LR_Fintech (1).ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VSxAH1WKkKxs",
        "outputId": "bbbd5802-5779-4941-e19a-8fae073f6c63"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "nUQvtCuVpyn_"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "9X6l5ptdpF-q",
        "outputId": "5c3b782c-ff52-4018-dc16-ac9104d2bc1d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        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>loan_id</th>\n",
              "      <th>no_of_dependents</th>\n",
              "      <th>education</th>\n",
              "      <th>self_employed</th>\n",
              "      <th>income_annum</th>\n",
              "      <th>loan_amount</th>\n",
              "      <th>loan_term</th>\n",
              "      <th>cibil_score</th>\n",
              "      <th>residential_assets_value</th>\n",
              "      <th>commercial_assets_value</th>\n",
              "      <th>luxury_assets_value</th>\n",
              "      <th>bank_asset_value</th>\n",
              "      <th>loan_status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>9600000</td>\n",
              "      <td>29900000</td>\n",
              "      <td>12</td>\n",
              "      <td>778</td>\n",
              "      <td>2400000</td>\n",
              "      <td>17600000</td>\n",
              "      <td>22700000</td>\n",
              "      <td>8000000</td>\n",
              "      <td>Approved</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>4100000</td>\n",
              "      <td>12200000</td>\n",
              "      <td>8</td>\n",
              "      <td>417</td>\n",
              "      <td>2700000</td>\n",
              "      <td>2200000</td>\n",
              "      <td>8800000</td>\n",
              "      <td>3300000</td>\n",
              "      <td>Rejected</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>9100000</td>\n",
              "      <td>29700000</td>\n",
              "      <td>20</td>\n",
              "      <td>506</td>\n",
              "      <td>7100000</td>\n",
              "      <td>4500000</td>\n",
              "      <td>33300000</td>\n",
              "      <td>12800000</td>\n",
              "      <td>Rejected</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>8200000</td>\n",
              "      <td>30700000</td>\n",
              "      <td>8</td>\n",
              "      <td>467</td>\n",
              "      <td>18200000</td>\n",
              "      <td>3300000</td>\n",
              "      <td>23300000</td>\n",
              "      <td>7900000</td>\n",
              "      <td>Rejected</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>9800000</td>\n",
              "      <td>24200000</td>\n",
              "      <td>20</td>\n",
              "      <td>382</td>\n",
              "      <td>12400000</td>\n",
              "      <td>8200000</td>\n",
              "      <td>29400000</td>\n",
              "      <td>5000000</td>\n",
              "      <td>Rejected</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   loan_id   no_of_dependents      education  self_employed   income_annum  \\\n",
              "0        1                  2       Graduate             No        9600000   \n",
              "1        2                  0   Not Graduate            Yes        4100000   \n",
              "2        3                  3       Graduate             No        9100000   \n",
              "3        4                  3       Graduate             No        8200000   \n",
              "4        5                  5   Not Graduate            Yes        9800000   \n",
              "\n",
              "    loan_amount   loan_term   cibil_score   residential_assets_value  \\\n",
              "0      29900000          12           778                    2400000   \n",
              "1      12200000           8           417                    2700000   \n",
              "2      29700000          20           506                    7100000   \n",
              "3      30700000           8           467                   18200000   \n",
              "4      24200000          20           382                   12400000   \n",
              "\n",
              "    commercial_assets_value   luxury_assets_value   bank_asset_value  \\\n",
              "0                  17600000              22700000            8000000   \n",
              "1                   2200000               8800000            3300000   \n",
              "2                   4500000              33300000           12800000   \n",
              "3                   3300000              23300000            7900000   \n",
              "4                   8200000              29400000            5000000   \n",
              "\n",
              "   loan_status  \n",
              "0     Approved  \n",
              "1     Rejected  \n",
              "2     Rejected  \n",
              "3     Rejected  \n",
              "4     Rejected  "
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset = 'loan_approval_dataset.csv'\n",
        "df = pd.read_csv(dataset)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DRR8-7ygp8ni",
        "outputId": "6d20b503-eb7e-4a9d-acc3-b98e236db664"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(4269, 13)"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape #4269 rows of data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "ZJJtjteKqF9r",
        "outputId": "1388378f-c95c-4146-c1d8-df3889c892f0"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loan_id</th>\n",
              "      <th>no_of_dependents</th>\n",
              "      <th>education</th>\n",
              "      <th>self_employed</th>\n",
              "      <th>income_annum</th>\n",
              "      <th>loan_amount</th>\n",
              "      <th>loan_term</th>\n",
              "      <th>cibil_score</th>\n",
              "      <th>residential_assets_value</th>\n",
              "      <th>commercial_assets_value</th>\n",
              "      <th>luxury_assets_value</th>\n",
              "      <th>bank_asset_value</th>\n",
              "      <th>loan_status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>9600000</td>\n",
