LoanPayments.ipynb

{
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  "metadata": {
    "colab": {
      "name": "LoanPayments.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyO1nD+85VKoAjmWyH4rjwVX",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tselest/ML_DL_projects/blob/main/LoanPayments.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sQTi21haddOf",
        "outputId": "a349db8e-66a5-4731-865f-850025c91b92"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QBiiBrqoXg63"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eKVbGQVfY2c8"
      },
      "source": [
        "info_club = pd.read_csv('/content/gdrive/MyDrive/data/lending_club_info.csv', index_col='LoanStatNew')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 948
        },
        "id": "6yDW9PC0ertV",
        "outputId": "c84392e9-556c-49ed-80fd-3d610821c1f6"
      },
      "source": [
        "info_club"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Description</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LoanStatNew</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>loan_amnt</th>\n",
              "      <td>The listed amount of the loan applied for by t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>term</th>\n",
              "      <td>The number of payments on the loan. Values are...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>int_rate</th>\n",
              "      <td>Interest Rate on the loan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>installment</th>\n",
              "      <td>The monthly payment owed by the borrower if th...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>grade</th>\n",
              "      <td>LC assigned loan grade</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sub_grade</th>\n",
              "      <td>LC assigned loan subgrade</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>emp_title</th>\n",
              "      <td>The job title supplied by the Borrower when ap...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>emp_length</th>\n",
              "      <td>Employment length in years. Possible values ar...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>home_ownership</th>\n",
              "      <td>The home ownership status provided by the borr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>annual_inc</th>\n",
              "      <td>The self-reported annual income provided by th...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>verification_status</th>\n",
              "      <td>Indicates if income was verified by LC, not ve...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>issue_d</th>\n",
              "      <td>The month which the loan was funded</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_status</th>\n",
              "      <td>Current status of the loan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>purpose</th>\n",
              "      <td>A category provided by the borrower for the lo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title</th>\n",
              "      <td>The loan title provided by the borrower</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>zip_code</th>\n",
              "      <td>The first 3 numbers of the zip code provided b...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>addr_state</th>\n",
              "      <td>The state provided by the borrower in the loan...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dti</th>\n",
              "      <td>A ratio calculated using the borrower’s total ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>earliest_cr_line</th>\n",
              "      <td>The month the borrower's earliest reported cre...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>open_acc</th>\n",
              "      <td>The number of open credit lines in the borrowe...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pub_rec</th>\n",
              "      <td>Number of derogatory public records</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>revol_bal</th>\n",
              "      <td>Total credit revolving balance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>revol_util</th>\n",
              "      <td>Revolving line utilization rate, or the amount...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_acc</th>\n",
              "      <td>The total number of credit lines currently in ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>initial_list_status</th>\n",
              "      <td>The initial listing status of the loan. Possib...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>application_type</th>\n",
              "      <td>Indicates whether the loan is an individual ap...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mort_acc</th>\n",
              "      <td>Number of mortgage accounts.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "      <td>Number of public record bankruptcies</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                            Description\n",
              "LoanStatNew                                                            \n",
              "loan_amnt             The listed amount of the loan applied for by t...\n",
              "term                  The number of payments on the loan. Values are...\n",
              "int_rate                                      Interest Rate on the loan\n",
              "installment           The monthly payment owed by the borrower if th...\n",
              "grade                                            LC assigned loan grade\n",
              "sub_grade                                     LC assigned loan subgrade\n",
              "emp_title             The job title supplied by the Borrower when ap...\n",
              "emp_length            Employment length in years. Possible values ar...\n",
              "home_ownership        The home ownership status provided by the borr...\n",
              "annual_inc            The self-reported annual income provided by th...\n",
              "verification_status   Indicates if income was verified by LC, not ve...\n",
              "issue_d                             The month which the loan was funded\n",
              "loan_status                                  Current status of the loan\n",
              "purpose               A category provided by the borrower for the lo...\n",
              "title                           The loan title provided by the borrower\n",
              "zip_code              The first 3 numbers of the zip code provided b...\n",
              "addr_state            The state provided by the borrower in the loan...\n",
              "dti                   A ratio calculated using the borrower’s total ...\n",
              "earliest_cr_line      The month the borrower's earliest reported cre...\n",
              "open_acc              The number of open credit lines in the borrowe...\n",
              "pub_rec                             Number of derogatory public records\n",
              "revol_bal                                Total credit revolving balance\n",
              "revol_util            Revolving line utilization rate, or the amount...\n",
              "total_acc             The total number of credit lines currently in ...\n",
              "initial_list_status   The initial listing status of the loan. Possib...\n",
              "application_type      Indicates whether the loan is an individual ap...\n",
              "mort_acc                                   Number of mortgage accounts.\n",
              "pub_rec_bankruptcies               Number of public record bankruptcies"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0F9-dIDDfHuR",
        "outputId": "d14762af-7dea-4cda-b093-268ccc873db8"
      },
      "source": [
        "for index, row in info_club.iterrows():\n",
        "  print(index + \": \" + row['Description'])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "loan_amnt: The listed amount of the loan applied for by the borrower. If at some point in time, the credit department reduces the loan amount, then it will be reflected in this value.\n",
            "term: The number of payments on the loan. Values are in months and can be either 36 or 60.\n",
            "int_rate: Interest Rate on the loan\n",
            "installment: The monthly payment owed by the borrower if the loan originates.\n",
            "grade: LC assigned loan grade\n",
            "sub_grade: LC assigned loan subgrade\n",
            "emp_title: The job title supplied by the Borrower when applying for the loan.*\n",
            "emp_length: Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years. \n",
            "home_ownership: The home ownership status provided by the borrower during registration or obtained from the credit report. Our values are: RENT, OWN, MORTGAGE, OTHER\n",
            "annual_inc: The self-reported annual income provided by the borrower during registration.\n",
            "verification_status: Indicates if income was verified by LC, not verified, or if the income source was verified\n",
            "issue_d: The month which the loan was funded\n",
            "loan_status: Current status of the loan\n",
            "purpose: A category provided by the borrower for the loan request. \n",
            "title: The loan title provided by the borrower\n",
            "zip_code: The first 3 numbers of the zip code provided by the borrower in the loan application.\n",
            "addr_state: The state provided by the borrower in the loan application\n",
            "dti: A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income.\n",
            "earliest_cr_line: The month the borrower's earliest reported credit line was opened\n",
            "open_acc: The number of open credit lines in the borrower's credit file.\n",
            "pub_rec: Number of derogatory public records\n",
            "revol_bal: Total credit revolving balance\n",
            "revol_util: Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit.\n",
            "total_acc: The total number of credit lines currently in the borrower's credit file\n",
            "initial_list_status: The initial listing status of the loan. Possible values are – W, F\n",
