coerce')\n",
        "\n",
        "# Interpolation for missing values\n",
        "train_df.loc[:, 'phone_digitalage'] = train_df['phone_digitalage'].interpolate()\n",
        "train_df.loc[:, 'phone_nameMatchScore'] = train_df['phone_nameMatchScore'].interpolate()\n",
        "train_df['phone_phoneFootprintStrengthOverall'] = train_df['phone_phoneFootprintStrengthOverall'].astype('object')\n",
        "train_df['phone_phoneFootprintStrengthOverall'] = train_df['phone_phoneFootprintStrengthOverall'].fillna('Low')"
      ],
      "metadata": {
        "id": "rvviq8U6cJJa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "48HMFxDMj7yi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_df['Cibil Score'] = train_df['Cibil Score'].astype('float64')\n"
      ],
      "metadata": {
        "id": "vLYT8oB-jzCF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "EXaYPCDUatXH",
        "outputId": "1299a740-4fc7-4380-80a5-8211b133c301"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  AADHAR VERIFIED  Cibil Score  TOTAL ASSET COST  APPLIED AMOUNT  \\\n",
              "0              NO   726.000000      95041.000000           85000   \n",
              "1              NO   706.402118      97612.518397           90000   \n",
              "2              NO   737.000000     119436.000000           75000   \n",
              "3              NO   713.000000      87000.000000           78500   \n",
              "4              NO   669.000000      93604.800000           70000   \n",
              "\n",
              "  MARITAL STATUS GENDER  AGE  phone_digitalage  phone_nameMatchScore  \\\n",
              "0        Married   Male   44            5324.0             67.222222   \n",
              "1         Single   Male   25            1998.0            100.000000   \n",
              "2         Single   Male   22              -1.0             -1.000000   \n",
              "3         Single   Male   24            1998.0             72.777778   \n",
              "4         Single   Male   33            1998.0             68.095238   \n",
              "\n",
              "  phone_phoneFootprintStrengthOverall  \n",
              "0                                High  \n",
              "1                                High  \n",
              "2                                 Low  \n",
              "3                                High  \n",
              "4                                High  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a5d7b0a3-691a-4ee7-8b52-92559af5b930\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AADHAR VERIFIED</th>\n",
              "      <th>Cibil Score</th>\n",
              "      <th>TOTAL ASSET COST</th>\n",
              "      <th>APPLIED AMOUNT</th>\n",
              "      <th>MARITAL STATUS</th>\n",
              "      <th>GENDER</th>\n",
              "      <th>AGE</th>\n",
              "      <th>phone_digitalage</th>\n",
              "      <th>phone_nameMatchScore</th>\n",
              "      <th>phone_phoneFootprintStrengthOverall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NO</td>\n",
              "      <td>726.000000</td>\n",
              "      <td>95041.000000</td>\n",
              "      <td>85000</td>\n",
              "      <td>Married</td>\n",
              "      <td>Male</td>\n",
              "      <td>44</td>\n",
              "      <td>5324.0</td>\n",
              "      <td>67.222222</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NO</td>\n",
              "      <td>706.402118</td>\n",
              "      <td>97612.518397</td>\n",
              "      <td>90000</td>\n",
              "      <td>Single</td>\n",
              "      <td>Male</td>\n",
              "      <td>25</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NO</td>\n",
              "      <td>737.000000</td>\n",
              "      <td>119436.000000</td>\n",
              "      <td>75000</td>\n",
              "      <td>Single</td>\n",
              "      <td>Male</td>\n",
              "      <td>22</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NO</td>\n",
              "      <td>713.000000</td>\n",
              "      <td>87000.000000</td>\n",
              "      <td>78500</td>\n",
              "      <td>Single</td>\n",
              "      <td>Male</td>\n",
              "      <td>24</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>72.777778</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NO</td>\n",
              "      <td>669.000000</td>\n",
              "      <td>93604.800000</td>\n",
              "      <td>70000</td>\n",
              "      <td>Single</td>\n",
              "      <td>Male</td>\n",
              "      <td>33</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>68.095238</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a5d7b0a3-691a-4ee7-8b52-92559af5b930')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a5d7b0a3-691a-4ee7-8b52-92559af5b930 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a5d7b0a3-691a-4ee7-8b52-92559af5b930');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2ddc1e77-f1e5-4de7-93f0-745e030c940e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2ddc1e77-f1e5-4de7-93f0-745e030c940e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2ddc1e77-f1e5-4de7-93f0-745e030c940e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_df",
              "summary": "{\n  \"name\": \"train_df\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"AADHAR VERIFIED\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"NO\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cibil Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 48.98195005904035,\n        \"min\": 521.0,\n        \"max\": 826.0,\n        \"num_unique_values\": 532,\n        \"samples\": [\n          762.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TOTAL ASSET COST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12562.871975691769,\n        \"min\": 51873.0,\n        \"max\": 241116.0,\n        \"num_unique_values\": 3895,\n        \"samples\": [\n          106750.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"APPLIED AMOUNT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27000,\n        \"min\": 10400,\n        \"max\": 1420000,\n        \"num_unique_values\": 1230,\n        \"samples\": [\n          150600\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MARITAL STATUS\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Single\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GENDER\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 0,\n        \"max\": 79,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"phone_digitalage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 984.044087916326,\n        \"min\": -1.0,\n        \"max\": 6311.0,\n        \"num_unique_values\": 1784,\n        \"samples\": [\n          381.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"phone_nameMatchScore\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42.10384190492454,\n        \"min\": -1.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 1009,\n        \"samples\": [\n          38.86639676\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"phone_phoneFootprintStrengthOverall\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Low\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_df.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m7VYeXCWcvoa",
        "outputId": "a58992d0-3608-44a5-b970-38996229b2be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AADHAR VERIFIED                        0\n",
            "Cibil Score                            0\n",
            "TOTAL ASSET COST                       0\n",
            "APPLIED AMOUNT                         0\n",
            "MARITAL STATUS                         0\n",
            "GENDER                                 0\n",
            "AGE                                    0\n",
            "phone_digitalage                       0\n",
            "phone_nameMatchScore                   0\n",
            "phone_phoneFootprintStrengthOverall    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQdUYvBog3Au",
        "outputId": "4ca89cb4-a8a3-49b2-8f60-e0ace2749778"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 10 columns):\n",
            " #   Column                               Non-Null Count  Dtype  \n",
            "---  ------                               --------------  -----  \n",
            " 0   AADHAR VERIFIED                      10000 non-null  object \n",
            " 1   Cibil Score                          10000 non-null  float64\n",
            " 2   TOTAL ASSET COST                     10000 non-null  float64\n",
            " 3   APPLIED AMOUNT                       10000 non-null  int64  \n",
            " 4   MARITAL STATUS                       10000 non-null  object \n",