              "      <td>29900000</td>\n",
              "      <td>12</td>\n",
              "      <td>778</td>\n",
              "      <td>2400000</td>\n",
              "      <td>17600000</td>\n",
              "      <td>22700000</td>\n",
              "      <td>8000000</td>\n",
              "      <td>Approved</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>4100000</td>\n",
              "      <td>12200000</td>\n",
              "      <td>8</td>\n",
              "      <td>417</td>\n",
              "      <td>2700000</td>\n",
              "      <td>2200000</td>\n",
              "      <td>8800000</td>\n",
              "      <td>3300000</td>\n",
              "      <td>Rejected</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>9100000</td>\n",
              "      <td>29700000</td>\n",
              "      <td>20</td>\n",
              "      <td>506</td>\n",
              "      <td>7100000</td>\n",
              "      <td>4500000</td>\n",
              "      <td>33300000</td>\n",
              "      <td>12800000</td>\n",
              "      <td>Rejected</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>8200000</td>\n",
              "      <td>30700000</td>\n",
              "      <td>8</td>\n",
              "      <td>467</td>\n",
              "      <td>18200000</td>\n",
              "      <td>3300000</td>\n",
              "      <td>23300000</td>\n",
              "      <td>7900000</td>\n",
              "      <td>Rejected</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>9800000</td>\n",
              "      <td>24200000</td>\n",
              "      <td>20</td>\n",
              "      <td>382</td>\n",
              "      <td>12400000</td>\n",
              "      <td>8200000</td>\n",
              "      <td>29400000</td>\n",
              "      <td>5000000</td>\n",
              "      <td>Rejected</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   loan_id   no_of_dependents      education  self_employed   income_annum  \\\n",
              "0        1                  2       Graduate             No        9600000   \n",
              "1        2                  0   Not Graduate            Yes        4100000   \n",
              "2        3                  3       Graduate             No        9100000   \n",
              "3        4                  3       Graduate             No        8200000   \n",
              "4        5                  5   Not Graduate            Yes        9800000   \n",
              "\n",
              "    loan_amount   loan_term   cibil_score   residential_assets_value  \\\n",
              "0      29900000          12           778                    2400000   \n",
              "1      12200000           8           417                    2700000   \n",
              "2      29700000          20           506                    7100000   \n",
              "3      30700000           8           467                   18200000   \n",
              "4      24200000          20           382                   12400000   \n",
              "\n",
              "    commercial_assets_value   luxury_assets_value   bank_asset_value  \\\n",
              "0                  17600000              22700000            8000000   \n",
              "1                   2200000               8800000            3300000   \n",
              "2                   4500000              33300000           12800000   \n",
              "3                   3300000              23300000            7900000   \n",
              "4                   8200000              29400000            5000000   \n",
              "\n",
              "   loan_status  \n",
              "0     Approved  \n",
              "1     Rejected  \n",
              "2     Rejected  \n",
              "3     Rejected  \n",
              "4     Rejected  "
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.dropna()\n",
        "df.drop_duplicates()\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L7EMrBE1r79h",
        "outputId": "f8c6b64e-c172-41a5-aa32-51006705f5dd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index(['loan_id', 'no_of_dependents', 'education', 'self_employed',\n",
            "       'income_annum', 'loan_amount', 'loan_term', 'cibil_score',\n",
            "       'residential_assets_value', 'commercial_assets_value',\n",
            "       'luxury_assets_value', 'bank_asset_value', 'loan_status'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "df.columns = df.columns.str.strip()\n",
        "print(df.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "VA83vK4pqcSq",
        "outputId": "926c7b3c-d497-4bfd-8678-c94d1670e79d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
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              "    .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>loan_id</th>\n",
              "      <th>no_of_dependents</th>\n",
              "      <th>education</th>\n",
              "      <th>self_employed</th>\n",
              "      <th>income_annum</th>\n",
              "      <th>loan_amount</th>\n",
              "      <th>loan_term</th>\n",
              "      <th>cibil_score</th>\n",
              "      <th>residential_assets_value</th>\n",
              "      <th>commercial_assets_value</th>\n",
              "      <th>luxury_assets_value</th>\n",
              "      <th>bank_asset_value</th>\n",
              "      <th>loan_status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>9600000</td>\n",
              "      <td>29900000</td>\n",
              "      <td>12</td>\n",
              "      <td>778</td>\n",
              "      <td>2400000</td>\n",
              "      <td>17600000</td>\n",
              "      <td>22700000</td>\n",
              "      <td>8000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4100000</td>\n",
              "      <td>12200000</td>\n",
              "      <td>8</td>\n",
              "      <td>417</td>\n",
              "      <td>2700000</td>\n",
              "      <td>2200000</td>\n",
              "      <td>8800000</td>\n",