            "application_type: Indicates whether the loan is an individual application or a joint application with two co-borrowers\n",
            "mort_acc: Number of mortgage accounts.\n",
            "pub_rec_bankruptcies: Number of public record bankruptcies\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f-YUNj44fLFy"
      },
      "source": [
        "loan = pd.read_csv('/content/gdrive/MyDrive/data/lending_club_loan_two.csv')"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "RrdFZyhpgnFh",
        "outputId": "2591cf7d-cd0a-49cd-ea0f-1c17110c9ae4"
      },
      "source": [
        "loan.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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_amnt</th>\n",
              "      <th>term</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>grade</th>\n",
              "      <th>sub_grade</th>\n",
              "      <th>emp_title</th>\n",
              "      <th>emp_length</th>\n",
              "      <th>home_ownership</th>\n",
              "      <th>annual_inc</th>\n",
              "      <th>verification_status</th>\n",
              "      <th>issue_d</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>purpose</th>\n",
              "      <th>title</th>\n",
              "      <th>dti</th>\n",
              "      <th>earliest_cr_line</th>\n",
              "      <th>open_acc</th>\n",
              "      <th>pub_rec</th>\n",
              "      <th>revol_bal</th>\n",
              "      <th>revol_util</th>\n",
              "      <th>total_acc</th>\n",
              "      <th>initial_list_status</th>\n",
              "      <th>application_type</th>\n",
              "      <th>mort_acc</th>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "      <th>address</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.44</td>\n",
              "      <td>329.48</td>\n",
              "      <td>B</td>\n",
              "      <td>B4</td>\n",
              "      <td>Marketing</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>117000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Jan-2015</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>vacation</td>\n",
              "      <td>Vacation</td>\n",
              "      <td>26.24</td>\n",
              "      <td>Jun-1990</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>36369.0</td>\n",
              "      <td>41.8</td>\n",
              "      <td>25.0</td>\n",
              "      <td>w</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0174 Michelle Gateway\\r\\nMendozaberg, OK 22690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.99</td>\n",
              "      <td>265.68</td>\n",
              "      <td>B</td>\n",
              "      <td>B5</td>\n",
              "      <td>Credit analyst</td>\n",
              "      <td>4 years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>65000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Jan-2015</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>22.05</td>\n",
              "      <td>Jul-2004</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20131.0</td>\n",
              "      <td>53.3</td>\n",
              "      <td>27.0</td>\n",
              "      <td>f</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1076 Carney Fort Apt. 347\\r\\nLoganmouth, SD 05113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15600.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>10.49</td>\n",
              "      <td>506.97</td>\n",
              "      <td>B</td>\n",
              "      <td>B3</td>\n",
              "      <td>Statistician</td>\n",
              "      <td>&lt; 1 year</td>\n",
              "      <td>RENT</td>\n",
              "      <td>43057.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Jan-2015</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>Credit card refinancing</td>\n",
              "      <td>12.79</td>\n",
              "      <td>Aug-2007</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11987.0</td>\n",
              "      <td>92.2</td>\n",
              "      <td>26.0</td>\n",
              "      <td>f</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>87025 Mark Dale Apt. 269\\r\\nNew Sabrina, WV 05113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7200.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>6.49</td>\n",
              "      <td>220.65</td>\n",
              "      <td>A</td>\n",
              "      <td>A2</td>\n",
              "      <td>Client Advocate</td>\n",
              "      <td>6 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>54000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Nov-2014</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>Credit card refinancing</td>\n",
              "      <td>2.60</td>\n",
              "      <td>Sep-2006</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5472.0</td>\n",
              "      <td>21.5</td>\n",
              "      <td>13.0</td>\n",
              "      <td>f</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>823 Reid Ford\\r\\nDelacruzside, MA 00813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24375.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>17.27</td>\n",
              "      <td>609.33</td>\n",
              "      <td>C</td>\n",
              "      <td>C5</td>\n",
              "      <td>Destiny Management Inc.</td>\n",
              "      <td>9 years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>55000.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>Apr-2013</td>\n",
              "      <td>Charged Off</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>Credit Card Refinance</td>\n",
              "      <td>33.95</td>\n",
              "      <td>Mar-1999</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24584.0</td>\n",
              "      <td>69.8</td>\n",
              "      <td>43.0</td>\n",
              "      <td>f</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>679 Luna Roads\\r\\nGreggshire, VA 11650</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   loan_amnt  ...                                            address\n",
              "0    10000.0  ...     0174 Michelle Gateway\\r\\nMendozaberg, OK 22690\n",
              "1     8000.0  ...  1076 Carney Fort Apt. 347\\r\\nLoganmouth, SD 05113\n",
              "2    15600.0  ...  87025 Mark Dale Apt. 269\\r\\nNew Sabrina, WV 05113\n",
              "3     7200.0  ...            823 Reid Ford\\r\\nDelacruzside, MA 00813\n",
              "4    24375.0  ...             679 Luna Roads\\r\\nGreggshire, VA 11650\n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "wxly6HGZgpbR",
        "outputId": "ee97d441-48cb-42fb-b619-bae073c3b27f"
      },
      "source": [
        "loan.describe().T"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>loan_amnt</th>\n",
              "      <td>396030.0</td>\n",
              "      <td>14113.888089</td>\n",
              "      <td>8357.441341</td>\n",
              "      <td>500.00</td>\n",
              "      <td>8000.00</td>\n",
              "      <td>12000.00</td>\n",
              "      <td>20000.00</td>\n",
              "      <td>40000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>int_rate</th>\n",
              "      <td>396030.0</td>\n",
              "      <td>13.639400</td>\n",
              "      <td>4.472157</td>\n",
              "      <td>5.32</td>\n",
              "      <td>10.49</td>\n",
              "      <td>13.33</td>\n",
              "      <td>16.49</td>\n",
              "      <td>30.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>installment</th>\n",
              "      <td>396030.0</td>\n",
              "      <td>431.849698</td>\n",
              "      <td>250.727790</td>\n",
              "      <td>16.08</td>\n",
              "      <td>250.33</td>\n",
              "      <td>375.43</td>\n",
              "      <td>567.30</td>\n",
              "      <td>1533.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>annual_inc</th>\n",
              "      <td>396030.0</td>\n",
              "      <td>74203.175798</td>\n",
              "      <td>61637.621158</td>\n",
              "      <td>0.00</td>\n",
              "      <td>45000.00</td>\n",
              "      <td>64000.00</td>\n",
              "      <td>90000.00</td>\n",
              "      <td>8706582.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dti</th>\n",
              "      <td>396030.0</td>\n",
              "      <td>17.379514</td>\n",
              "      <td>18.019092</td>\n",
              "      <td>0.00</td>\n",
              "      <td>11.28</td>\n",
              "      <td>16.91</td>\n",
              "      <td>22.98</td>\n",
              "      <td>9999.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>open_acc</th>\n",
              "      <td>396030.0</td>\n",
              "      <td>11.311153</td>\n",
              "      <td>5.137649</td>\n",
              "      <td>0.00</td>\n",
              "      <td>8.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>14.00</td>\n",
              "      <td>90.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pub_rec</th>\n",
              "      <td>396030.0</td>\n",
              "      <td>0.178191</td>\n",
              "      <td>0.530671</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>86.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>revol_bal</th>\n",
              "      <td>396030.0</td>\n",
              "      <td>15844.539853</td>\n",
              "      <td>20591.836109</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6025.00</td>\n",
              "      <td>11181.00</td>\n",
              "      <td>19620.00</td>\n",
              "      <td>1743266.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>revol_util</th>\n",
              "      <td>395754.0</td>\n",
              "      <td>53.791749</td>\n",
              "      <td>24.452193</td>\n",
              "      <td>0.00</td>\n",
              "      <td>35.80</td>\n",
              "      <td>54.80</td>\n",
              "      <td>72.90</td>\n",
              "      <td>892.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_acc</th>\n",
              "      <td>396030.0</td>\n",