            " 5   GENDER                               10000 non-null  object \n",
            " 6   AGE                                  10000 non-null  int64  \n",
            " 7   phone_digitalage                     10000 non-null  float64\n",
            " 8   phone_nameMatchScore                 10000 non-null  float64\n",
            " 9   phone_phoneFootprintStrengthOverall  10000 non-null  object \n",
            "dtypes: float64(4), int64(2), object(4)\n",
            "memory usage: 781.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Select columns with object data type\n",
        "pd.set_option('display.max_columns', None)\n",
        "object_columns = train_df.select_dtypes(include='object')"
      ],
      "metadata": {
        "id": "Pra9cEaNRlB4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "object_columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "KXu7Tbc-R_xZ",
        "outputId": "7b522f15-4f5c-41b1-b903-8c30e9ad2701"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     AADHAR VERIFIED MARITAL STATUS GENDER phone_phoneFootprintStrengthOverall\n",
              "0                 NO        Married   Male                                High\n",
              "1                 NO         Single   Male                                High\n",
              "2                 NO         Single   Male                                 Low\n",
              "3                 NO         Single   Male                                High\n",
              "4                 NO         Single   Male                                High\n",
              "...              ...            ...    ...                                 ...\n",
              "9995              NO        Married   Male                              Medium\n",
              "9996              NO        Married   Male                                High\n",
              "9997              NO        Married   Male                              Medium\n",
              "9998              NO        Married   Male                                 Low\n",
              "9999              NO        Married   Male                                High\n",
              "\n",
              "[10000 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9219c216-8892-40eb-9a4a-861d24faa86e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AADHAR VERIFIED</th>\n",
              "      <th>MARITAL STATUS</th>\n",
              "      <th>GENDER</th>\n",
              "      <th>phone_phoneFootprintStrengthOverall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NO</td>\n",
              "      <td>Married</td>\n",
              "      <td>Male</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NO</td>\n",
              "      <td>Single</td>\n",
              "      <td>Male</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NO</td>\n",
              "      <td>Single</td>\n",
              "      <td>Male</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NO</td>\n",
              "      <td>Single</td>\n",
              "      <td>Male</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NO</td>\n",
              "      <td>Single</td>\n",
              "      <td>Male</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>NO</td>\n",
              "      <td>Married</td>\n",
              "      <td>Male</td>\n",
              "      <td>Medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>NO</td>\n",
              "      <td>Married</td>\n",
              "      <td>Male</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>NO</td>\n",
              "      <td>Married</td>\n",
              "      <td>Male</td>\n",
              "      <td>Medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>NO</td>\n",
              "      <td>Married</td>\n",
              "      <td>Male</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>NO</td>\n",
              "      <td>Married</td>\n",
              "      <td>Male</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9219c216-8892-40eb-9a4a-861d24faa86e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9219c216-8892-40eb-9a4a-861d24faa86e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9219c216-8892-40eb-9a4a-861d24faa86e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4c27057d-59d0-45b9-a195-9ab7059636e9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4c27057d-59d0-45b9-a195-9ab7059636e9')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4c27057d-59d0-45b9-a195-9ab7059636e9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_b822bdfe-0aba-4132-a298-06ad19a65d1b\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('object_columns')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_b822bdfe-0aba-4132-a298-06ad19a65d1b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('object_columns');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "object_columns",
              "summary": "{\n  \"name\": \"object_columns\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"AADHAR VERIFIED\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"NO\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MARITAL STATUS\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Single\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GENDER\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"phone_phoneFootprintStrengthOverall\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Low\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "object_columns.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wSAvxH12TNl2",
        "outputId": "d652609e-05f0-4c7e-ca06-d8a33e84a721"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 4 columns):\n",
            " #   Column                               Non-Null Count  Dtype \n",
            "---  ------                               --------------  ----- \n",
            " 0   AADHAR VERIFIED                      10000 non-null  object\n",
            " 1   MARITAL STATUS                       10000 non-null  object\n",
            " 2   GENDER                               10000 non-null  object\n",
            " 3   phone_phoneFootprintStrengthOverall  10000 non-null  object\n",
            "dtypes: object(4)\n",
            "memory usage: 312.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use value_counts() on each column\n",
        "for col in object_columns.columns:\n",
        "  print(f\"\\nUnique Value Counts for Column '{col}':\")\n",
        "  print(object_columns[col].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T4OPrnpnThfq",
        "outputId": "64bd1dee-3703-44fe-8ef9-ae219ef3f432"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Unique Value Counts for Column 'AADHAR VERIFIED':\n",
            "AADHAR VERIFIED\n",
            "NO    10000\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Unique Value Counts for Column 'MARITAL STATUS':\n",
            "MARITAL STATUS\n",
            "Single     6167\n",
            "Married    3833\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Unique Value Counts for Column 'GENDER':\n",
            "GENDER\n",
            "Male      8408\n",
            "Female    1592\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Unique Value Counts for Column 'phone_phoneFootprintStrengthOverall':\n",
            "phone_phoneFootprintStrengthOverall\n",
            "Medium       4599\n",
            "High         4141\n",
            "Low          1185\n",
            "Very High      44\n",
            "Very Low       31\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Select columns with object data type\n",
        "pd.set_option('display.max_columns', None)\n",
        "num_columns = train_df.select_dtypes(include='float64')"
      ],
      "metadata": {
        "id": "NEHS9majSwld"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_columns.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6qZ2JKxZS2-f",
        "outputId": "dc7bc12f-6eae-4d60-a324-a9973c2d474e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 4 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   Cibil Score           10000 non-null  float64\n",
            " 1   TOTAL ASSET COST      10000 non-null  float64\n",
            " 2   phone_digitalage      10000 non-null  float64\n",
            " 3   phone_nameMatchScore  10000 non-null  float64\n",
            "dtypes: float64(4)\n",
            "memory usage: 312.6 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JywNvhiEkkBn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "\n",
        "# Initialize LabelEncoder\n",
        "le = LabelEncoder()\n",
        "\n",
        "# Label encode object columns\n",
        "encoded_object_df = object_columns.copy()\n",
        "for col in object_columns.columns:\n",
        "    encoded_object_df[col] = le.fit_transform(object_columns[col])\n",