              "      <td>3300000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>9100000</td>\n",
              "      <td>29700000</td>\n",
              "      <td>20</td>\n",
              "      <td>506</td>\n",
              "      <td>7100000</td>\n",
              "      <td>4500000</td>\n",
              "      <td>33300000</td>\n",
              "      <td>12800000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>8200000</td>\n",
              "      <td>30700000</td>\n",
              "      <td>8</td>\n",
              "      <td>467</td>\n",
              "      <td>18200000</td>\n",
              "      <td>3300000</td>\n",
              "      <td>23300000</td>\n",
              "      <td>7900000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9800000</td>\n",
              "      <td>24200000</td>\n",
              "      <td>20</td>\n",
              "      <td>382</td>\n",
              "      <td>12400000</td>\n",
              "      <td>8200000</td>\n",
              "      <td>29400000</td>\n",
              "      <td>5000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   loan_id  no_of_dependents  education  self_employed  income_annum  \\\n",
              "0        1                 2          1              0       9600000   \n",
              "1        2                 0          0              1       4100000   \n",
              "2        3                 3          1              0       9100000   \n",
              "3        4                 3          1              0       8200000   \n",
              "4        5                 5          0              1       9800000   \n",
              "\n",
              "   loan_amount  loan_term  cibil_score  residential_assets_value  \\\n",
              "0     29900000         12          778                   2400000   \n",
              "1     12200000          8          417                   2700000   \n",
              "2     29700000         20          506                   7100000   \n",
              "3     30700000          8          467                  18200000   \n",
              "4     24200000         20          382                  12400000   \n",
              "\n",
              "   commercial_assets_value  luxury_assets_value  bank_asset_value  loan_status  \n",
              "0                 17600000             22700000           8000000            1  \n",
              "1                  2200000              8800000           3300000            0  \n",
              "2                  4500000             33300000          12800000            0  \n",
              "3                  3300000             23300000           7900000            0  \n",
              "4                  8200000             29400000           5000000            0  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Encoding values\n",
        "status_map = {' Rejected': 0, ' Approved': 1}\n",
        "status_map2 = {' Not Graduate': 0, ' Graduate': 1}\n",
        "status_map3 = {' No': 0, ' Yes': 1}\n",
        "\n",
        "df['loan_status'].replace(status_map, inplace=True)\n",
        "df['education'].replace(status_map2, inplace=True)\n",
        "df['self_employed'].replace(status_map3, inplace=True)\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "WQHl1mQ7trM4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      loan_id  no_of_dependents  education  self_employed  income_annum  \\\n",
            "0           1                 2          1              0       9600000   \n",
            "1           2                 0          0              1       4100000   \n",
            "2           3                 3          1              0       9100000   \n",
            "3           4                 3          1              0       8200000   \n",
            "4           5                 5          0              1       9800000   \n",
            "...       ...               ...        ...            ...           ...   \n",
            "4264     4265                 5          1              1       1000000   \n",
            "4265     4266                 0          0              1       3300000   \n",
            "4266     4267                 2          0              0       6500000   \n",
            "4267     4268                 1          0              0       4100000   \n",
            "4268     4269                 1          1              0       9200000   \n",
            "\n",
            "      loan_amount  loan_term  cibil_score  residential_assets_value  \\\n",
            "0        29900000         12          778                   2400000   \n",
            "1        12200000          8          417                   2700000   \n",
            "2        29700000         20          506                   7100000   \n",
            "3        30700000          8          467                  18200000   \n",
            "4        24200000         20          382                  12400000   \n",
            "...           ...        ...          ...                       ...   \n",
            "4264      2300000         12          317                   2800000   \n",
            "4265     11300000         20          559                   4200000   \n",
            "4266     23900000         18          457                   1200000   \n",
            "4267     12800000          8          780                   8200000   \n",
            "4268     29700000         10          607                  17800000   \n",
            "\n",
            "      commercial_assets_value  luxury_assets_value  bank_asset_value  \n",
            "0                    17600000             22700000           8000000  \n",
            "1                     2200000              8800000           3300000  \n",
            "2                     4500000             33300000          12800000  \n",
            "3                     3300000             23300000           7900000  \n",
            "4                     8200000             29400000           5000000  \n",
            "...                       ...                  ...               ...  \n",