              "      <td>25.414744</td>\n",
              "      <td>11.886991</td>\n",
              "      <td>2.00</td>\n",
              "      <td>17.00</td>\n",
              "      <td>24.00</td>\n",
              "      <td>32.00</td>\n",
              "      <td>151.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mort_acc</th>\n",
              "      <td>358235.0</td>\n",
              "      <td>1.813991</td>\n",
              "      <td>2.147930</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>34.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "      <td>395495.0</td>\n",
              "      <td>0.121648</td>\n",
              "      <td>0.356174</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>8.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         count          mean  ...       75%         max\n",
              "loan_amnt             396030.0  14113.888089  ...  20000.00    40000.00\n",
              "int_rate              396030.0     13.639400  ...     16.49       30.99\n",
              "installment           396030.0    431.849698  ...    567.30     1533.81\n",
              "annual_inc            396030.0  74203.175798  ...  90000.00  8706582.00\n",
              "dti                   396030.0     17.379514  ...     22.98     9999.00\n",
              "open_acc              396030.0     11.311153  ...     14.00       90.00\n",
              "pub_rec               396030.0      0.178191  ...      0.00       86.00\n",
              "revol_bal             396030.0  15844.539853  ...  19620.00  1743266.00\n",
              "revol_util            395754.0     53.791749  ...     72.90      892.30\n",
              "total_acc             396030.0     25.414744  ...     32.00      151.00\n",
              "mort_acc              358235.0      1.813991  ...      3.00       34.00\n",
              "pub_rec_bankruptcies  395495.0      0.121648  ...      0.00        8.00\n",
              "\n",
              "[12 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8OLGogYCgutf"
      },
      "source": [
        "## 1. Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "fCKxBcNeg5Cx",
        "outputId": "00f7874d-849c-4ade-c851-c28227fe0285"
      },
      "source": [
        "# countplot for the loan status (fully paid vs charged off)\n",
        "\n",
        "sns.countplot(x='loan_status', data=loan)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f74bc6e0d90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "nHZmRwaHg5ar",
        "outputId": "a4da1cbd-94f6-4a0f-e327-b745dba9f5b3"
      },
      "source": [
        "plt.figure(figsize=(10,4))\n",
        "sns.histplot(data=loan, x='loan_amnt', bins=40)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f74c0d7b590>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "xCLuz5oej3DW",
        "outputId": "6422a060-308c-4e58-8508-91fbef7f788a"
      },
      "source": [
        "# exploring correlation between continuous feature variables\n",
        "\n",
        "corr = loan.corr()\n",
        "plt.figure(figsize=(12,7))\n",
        "sns.heatmap(corr, annot=True, cmap='YlGnBu')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f74baec6950>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "yQXqmjRUkym-",
        "outputId": "d660cd6e-afd5-42f7-a0c9-633b7c4ee7cc"
      },
      "source": [
        "sns.scatterplot(x = 'installment', \n",
        "                y='loan_amnt', \n",
        "                data=loan, \n",
        "                alpha=0.4, \n",
        "                marker=\"+\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f74badfbb10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "BKMMU9pWoZoe",
        "outputId": "c0aa295a-9fe5-499a-8623-e958d9b1596f"
      },
      "source": [
        "sns.boxplot(x='loan_amnt', y='loan_status',data=loan)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f74bc6dbdd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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h5s6INC6j0SWXXMJJJ53UfOBBdPLd4bbM/M/M3ATcDkyvxx8WEbdGxApgDlXBbfYdgIh4KbBjZv6oHv+thnn+DJgfEbcDtwLTgL2AQ4GLM3NjZj4IXNem74s64/ERsSwilj322GPtXJUktcS1117Lhg0bANiwYQPXXHPNqJbRTu3c87oTeN4FEQ2ebRjeCEyqzyt9GZiVmb+KiE8D2zfM9/Qw1hvAwsz8/lYjI+Zt64mZ+ZuIeDoiXtVv7+tA4MZhrLtxWecD5wPMmjUrtzH7sPX09ABw9tlnt2qRXWnRokU8+8CyTscYt3Z78Sa223PGuP850vMNtbd9+OGHc+WVV7JhwwYmTZrEm970pqaX37iMdmrnntd1wHYRcfzmERGx3zbODW0uql9HxFQGKb/MfAJ4KiL+Wz3qPQ2Tvw+cEBGT63XuHRE7ADcBx9TnxHanOic3kLOAcyJiSv38w6nOc31rkPklaVxYsGABEyZUtTBx4kTmz58/qmU0ete73jXqfI3aVl5Zna17O3B4fan8nVRX7j08xHOeAL4GrKQqoR8PsYpjga/Vhwd3AJ6sx3+d6nzWT+rzYl+l2sO8DFhVT1sC/Oh5S6wsrte7IiJ+AfwNcFRmrtvmNy1JBZs2bRpz584lIpg7dy7Tpk0b1TIatfJ8F7T3sCH1uaV3DzBpFXBDw3wnNwyfDpw+wLJm9xt1Z2buBxARH6O6qIL6HNon6q/+Th5gXP/1JHBG/TXQ9NkNw6upLkyRpHFhwYIFrF69ekR7Xf2X0dPTw9KlS1u+1wVtLq82e0tEfJzqe3gA+EBn40hS+aZNm8Y555zTsmV89KMfbUWs5ym2vDLzO9RXH0qSXlj8QxpJUnEsL0lScSwvSVJxLC9JUnEsL0lScSwvSVJxLC9JUnEsL0lScSwvSVJxLC9JUnEsL0lScSwvSVJxLC9JUnEsL0lScYq9JcoL1YwZMzodQVLBxst7iOVVmIULF3Y6gqSCjZf3EA8bSpKKY3lJkopjeUmSimN5SZKKY3lJkopjeUmSimN5SZKKY3lJkopjeUmSimN5SZKKY3lJkopjeUmSimN5SZKKY3lJkopjeUmSimN5SZKKY3lJkopjeUmSimN5SZKKM6nTAaTB/HLtRM78yU6djjGkB56aCND1Ofv75dqJ7NXpENIoWF7qSjNmzOh0hGHZYc0aALbr6elwkubsRTnbWBqI5aWutHDhwk5HkNTFPOclSSqO5SVJKo7lJUkqjuUlSSqO5SVJKo7lJUkqjuUlSSqO5SVJKo7lJUkqTmRmpzOMexHxGPBAE095GfDrNsUZjW7NBd2brVtzQfdm69ZcYLaRGE2uPTNz14EmWF5dKCKWZeasTufor1tzQfdm69Zc0L3ZujUXmG0k2pXLw4aSpOJYXpKk4lhe3en8TgcYRLfmgu7N1q25oHuzdWsuMNtItCWX57wkScVxz0uSVBzLS5JUHMuri0TE3Ij4RUT0RsTHxnC9qyNiRUTcHhHL6nG7RMQ1EbGq/nfnenxExDl1xp9FxAENy1lQz78qIhaMIMc3IuLRiFjZMK5lOSLiwPr77K2fG6PM9umIWFNvt9sjYl7DtI/X6/lFRLy5YfyAr3FEvDIibq3HfyciXjTMXK+IiOsj4ucRcWdELOqG7TZErm7YZttHxG0RcUed7YyhlhcR29WPe+vp00eaeRTZLoiI+xu22/71+LH+PZgYET+NiCs6vs0y068u+AImAvcCrwJeBNwBzByjda8GXtZv3D8AH6uHPwZ8rh6eBywFAng9cGs9fhfgvvrfnevhnZvMcShwALCyHTmA2+p5o37uEaPM9mngrwaYd2b9+m0HvLJ+XScO9RoD/wq8px7+CnDCMHPtDhxQD+8I3FOvv6PbbYhc3bDNAphaD08Gbq2/vwGXB5wIfKUefg/wnZFmHkW2C4B3DjD/WP8efAT4FnDFUK/BWGwz97y6x58AvZl5X2b+Dvg2cFQH8xwFXFgPXwgc3TB+SVZuAV4aEbsDbwauyczHM/O/gGuAuc2sMDNvAh5vR4562k6ZeUtWv0VLGpY10myDOQr4dmY+m5n3A71Ur++Ar3H9P985wKUDfJ/byvVQZv6kHn4KuAvoocPbbYhcgxnLbZaZubZ+OLn+yiGW17gtLwX+tF5/U5lHmW0wY/Z7EBF7AG8Bvl4/Huo1aPs2s7y6Rw/wq4bH/8nQv+ytlMDVEbE8Io6vx+2WmQ/Vww8Du9XDg+VsV/5W5eiph1ud7+T6cM03oj40N4Js04AnMnPDaLLVh2b+mOp/612z3frlgi7YZvXhr9uBR6ne2O8dYnlbMtTTn6zX35bfhf7ZMnPzdvtsvd2+EBHb9c82zAyjeT2/CPw1sKl+PNRr0PZtZnkJ4JDMPAA4AjgpIg5tnFj/D63jf1PRLTkanAe8GtgfeAj4fKeCRMRU4LvAhzLzN43TOrndBsjVFdssMzdm5v7AHlT/6/+jTuQYSP9sEfEa4ONUGQ+iOhT40bHMFBFHAo9m5vKxXO9QLK/usQZ4RcPjPepxbZeZa+p/HwUuo/plfqQ+xED976PbyNmu/K3KsaYeblm+zHykfqPZBHyNaruNJFsf1eGeSSPJFhGTqQriosz8t3p0x7fbQLm6ZZttlplPANcDbxhieVsy1NNfUq+/rb8LDdnm1odhMzOfBb7JyLfbSF/Pg4G3RcRqqkN6c4Cz6eQ2G+qEmF9j9wVMojqp+kqeO2G57xisdwdgx4bhH1KdqzqLrU/4/0M9/Ba2PkF8Wz1+F+B+qpPDO9fDu4wgz3S2viiiZTl4/onqeaPMtnvD8IepjuUD7MvWJ6XvozohPehrDFzC1ie+TxxmpqA6b/HFfuM7ut2GyNUN22xX4KX18BTgZuDIwZYHnMTWFx/860gzjyLb7g3b9YvA33fw92A2z12w0bFt1pE3ar8G/aGYR3VV1r3AaWO0zlfVPyh3AHduXi/V8en/AFYB1zb84Adwbp1xBTCrYVl/QXUCthf44AiyXEx1KGk91THvY1uZA5gFrKyf8yXqT5gZRbZ/qdf9M+B7bP3GfFq9nl/QcDXXYK9x/TrcVme+BNhumLkOoTok+DPg9vprXqe32xC5umGb7Qf8tM6wEvjkUMsDtq8f99bTXzXSzKPIdl293VYC/4fnrkgc09+D+vmzea68OrbN/HgoSVJxPOclSSqO5SVJKo7lJUkqjuUlSSqO5SVJKo7lJUkqjuUldbGIWLvtubpXRBwdETM7nUPjj+UlqZ2OproNhtRSlpdUgPqmg2dFxMr6RoLH1OOnRsR/RMRP6vFH1eOnR8RdEfG1+qaGV0fElCGWf1xE/Li+CeJ3I+LF9fgLIuK8iLglIu6LiNn1p8HfFREXNDx/bUR8tn7+LRGxW0S8EXgbcFZUN1B8dVs3kl5QLC+pDH9O9UnsrwMOpyqE3YHfAm/P6q4AhwGfb7gz7l7AuZm5L/AE8I4hlv9vmXlQZr6O6t5bxzZM25nqg2s/TPWRTl+g+oy610Z9R1+qz8W8pX7+TcBxmfnDev5TM3P/zLx3dJtAeo7lJZXhEODirD6R/RHgRqrbYwRwZkT8jOozDHt47t5d92fm7fXwcqoPFh7MayLi5ohYAbyPqpw2+/esPkduBfBIZq7I6lPh72xY5u+AK4a5LmnUJm17Fkld7H1Un0R+YGaur29ZsX097dmG+TZSfUr5YC4Ajs7MOyLiA1QfvrrZ5uVs6rfMTTz3HrI+n/ug1I343qI2c89LKsPNwDH1XXZ3BQ6l+rTul1DdJHB9RBwG7DnC5e8IPFTfg+t9LUlceapettRSlpdUhsuobpNxB9XtMf46Mx8GLgJm1Yf75gN3j3D5fwPcCvxgFMsYyLeBUyPip16woVbyliiSpOK45yVJKo4nVaUXkIg4Fzi43+izM/ObncgjjZSHDSVJxfGwoSSpOJaXJKk4lpckqTiWlySpOP8fgi3QhzzbTTwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "4045J09opwO5",
        "outputId": "e6bb8c46-9e17-4e29-884d-d3a72e9a9480"
      },
      "source": [
        "loan.groupby('loan_status')['loan_amnt'].describe()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_status</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Charged Off</th>\n",
              "      <td>77673.0</td>\n",
              "      <td>15126.300967</td>\n",
              "      <td>8505.090557</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>8525.0</td>\n",
              "      <td>14000.0</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>40000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fully Paid</th>\n",
              "      <td>318357.0</td>\n",
              "      <td>13866.878771</td>\n",
              "      <td>8302.319699</td>\n",
              "      <td>500.0</td>\n",
              "      <td>7500.0</td>\n",
              "      <td>12000.0</td>\n",
              "      <td>19225.0</td>\n",
              "      <td>40000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                count          mean          std  ...      50%      75%      max\n",
              "loan_status                                       ...                           \n",
              "Charged Off   77673.0  15126.300967  8505.090557  ...  14000.0  20000.0  40000.0\n",
              "Fully Paid   318357.0  13866.878771  8302.319699  ...  12000.0  19225.0  40000.0\n",
              "\n",
              "[2 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LU0zBJr2qFsd",
        "outputId": "43f4fdb1-87c8-4b28-fe88-d8ad62f245fb"
      },
      "source": [
        "print(sorted(loan['grade'].unique()))\n",
        "print('\\n')\n",
        "print(sorted(loan['sub_grade'].unique()))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['A', 'B', 'C', 'D', 'E', 'F', 'G']\n",
            "\n",
            "\n",
            "['A1', 'A2', 'A3', 'A4', 'A5', 'B1', 'B2', 'B3', 'B4', 'B5', 'C1', 'C2', 'C3', 'C4', 'C5', 'D1', 'D2', 'D3', 'D4', 'D5', 'E1', 'E2', 'E3', 'E4', 'E5', 'F1', 'F2', 'F3', 'F4', 'F5', 'G1', 'G2', 'G3', 'G4', 'G5']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "oxmexK1asAkW",
        "outputId": "01d312e8-9480-4590-b978-4175c4e849ed"
      },
      "source": [