        "\n",
        "# Initialize StandardScaler\n",
        "scaler = StandardScaler()\n",
        "\n",
        "# Scale numerical columns\n",
        "scaled_numerical_df = pd.DataFrame(scaler.fit_transform(num_columns), columns=num_columns.columns)\n",
        "\n",
        "# Combine encoded object columns with scaled numerical columns\n",
        "final_df = pd.concat([encoded_object_df, scaled_numerical_df], axis=1)\n",
        "\n",
        "# Display the updated DataFrame\n",
        "print(final_df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FK34NYDRS5yA",
        "outputId": "e62c49ab-f5c4-4131-a533-505e34fec5f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   AADHAR VERIFIED  MARITAL STATUS  GENDER  \\\n",
            "0                0               0       1   \n",
            "1                0               1       1   \n",
            "2                0               1       1   \n",
            "3                0               1       1   \n",
            "4                0               1       1   \n",
            "\n",
            "   phone_phoneFootprintStrengthOverall  Cibil Score  TOTAL ASSET COST  \\\n",
            "0                                    0     0.237096         -0.177271   \n",
            "1                                    0    -0.163028          0.027431   \n",
            "2                                    1     0.461680          1.764659   \n",
            "3                                    0    -0.028321         -0.817364   \n",
            "4                                    0    -0.926656         -0.291598   \n",
            "\n",
            "   phone_digitalage  phone_nameMatchScore  \n",
            "0          3.730830              0.275426  \n",
            "1          0.350731              1.053963  \n",
            "2         -1.680783             -1.344988  \n",
            "3          0.350731              0.407381  \n",
            "4          0.350731              0.296161  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "o106E4NRkbRn",
        "outputId": "b3d1ef55-aec9-4636-dcd1-96f86bc86acb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   AADHAR VERIFIED  MARITAL STATUS  GENDER  \\\n",
              "0                0               0       1   \n",
              "1                0               1       1   \n",
              "2                0               1       1   \n",
              "3                0               1       1   \n",
              "4                0               1       1   \n",
              "\n",
              "   phone_phoneFootprintStrengthOverall  Cibil Score  TOTAL ASSET COST  \\\n",
              "0                                    0     0.237096         -0.177271   \n",
              "1                                    0    -0.163028          0.027431   \n",
              "2                                    1     0.461680          1.764659   \n",
              "3                                    0    -0.028321         -0.817364   \n",
              "4                                    0    -0.926656         -0.291598   \n",
              "\n",
              "   phone_digitalage  phone_nameMatchScore  \n",
              "0          3.730830              0.275426  \n",
              "1          0.350731              1.053963  \n",
              "2         -1.680783             -1.344988  \n",
              "3          0.350731              0.407381  \n",
              "4          0.350731              0.296161  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-75087163-9d8d-4b35-8426-0c83e91910bc\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AADHAR VERIFIED</th>\n",
              "      <th>MARITAL STATUS</th>\n",
              "      <th>GENDER</th>\n",
              "      <th>phone_phoneFootprintStrengthOverall</th>\n",
              "      <th>Cibil Score</th>\n",
              "      <th>TOTAL ASSET COST</th>\n",
              "      <th>phone_digitalage</th>\n",
              "      <th>phone_nameMatchScore</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.237096</td>\n",
              "      <td>-0.177271</td>\n",
              "      <td>3.730830</td>\n",
              "      <td>0.275426</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.163028</td>\n",
              "      <td>0.027431</td>\n",
              "      <td>0.350731</td>\n",
              "      <td>1.053963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.461680</td>\n",
              "      <td>1.764659</td>\n",
              "      <td>-1.680783</td>\n",
              "      <td>-1.344988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.028321</td>\n",
              "      <td>-0.817364</td>\n",
              "      <td>0.350731</td>\n",
              "      <td>0.407381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.926656</td>\n",
              "      <td>-0.291598</td>\n",
              "      <td>0.350731</td>\n",
              "      <td>0.296161</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-75087163-9d8d-4b35-8426-0c83e91910bc')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-75087163-9d8d-4b35-8426-0c83e91910bc button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-75087163-9d8d-4b35-8426-0c83e91910bc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4c392e9d-a02d-415f-90e3-44314acda37b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4c392e9d-a02d-415f-90e3-44314acda37b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4c392e9d-a02d-415f-90e3-44314acda37b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "final_df",
              "summary": "{\n  \"name\": \"final_df\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"AADHAR VERIFIED\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MARITAL STATUS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GENDER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"phone_phoneFootprintStrengthOverall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cibil Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000500037503124,\n        \"min\": -3.94832833944004,\n        \"max\": 2.2787665552343346,\n        \"num_unique_values\": 532,\n        \"samples\": [\n          0.9720974625813509\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TOTAL ASSET COST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000500037503124,\n        \"min\": -3.6135999462623554,\n        \"max\": 11.450826661436103,\n        \"num_unique_values\": 3895,\n        \"samples\": [\n          0.7548075454317541\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"phone_digitalage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000500037503124,\n        \"min\": -1.6807832805665033,\n        \"max\": 4.733884213689826,\n        \"num_unique_values\": 1784,\n        \"samples\": [\n          -1.292569880248709\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"phone_nameMatchScore\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000500037503124,\n        \"min\": -1.3449879598536498,\n        \"max\": 1.0539629626503564,\n        \"num_unique_values\": 1009,\n        \"samples\": [\n          -0.3980817293159001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_df.shape, target.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KiykShWIl__C",
        "outputId": "cf569572-c673-4cff-bbc0-c2feac5c28da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((10000, 8), (10000,))"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# for data processing and manipulation\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# scikit-learn modules for feature selection and model evaluation\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.feature_selection import RFE, SelectKBest, SelectFromModel, chi2, f_classif\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, roc_auc_score, precision_score, recall_score, f1_score\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.feature_selection import SelectFromModel\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "\n",
        "# libraries for visualization\n",
        "import seaborn as sns\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "HiqrRWROm5nI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X, Y = final_df, target"
      ],
      "metadata": {
        "id": "Yh7d2CUMmI8p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LWmJfwJPm4hp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def fit_model(X, Y):\n",
        "    '''Use a RandomForestClassifier for this problem.'''\n",
        "\n",
        "    # define the model to use\n",
        "    model = RandomForestClassifier(criterion='entropy', random_state=47)\n",
        "\n",
        "    # Train the model\n",
        "    model.fit(X, Y)\n",
        "\n",
        "    return model"
      ],
      "metadata": {
