Loan_Data_Set.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73,
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            }
          }
        },
        "id": "wH3d-cIA8BJp",
        "outputId": "99f1fa66-aa3d-40aa-92e1-80d881d91c18"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-29851ff4-0efc-44e1-be3e-61ca57f19517\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-29851ff4-0efc-44e1-be3e-61ca57f19517\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving loan.csv to loan.csv\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "  \n",
        "  \n",
        "uploaded = files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "OuuhH8Z0-rDT"
      },
      "outputs": [],
      "source": [
        "## import libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the CSV file"
      ],
      "metadata": {
        "id": "IziEqjF7mxx5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "n5c7EZuL-thU"
      },
      "outputs": [],
      "source": [
        "loan = pd.read_csv(\"loan.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMhUZLykA-qx"
      },
      "source": [
        "**Check the file shape how many row and columns in csv file**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F0DPQkPqAkEi",
        "outputId": "d63a2b9c-0bc3-43f3-89ed-dea0ea97d6a7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(39717, 111)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "loan.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yPL4l6gSAnOo",
        "outputId": "42b438aa-9666-4348-cbe4-a5fc7c61faa4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-12f57b05-b1a0-4d4a-bd87-38d54c6e2ba6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>member_id</th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>funded_amnt</th>\n",
              "      <th>funded_amnt_inv</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>pymnt_plan</th>\n",
              "      <th>url</th>\n",
              "      <th>desc</th>\n",
              "      <th>purpose</th>\n",
              "      <th>title</th>\n",
              "      <th>zip_code</th>\n",
              "      <th>addr_state</th>\n",
              "      <th>dti</th>\n",
              "      <th>delinq_2yrs</th>\n",
              "      <th>earliest_cr_line</th>\n",
              "      <th>inq_last_6mths</th>\n",
              "      <th>mths_since_last_delinq</th>\n",
              "      <th>mths_since_last_record</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>out_prncp</th>\n",
              "      <th>out_prncp_inv</th>\n",
              "      <th>total_pymnt</th>\n",
              "      <th>total_pymnt_inv</th>\n",
              "      <th>...</th>\n",
              "      <th>inq_fi</th>\n",
              "      <th>total_cu_tl</th>\n",
              "      <th>inq_last_12m</th>\n",
              "      <th>acc_open_past_24mths</th>\n",
              "      <th>avg_cur_bal</th>\n",
              "      <th>bc_open_to_buy</th>\n",
              "      <th>bc_util</th>\n",
              "      <th>chargeoff_within_12_mths</th>\n",
              "      <th>delinq_amnt</th>\n",
              "      <th>mo_sin_old_il_acct</th>\n",
              "      <th>mo_sin_old_rev_tl_op</th>\n",
              "      <th>mo_sin_rcnt_rev_tl_op</th>\n",
              "      <th>mo_sin_rcnt_tl</th>\n",
              "      <th>mort_acc</th>\n",
              "      <th>mths_since_recent_bc</th>\n",
              "      <th>mths_since_recent_bc_dlq</th>\n",
              "      <th>mths_since_recent_inq</th>\n",
              "      <th>mths_since_recent_revol_delinq</th>\n",
              "      <th>num_accts_ever_120_pd</th>\n",
              "      <th>num_actv_bc_tl</th>\n",
              "      <th>num_actv_rev_tl</th>\n",
              "      <th>num_bc_sats</th>\n",
              "      <th>num_bc_tl</th>\n",
              "      <th>num_il_tl</th>\n",
              "      <th>num_op_rev_tl</th>\n",
              "      <th>num_rev_accts</th>\n",
              "      <th>num_rev_tl_bal_gt_0</th>\n",
              "      <th>num_sats</th>\n",
              "      <th>num_tl_120dpd_2m</th>\n",
              "      <th>num_tl_30dpd</th>\n",
              "      <th>num_tl_90g_dpd_24m</th>\n",
              "      <th>num_tl_op_past_12m</th>\n",
              "      <th>pct_tl_nvr_dlq</th>\n",
              "      <th>percent_bc_gt_75</th>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "      <th>tax_liens</th>\n",
              "      <th>tot_hi_cred_lim</th>\n",
              "      <th>total_bal_ex_mort</th>\n",
              "      <th>total_bc_limit</th>\n",