        "sns.countplot(x='grade', \n",
        "              data=loan, \n",
        "              hue='loan_status', \n",
        "              order = sorted(loan['grade'].unique()))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f74b0b0a450>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "QUQm68mAsty5",
        "outputId": "56cc90ba-e67d-4469-8958-e88c52ed1753"
      },
      "source": [
        "sub_sort = sorted(loan['sub_grade'].unique())\n",
        "\n",
        "plt.figure(figsize=(14,6))\n",
        "sns.countplot(x='sub_grade', \n",
        "              data=loan, \n",
        "              hue='loan_status',\n",
        "              palette='gnuplot',\n",
        "              order=sub_sort)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f74b0a4ecd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "WnjoxMd4vXvL",
        "outputId": "0ff7ab6b-088f-49f5-a52e-31fec3b9d78d"
      },
      "source": [
        "plt.figure(figsize=(14,6))\n",
        "sns.countplot(x='sub_grade', \n",
        "              data=loan, \n",
        "              palette='PuOr',\n",
        "              order=sub_sort)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f74b0b14390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "6eOHxDg7s20D",
        "outputId": "59db30af-1d75-4367-cc2d-4f4d64d0a0eb"
      },
      "source": [
        "fg_sub = loan[(loan['grade']=='F') | (loan['grade']=='G')]\n",
        "sub_order = sorted(fg_sub['sub_grade'].unique())\n",
        "\n",
        "plt.figure(figsize=(14,6))\n",
        "sns.countplot(x='sub_grade', \n",
        "              data=fg_sub, \n",
        "              palette='PuOr',\n",
        "              hue='loan_status',\n",
        "              order=sub_order)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f74b08d6f50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vq0WjmkWxRVb"
      },
      "source": [
        "loan['loan_repaid'] = loan['loan_status'].map({'Fully Paid':1, 'Charged Off':0})"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "bHHjAYeQyPyE",
        "outputId": "a9ca21cf-4350-4541-c6a2-bad193cf436d"
      },
      "source": [
        "loan[['loan_status', 'loan_repaid']]"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "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_status</th>\n",
              "      <th>loan_repaid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Charged Off</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396025</th>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396026</th>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396027</th>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396028</th>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396029</th>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>396030 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        loan_status  loan_repaid\n",
              "0        Fully Paid            1\n",
              "1        Fully Paid            1\n",
              "2        Fully Paid            1\n",
              "3        Fully Paid            1\n",
              "4       Charged Off            0\n",
              "...             ...          ...\n",
              "396025   Fully Paid            1\n",
              "396026   Fully Paid            1\n",
              "396027   Fully Paid            1\n",
              "396028   Fully Paid            1\n",
              "396029   Fully Paid            1\n",
              "\n",
              "[396030 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "-mey3QgnySva",
        "outputId": "fd34cdb2-1a5c-4bc8-f2df-d342847c3cb8"
      },
      "source": [
        "loan.corr()['loan_repaid'].sort_values().drop('loan_repaid').plot(kind='bar', color='#a98d19')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f74b068acd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FZ_k6j-Wyvlg"
      },
      "source": [
        "## 2. Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E2qEewNiGEQI"
      },
      "source": [
        "### 2.1 Missing Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C9z93W9tzyNq",
        "outputId": "142dff26-1a1f-4516-e953-0e9ff7c3df37"
      },
      "source": [
        "len(loan)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "396030"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e729AZMDzyLP",
        "outputId": "f3006287-2f2f-40fe-f6e6-188b3cffb801"
      },
      "source": [
        "100 * loan.isnull().sum()/len(loan)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_amnt               0.000000\n",
              "term                    0.000000\n",
              "int_rate                0.000000\n",
              "installment             0.000000\n",
              "grade                   0.000000\n",
              "sub_grade               0.000000\n",
              "emp_title               5.789208\n",
              "emp_length              4.621115\n",
              "home_ownership          0.000000\n",
              "annual_inc              0.000000\n",
              "verification_status     0.000000\n",
              "issue_d                 0.000000\n",
              "loan_status             0.000000\n",
              "purpose                 0.000000\n",
              "title                   0.443148\n",
              "dti                     0.000000\n",
              "earliest_cr_line        0.000000\n",
              "open_acc                0.000000\n",
              "pub_rec                 0.000000\n",
              "revol_bal               0.000000\n",
              "revol_util              0.069692\n",
              "total_acc               0.000000\n",
              "initial_list_status     0.000000\n",
              "application_type        0.000000\n",
              "mort_acc                9.543469\n",
              "pub_rec_bankruptcies    0.135091\n",
              "address                 0.000000\n",
              "loan_repaid             0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "61-veo4nzyIy",
        "outputId": "3d134068-d203-4472-9ac0-6428295cf196"
      },
      "source": [
        "loan['emp_title'].nunique()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "173105"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2peAaLNrzyGN",
        "outputId": "f7f4550c-30e1-4568-ed5a-eb2acd6c6dd5"
      },
      "source": [
        "loan['emp_title'].value_counts()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Teacher                        4389\n",
              "Manager                        4250\n",
              "Registered Nurse               1856\n",
              "RN                             1846\n",
              "Supervisor                     1830\n",
              "                               ... \n",
              "Florida Pest Management           1\n",
              "logistician                       1\n",
              "Criminal Justice Specialist       1\n",
              "Evaluation Consultant             1\n",
              "Calsense                          1\n",
              "Name: emp_title, Length: 173105, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jFvOXseFzyAO"
      },
      "source": [
        "loan = loan.drop('emp_title', axis=1)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UtgGBXX_zx3b",
        "outputId": "d0151da7-e3d0-4a0e-fb04-2493b80e24fe"
      },
      "source": [
        "sorted(loan['emp_length'].dropna().unique())"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['1 year',\n",
              " '10+ years',\n",
              " '2 years',\n",
              " '3 years',\n",
              " '4 years',\n",
              " '5 years',\n",
              " '6 years',\n",
              " '7 years',\n",
              " '8 years',\n",
              " '9 years',\n",
              " '< 1 year']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "h7ZOZ6-B-bMT",
        "outputId": "9cccb691-2f59-42be-a231-5d2fe91ae11a"
      },
      "source": [
        "emp_length_order = ['< 1 year', '1 year', '2 years',\n",
        "                    '3 years','4 years', '5 years',\n",
        "                    '6 years','7 years','8 years',\n",
        "                    '9 years','10+ years']\n",
        "\n",
        "plt.figure(figsize=(13,4))\n",
        "sns.countplot(x = 'emp_length', data = loan, order = emp_length_order, hue='loan_status')"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f74bae66c90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PNlttN4l_N18",
        "outputId": "e3138c3c-7ce1-4a1c-cfa4-ecd659d6a9ce"
      },
      "source": [
        "emp_co = loan[loan['loan_status']=='Charged Off'].groupby(\"emp_length\").count()['loan_status']\n",
        "emp_fp = loan[loan['loan_status']=='Fully Paid'].groupby(\"emp_length\").count()['loan_status']\n",
        "\n",
        "emp_co/(emp_co + emp_fp)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "emp_length\n",
              "1 year       0.199135\n",
              "10+ years    0.184186\n",
              "2 years      0.193262\n",
              "3 years      0.195231\n",
              "4 years      0.192385\n",
              "5 years      0.192187\n",
              "6 years      0.189194\n",
              "7 years      0.194774\n",
              "8 years      0.199760\n",
              "9 years      0.200470\n",
              "< 1 year     0.206872\n",
              "Name: loan_status, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "AaADo5y3AMUA",
        "outputId": "506a0b4b-30de-454e-eaea-166c0e9753c3"
      },
      "source": [
        "(emp_co/(emp_co + emp_fp)).plot(kind='bar')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f74b0566050>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_aq1KP0LBOwS"
      },
      "source": [
        "loan = loan.drop('emp_length', axis=1)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ukxIhBMzBk8z",
        "outputId": "21a02964-77e1-40a5-c182-866f9ab41905"
      },
      "source": [
        "loan.isnull().sum()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_amnt                   0\n",
              "term                        0\n",
              "int_rate                    0\n",
              "installment                 0\n",
              "grade                       0\n",