        "id": "U2B9AtlFml-O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_metrics(model, X_test_scaled, Y_test):\n",
        "    '''Get model evaluation metrics on the test set.'''\n",
        "\n",
        "    # Get model predictions\n",
        "    y_predict_r = model.predict(X_test_scaled)\n",
        "\n",
        "    # Calculate evaluation metrics for assesing performance of the model.\n",
        "    acc = accuracy_score(Y_test, y_predict_r)\n",
        "    roc = roc_auc_score(Y_test, y_predict_r)\n",
        "    prec = precision_score(Y_test, y_predict_r)\n",
        "    rec = recall_score(Y_test, y_predict_r)\n",
        "    f1 = f1_score(Y_test, y_predict_r)\n",
        "\n",
        "    return acc, roc, prec, rec, f1"
      ],
      "metadata": {
        "id": "oUpVaoU7mpCW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_and_get_metrics(X, Y):\n",
        "    '''Train a Random Forest Classifier and get evaluation metrics'''\n",
        "\n",
        "    # Split train and test sets\n",
        "    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2,stratify=Y, random_state = 123)\n",
        "\n",
        "    # All features of dataset are float values. You normalize all features of the train and test dataset here.\n",
        "    scaler = StandardScaler().fit(X_train)\n",
        "    X_train_scaled = scaler.transform(X_train)\n",
        "    X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "    # Call the fit model function to train the model on the normalized features and the diagnosis values\n",
        "    model = fit_model(X_train_scaled, Y_train)\n",
        "\n",
        "    # Make predictions on test dataset and calculate metrics.\n",
        "    acc, roc, prec, rec, f1 = calculate_metrics(model, X_test_scaled, Y_test)\n",
        "\n",
        "    return acc, roc, prec, rec, f1"
      ],
      "metadata": {
        "id": "JGkUxHJ5mqtF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_model_on_features(X, Y):\n",
        "    '''Train model and display evaluation metrics.'''\n",
        "\n",
        "    # Train the model, predict values and get metrics\n",
        "    acc, roc, prec, rec, f1 = train_and_get_metrics(X, Y)\n",
        "\n",
        "    # Construct a dataframe to display metrics.\n",
        "    display_df = pd.DataFrame([[acc, roc, prec, rec, f1, X.shape[1]]], columns=[\"Accuracy\", \"ROC\", \"Precision\", \"Recall\", \"F1 Score\", 'Feature Count'])\n",
        "\n",
        "    return display_df"
      ],
      "metadata": {
        "id": "TYBLrKdomr3d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate evaluation metrics\n",
        "all_features_eval_df = evaluate_model_on_features(X, Y)\n",
        "all_features_eval_df.index = ['All features']\n",
        "\n",
        "# Initialize results dataframe\n",
        "results = all_features_eval_df\n",
        "\n",
        "# Check the metrics\n",
        "results.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "DB2F6tJfmtcW",
        "outputId": "23bf13de-d4b7-4d00-a9c1-3d1ac30676e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Accuracy       ROC  Precision    Recall  F1 Score  Feature Count\n",
              "All features    0.8485  0.817462   0.869099  0.910112  0.889133              8"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-44c91fcb-646f-484a-b455-b15fd9f33619\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>ROC</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1 Score</th>\n",
              "      <th>Feature Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>All features</th>\n",
              "      <td>0.8485</td>\n",
              "      <td>0.817462</td>\n",
              "      <td>0.869099</td>\n",
              "      <td>0.910112</td>\n",
              "      <td>0.889133</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44c91fcb-646f-484a-b455-b15fd9f33619')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-44c91fcb-646f-484a-b455-b15fd9f33619 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-44c91fcb-646f-484a-b455-b15fd9f33619');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results",
              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.8485,\n        \"max\": 0.8485,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.8485\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.8174621948128749,\n        \"max\": 0.8174621948128749,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.8174621948128749\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.869098712446352,\n        \"max\": 0.869098712446352,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.869098712446352\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.9101123595505618,\n        \"max\": 0.9101123595505618,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.9101123595505618\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1 Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.8891328210757409,\n        \"max\": 0.8891328210757409,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.8891328210757409\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Feature Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 8,\n        \"max\": 8,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Set the figure size\n",
        "plt.figure(figsize=(10, 8))  # Adjust size for better visibility\n",
        "\n",
        "# Calculate the correlation matrix\n",
        "cor = X.corr()\n",
        "\n",
        "# Create the heatmap with minimalistic design\n",
        "sns.heatmap(\n",
        "    cor,\n",
        "    annot=True,  # Show correlation values on the heatmap\n",
        "    cmap='coolwarm',  # Simple color map for the heatmap\n",
        "    fmt='.2f',  # Format for the annotation text\n",
        "    linewidths=0.3,  # Minimal width of the lines separating the cells\n",
        "    linecolor='lightgrey',  # Light color for the lines separating the cells\n",
        "    cbar_kws={\n",
        "        'shrink': .8,  # Shrink color bar for better fit\n",
        "        'label': 'Correlation'  # Label for the color bar\n",
        "    },\n",
        "    annot_kws={\"size\": 8, \"color\": \"black\"}  # Smaller, minimal annotation text\n",
        ")\n",
        "\n",
        "# Add plot title and axis labels\n",
        "plt.title('Feature Correlation Matrix', size=16, weight='bold')  # Title of the heatmap\n",
        "plt.xlabel('Features', size=12)  # X-axis label\n",
        "plt.ylabel('Features', size=12)  # Y-axis label\n",
        "\n",
        "# Remove unnecessary grid lines\n",
        "plt.grid(False)\n",
        "\n",
        "# Display the plot\n",
        "plt.tight_layout()  # Adjust layout to prevent clipping of labels\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "cZwo4TJ0mvLH",
        "outputId": "87f851b2-0ebe-470c-f7ba-cd4ef6e3c676"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the absolute value of the correlation\n",
        "cor_target = abs(X.corrwith(Y))\n",
        "\n",
        "# Select highly correlated features (threshold = 0.02)\n",
        "highly_correlated_features = cor_target[cor_target > 0.02]\n",
        "\n",
        "# Collect the names of the features\n",
        "names = highly_correlated_features.index.tolist()\n",
        "\n",
        "# Display the results\n",
        "print(\"Highly correlated features:\")\n",
        "print(names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mo9gUw5WnBjX",
        "outputId": "c7a484cb-05f0-413a-d5b0-de26e7110299"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Highly correlated features:\n",
            "['MARITAL STATUS', 'GENDER', 'Cibil Score', 'TOTAL ASSET COST', 'phone_digitalage']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model with new features\n",
        "strong_features_eval_df = evaluate_model_on_features(X[names], Y)\n",
        "strong_features_eval_df.index = ['Strong features']\n",
        "\n",
        "# Append to results and display\n",
        "results = pd.concat([results, strong_features_eval_df], ignore_index=True)\n",
        "results.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "xgJZ7BGIn1uJ",
        "outputId": "c3033d65-f0e7-4e24-91a0-35551b5596db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Accuracy       ROC  Precision    Recall  F1 Score  Feature Count\n",
              "0    0.8485  0.817462   0.869099  0.910112  0.889133              8\n",
              "1    0.8505  0.833300   0.890649  0.884644  0.887636              5"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1e4759fe-e6e0-41c0-8cfb-09e71b8194f4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>ROC</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1 Score</th>\n",
              "      <th>Feature Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.8485</td>\n",
              "      <td>0.817462</td>\n",