              "      <th>total_il_high_credit_limit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1077501</td>\n",
              "      <td>1296599</td>\n",
              "      <td>5000</td>\n",
              "      <td>5000</td>\n",
              "      <td>4975.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>10.65%</td>\n",
              "      <td>162.87</td>\n",
              "      <td>B</td>\n",
              "      <td>B2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>24000.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>Borrower added on 12/22/11 &gt; I need to upgra...</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>Computer</td>\n",
              "      <td>860xx</td>\n",
              "      <td>AZ</td>\n",
              "      <td>27.65</td>\n",
              "      <td>0</td>\n",
              "      <td>Jan-85</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>13648</td>\n",
              "      <td>83.70%</td>\n",
              "      <td>9</td>\n",
              "      <td>f</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>5863.155187</td>\n",
              "      <td>5833.84</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1077430</td>\n",
              "      <td>1314167</td>\n",
              "      <td>2500</td>\n",
              "      <td>2500</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>15.27%</td>\n",
              "      <td>59.83</td>\n",
              "      <td>C</td>\n",
              "      <td>C4</td>\n",
              "      <td>Ryder</td>\n",
              "      <td>&lt; 1 year</td>\n",
              "      <td>RENT</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Charged Off</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>Borrower added on 12/22/11 &gt; I plan to use t...</td>\n",
              "      <td>car</td>\n",
              "      <td>bike</td>\n",
              "      <td>309xx</td>\n",
              "      <td>GA</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0</td>\n",
              "      <td>Apr-99</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1687</td>\n",
              "      <td>9.40%</td>\n",
              "      <td>4</td>\n",
              "      <td>f</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1008.710000</td>\n",
              "      <td>1008.71</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1077175</td>\n",
              "      <td>1313524</td>\n",
              "      <td>2400</td>\n",
              "      <td>2400</td>\n",
              "      <td>2400.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>15.96%</td>\n",
              "      <td>84.33</td>\n",
              "      <td>C</td>\n",
              "      <td>C5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>12252.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>small_business</td>\n",
              "      <td>real estate business</td>\n",
              "      <td>606xx</td>\n",
              "      <td>IL</td>\n",
              "      <td>8.72</td>\n",
              "      <td>0</td>\n",
              "      <td>Nov-01</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2956</td>\n",
              "      <td>98.50%</td>\n",
              "      <td>10</td>\n",
              "      <td>f</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3005.666844</td>\n",
              "      <td>3005.67</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1076863</td>\n",
              "      <td>1277178</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>13.49%</td>\n",
              "      <td>339.31</td>\n",
              "      <td>C</td>\n",
              "      <td>C1</td>\n",
              "      <td>AIR RESOURCES BOARD</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>49200.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>Borrower added on 12/21/11 &gt; to pay for prop...</td>\n",
              "      <td>other</td>\n",
              "      <td>personel</td>\n",
              "      <td>917xx</td>\n",
              "      <td>CA</td>\n",
              "      <td>20.00</td>\n",
              "      <td>0</td>\n",
              "      <td>Feb-96</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>5598</td>\n",
              "      <td>21%</td>\n",
              "      <td>37</td>\n",
              "      <td>f</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>12231.890000</td>\n",
              "      <td>12231.89</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1075358</td>\n",
              "      <td>1311748</td>\n",
              "      <td>3000</td>\n",
              "      <td>3000</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>12.69%</td>\n",