            "4264                   500000              3300000            800000  \n",
            "4265                  2900000             11000000           1900000  \n",
            "4266                 12400000             18100000           7300000  \n",
            "4267                   700000             14100000           5800000  \n",
            "4268                 11800000             35700000          12000000  \n",
            "\n",
            "[4269 rows x 12 columns]\n",
            "0       1\n",
            "1       0\n",
            "2       0\n",
            "3       0\n",
            "4       0\n",
            "       ..\n",
            "4264    0\n",
            "4265    1\n",
            "4266    0\n",
            "4267    1\n",
            "4268    1\n",
            "Name: loan_status, Length: 4269, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "X = df.drop('loan_status', axis=1)  # Features\n",
        "y = df['loan_status']  # Target variable\n",
        "print(X)\n",
        "print(y)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "KRZGeF0jvCvl"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "0GeldBEMvJk5",
        "outputId": "c8ca88ac-55de-416e-8623-f49512606501"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "logistic_model = LogisticRegression()\n",
        "logistic_model.fit(X_train, y_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tx-HyKrivS9V",
        "outputId": "f5514789-db21-42d3-c589-856b00068230"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy of Logistic Regression Model: 0.6370023419203747\n"
          ]
        }
      ],
      "source": [
        "y_pred = logistic_model.predict(X_test)\n",
        "from sklearn.metrics import accuracy_score\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Accuracy of Logistic Regression Model: {accuracy}\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "7rbeyk6RvbaN",
        "outputId": "0a80f255-5356-4d5b-e236-b55166a1d09a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestClassifier(random_state=42)"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rf_classifier2 = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rf_classifier2.fit(X_train, y_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bOoTe2BPvlIb",
        "outputId": "c289dccf-0646-4df4-fda8-2374979914ba"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy of Random Forest Classifier: 0.977751756440281\n"
          ]
        }
      ],
      "source": [
        "rf_accuracy = rf_classifier2.score(X_test, y_test)\n",
        "print(f\"Accuracy of Random Forest Classifier: {rf_accuracy}\")\n",
        "#can test this out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wuh4orUUvqUq",
        "outputId": "881fd946-5fd1-4035-acdb-afc3bce8f36d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy of Gradient Boosting Classifier: 0.9754098360655737\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "gb_classifier = GradientBoostingClassifier(n_estimators=100, random_state=42)\n",
        "gb_classifier.fit(X_train, y_train)\n",
        "gb_accuracy = gb_classifier.score(X_test, y_test)\n",
        "print(f\"Accuracy of Gradient Boosting Classifier: {gb_accuracy}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gUoJwKsrv09p",
        "outputId": "f6f6bfdf-cc7c-4720-e034-511acf85e218"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   loan_id  no_of_dependents  education  self_employed  income_annum  \\\n",
            "0      102                 0          1              0         25000   \n",
            "\n",
            "   loan_amount  loan_term  cibil_score  residential_assets_value  \\\n",
            "0        50000          5          800                         0   \n",
            "\n",
            "   commercial_assets_value  luxury_assets_value  bank_asset_value  \n",
            "0                        0                    0                 0  \n",
            "Prediction: 0\n"
          ]
        }
      ],
      "source": [
        "custom_entry = {\n",
        "    'loan_id': 102,\n",
        "    'no_of_dependents': 0,\n",
        "    'education': 1,\n",
        "    'self_employed': 0,\n",
        "    'income_annum': 25000,\n",
        "    'loan_amount': 50000,\n",
        "    'loan_term': 5,\n",
        "    'cibil_score': 800,\n",
        "    'residential_assets_value': 0,\n",
        "    'commercial_assets_value': 0,\n",
        "    'luxury_assets_value': 0,\n",
        "    'bank_asset_value': 0\n",
        "}\n",
        "\n",
        "# Convert the dictionary to a DataFrame\n",
        "custom_entry_df = pd.DataFrame([custom_entry])\n",
        "\n",
        "print(custom_entry_df)\n",
        "\n",
        "prediction = rf_classifier.predict(custom_entry_df)\n",
        "# print(\"Prediction:\", prediction, type(prediction))\n",
        "print(\"Prediction:\", prediction[0])\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "ADDED BY SHAYAAN CONVERTING THE RF_CLASSIFIER TO A PKL"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pickle\n",
        "\n",
        "# Save the model as a pickle in a file\n",
        "model_pickle_path = \"eligibility_check_model.pkl\"\n",
        "with open(model_pickle_path, 'wb') as file:\n",
        "    pickle.dump(rf_classifier, file)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "iS133PbcSAxP"
      },
      "outputs": [],
      "source": [
        "X = df[[' no_of_dependents', ' education', ' self_employed', ' income_annum', ' loan_term', ' cibil_score', ' residential_assets_value', ' commercial_assets_value', ' luxury_assets_value', ' bank_asset_value']]\n",
        "y = df[' loan_amount']\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jjizNtenSdWt",
        "outputId": "d2d626fd-97db-467b-a9d7-d8847915aa21"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Random Forest Mean Squared Error: 12381645222482.436\n",
            "Random Forest R-squared Score: 0.8455861996283656\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "# Initialize the Random Forest Regressor model\n",
        "rf_model = RandomForestRegressor()\n",
        "\n",
        "# Train the model\n",