              "      <td>0.869099</td>\n",
              "      <td>0.910112</td>\n",
              "      <td>0.889133</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.8505</td>\n",
              "      <td>0.833300</td>\n",
              "      <td>0.890649</td>\n",
              "      <td>0.884644</td>\n",
              "      <td>0.887636</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e4759fe-e6e0-41c0-8cfb-09e71b8194f4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1e4759fe-e6e0-41c0-8cfb-09e71b8194f4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1e4759fe-e6e0-41c0-8cfb-09e71b8194f4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1f0a33d6-7625-4a77-8850-792b8bd3a9b8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1f0a33d6-7625-4a77-8850-792b8bd3a9b8')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1f0a33d6-7625-4a77-8850-792b8bd3a9b8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results",
              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0014142135623730963,\n        \"min\": 0.8485,\n        \"max\": 0.8505,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8505,\n          0.8485\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.011198694875934589,\n        \"min\": 0.8174621948128749,\n        \"max\": 0.8332995409872996,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8332995409872996,\n          0.8174621948128749\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.015238048372723934,\n        \"min\": 0.869098712446352,\n        \"max\": 0.8906485671191554,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8906485671191554,\n          0.869098712446352\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.018008712030219184,\n        \"min\": 0.8846441947565543,\n        \"max\": 0.9101123595505618,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8846441947565543,\n          0.9101123595505618\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1 Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0010582518321292087,\n        \"min\": 0.8876362269823376,\n        \"max\": 0.8891328210757409,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8876362269823376,\n          0.8891328210757409\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Feature Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 5,\n        \"max\": 8,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def univariate_selection():\n",
        "    \"\"\"\n",
        "    Performs univariate feature selection using the SelectKBest method with the f-classif scoring function.\n",
        "\n",
        "    Args:\n",
        "        X: The feature matrix.\n",
        "        Y: The target variable.\n",
        "\n",
        "    Returns:\n",
        "        A list of feature names selected by the univariate selection process.\n",
        "    \"\"\"\n",
        "\n",
        "    # Split train and test sets\n",
        "    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=123)\n",
        "\n",
        "    # Normalize all features of the train and test datasets\n",
        "    scaler = StandardScaler().fit(X_train)\n",
        "    X_train_scaled = scaler.transform(X_train)\n",
        "    X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "    # Select top 5 features based on f-test\n",
        "    selector = SelectKBest(f_classif, k=5)\n",
        "\n",
        "    # Fit to scaled data, then transform it\n",
        "    X_new = selector.fit_transform(X_train_scaled, Y_train)\n",
        "\n",
        "    # Get selected feature indices\n",
        "    feature_idx = selector.get_support()\n",
        "\n",
        "    # Print the results\n",
        "    for name, included in zip(X.columns, feature_idx):\n",
        "        print(\"%s: %s\" % (name, included))\n",
        "\n",
        "    # Extract selected feature names\n",
        "    feature_names = X.columns[feature_idx]\n",
        "\n",
        "    return feature_names"
      ],
      "metadata": {
        "id": "Uye7jn8zqVYH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "univariate_feature_names = univariate_selection()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4XpLJkzFqybk",
        "outputId": "02dee371-ec40-4d98-b39d-17ba5d7626f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AADHAR VERIFIED: False\n",
            "MARITAL STATUS: True\n",
            "GENDER: True\n",
            "phone_phoneFootprintStrengthOverall: False\n",
            "Cibil Score: True\n",
            "TOTAL ASSET COST: True\n",
            "phone_digitalage: True\n",
            "phone_nameMatchScore: False\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [0] are constant.\n",
            "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
            "  f = msb / msw\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate and check model metrics\n",
        "univariate_eval_df = evaluate_model_on_features(X, Y)\n",
        "univariate_eval_df.index = ['F-test']\n",
        "\n",
        "# Append to results and display\n",
        "results = pd.concat([results, univariate_eval_df], ignore_index=True)\n",
        "results.head(n=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "HGxhopUSq3Dm",
        "outputId": "7ec64d1e-ce8b-49bc-bfa8-57d6aae9f7a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Accuracy       ROC  Precision    Recall  F1 Score  Feature Count\n",
              "0    0.8485  0.817462   0.869099  0.910112  0.889133              8\n",
              "1    0.8505  0.833300   0.890649  0.884644  0.887636              5\n",
              "2    0.8485  0.817462   0.869099  0.910112  0.889133              8"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bd33a256-c5b8-47b7-99ac-12de0e2472ca\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>ROC</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1 Score</th>\n",
              "      <th>Feature Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.8485</td>\n",
              "      <td>0.817462</td>\n",
              "      <td>0.869099</td>\n",
              "      <td>0.910112</td>\n",
              "      <td>0.889133</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.8505</td>\n",
              "      <td>0.833300</td>\n",
              "      <td>0.890649</td>\n",
              "      <td>0.884644</td>\n",
              "      <td>0.887636</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.8485</td>\n",
              "      <td>0.817462</td>\n",
              "      <td>0.869099</td>\n",
              "      <td>0.910112</td>\n",
              "      <td>0.889133</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bd33a256-c5b8-47b7-99ac-12de0e2472ca')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bd33a256-c5b8-47b7-99ac-12de0e2472ca button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bd33a256-c5b8-47b7-99ac-12de0e2472ca');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-43bcff63-b799-4e62-82f5-f6939b8e05e6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-43bcff63-b799-4e62-82f5-f6939b8e05e6')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-43bcff63-b799-4e62-82f5-f6939b8e05e6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results",
              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0011547005383792527,\n        \"min\": 0.8485,\n        \"max\": 0.8505,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8505,\n          0.8485\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.009143696077053438,\n        \"min\": 0.8174621948128749,\n        \"max\": 0.8332995409872996,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8332995409872996,\n          0.8174621948128749\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.012441814396340392,\n        \"min\": 0.869098712446352,\n        \"max\": 0.8906485671191554,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8906485671191554,\n          0.869098712446352\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.014704051799585971,\n        \"min\": 0.8846441947565543,\n        \"max\": 0.9101123595505618,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8846441947565543,\n          0.9101123595505618\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1 Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0008640590026940008,\n        \"min\": 0.8876362269823376,\n        \"max\": 0.8891328210757409,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8876362269823376,\n          0.8891328210757409\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Feature Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 5,\n        \"max\": 8,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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.ensemble import RandomForestClassifier\n",
        "from sklearn.feature_selection import SelectFromModel\n",
        "\n",