              "      <td>67.79</td>\n",
              "      <td>B</td>\n",
              "      <td>B5</td>\n",
              "      <td>University Medical Group</td>\n",
              "      <td>1 year</td>\n",
              "      <td>RENT</td>\n",
              "      <td>80000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>Borrower added on 12/21/11 &gt; I plan on combi...</td>\n",
              "      <td>other</td>\n",
              "      <td>Personal</td>\n",
              "      <td>972xx</td>\n",
              "      <td>OR</td>\n",
              "      <td>17.94</td>\n",
              "      <td>0</td>\n",
              "      <td>Jan-96</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>27783</td>\n",
              "      <td>53.90%</td>\n",
              "      <td>38</td>\n",
              "      <td>f</td>\n",
              "      <td>524.06</td>\n",
              "      <td>524.06</td>\n",
              "      <td>3513.330000</td>\n",
              "      <td>3513.33</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1075269</td>\n",
              "      <td>1311441</td>\n",
              "      <td>5000</td>\n",
              "      <td>5000</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>7.90%</td>\n",
              "      <td>156.46</td>\n",
              "      <td>A</td>\n",
              "      <td>A4</td>\n",
              "      <td>Veolia Transportaton</td>\n",
              "      <td>3 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>36000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>wedding</td>\n",
              "      <td>My wedding loan I promise to pay back</td>\n",
              "      <td>852xx</td>\n",
              "      <td>AZ</td>\n",
              "      <td>11.20</td>\n",
              "      <td>0</td>\n",
              "      <td>Nov-04</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>7963</td>\n",
              "      <td>28.30%</td>\n",
              "      <td>12</td>\n",
              "      <td>f</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>5632.210000</td>\n",
              "      <td>5632.21</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1069639</td>\n",
              "      <td>1304742</td>\n",
              "      <td>7000</td>\n",
              "      <td>7000</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>15.96%</td>\n",
              "      <td>170.08</td>\n",
              "      <td>C</td>\n",
              "      <td>C5</td>\n",
              "      <td>Southern Star Photography</td>\n",
              "      <td>8 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>47004.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>Borrower added on 12/18/11 &gt; I am planning o...</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>Loan</td>\n",
              "      <td>280xx</td>\n",
              "      <td>NC</td>\n",
              "      <td>23.51</td>\n",
              "      <td>0</td>\n",
              "      <td>Jul-05</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>17726</td>\n",
              "      <td>85.60%</td>\n",
              "      <td>11</td>\n",
              "      <td>f</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>10110.840000</td>\n",
              "      <td>10110.84</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7 rows × 111 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-12f57b05-b1a0-4d4a-bd87-38d54c6e2ba6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-12f57b05-b1a0-4d4a-bd87-38d54c6e2ba6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-12f57b05-b1a0-4d4a-bd87-38d54c6e2ba6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        id  member_id  ...  total_bc_limit  total_il_high_credit_limit\n",
              "0  1077501    1296599  ...             NaN                         NaN\n",
              "1  1077430    1314167  ...             NaN                         NaN\n",
              "2  1077175    1313524  ...             NaN                         NaN\n",
              "3  1076863    1277178  ...             NaN                         NaN\n",
              "4  1075358    1311748  ...             NaN                         NaN\n",
              "5  1075269    1311441  ...             NaN                         NaN\n",
              "6  1069639    1304742  ...             NaN                         NaN\n",
              "\n",
              "[7 rows x 111 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "loan.head(7)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vqmx8CVRBaWS"
      },
      "source": [