        "rf_model.fit(X_train, y_train)\n",
        "\n",
        "# Predict loan amounts using Random Forest\n",
        "y_pred_rf = rf_model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print('Random Forest Mean Squared Error:', mean_squared_error(y_test, y_pred_rf))\n",
        "print('Random Forest R-squared Score:', r2_score(y_test, y_pred_rf))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pcV-scf-Tgh6",
        "outputId": "dedf5999-d325-4524-d1be-a58573181b64"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Huber Regression Mean Squared Error: 11891761670847.648\n",
            "Huber Regression R-squared Score: 0.8516956285118653\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import HuberRegressor\n",
        "\n",
        "# Create a Huber Regression model\n",
        "huber_model = HuberRegressor(epsilon=1.35)  # You can adjust the epsilon parameter\n",
        "\n",
        "# Train the model\n",
        "huber_model.fit(X_train, y_train)\n",
        "\n",
        "# Predict loan amounts using Huber Regression\n",
        "y_pred_huber = huber_model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print('Huber Regression Mean Squared Error:', mean_squared_error(y_test, y_pred_huber))\n",
        "print('Huber Regression R-squared Score:', r2_score(y_test, y_pred_huber))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aENUryg7Trn-",
        "outputId": "ec1bbdf0-4b18-4e2e-e0ca-6c2de12133d1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Bayesian Ridge Regression Mean Squared Error: 11908713976547.045\n",
            "Bayesian Ridge Regression R-squared Score: 0.8514842131546111\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import BayesianRidge\n",
        "\n",
        "# Create a Bayesian Ridge Regression model\n",
        "bayesian_ridge_model = BayesianRidge()\n",
        "\n",
        "# Train the model\n",
        "bayesian_ridge_model.fit(X_train, y_train)\n",
        "\n",
        "# Predict loan amounts using Bayesian Ridge Regression\n",
        "y_pred_bayesian_ridge = bayesian_ridge_model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print('Bayesian Ridge Regression Mean Squared Error:', mean_squared_error(y_test, y_pred_bayesian_ridge))\n",
        "print('Bayesian Ridge Regression R-squared Score:', r2_score(y_test, y_pred_bayesian_ridge))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LUwFSwxmWxId",
        "outputId": "fd43c7f7-a421-4118-8d6f-f9c8fed52bb3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.15.0)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (23.5.26)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.5.4)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
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            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (16.0.6)\n",
            "Requirement already satisfied: ml-dtypes~=0.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.23.5)\n",
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            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow) (23.2)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.20.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow) (67.7.2)\n",
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            "Requirement already satisfied: tensorflow-estimator<2.16,>=2.15.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.15.0)\n",
            "Requirement already satisfied: keras<2.16,>=2.15.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.15.0)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow) (0.42.0)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (2.17.3)\n",
            "Requirement already satisfied: google-auth-oauthlib<2,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (1.2.0)\n",
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            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (3.0.1)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (5.3.2)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (0.3.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow) (1.3.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (2023.11.17)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.16,>=2.15->tensorflow) (2.1.3)\n",
            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (0.5.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow) (3.2.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-gizKNLoWo9A",
        "outputId": "98445874-a0de-442c-de13-f0f2962ee867"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "97/97 [==============================] - 1s 4ms/step - loss: 310972510109696.0000 - val_loss: 325111542448128.0000\n",
            "Epoch 2/100\n",
            "97/97 [==============================] - 0s 3ms/step - loss: 310971704803328.0000 - val_loss: 325109495627776.0000\n",
            "Epoch 3/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 310967476944896.0000 - val_loss: 325101744553984.0000\n",
            "Epoch 4/100\n",
            "97/97 [==============================] - 0s 3ms/step - loss: 310955867111424.0000 - val_loss: 325083558051840.0000\n",
            "Epoch 5/100\n",
            "97/97 [==============================] - 0s 3ms/step - loss: 310932882325504.0000 - val_loss: 325050372718592.0000\n",
            "Epoch 6/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 310894428946432.0000 - val_loss: 324999437090816.0000\n",
            "Epoch 7/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 310837352857600.0000 - val_loss: 324926993072128.0000\n",
            "Epoch 8/100\n",
            "97/97 [==============================] - 0s 1ms/step - loss: 310757963071488.0000 - val_loss: 324828678586368.0000\n",
            "Epoch 9/100\n",
            "97/97 [==============================] - 0s 1ms/step - loss: 310653508124672.0000 - val_loss: 324706339127296.0000\n",
            "Epoch 10/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 310528081657856.0000 - val_loss: 324557391003648.0000\n",
            "Epoch 11/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 310377925574656.0000 - val_loss: 324378277445632.0000\n",