        "def feature_importances_from_tree_based_model(X, Y, test_size=0.2, random_state=123):\n",
        "    \"\"\"\n",
        "    Calculates feature importances from a Random Forest Classifier model.\n",
        "\n",
        "    Args:\n",
        "        X (pandas.DataFrame): The input features.\n",
        "        Y (pandas.Series): The target variable.\n",
        "        test_size (float, optional): The proportion of data to use for the test set. Defaults to 0.2.\n",
        "        random_state (int, optional): Seed for random number generation. Defaults to 123.\n",
        "\n",
        "    Returns:\n",
        "        sklearn.ensemble.RandomForestClassifier: The fitted Random Forest Classifier model.\n",
        "        pandas.Index: The feature names.\n",
        "    \"\"\"\n",
        "\n",
        "    # Split train and test set\n",
        "    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size, stratify=Y, random_state=random_state)\n",
        "\n",
        "    # Standardize features (assuming numerical features)\n",
        "    scaler = StandardScaler().fit(X_train)\n",
        "    X_train_scaled = scaler.transform(X_train)\n",
        "    X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "    # Define and fit the Random Forest Classifier model\n",
        "    model = RandomForestClassifier(random_state=random_state)\n",
        "    model.fit(X_train_scaled, Y_train)\n",
        "\n",
        "    # Plot feature importance\n",
        "    plt.figure(figsize=(12, 8))  # Increase figure size for better readability\n",
        "    feat_importances = pd.Series(model.feature_importances_, index=X.columns).sort_values(ascending=False)\n",
        "    feat_importances.plot(kind='barh', color='teal')  # Use a modern color for bars\n",
        "\n",
        "    plt.xlabel(\"Feature Importance\")\n",
        "    plt.ylabel(\"Feature\")\n",
        "    plt.title(\"Feature Importances from Random Forest Classifier\", fontsize=16, weight='bold')\n",
        "    plt.grid(axis='x', linestyle='--', alpha=0.6)  # Add grid lines for better visualization\n",
        "\n",
        "    # Annotate bars with importance values\n",
        "    for index, value in enumerate(feat_importances):\n",
        "        plt.text(value + 0.01, index, f\"{value:.3f}\", va='center', fontsize=10, color='black', weight='bold')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    return model, X.columns\n",
        "\n",
        "def select_features_from_model(model, feature_names, threshold=0.013):\n",
        "    \"\"\"\n",
        "    Selects features based on importance from a fitted model.\n",
        "\n",
        "    Args:\n",
        "        model (sklearn.base.Transformer): The fitted model from which to select features.\n",
        "        feature_names (pandas.Index): The names of the features.\n",
        "        threshold (float, optional): The minimum importance threshold for feature selection. Defaults to 0.013.\n",
        "\n",
        "    Returns:\n",
        "        list: A list of selected feature names.\n",
        "    \"\"\"\n",
        "\n",
        "    selector = SelectFromModel(model, prefit=True, threshold=threshold)\n",
        "    feature_idx = selector.get_support()\n",
        "    feature_names_selected = feature_names[feature_idx]\n",
        "\n",
        "    return feature_names_selected\n",
        "\n",
        "# Assuming you have your data in X and Y variables\n",
        "model, feature_names = feature_importances_from_tree_based_model(X, Y)\n",
        "selected_feature_names = select_features_from_model(model, feature_names)\n",
        "\n",
        "print(\"Selected features based on importance threshold (0.013):\")\n",
        "print(selected_feature_names)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 876
        },
        "id": "3RU-XB3arewd",
        "outputId": "a5cef3fa-f3b0-4d65-e764-6220c911f354"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected features based on importance threshold (0.013):\n",
            "Index(['MARITAL STATUS', 'phone_phoneFootprintStrengthOverall', 'Cibil Score',\n",
            "       'TOTAL ASSET COST', 'phone_digitalage', 'phone_nameMatchScore'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def run_rfe():\n",
        "\n",
        "    # Split train and test sets\n",
        "    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2,stratify=Y, random_state = 123)\n",
        "\n",
        "    # All features of dataset are float values. You normalize all features of the train and test dataset here.\n",
        "    scaler = StandardScaler().fit(X_train)\n",
        "    X_train_scaled = scaler.transform(X_train)\n",
        "    X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "    # Define the model\n",
        "    model = RandomForestClassifier(criterion='entropy', random_state=47)\n",
        "\n",
        "    # Wrap RFE around the model\n",
        "    rfe = RFE(model, n_features_to_select=20)\n",
        "\n",
        "    # Fit RFE\n",
        "    rfe = rfe.fit(X_train_scaled, Y_train)\n",
        "    feature_names = X.columns[rfe.get_support()]\n",
        "\n",
        "    return feature_names\n",
        "\n",
        "rfe_feature_names = run_rfe()"
      ],
      "metadata": {
        "id": "kXv5hVOJwChl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate and check model metrics\n",
        "rfe_eval_df = evaluate_model_on_features(X[rfe_feature_names], Y)\n",
        "rfe_eval_df.index = ['RFE']\n",
        "\n",
        "# Append to results and display\n",
        "results = pd.concat([results, rfe_eval_df], ignore_index=True)\n",
        "results.head(n=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "kPDEEzONsbPn",
        "outputId": "d2a508df-90e4-4d6e-d9cb-123676ec7d4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Accuracy       ROC  Precision    Recall  F1 Score  Feature Count\n",
              "0    0.8485  0.817462   0.869099  0.910112  0.889133              8\n",
              "1    0.8505  0.833300   0.890649  0.884644  0.887636              5\n",
              "2    0.8485  0.817462   0.869099  0.910112  0.889133              8\n",
              "3    0.8485  0.817462   0.869099  0.910112  0.889133              8\n",
              "4    0.8485  0.817462   0.869099  0.910112  0.889133              8\n",
              "5    0.8485  0.817462   0.869099  0.910112  0.889133              8\n",
              "6    0.8485  0.817462   0.869099  0.910112  0.889133              8"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ad518a45-3aab-45b7-be0d-33602aa7fa2a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>ROC</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1 Score</th>\n",
              "      <th>Feature Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.8485</td>\n",
              "      <td>0.817462</td>\n",
              "      <td>0.869099</td>\n",
              "      <td>0.910112</td>\n",
              "      <td>0.889133</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.8505</td>\n",
              "      <td>0.833300</td>\n",
              "      <td>0.890649</td>\n",
              "      <td>0.884644</td>\n",
              "      <td>0.887636</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.8485</td>\n",
              "      <td>0.817462</td>\n",
              "      <td>0.869099</td>\n",
              "      <td>0.910112</td>\n",
              "      <td>0.889133</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.8485</td>\n",
              "      <td>0.817462</td>\n",
              "      <td>0.869099</td>\n",
              "      <td>0.910112</td>\n",
              "      <td>0.889133</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.8485</td>\n",
              "      <td>0.817462</td>\n",
              "      <td>0.869099</td>\n",
              "      <td>0.910112</td>\n",
              "      <td>0.889133</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.8485</td>\n",
              "      <td>0.817462</td>\n",
              "      <td>0.869099</td>\n",
              "      <td>0.910112</td>\n",
              "      <td>0.889133</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.8485</td>\n",
              "      <td>0.817462</td>\n",
              "      <td>0.869099</td>\n",
              "      <td>0.910112</td>\n",
              "      <td>0.889133</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ad518a45-3aab-45b7-be0d-33602aa7fa2a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ad518a45-3aab-45b7-be0d-33602aa7fa2a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ad518a45-3aab-45b7-be0d-33602aa7fa2a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-08c96a2f-f948-46ab-92f9-8d32076861fb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-08c96a2f-f948-46ab-92f9-8d32076861fb')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-08c96a2f-f948-46ab-92f9-8d32076861fb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results",