              "sub_grade                   0\n",
              "home_ownership              0\n",
              "annual_inc                  0\n",
              "verification_status         0\n",
              "issue_d                     0\n",
              "loan_status                 0\n",
              "purpose                     0\n",
              "title                    1755\n",
              "dti                         0\n",
              "earliest_cr_line            0\n",
              "open_acc                    0\n",
              "pub_rec                     0\n",
              "revol_bal                   0\n",
              "revol_util                276\n",
              "total_acc                   0\n",
              "initial_list_status         0\n",
              "application_type            0\n",
              "mort_acc                37795\n",
              "pub_rec_bankruptcies      535\n",
              "address                     0\n",
              "loan_repaid                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0pgNdKEjBou_"
      },
      "source": [
        "loan = loan.drop('title', axis =1)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fusPBgA4B5mC",
        "outputId": "912f1bb4-2120-4932-c910-d5bbedf3eb50"
      },
      "source": [
        "loan['mort_acc'].value_counts()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0     139777\n",
              "1.0      60416\n",
              "2.0      49948\n",
              "3.0      38049\n",
              "4.0      27887\n",
              "5.0      18194\n",
              "6.0      11069\n",
              "7.0       6052\n",
              "8.0       3121\n",
              "9.0       1656\n",
              "10.0       865\n",
              "11.0       479\n",
              "12.0       264\n",
              "13.0       146\n",
              "14.0       107\n",
              "15.0        61\n",
              "16.0        37\n",
              "17.0        22\n",
              "18.0        18\n",
              "19.0        15\n",
              "20.0        13\n",
              "24.0        10\n",
              "22.0         7\n",
              "21.0         4\n",
              "25.0         4\n",
              "27.0         3\n",
              "23.0         2\n",
              "32.0         2\n",
              "26.0         2\n",
              "31.0         2\n",
              "30.0         1\n",
              "28.0         1\n",
              "34.0         1\n",
              "Name: mort_acc, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aCEdCwoECYiO",
        "outputId": "e4c87112-ee64-4f63-9f5a-99f89e8bef7e"
      },
      "source": [
        "loan.corr()['mort_acc'].sort_values()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "int_rate               -0.082583\n",
              "dti                    -0.025439\n",
              "revol_util              0.007514\n",
              "pub_rec                 0.011552\n",
              "pub_rec_bankruptcies    0.027239\n",
              "loan_repaid             0.073111\n",
              "open_acc                0.109205\n",
              "installment             0.193694\n",
              "revol_bal               0.194925\n",
              "loan_amnt               0.222315\n",
              "annual_inc              0.236320\n",
              "total_acc               0.381072\n",
              "mort_acc                1.000000\n",
              "Name: mort_acc, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BPRBPac7C7Wc"
      },
      "source": [
        "total_acc_avg = loan.groupby('total_acc').mean()['mort_acc']\n",
        "\n",
        "def fill_mort_acc(total_acc, mort_acc):\n",
        "  if np.isnan(mort_acc):\n",
        "    return total_acc_avg[total_acc]\n",
        "  else:\n",
        "    return mort_acc"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_U2wi9feDSzE"
      },
      "source": [
        "loan['mort_acc'] = loan.apply(lambda x: fill_mort_acc(x['total_acc'], x['mort_acc']), axis=1)"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rAV6FJd3E6IA",
        "outputId": "52fa3998-3fe1-40c8-c371-d87e7e358111"
      },
      "source": [
        "loan.isnull().sum()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_amnt                 0\n",
              "term                      0\n",
              "int_rate                  0\n",
              "installment               0\n",
              "grade                     0\n",
              "sub_grade                 0\n",
              "home_ownership            0\n",
              "annual_inc                0\n",
              "verification_status       0\n",
              "issue_d                   0\n",
              "loan_status               0\n",
              "purpose                   0\n",
              "dti                       0\n",
              "earliest_cr_line          0\n",
              "open_acc                  0\n",
              "pub_rec                   0\n",
              "revol_bal                 0\n",
              "revol_util              276\n",
              "total_acc                 0\n",
              "initial_list_status       0\n",
              "application_type          0\n",
              "mort_acc                  0\n",
              "pub_rec_bankruptcies    535\n",
              "address                   0\n",
              "loan_repaid               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4JV2_Zp8E8-X",
        "outputId": "da5e525f-940a-4d34-9c26-b94313001f8f"
      },
      "source": [
        "loan = loan.dropna()\n",
        "loan.isnull().sum()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_amnt               0\n",
              "term                    0\n",
              "int_rate                0\n",
              "installment             0\n",
              "grade                   0\n",
              "sub_grade               0\n",
              "home_ownership          0\n",
              "annual_inc              0\n",
              "verification_status     0\n",
              "issue_d                 0\n",
              "loan_status             0\n",
              "purpose                 0\n",
              "dti                     0\n",
              "earliest_cr_line        0\n",
              "open_acc                0\n",
              "pub_rec                 0\n",
              "revol_bal               0\n",
              "revol_util              0\n",
              "total_acc               0\n",
              "initial_list_status     0\n",
              "application_type        0\n",
              "mort_acc                0\n",
              "pub_rec_bankruptcies    0\n",
              "address                 0\n",
              "loan_repaid             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xv1KitvuFIed"
      },
      "source": [
        "## 2.2 Categorical Variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nXDGj0VFFUmp"
      },
      "source": [
        "# Converting categorical data to numerical by using one-hot-encoding"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2wuZWOEXGZbZ",
        "outputId": "e4250d9b-2c4a-4ea5-ad94-ce55d0052746"
      },
      "source": [
        "loan.select_dtypes(['object']).columns"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['term', 'grade', 'sub_grade', 'home_ownership', 'verification_status',\n",
              "       'issue_d', 'loan_status', 'purpose', 'earliest_cr_line',\n",
              "       'initial_list_status', 'application_type', 'address'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ajm2ImL2GgUx",
        "outputId": "9d74fc3e-8fcc-4bc4-dbe0-1062fcc7c491"
      },
      "source": [
        "# term \n",
        "\n",
        "loan['term'].value_counts()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " 36 months    301247\n",
              " 60 months     93972\n",
              "Name: term, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zUN_ARRPGvFn"
      },
      "source": [
        "loan['term'] = loan['term'].apply(lambda term: int(term[:3]))"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sHwK86atHLhl",
        "outputId": "189dc4b2-c5bf-4487-bf06-cd847fa1b744"
      },
      "source": [
        "loan['term']"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         36\n",
              "1         36\n",
              "2         36\n",
              "3         36\n",
              "4         60\n",
              "          ..\n",
              "396025    60\n",
              "396026    36\n",
              "396027    36\n",
              "396028    60\n",
              "396029    36\n",
              "Name: term, Length: 395219, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QXPjr8KMHSsN"
      },
      "source": [
        "# grade is a part of sub_grade so we can just drop it\n",
        "\n",
        "loan = loan.drop('grade', axis=1)"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LVbTVdgyHpMs"
      },
      "source": [
        "# converting subgrade to dummy variables\n",
        "\n",
        "dummies = pd.get_dummies(loan['sub_grade'], drop_first = True)\n",
        "\n",
        "loan = pd.concat([loan.drop('sub_grade', axis=1), dummies], axis=1)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UuaNvrYbIbtE",
        "outputId": "17d98b73-027c-4c13-cab2-c73e86e84470"
      },
      "source": [
        "loan.columns"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['loan_amnt', 'term', 'int_rate', 'installment', 'home_ownership',\n",
              "       'annual_inc', 'verification_status', 'issue_d', 'loan_status',\n",
              "       'purpose', 'dti', 'earliest_cr_line', 'open_acc', 'pub_rec',\n",
              "       'revol_bal', 'revol_util', 'total_acc', 'initial_list_status',\n",
              "       'application_type', 'mort_acc', 'pub_rec_bankruptcies', 'address',\n",
              "       'loan_repaid', 'A2', 'A3', 'A4', 'A5', 'B1', 'B2', 'B3', 'B4', 'B5',\n",
              "       'C1', 'C2', 'C3', 'C4', 'C5', 'D1', 'D2', 'D3', 'D4', 'D5', 'E1', 'E2',\n",
              "       'E3', 'E4', 'E5', 'F1', 'F2', 'F3', 'F4', 'F5', 'G1', 'G2', 'G3', 'G4',\n",
              "       'G5'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sNnHsH-GIeS6",