            "Epoch 12/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 310190356299776.0000 - val_loss: 324157757718528.0000\n",
            "Epoch 13/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 309974634856448.0000 - val_loss: 323904891518976.0000\n",
            "Epoch 14/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 309728211107840.0000 - val_loss: 323613236396032.0000\n",
            "Epoch 15/100\n",
            "97/97 [==============================] - 0s 1ms/step - loss: 309445112365056.0000 - val_loss: 323286651109376.0000\n",
            "Epoch 16/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 309103930900480.0000 - val_loss: 322927752904704.0000\n",
            "Epoch 17/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 308768755679232.0000 - val_loss: 322536575336448.0000\n",
            "Epoch 18/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 308382946820096.0000 - val_loss: 322099293978624.0000\n",
            "Epoch 19/100\n",
            "97/97 [==============================] - 0s 1ms/step - loss: 307964791488512.0000 - val_loss: 321624297439232.0000\n",
            "Epoch 20/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 307506270175232.0000 - val_loss: 321102794457088.0000\n",
            "Epoch 21/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 306974432428032.0000 - val_loss: 320533845508096.0000\n",
            "Epoch 22/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 306470579077120.0000 - val_loss: 319939562962944.0000\n",
            "Epoch 23/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 305871867346944.0000 - val_loss: 319293539483648.0000\n",
            "Epoch 24/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 305267082264576.0000 - val_loss: 318622081744896.0000\n",
            "Epoch 25/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 304640319029248.0000 - val_loss: 317903748464640.0000\n",
            "Epoch 26/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 303938058321920.0000 - val_loss: 317131057004544.0000\n",
            "Epoch 27/100\n",
            "97/97 [==============================] - 0s 1ms/step - loss: 303235764060160.0000 - val_loss: 316311221567488.0000\n",
            "Epoch 28/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 302398178328576.0000 - val_loss: 315448168022016.0000\n",
            "Epoch 29/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 301554989006848.0000 - val_loss: 314531091841024.0000\n",
            "Epoch 30/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 300699283881984.0000 - val_loss: 313578414407680.0000\n",
            "Epoch 31/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 299764927168512.0000 - val_loss: 312571244576768.0000\n",
            "Epoch 32/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 298902678929408.0000 - val_loss: 311498106732544.0000\n",
            "Epoch 33/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 297818233241600.0000 - val_loss: 310369671184384.0000\n",
            "Epoch 34/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 296829451239424.0000 - val_loss: 309174328098816.0000\n",
            "Epoch 35/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 295548745678848.0000 - val_loss: 307951201943552.0000\n",
            "Epoch 36/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 294447589883904.0000 - val_loss: 306679891623936.0000\n",
            "Epoch 37/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 293103365783552.0000 - val_loss: 305336808374272.0000\n",
            "Epoch 38/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 291956542406656.0000 - val_loss: 303960875335680.0000\n",
            "Epoch 39/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 290634564894720.0000 - val_loss: 302523973894144.0000\n",
            "Epoch 40/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 289267926433792.0000 - val_loss: 301023319031808.0000\n",
            "Epoch 41/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 288029767892992.0000 - val_loss: 299481190891520.0000\n",
            "Epoch 42/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 286355099746304.0000 - val_loss: 297881986662400.0000\n",
            "Epoch 43/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 284768914636800.0000 - val_loss: 296283319304192.0000\n",
            "Epoch 44/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 283479686250496.0000 - val_loss: 294634924277760.0000\n",
            "Epoch 45/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 281958630293504.0000 - val_loss: 292933077041152.0000\n",
            "Epoch 46/100\n",
            "97/97 [==============================] - 0s 3ms/step - loss: 280146556747776.0000 - val_loss: 291189857189888.0000\n",
            "Epoch 47/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 278602063020032.0000 - val_loss: 289382850363392.0000\n",
            "Epoch 48/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 276864597753856.0000 - val_loss: 287519639863296.0000\n",
            "Epoch 49/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 275177430581248.0000 - val_loss: 285580428574720.0000\n",
            "Epoch 50/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 273316300455936.0000 - val_loss: 283637794734080.0000\n",
            "Epoch 51/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 271459716956160.0000 - val_loss: 281625065684992.0000\n",
            "Epoch 52/100\n",
            "97/97 [==============================] - 0s 3ms/step - loss: 269628433498112.0000 - val_loss: 279667567230976.0000\n",
            "Epoch 53/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 268136150466560.0000 - val_loss: 277672655585280.0000\n",
            "Epoch 54/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 265840524001280.0000 - val_loss: 275610114981888.0000\n",
            "Epoch 55/100\n",
            "97/97 [==============================] - 0s 3ms/step - loss: 263899651768320.0000 - val_loss: 273507292282880.0000\n",
            "Epoch 56/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 261747235618816.0000 - val_loss: 271345380229120.0000\n",
            "Epoch 57/100\n",