              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0007559289460184551,\n        \"min\": 0.8485,\n        \"max\": 0.8505,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8505,\n          0.8485\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0059859542006811635,\n        \"min\": 0.8174621948128749,\n        \"max\": 0.8332995409872996,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8332995409872996,\n          0.8174621948128749\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.008145079464831591,\n        \"min\": 0.869098712446352,\n        \"max\": 0.8906485671191554,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8906485671191554,\n          0.869098712446352\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.009626061484879194,\n        \"min\": 0.8846441947565543,\n        \"max\": 0.9101123595505618,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8846441947565543,\n          0.9101123595505618\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1 Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0005656593978219014,\n        \"min\": 0.8876362269823376,\n        \"max\": 0.8891328210757409,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8876362269823376,\n          0.8891328210757409\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Feature Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 5,\n        \"max\": 8,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate and check model metrics\n",
        "feat_imp_eval_df = evaluate_model_on_features(X[feature_imp_feature_names], Y)\n",
        "feat_imp_eval_df.index = ['Feature Importance']\n",
        "\n",
        "# Append to results and display\n",
        "results = pd.concat([results, rfe_eval_df], ignore_index=True)\n",
        "results.head(n=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5-IWsKoCtEv_",
        "outputId": "9b41039d-30ab-450a-cbb1-60f6377c996a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Accuracy       ROC  Precision    Recall  F1 Score  Feature Count\n",
              "0    0.8485  0.817462   0.869099  0.910112  0.889133              8\n",
              "1    0.8505  0.833300   0.890649  0.884644  0.887636              5\n",
              "2    0.8485  0.817462   0.869099  0.910112  0.889133              8\n",
              "3    0.8485  0.817462   0.869099  0.910112  0.889133              8\n",
              "4    0.8485  0.817462   0.869099  0.910112  0.889133              8"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-40727aeb-528d-43b6-bc43-d8ca3e313419\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>ROC</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1 Score</th>\n",
              "      <th>Feature Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.8485</td>\n",
              "      <td>0.817462</td>\n",
              "      <td>0.869099</td>\n",
              "      <td>0.910112</td>\n",
              "      <td>0.889133</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.8505</td>\n",
              "      <td>0.833300</td>\n",
              "      <td>0.890649</td>\n",
              "      <td>0.884644</td>\n",
              "      <td>0.887636</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.8485</td>\n",
              "      <td>0.817462</td>\n",
              "      <td>0.869099</td>\n",
              "      <td>0.910112</td>\n",
              "      <td>0.889133</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.8485</td>\n",
              "      <td>0.817462</td>\n",
              "      <td>0.869099</td>\n",
              "      <td>0.910112</td>\n",
              "      <td>0.889133</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.8485</td>\n",
              "      <td>0.817462</td>\n",
              "      <td>0.869099</td>\n",
              "      <td>0.910112</td>\n",
              "      <td>0.889133</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-40727aeb-528d-43b6-bc43-d8ca3e313419')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-40727aeb-528d-43b6-bc43-d8ca3e313419 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-40727aeb-528d-43b6-bc43-d8ca3e313419');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-615d7316-7787-4ce8-9198-98c73cb7d3e2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-615d7316-7787-4ce8-9198-98c73cb7d3e2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-615d7316-7787-4ce8-9198-98c73cb7d3e2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results",
              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0008944271909999167,\n        \"min\": 0.8485,\n        \"max\": 0.8505,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8505,\n          0.8485\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.007082676525842011,\n        \"min\": 0.8174621948128749,\n        \"max\": 0.8332995409872996,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8332995409872996,\n          0.8174621948128749\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.009637387990726005,\n        \"min\": 0.869098712446352,\n        \"max\": 0.8906485671191554,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8906485671191554,\n          0.869098712446352\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.011389709548313533,\n        \"min\": 0.8846441947565543,\n        \"max\": 0.9101123595505618,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8846441947565543,\n          0.9101123595505618\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1 Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.000669297225514891,\n        \"min\": 0.8876362269823376,\n        \"max\": 0.8891328210757409,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8876362269823376,\n          0.8891328210757409\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Feature Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 5,\n        \"max\": 8,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def run_l1_regularization():\n",
        "\n",
        "    # Split train and test set\n",
        "    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2,stratify=Y, random_state = 123)\n",
        "\n",
        "    # All features of dataset are float values. You normalize all features of the train and test dataset here.\n",
        "    scaler = StandardScaler().fit(X_train)\n",
        "    X_train_scaled = scaler.transform(X_train)\n",
        "    X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "    # Select L1 regulated features from LinearSVC output\n",
        "    selection = SelectFromModel(LinearSVC(C=1, penalty='l1', dual=False))\n",
        "    selection.fit(X_train_scaled, Y_train)\n",
        "\n",
        "    feature_names = X.columns[(selection.get_support())]\n",
        "\n",
        "    return feature_names\n",
        "\n",
        "l1reg_feature_names = run_l1_regularization()"
      ],
      "metadata": {
        "id": "7vq_hyQUtRXt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# TENSORFLOW -DL\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler, OrdinalEncoder\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, BatchNormalization, Activation, Dropout\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "import joblib\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "metadata": {
        "id": "zswlP5l9weqC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=42)"
      ],
      "metadata": {
        "id": "yVkW88yrwhez"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential([\n",
        "    # Input layer: 128 neurons, accepting input shape (number of features)\n",
        "    Dense(128, input_shape=(X_train_split.shape[1],)),\n",
        "    # Batch normalization to standardize inputs\n",
        "    BatchNormalization(),\n",
        "    # ReLU activation function for non-linearity\n",
        "    Activation('relu'),\n",
        "    # Dropout for regularization, preventing overfitting\n",
        "    Dropout(0.2),\n",
        "\n",
        "    # Hidden layer 1: 256 neurons\n",
        "    Dense(256),\n",
        "    BatchNormalization(),\n",
        "    Activation('relu'),\n",
        "    Dropout(0.5),\n",
        "\n",
        "    # Hidden layer 2: 64 neurons\n",
        "    Dense(64),\n",
        "    BatchNormalization(),\n",
        "    Activation('relu'),\n",
        "    Dropout(0.2),\n",
        "\n",
        "    # Hidden layer 3: 16 neurons\n",
        "    Dense(16),\n",
        "    BatchNormalization(),\n",
        "    Activation('relu'),\n",
        "    Dropout(0.1),\n",
        "\n",
        "    # Output layer: 1 neuron, sigmoid activation for binary classification\n",
        "    Dense(1, activation='sigmoid')\n",