        "outputId": "eec3a8e6-8e42-4dcd-e260-00aff173136b"
      },
      "source": [
        "# verification status\n",
        "\n",
        "print(loan['verification_status'].value_counts())\n",
        "print()\n",
        "# application type\n",
        "print('-'*10)\n",
        "print(loan['application_type'].value_counts())\n",
        "print()\n",
        "#initial status\n",
        "print('-'*10)\n",
        "print(loan['initial_list_status'].value_counts())\n",
        "print()\n",
        "# purpose\n",
        "print('-'*10)\n",
        "print(loan['purpose'].value_counts())\n"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Verified           139451\n",
            "Source Verified    131301\n",
            "Not Verified       124467\n",
            "Name: verification_status, dtype: int64\n",
            "\n",
            "----------\n",
            "INDIVIDUAL    394508\n",
            "JOINT            425\n",
            "DIRECT_PAY       286\n",
            "Name: application_type, dtype: int64\n",
            "\n",
            "----------\n",
            "f    237346\n",
            "w    157873\n",
            "Name: initial_list_status, dtype: int64\n",
            "\n",
            "----------\n",
            "debt_consolidation    234169\n",
            "credit_card            82923\n",
            "home_improvement       23961\n",
            "other                  21059\n",
            "major_purchase          8756\n",
            "small_business          5656\n",
            "car                     4670\n",
            "medical                 4175\n",
            "moving                  2842\n",
            "vacation                2442\n",
            "house                   2197\n",
            "wedding                 1794\n",
            "renewable_energy         329\n",
            "educational              246\n",
            "Name: purpose, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uDKV5ubTIzv8"
      },
      "source": [
        "dummies = pd.get_dummies(loan[['verification_status', 'application_type', 'initial_list_status', 'purpose']], drop_first = True)\n",
        "\n",
        "loan = pd.concat([loan.drop(['verification_status', 'application_type', 'initial_list_status', 'purpose'], axis=1), dummies], axis=1)"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aADbWhsvKDzw",
        "outputId": "95a1a3c9-e441-43ee-b6ad-5436b3150961"
      },
      "source": [
        "loan.columns"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['loan_amnt', 'term', 'int_rate', 'installment', 'home_ownership',\n",
              "       'annual_inc', 'issue_d', 'loan_status', 'dti', 'earliest_cr_line',\n",
              "       'open_acc', 'pub_rec', 'revol_bal', 'revol_util', 'total_acc',\n",
              "       'mort_acc', 'pub_rec_bankruptcies', 'address', 'loan_repaid', 'A2',\n",
              "       'A3', 'A4', 'A5', 'B1', 'B2', 'B3', 'B4', 'B5', 'C1', 'C2', 'C3', 'C4',\n",
              "       'C5', 'D1', 'D2', 'D3', 'D4', 'D5', 'E1', 'E2', 'E3', 'E4', 'E5', 'F1',\n",
              "       'F2', 'F3', 'F4', 'F5', 'G1', 'G2', 'G3', 'G4', 'G5',\n",
              "       'verification_status_Source Verified', 'verification_status_Verified',\n",
              "       'application_type_INDIVIDUAL', 'application_type_JOINT',\n",
              "       'initial_list_status_w', 'purpose_credit_card',\n",
              "       'purpose_debt_consolidation', 'purpose_educational',\n",
              "       'purpose_home_improvement', 'purpose_house', 'purpose_major_purchase',\n",
              "       'purpose_medical', 'purpose_moving', 'purpose_other',\n",
              "       'purpose_renewable_energy', 'purpose_small_business',\n",
              "       'purpose_vacation', 'purpose_wedding'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XdcJnaF4KI11",
        "outputId": "058b794e-45cc-4c32-8eda-f0ea6f8a1107"
      },
      "source": [
        "# home ownership\n",
        "\n",
        "print(loan['home_ownership'].value_counts())\n",
        "print('-'*10)\n",
        "\n",
        "\n",
        "# replace none and any with other, i.e. have 110+29+3=142 for the other category\n",
        "# reduce number of categories, from 6 to 4\n",
        "\n",
        "loan['home_ownership'] = loan['home_ownership'].replace(['NONE', 'ANY'], 'OTHER')\n",
        "print(loan['home_ownership'].value_counts())\n"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MORTGAGE    198022\n",
            "RENT        159395\n",
            "OWN          37660\n",
            "OTHER          110\n",
            "NONE            29\n",
            "ANY              3\n",
            "Name: home_ownership, dtype: int64\n",
            "----------\n",
            "MORTGAGE    198022\n",
            "RENT        159395\n",
            "OWN          37660\n",
            "OTHER          142\n",
            "Name: home_ownership, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h6o6kFxwKQ2j"
      },
      "source": [
        "# converting home ownership into dummy variables\n",
        "\n",
        "dummies = pd.get_dummies(loan['home_ownership'], drop_first = True)\n",
        "\n",
        "loan = pd.concat([loan.drop('home_ownership', axis=1), dummies], axis=1)"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FzpORLi6LQkd",
        "outputId": "01dc3fc3-cec1-4b46-8959-fc3eb9a8ab9c"
      },
      "source": [
        "loan.columns"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['loan_amnt', 'term', 'int_rate', 'installment', 'annual_inc', 'issue_d',\n",
              "       'loan_status', 'dti', 'earliest_cr_line', 'open_acc', 'pub_rec',\n",
              "       'revol_bal', 'revol_util', 'total_acc', 'mort_acc',\n",
              "       'pub_rec_bankruptcies', 'address', 'loan_repaid', 'A2', 'A3', 'A4',\n",
              "       'A5', 'B1', 'B2', 'B3', 'B4', 'B5', 'C1', 'C2', 'C3', 'C4', 'C5', 'D1',\n",
              "       'D2', 'D3', 'D4', 'D5', 'E1', 'E2', 'E3', 'E4', 'E5', 'F1', 'F2', 'F3',\n",
              "       'F4', 'F5', 'G1', 'G2', 'G3', 'G4', 'G5',\n",
              "       'verification_status_Source Verified', 'verification_status_Verified',\n",
              "       'application_type_INDIVIDUAL', 'application_type_JOINT',\n",
              "       'initial_list_status_w', 'purpose_credit_card',\n",
              "       'purpose_debt_consolidation', 'purpose_educational',\n",
              "       'purpose_home_improvement', 'purpose_house', 'purpose_major_purchase',\n",
              "       'purpose_medical', 'purpose_moving', 'purpose_other',\n",
              "       'purpose_renewable_energy', 'purpose_small_business',\n",
              "       'purpose_vacation', 'purpose_wedding', 'OTHER', 'OWN', 'RENT'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jsF27LTkLSbT",
        "outputId": "1bf26a67-30d8-4eb9-e031-33e2cb828a22"
      },
      "source": [
        "# address - extracting the zip code\n",
        "\n",
        "loan['zip_code'] = loan['address'].apply(lambda x:x[-5:])\n",
        "print(loan['zip_code'].value_counts()) # just 10 zip codes!\n"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "70466    56880\n",
            "22690    56413\n",
            "30723    56402\n",
            "48052    55811\n",
            "00813    45725\n",
            "29597    45393\n",
            "05113    45300\n",
            "11650    11210\n",
            "93700    11126\n",
            "86630    10959\n",
            "Name: zip_code, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K30FhN8oLkO_"
      },
      "source": [
        "# converting zip code into dummy variables\n",
        "\n",
        "dummies = pd.get_dummies(loan['zip_code'], drop_first = True)\n",
        "\n",
        "loan = pd.concat([loan.drop('zip_code', axis=1), dummies], axis=1)"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sF3yq3WDMMO4",
        "outputId": "f3a6f180-83be-4a8b-bf6e-5b3325b204d2"
      },
      "source": [
        "loan.columns"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['loan_amnt', 'term', 'int_rate', 'installment', 'annual_inc', 'issue_d',\n",
              "       'loan_status', 'dti', 'earliest_cr_line', 'open_acc', 'pub_rec',\n",
              "       'revol_bal', 'revol_util', 'total_acc', 'mort_acc',\n",
              "       'pub_rec_bankruptcies', 'address', 'loan_repaid', 'A2', 'A3', 'A4',\n",
              "       'A5', 'B1', 'B2', 'B3', 'B4', 'B5', 'C1', 'C2', 'C3', 'C4', 'C5', 'D1',\n",
              "       'D2', 'D3', 'D4', 'D5', 'E1', 'E2', 'E3', 'E4', 'E5', 'F1', 'F2', 'F3',\n",
              "       'F4', 'F5', 'G1', 'G2', 'G3', 'G4', 'G5',\n",
              "       'verification_status_Source Verified', 'verification_status_Verified',\n",
              "       'application_type_INDIVIDUAL', 'application_type_JOINT',\n",
              "       'initial_list_status_w', 'purpose_credit_card',\n",
              "       'purpose_debt_consolidation', 'purpose_educational',\n",
              "       'purpose_home_improvement', 'purpose_house', 'purpose_major_purchase',\n",
              "       'purpose_medical', 'purpose_moving', 'purpose_other',\n",
              "       'purpose_renewable_energy', 'purpose_small_business',\n",
              "       'purpose_vacation', 'purpose_wedding', 'OTHER', 'OWN', 'RENT', '05113',\n",
              "       '11650', '22690', '29597', '30723', '48052', '70466', '86630', '93700'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rAHxr2EcMN_l"
      },
      "source": [
        "# issue_d >>> which month the loan was funded\n",
        "\n",
        "loan = loan.drop('issue_d', axis=1)"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hPtLQ8EpMrFN",
        "outputId": "1297de51-cfab-4499-f190-e745ebd115d2"
      },
      "source": [
        "# earliest_cr_line\n",
        "\n",
        "loan['earliest_cr_line'] = loan['earliest_cr_line'].apply(lambda x:int(x[-4:]))\n",
        "print(loan['earliest_cr_line'].value_counts())\n"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2000    29302\n",
            "2001    29031\n",
            "1999    26444\n",
            "2002    25849\n",
            "2003    23623\n",
            "        ...  \n",