            "97/97 [==============================] - 0s 3ms/step - loss: 259683587719168.0000 - val_loss: 269134831026176.0000\n",
            "Epoch 58/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 258005899673600.0000 - val_loss: 266849254113280.0000\n",
            "Epoch 59/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 255634087870464.0000 - val_loss: 264553862529024.0000\n",
            "Epoch 60/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 253771733008384.0000 - val_loss: 262256759668736.0000\n",
            "Epoch 61/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 251099642593280.0000 - val_loss: 259876206936064.0000\n",
            "Epoch 62/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 249009134370816.0000 - val_loss: 257424166486016.0000\n",
            "Epoch 63/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 247021470482432.0000 - val_loss: 254939444019200.0000\n",
            "Epoch 64/100\n",
            "97/97 [==============================] - 0s 3ms/step - loss: 244674337964032.0000 - val_loss: 252450896347136.0000\n",
            "Epoch 65/100\n",
            "97/97 [==============================] - 0s 1ms/step - loss: 242889678389248.0000 - val_loss: 249943390420992.0000\n",
            "Epoch 66/100\n",
            "97/97 [==============================] - 0s 3ms/step - loss: 239726669856768.0000 - val_loss: 247410265686016.0000\n",
            "Epoch 67/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 237576401190912.0000 - val_loss: 244862360223744.0000\n",
            "Epoch 68/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 234766905376768.0000 - val_loss: 242262848045056.0000\n",
            "Epoch 69/100\n",
            "97/97 [==============================] - 0s 3ms/step - loss: 232597896560640.0000 - val_loss: 239695464235008.0000\n",
            "Epoch 70/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 229741625868288.0000 - val_loss: 237047113580544.0000\n",
            "Epoch 71/100\n",
            "97/97 [==============================] - 0s 3ms/step - loss: 227288226463744.0000 - val_loss: 234378428940288.0000\n",
            "Epoch 72/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 224883464208384.0000 - val_loss: 231696037314560.0000\n",
            "Epoch 73/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 222533546672128.0000 - val_loss: 228967239909376.0000\n",
            "Epoch 74/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 220072698183680.0000 - val_loss: 226228040630272.0000\n",
            "Epoch 75/100\n",
            "97/97 [==============================] - 0s 3ms/step - loss: 217640404516864.0000 - val_loss: 223483824963584.0000\n",
            "Epoch 76/100\n",
            "97/97 [==============================] - 0s 3ms/step - loss: 214926958264320.0000 - val_loss: 220711843004416.0000\n",
            "Epoch 77/100\n",
            "97/97 [==============================] - 0s 3ms/step - loss: 211844480368640.0000 - val_loss: 217851394785280.0000\n",
            "Epoch 78/100\n",
            "97/97 [==============================] - 0s 3ms/step - loss: 210211386163200.0000 - val_loss: 215029316059136.0000\n",
            "Epoch 79/100\n",
            "97/97 [==============================] - 0s 3ms/step - loss: 207554059698176.0000 - val_loss: 212214401204224.0000\n",
            "Epoch 80/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 204230711312384.0000 - val_loss: 209356251463680.0000\n",
            "Epoch 81/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 201862036848640.0000 - val_loss: 206461997154304.0000\n",
            "Epoch 82/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 198723221061632.0000 - val_loss: 203575275814912.0000\n",
            "Epoch 83/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 195787006935040.0000 - val_loss: 200646594658304.0000\n",
            "Epoch 84/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 193355988336640.0000 - val_loss: 197735512801280.0000\n",
            "Epoch 85/100\n",
            "97/97 [==============================] - 0s 1ms/step - loss: 191018938925056.0000 - val_loss: 194804818378752.0000\n",
            "Epoch 86/100\n",
            "97/97 [==============================] - 0s 1ms/step - loss: 187516678307840.0000 - val_loss: 191856994418688.0000\n",
            "Epoch 87/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 185295475572736.0000 - val_loss: 188913180213248.0000\n",
            "Epoch 88/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 181837506805760.0000 - val_loss: 185908565377024.0000\n",
            "Epoch 89/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 179341963034624.0000 - val_loss: 182983961083904.0000\n",
            "Epoch 90/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 176317634969600.0000 - val_loss: 180019292798976.0000\n",
            "Epoch 91/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 173754109919232.0000 - val_loss: 177089437237248.0000\n",
            "Epoch 92/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 170936628150272.0000 - val_loss: 174157232865280.0000\n",
            "Epoch 93/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 168265712140288.0000 - val_loss: 171217680072704.0000\n",
            "Epoch 94/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 164956255289344.0000 - val_loss: 168255578701824.0000\n",
            "Epoch 95/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 162799913271296.0000 - val_loss: 165353573318656.0000\n",
            "Epoch 96/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 159891968753664.0000 - val_loss: 162457658064896.0000\n",
            "Epoch 97/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 156835445211136.0000 - val_loss: 159532953108480.0000\n",
            "Epoch 98/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 154386189778944.0000 - val_loss: 156669669539840.0000\n",
            "Epoch 99/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 150588331393024.0000 - val_loss: 153798651674624.0000\n",
            "Epoch 100/100\n",
            "97/97 [==============================] - 0s 2ms/step - loss: 149386411638784.0000 - val_loss: 151008231555072.0000\n",