        "])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vFWlAL_2wmxD",
        "outputId": "6e9e14e3-45e0-4e2b-afa5-a3434cedd9a1"
      },
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    loss='binary_crossentropy',\n",
        "    optimizer='RMSprop',\n",
        "    metrics=['accuracy']\n",
        ")\n"
      ],
      "metadata": {
        "id": "ZtAejDJ6wtAE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Define EarlyStopping\n",
        "early_stopping = EarlyStopping(\n",
        "    monitor='val_loss',\n",
        "    patience=5,\n",
        "    verbose=1\n",
        ")"
      ],
      "metadata": {
        "id": "tzUjpfL5w4xH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model using the early stopping callback\n",
        "history = model.fit(\n",
        "    X_train,\n",
        "    y_train,\n",
        "    validation_data=(X_val, y_val),\n",
        "    epochs=20,\n",
        "    batch_size=32,\n",
        "    callbacks=[early_stopping]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q1u9__aSw7ig",
        "outputId": "4f3689a7-423e-49cf-a6fd-b388808b0b87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 7ms/step - accuracy: 0.7709 - loss: 0.4821 - val_accuracy: 0.8463 - val_loss: 0.3668\n",
            "Epoch 2/20\n",
            "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.8376 - loss: 0.3590 - val_accuracy: 0.8507 - val_loss: 0.3248\n",
            "Epoch 3/20\n",
            "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8522 - loss: 0.3385 - val_accuracy: 0.8508 - val_loss: 0.3183\n",
            "Epoch 4/20\n",
            "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.8388 - loss: 0.3437 - val_accuracy: 0.8545 - val_loss: 0.3140\n",
            "Epoch 5/20\n",
            "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - accuracy: 0.8455 - loss: 0.3309 - val_accuracy: 0.8522 - val_loss: 0.3110\n",
            "Epoch 6/20\n",
            "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.8498 - loss: 0.3298 - val_accuracy: 0.8553 - val_loss: 0.3107\n",
            "Epoch 7/20\n",
            "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.8417 - loss: 0.3364 - val_accuracy: 0.8538 - val_loss: 0.3150\n",
            "Epoch 8/20\n",
            "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.8478 - loss: 0.3308 - val_accuracy: 0.8522 - val_loss: 0.3089\n",
            "Epoch 9/20\n",
            "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8486 - loss: 0.3196 - val_accuracy: 0.8543 - val_loss: 0.3101\n",
            "Epoch 10/20\n",
            "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.8498 - loss: 0.3324 - val_accuracy: 0.8530 - val_loss: 0.3079\n",
            "Epoch 11/20\n",
            "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - accuracy: 0.8526 - loss: 0.3266 - val_accuracy: 0.8512 - val_loss: 0.3103\n",
            "Epoch 12/20\n",
            "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8536 - loss: 0.3203 - val_accuracy: 0.8550 - val_loss: 0.3095\n",
            "Epoch 13/20\n",
            "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - accuracy: 0.8500 - loss: 0.3260 - val_accuracy: 0.8572 - val_loss: 0.3081\n",
            "Epoch 14/20\n",
            "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8474 - loss: 0.3237 - val_accuracy: 0.8555 - val_loss: 0.3066\n",
            "Epoch 15/20\n",
            "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8510 - loss: 0.3329 - val_accuracy: 0.8570 - val_loss: 0.3067\n",
            "Epoch 16/20\n",
            "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8473 - loss: 0.3220 - val_accuracy: 0.8560 - val_loss: 0.3071\n",
            "Epoch 17/20\n",
            "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8565 - loss: 0.3151 - val_accuracy: 0.8570 - val_loss: 0.3078\n",
            "Epoch 18/20\n",
            "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.8524 - loss: 0.3187 - val_accuracy: 0.8568 - val_loss: 0.3054\n",
            "Epoch 19/20\n",
            "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - accuracy: 0.8500 - loss: 0.3226 - val_accuracy: 0.8565 - val_loss: 0.3073\n",
            "Epoch 20/20\n",
            "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.8623 - loss: 0.3070 - val_accuracy: 0.8577 - val_loss: 0.3047\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5. Evaluate Model\n",
        "val_preds = (model.predict(X_val) > 0.5).astype(\"int32\")\n",
        "print(\"Classification Report on Validation Data:\\n\", classification_report(y_val, val_preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MgsRE6wPw86l",
        "outputId": "d0f281f2-aa55-4bfc-c2ae-ce5def11620b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m188/188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n",
            "Classification Report on Validation Data:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.76      0.83      0.80      1995\n",
            "           1       0.91      0.87      0.89      4005\n",
            "\n",
            "    accuracy                           0.86      6000\n",
            "   macro avg       0.84      0.85      0.84      6000\n",
            "weighted avg       0.86      0.86      0.86      6000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 6. Make Predictions\n",
        "test_preds = (model.predict(X_test[:len(test)]) > 0.5).astype(\"int32\")\n",
        "\n",
        "test['Prediction'] = test_preds\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OjYNvuEAyGTu",
        "outputId": "6501c8f6-d7c9-4534-ab3d-671153646d8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save predictions to CSV\n",
        "test[['UID', 'Prediction']].to_csv('predictions.csv', index=False)"
      ],
      "metadata": {
        "id": "wibQIF0PzJy1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn.preprocessing\n",
        "\n",
        "#save the model for future use\n",
        "model.save('model.h5')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fugfAcLszoyV",
        "outputId": "f27c5d95-a379-4cd9-ee70-b09df1af1db5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import libraries (assumed)\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Function to plot training history\n",
        "def plot_history(history):\n",
        "  \"\"\"\n",
        "  This function takes a 'history' object, typically generated by fitting a model\n",
        "  using libraries like Keras or TensorFlow, and plots the training and validation\n",
        "  accuracy and loss curves.\n",
        "\n",
        "  Args:\n",
        "      history: A dictionary containing the training history. It should have keys\n",
        "               like 'accuracy', 'val_accuracy', 'loss', and 'val_loss'.\n",
        "  \"\"\"\n",
        "\n",
        "  # Configure the figure size\n",
        "  plt.figure(figsize=(18, 8))  # Adjust the figure size for better readability\n",
        "\n",
        "  # Accuracy subplot\n",
        "  plt.subplot(1, 2, 1)  # Create a subplot occupying 1 row, 2 columns, position 1\n",
        "\n",
        "  # Plot training and validation accuracy\n",
        "  plt.plot(history.history['accuracy'], label='Training accuracy')\n",
        "  plt.plot(history.history['val_accuracy'], label='Validation accuracy')\n",
        "\n",
        "  # Set labels and title\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.legend(loc='lower right')  # Place the legend in the lower right corner\n",
        "  plt.title('Training and Validation Accuracy')\n",
        "  plt.grid(True)  # Add grid lines for better visualization\n",
        "\n",
        "  # Loss subplot\n",
        "  plt.subplot(1, 2, 2)  # Create a subplot occupying 1 row, 2 columns, position 2\n",
        "\n",
        "  # Plot training and validation loss\n",
        "  plt.plot(history.history['loss'], label='Training loss')\n",
        "  plt.plot(history.history['val_loss'], label='Validation loss')\n",
        "\n",
        "  # Set labels and title\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('Loss')\n",
        "  plt.legend(loc='upper right')  # Place the legend in the upper right corner\n",
        "  plt.title('Training and Validation Loss')\n",
        "  plt.grid(True)  # Add grid lines for better visualization\n",
        "\n",
        "  # Display the plot\n",
        "  plt.show()\n",
        "\n",
        "plot_history(history)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "EW0RrhlK0QY1",
        "outputId": "6eb48bca-c647-435f-b650-25d92b8e29a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x800 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yBjdlDpAzsEL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BqvlKLZs1zrQ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}