            "1951        3\n",
            "1950        3\n",
            "1953        2\n",
            "1948        1\n",
            "1944        1\n",
            "Name: earliest_cr_line, Length: 65, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GCWVkwC1MyWl"
      },
      "source": [
        "### 2.3 Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8-3sgP5zNze7"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "# dropping loan_status column\n",
        "\n",
        "loan = loan.drop('loan_status', axis=1)\n"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Gk-v5tmQzoF"
      },
      "source": [
        "loan = loan.drop('address', axis=1)"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UOGl3ZD4OPfz"
      },
      "source": [
        "# assigning X,y\n",
        "\n",
        "X = loan.drop('loan_repaid', axis = 1).values\n",
        "y = loan['loan_repaid'].values"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JNeH5TwyOf5p"
      },
      "source": [
        "# train test splits\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
        "                                                    test_size = 0.2,\n",
        "                                                    random_state=101)\n",
        "\n",
        "# Normalizing the data\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test) # only transform X_test to prevent data leakage\n",
        "\n"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4W2nJG89PkHz"
      },
      "source": [
        "## Model creation "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l7Ok5LyrREuJ"
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OHaQgLwPREki"
      },
      "source": [
        "classifier = Sequential()\n",
        "\n",
        "classifier.add(Dense(X_train.shape[1] , activation='relu'))\n",
        "classifier.add(Dropout(0.2))\n",
        "\n",
        "# Adding hidden layers\n",
        "\n",
        "classifier.add(Dense(X_train.shape[1]//2 , activation='relu'))\n",
        "classifier.add(Dropout(0.2))\n",
        "\n",
        "classifier.add(Dense(X_train.shape[1]//4 , activation='relu'))\n",
        "classifier.add(Dropout(0.2))\n",
        "\n",
        "classifier.add(Dense(1 , activation='sigmoid')) \n",
        "\n",
        "\n",
        "classifier.compile(loss = 'binary_crossentropy', optimizer = 'adam')"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TeAGW3rxSNJ_",
        "outputId": "be62cb75-29e0-4a24-b4af-64f0233086d4"
      },
      "source": [
        "classifier.fit(x=X_train, y=y_train, epochs=25, batch_size=256, validation_data = (X_test, y_test))"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/25\n",
            "1236/1236 [==============================] - 7s 3ms/step - loss: 0.3541 - val_loss: 0.2646\n",
            "Epoch 2/25\n",
            "1236/1236 [==============================] - 3s 3ms/step - loss: 0.2649 - val_loss: 0.2629\n",
            "Epoch 3/25\n",
            "1236/1236 [==============================] - 3s 3ms/step - loss: 0.2609 - val_loss: 0.2627\n",
            "Epoch 4/25\n",
            "1236/1236 [==============================] - 3s 3ms/step - loss: 0.2626 - val_loss: 0.2620\n",
            "Epoch 5/25\n",
            "1236/1236 [==============================] - 3s 3ms/step - loss: 0.2607 - val_loss: 0.2622\n",
            "Epoch 6/25\n",
            "1236/1236 [==============================] - 3s 3ms/step - loss: 0.2612 - val_loss: 0.2619\n",
            "Epoch 7/25\n",
            "1236/1236 [==============================] - 3s 3ms/step - loss: 0.2600 - val_loss: 0.2619\n",
            "Epoch 8/25\n",
            "1236/1236 [==============================] - 3s 3ms/step - loss: 0.2596 - val_loss: 0.2617\n",
            "Epoch 9/25\n",
            "1236/1236 [==============================] - 3s 3ms/step - loss: 0.2588 - val_loss: 0.2623\n",
            "Epoch 10/25\n",
            "1236/1236 [==============================] - 3s 3ms/step - loss: 0.2600 - val_loss: 0.2621\n",
            "Epoch 11/25\n",
            "1236/1236 [==============================] - 3s 3ms/step - loss: 0.2586 - val_loss: 0.2617\n",
            "Epoch 12/25\n",
            "1236/1236 [==============================] - 3s 3ms/step - loss: 0.2592 - val_loss: 0.2620\n",
            "Epoch 13/25\n",
            "1236/1236 [==============================] - 3s 3ms/step - loss: 0.2562 - val_loss: 0.2612\n",
            "Epoch 14/25\n",
            "1236/1236 [==============================] - 3s 3ms/step - loss: 0.2564 - val_loss: 0.2619\n",
            "Epoch 15/25\n",
            "1236/1236 [==============================] - 3s 3ms/step - loss: 0.2565 - val_loss: 0.2609\n",
            "Epoch 16/25\n",
            "1236/1236 [==============================] - 3s 3ms/step - loss: 0.2574 - val_loss: 0.2612\n",
            "Epoch 17/25\n",
            "1236/1236 [==============================] - 3s 3ms/step - loss: 0.2570 - val_loss: 0.2611\n",
            "Epoch 18/25\n",
            "1236/1236 [==============================] - 3s 3ms/step - loss: 0.2569 - val_loss: 0.2614\n",
            "Epoch 19/25\n",
            "1236/1236 [==============================] - 3s 3ms/step - loss: 0.2555 - val_loss: 0.2612\n",
            "Epoch 20/25\n",
            "1236/1236 [==============================] - 3s 2ms/step - loss: 0.2563 - val_loss: 0.2613\n",
            "Epoch 21/25\n",
            "1236/1236 [==============================] - 3s 2ms/step - loss: 0.2568 - val_loss: 0.2619\n",
            "Epoch 22/25\n",
            "1236/1236 [==============================] - 3s 2ms/step - loss: 0.2562 - val_loss: 0.2621\n",
            "Epoch 23/25\n",
            "1236/1236 [==============================] - 3s 3ms/step - loss: 0.2558 - val_loss: 0.2613\n",
            "Epoch 24/25\n",
            "1236/1236 [==============================] - 3s 3ms/step - loss: 0.2564 - val_loss: 0.2612\n",
            "Epoch 25/25\n",
            "1236/1236 [==============================] - 3s 3ms/step - loss: 0.2556 - val_loss: 0.2610\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f7470193e50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iUipZwc5SdFy"
      },
      "source": [
        "# saving the model\n",
        "\n",
        "from tensorflow.keras.models import load_model\n",
        "\n",
        "classifier.save('/content/gdrive/MyDrive/Colab Notebooks/loan_classifier.h5')"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "jM1ElWzaTuNj",
        "outputId": "9bd66cde-9d70-4c90-caab-221ec16ecbb3"
      },
      "source": [
        "losses = pd.DataFrame(classifier.history.history)\n",
        "\n",
        "losses.plot()"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f742e046f50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FO6xsyixTlGL",
        "outputId": "114472a9-272e-40b8-f138-350717b98c1d"
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "predictions = classifier.predict_classes(X_test)\n",
        "\n",
        "print(classification_report(y_test, predictions))"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.45      0.61     15658\n",
            "           1       0.88      1.00      0.93     63386\n",
            "\n",
            "    accuracy                           0.89     79044\n",
            "   macro avg       0.92      0.72      0.77     79044\n",
            "weighted avg       0.90      0.89      0.87     79044\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FheG36UxUWMR",
        "outputId": "00c845c8-0fe6-4d77-8352-a3267380b54e"
      },
      "source": [
        "# Model's accuracy is 0.89. Not great not terrible. A model's accuracy that ALWAYS \n",
        "# predicts that one would pay the load is 80% (bottom threshold)\n",
        "# the true indicator of whether the model is good enough is the f1-score of 0 ('charged off'; 0.61)\n",
        "\n",
        "loan['loan_repaid'].value_counts()[1]/len(loan)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8038479931379817"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fwj5jTILUq7T",
        "outputId": "31ec27b6-f42a-4b70-8bb4-5a6bc5dbe550"
      },
      "source": [
        "confusion_matrix(y_test, predictions)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 7019,  8639],\n",
              "       [  238, 63148]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "StpBO4o8WUSF",
        "outputId": "d17a7ee4-13a8-40fe-ca8f-2abb0db2e0e7"
      },
      "source": [
        "# check model for new customer\n",
        "\n",
        "import random\n",
        "random.seed(101)\n",
        "random_ind = random.randint(0, len(loan))\n",
        "\n",
        "new_customer = loan.drop('loan_repaid', axis=1).iloc[random_ind]\n",
        "new_customer"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_amnt      25000.00\n",
              "term              60.00\n",
              "int_rate          18.24\n",
              "installment      638.11\n",
              "annual_inc     61665.00\n",
              "                 ...   \n",
              "30723              1.00\n",
              "48052              0.00\n",
              "70466              0.00\n",
              "86630              0.00\n",
              "93700              0.00\n",
              "Name: 305323, Length: 78, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kv43JaWaXHHB"
      },
      "source": [
        "new_customer = scaler.transform(new_customer.values.reshape(1, X_train.shape[1]))"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M94o3UKSXj0D",
        "outputId": "af8e433b-3461-45c2-8e0a-b5d6f766e71b"
      },
      "source": [
        "classifier.predict_classes(new_customer) # HE/SHE 'LL PAY IT"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WRmTnMA_XlP8",
        "outputId": "09ad9097-3aab-4acf-9b0d-bf8e68869a84"
      },
      "source": [
        "loan.iloc[random_ind]['loan_repaid'] == classifier.predict_classes(new_customer)"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ True]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MAELHXV9YEFm"
      },
      "source": [
        "# Kewl"
      ],
      "execution_count": 208,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q6qGqn8xeXTg"
      },
      "source": [