            "27/27 [==============================] - 0s 1ms/step\n",
            "Mean Squared Error: 139471371009403.05\n",
            "R-squared Score: -0.7393734074611333\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "\n",
        "X = df[[' no_of_dependents', ' education', ' self_employed', ' income_annum', ' loan_term', ' cibil_score', ' residential_assets_value', ' commercial_assets_value', ' luxury_assets_value', ' bank_asset_value']]\n",
        "y = df[' loan_amount']\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Scale features\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Build the neural network model\n",
        "model = Sequential()\n",
        "\n",
        "# Input layer and first hidden layer\n",
        "model.add(Dense(64, activation='relu', input_shape=(X_train.shape[1],)))\n",
        "model.add(Dropout(0.2))  # Adding dropout for regularization\n",
        "\n",
        "# Additional hidden layers\n",
        "model.add(Dense(32, activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "# Output layer\n",
        "model.add(Dense(1, activation='linear'))  # Linear activation for regression\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "\n",
        "# Train the model\n",
        "history = model.fit(X_train, y_train, epochs=100, batch_size=32, validation_split=0.1, verbose=1)\n",
        "\n",
        "# Evaluate the model\n",
        "predictions = model.predict(X_test)\n",
        "mse = mean_squared_error(y_test, predictions)\n",
        "r2 = r2_score(y_test, predictions)\n",
        "\n",
        "print(f\"Mean Squared Error: {mse}\")\n",
        "print(f\"R-squared Score: {r2}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "6B44XNxNXmu4"
      },
      "outputs": [],
      "source": [
        "#Generative AI approach\n",
        "new_data = pd.DataFrame({\n",
        "    ' no_of_dependents': [2],\n",
        "    ' education': [1],\n",
        "    ' self_employed': [1],\n",
        "    ' income_annum': [25000],\n",
        "    ' loan_term': [5],\n",
        "    ' cibil_score': [700],\n",
        "    ' residential_assets_value': [0],\n",
        "    ' commercial_assets_value': [25000],\n",
        "    ' luxury_assets_value': [0],\n",
        "    ' bank_asset_value': [0]\n",
        "})\n",
        "\n",
        "prompt = f\"Given the following data about an individual seeking microfinancing (in USD), where 0/1 in education means not graduate/graduate level education respectively, and similarily 1 for self employed , 0 for not: {new_data}. Predict an estimated amount of microloan  this individual would be eligible for as the first number in your response. Secondly given the loan_terms column (in years). Generate a concise repayment schedule table, particularly the interest and payment intervals. The loan is required for business capital, deduce the individua's credit history from their cibil score, and make ideal assumptions if you need more data.\"\n",
        "\n",
        "import google.generativeai as genai\n",
        "from google.colab import userdata\n",
        "\n",
        "from IPython.display import display\n",
        "from IPython.display import Markdown\n",
        "\n",
        "\n",
        "genai.configure(api_key='GOOGLE_CLOUD_KEY_HERE')\n",
        "\n",
        "model = genai.GenerativeModel('gemini-pro')\n",
        "response = model.generate_content(prompt)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "nAU9v3claeXR",
        "outputId": "d6eae33d-3398-44cc-9624-d3a9f6cb9daa"
      },
      "outputs": [
        {
          "data": {
            "text/markdown": [
              "**Estimated Microloan Amount:** $10,000\n",
              "\n",
              "**Repayment Schedule:**\n",
              "\n",
              "| Year | Interest Paid | Payment Made | Principal Balance |\n",
              "|---|---|---|---|\n",
              "| 1 | $1,000 | $2,000 | $9,000 |\n",
              "| 2 | $900 | $2,100 | $7,900 |\n",
              "| 3 | $790 | $2,210 | $5,790 |\n",
              "| 4 | $579 | $2,421 | $3,369 |\n",
              "| 5 | $337 | $2,663 | $706 |\n",
              "\n",
              "**Individual's Credit History:**\n",
              "\n",
              "Based on the cibil score of 700, the individual has a good credit history. This indicates that they have a history of paying their debts on time and in full. They are likely to be considered a low-risk borrower by lenders.\n",
              "\n",
              "**Assumptions:**\n",
              "\n",
              "* The individual is applying for a loan from a microfinance institution (MFI).\n",
              "* The MFI is willing to lend up to $10,000 to the individual.\n",
              "* The interest rate on the loan is 10%.\n",
              "* The loan term is 5 years.\n",
              "* The individual will make monthly payments on the loan."
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Markdown(response.text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4mVR4NzwS2Gx",
        "outputId": "ae6e9450-5ef7-4acd-f5c6-8c8a42f40b00"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 43ms/step\n",
            "Predicted Loan Amount: [[8.939369e+10]]\n"
          ]
        }
      ],
      "source": [
        "# Prepare new data for prediction (similar preprocessing as above)\n",
        "new_data = pd.DataFrame({\n",
        "    ' no_of_dependents': [2],\n",
        "    ' education': [1],\n",
        "    ' self_employed': [1],\n",
        "    ' income_annum': [25000],\n",
        "    ' loan_term': [5],\n",
        "    ' cibil_score': [700],\n",
        "    ' residential_assets_value': [0],\n",
        "    ' commercial_assets_value': [25000],\n",
        "    ' luxury_assets_value': [0],\n",
        "    ' bank_asset_value': [0]\n",
        "})\n",
        "\n",
        "# Perform one-hot encoding for new data if necessary\n",
        "\n",
        "# Make predictions\n",
        "predicted_loan_amount = huber_model.predict(new_data)\n",
        "print('Predicted Loan Amount:', predicted_loan_amount)\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}