        "## Keras-tuner and cross validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "apFqgzWjZFfM"
      },
      "source": [
        "## Let's try something different...\n",
        "\n",
        "num_fc_layers_min  = 1\n",
        "num_fc_layers_max  = 2\n",
        "\n",
        "\n",
        "def reg_wrapper(type, value):\n",
        "    if type == 'l2':\n",
        "        return regularizers.l2(value)\n",
        "    if type == 'l1':\n",
        "        return regularizers.l1(value)\n",
        "\n",
        "\n",
        "def build_classifier(hp):\n",
        "\n",
        "  classifier = Sequential()\n",
        "  # Flatten\n",
        "  classifier.add(Flatten(input_shape =( X_train.shape[1],)))\n",
        "  # First Hidden Layer\n",
        "  classifier.add(Dense(units=hp.Choice('units',[X_train.shape[1]]),\n",
        "                      activation='relu'))\n",
        "  \n",
        "  classifier.add(Dropout(hp.Choice('dropout',[0.2,0.4,0.5])))\n",
        "  #classifier.add(BatchNormalization())\n",
        "  \n",
        "  for i in range(hp.Int('num_fc_layers',min_value=num_fc_layers_min-1,max_value=num_fc_layers_max)):\n",
        "    classifier.add(Dense(units=hp.Choice('num_units_'+str(i+1),[32,64]), \n",
        "                        activation='relu'\n",
        "                        ,kernel_regularizer= reg_wrapper(hp.Choice('type', ['l1', 'l2']), \n",
        "                                                             hp.Choice('reg_value', \n",
        "                                                                       [0.01, 0.001, 0.1]))\n",
        "                        ))\n",
        "    classifier.add(Dropout(hp.Choice('dropout_'+str(i+1),[0.2,0.4,0.5])))\n",
        "    \n",
        "   # classifier.add(BatchNormalization()) \n",
        "    \n",
        "  # Output Layer\n",
        "  classifier.add(Dense(units=1, activation='sigmoid'))\n",
        "  #Compiling the neural network\n",
        "  classifier.compile(optimizer=tf.keras.optimizers.RMSprop(hp.Choice('learning_rate',\n",
        "                                                              values=[1e-2, 1e-3, 1e-4])),\n",
        "                    loss=tf.keras.losses.mean_squared_error,\n",
        "                    metrics=['mse','mae'])\n",
        "  \n",
        "  return classifier"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6nWFr1NJaGLE",
        "outputId": "eb61d46f-d871-4ad4-b768-cda36e5be529"
      },
      "source": [
        "!pip install -U keras-tuner"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting keras-tuner\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/20/ec/1ef246787174b1e2bb591c95f29d3c1310070cad877824f907faba3dade9/keras-tuner-1.0.2.tar.gz (62kB)\n",
            "\r\u001b[K     |█████▏                          | 10kB 13.3MB/s eta 0:00:01\r\u001b[K     |██████████▍                     | 20kB 16.7MB/s eta 0:00:01\r\u001b[K     |███████████████▋                | 30kB 10.6MB/s eta 0:00:01\r\u001b[K     |████████████████████▉           | 40kB 9.0MB/s eta 0:00:01\r\u001b[K     |██████████████████████████      | 51kB 7.2MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▎| 61kB 7.6MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 71kB 5.2MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: packaging in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (20.9)\n",
            "Requirement already satisfied, skipping upgrade: future in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (0.16.0)\n",
            "Requirement already satisfied, skipping upgrade: numpy in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (1.19.5)\n",
            "Requirement already satisfied, skipping upgrade: tabulate in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (0.8.9)\n",
            "Collecting terminaltables\n",
            "  Downloading https://files.pythonhosted.org/packages/9b/c4/4a21174f32f8a7e1104798c445dacdc1d4df86f2f26722767034e4de4bff/terminaltables-3.1.0.tar.gz\n",
            "Collecting colorama\n",
            "  Downloading https://files.pythonhosted.org/packages/44/98/5b86278fbbf250d239ae0ecb724f8572af1c91f4a11edf4d36a206189440/colorama-0.4.4-py2.py3-none-any.whl\n",
            "Requirement already satisfied, skipping upgrade: tqdm in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (4.41.1)\n",
            "Requirement already satisfied, skipping upgrade: requests in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (2.23.0)\n",
            "Requirement already satisfied, skipping upgrade: scipy in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (1.4.1)\n",
            "Requirement already satisfied, skipping upgrade: scikit-learn in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (0.22.2.post1)\n",
            "Requirement already satisfied, skipping upgrade: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->keras-tuner) (2.4.7)\n",
            "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner) (2.10)\n",
            "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner) (2020.12.5)\n",
            "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->keras-tuner) (1.0.1)\n",
            "Building wheels for collected packages: keras-tuner, terminaltables\n",
            "  Building wheel for keras-tuner (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-tuner: filename=keras_tuner-1.0.2-cp37-none-any.whl size=78938 sha256=9404d492a9c5b5bd91d90e9cfadb806f2a4023b6e6293aa1d4779204cd0dee12\n",
            "  Stored in directory: /root/.cache/pip/wheels/bb/a1/8a/7c3de0efb3707a1701b36ebbfdbc4e67aedf6d4943a1f463d6\n",
            "  Building wheel for terminaltables (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for terminaltables: filename=terminaltables-3.1.0-cp37-none-any.whl size=15356 sha256=13b17f1938d5b963c1d02a2f9993adb39e73750e119dea12d9370bd1cef7032a\n",
            "  Stored in directory: /root/.cache/pip/wheels/30/6b/50/6c75775b681fb36cdfac7f19799888ef9d8813aff9e379663e\n",
            "Successfully built keras-tuner terminaltables\n",
            "Installing collected packages: terminaltables, colorama, keras-tuner\n",
            "Successfully installed colorama-0.4.4 keras-tuner-1.0.2 terminaltables-3.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4RW4lPDcaQgw"
      },
      "source": [
        "from sklearn.model_selection import KFold\n",
        "\n",
        "from kerastuner import oracles, Objective\n",
        "import kerastuner\n",
        "\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n",
        "from tensorflow.keras import regularizers, optimizers"
      ],
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rp6HnF2-Z2G6"
      },
      "source": [
        "from tensorflow.keras.layers import Dense,Dropout,Flatten, BatchNormalization\n",
        "\n",
        "\n",
        "class CVTuner(kerastuner.engine.tuner.Tuner):\n",
        "  def run_trial(self, trial, x, y, batch_size=256, epochs=1):\n",
        "    cv = KFold(5)\n",
        "    #loo = LeaveOneOut()\n",
        "    val_losses = []\n",
        "    for train_indices, test_indices in cv.split(x):\n",
        "\n",
        "      X_train, X_test = X[train_indices], X[test_indices]\n",
        "      y_train, y_test = y[train_indices], y[test_indices]\n",
        "\n",
        "      # scale splits\n",
        "      X_train = scaler.fit_transform(X_train)\n",
        "      X_test = scaler.transform(X_test)\n",
        "\n",
        "      model = self.hypermodel.build(trial.hyperparameters)\n",
        "      model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, verbose = 0)\n",
        "      val_losses.append(model.evaluate(X_test, y_test))\n",
        "      \n",
        "    self.oracle.update_trial(trial.trial_id, {'val_loss': np.mean(val_losses)})\n",
        "    self.save_model(trial.trial_id, model)\n"
      ],
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "enGO3Ok8aE9V",
        "outputId": "0d81e23b-f8c1-4544-a420-02134b201d76"
      },
      "source": [
        "tuner_cv = CVTuner(\n",
        "    hypermodel=build_classifier,\n",
        "    oracle=kerastuner.oracles.BayesianOptimization(\n",
        "    objective='val_loss',\n",
        "    max_trials=20),\n",
        "    project_name='Tuner')\n",
        "\n",
        "tuner_cv.search(x = X,\n",
        "             y = y,\n",
        "             epochs=30,\n",
        "             batch_size=256)\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Trial 20 Complete [00h 05m 42s]\n",
            "val_loss: 0.10831377506256104\n",
            "\n",
            "Best val_loss So Far: 0.1077070971330007\n",
            "Total elapsed time: 02h 10m 26s\n",
            "INFO:tensorflow:Oracle triggered exit\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-FmQZC-bb2A"
      },
      "source": [
        "best_model = tuner_cv.get_best_models(num_models=1)[0]"
      ],
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JL62c3M_-k5v",
        "outputId": "31f76b72-a1c1-446b-9cc8-4e3f0e4738be"
      },
      "source": [
        "best_model.summary()"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten (Flatten)            (None, 78)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 78)                6162      \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 78)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 32)                2528      \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 33        \n",
            "=================================================================\n",
            "Total params: 8,723\n",
            "Trainable params: 8,723\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_XBYJ2RL_vGO",
        "outputId": "b21c81f1-e72e-4596-c224-4b0936fd586c"
      },
      "source": [
        "best_model.predict_classes(new_customer) # HE/SHE 'LL PAY IT"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cr70Zt-KABOj",
        "outputId": "a86c4e85-d55e-4d05-89c2-2cdfa7cc1bdc"
      },
      "source": [
        "loan.iloc[random_ind]['loan_repaid'] == best_model.predict_classes(new_customer)"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ True]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QgjoJjjGAGnL"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}