        "**Check the null values in columns have**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mK5Tx4SQAu5d",
        "outputId": "4cb103ac-336b-48b4-ce2f-4e64398d07ee"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                0\n",
              "member_id                         0\n",
              "loan_amnt                         0\n",
              "funded_amnt                       0\n",
              "funded_amnt_inv                   0\n",
              "                              ...  \n",
              "tax_liens                        39\n",
              "tot_hi_cred_lim               39717\n",
              "total_bal_ex_mort             39717\n",
              "total_bc_limit                39717\n",
              "total_il_high_credit_limit    39717\n",
              "Length: 111, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "loan.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "PLfNajiWCv7S",
        "outputId": "80472ebd-3896-42cd-e4b5-67e7eb8deabb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3e4f6086-4b95-416e-8ebf-9ace282af4cd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>member_id</th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>funded_amnt</th>\n",
              "      <th>funded_amnt_inv</th>\n",
              "      <th>installment</th>\n",
              "      <th>annual_inc</th>\n",
              "      <th>dti</th>\n",
              "      <th>delinq_2yrs</th>\n",
              "      <th>inq_last_6mths</th>\n",
              "      <th>mths_since_last_delinq</th>\n",
              "      <th>mths_since_last_record</th>\n",
              "      <th>open_acc</th>\n",
              "      <th>pub_rec</th>\n",
              "      <th>revol_bal</th>\n",
              "      <th>total_acc</th>\n",
              "      <th>out_prncp</th>\n",
              "      <th>out_prncp_inv</th>\n",
              "      <th>total_pymnt</th>\n",
              "      <th>total_pymnt_inv</th>\n",
              "      <th>total_rec_prncp</th>\n",
              "      <th>total_rec_int</th>\n",
              "      <th>total_rec_late_fee</th>\n",
              "      <th>recoveries</th>\n",
              "      <th>collection_recovery_fee</th>\n",
              "      <th>last_pymnt_amnt</th>\n",
              "      <th>collections_12_mths_ex_med</th>\n",
              "      <th>mths_since_last_major_derog</th>\n",
              "      <th>policy_code</th>\n",
              "      <th>annual_inc_joint</th>\n",
              "      <th>dti_joint</th>\n",
              "      <th>verification_status_joint</th>\n",
              "      <th>acc_now_delinq</th>\n",
              "      <th>tot_coll_amt</th>\n",
              "      <th>tot_cur_bal</th>\n",
              "      <th>open_acc_6m</th>\n",
              "      <th>open_il_6m</th>\n",
              "      <th>open_il_12m</th>\n",
              "      <th>open_il_24m</th>\n",
              "      <th>mths_since_rcnt_il</th>\n",
              "      <th>...</th>\n",
              "      <th>inq_fi</th>\n",
              "      <th>total_cu_tl</th>\n",
              "      <th>inq_last_12m</th>\n",
              "      <th>acc_open_past_24mths</th>\n",
              "      <th>avg_cur_bal</th>\n",
              "      <th>bc_open_to_buy</th>\n",
              "      <th>bc_util</th>\n",
              "      <th>chargeoff_within_12_mths</th>\n",
              "      <th>delinq_amnt</th>\n",
              "      <th>mo_sin_old_il_acct</th>\n",
              "      <th>mo_sin_old_rev_tl_op</th>\n",
              "      <th>mo_sin_rcnt_rev_tl_op</th>\n",
              "      <th>mo_sin_rcnt_tl</th>\n",
              "      <th>mort_acc</th>\n",
              "      <th>mths_since_recent_bc</th>\n",
              "      <th>mths_since_recent_bc_dlq</th>\n",
              "      <th>mths_since_recent_inq</th>\n",
              "      <th>mths_since_recent_revol_delinq</th>\n",
              "      <th>num_accts_ever_120_pd</th>\n",
              "      <th>num_actv_bc_tl</th>\n",
              "      <th>num_actv_rev_tl</th>\n",
              "      <th>num_bc_sats</th>\n",
              "      <th>num_bc_tl</th>\n",
              "      <th>num_il_tl</th>\n",
              "      <th>num_op_rev_tl</th>\n",
              "      <th>num_rev_accts</th>\n",
              "      <th>num_rev_tl_bal_gt_0</th>\n",
              "      <th>num_sats</th>\n",
              "      <th>num_tl_120dpd_2m</th>\n",
              "      <th>num_tl_30dpd</th>\n",
              "      <th>num_tl_90g_dpd_24m</th>\n",
              "      <th>num_tl_op_past_12m</th>\n",
              "      <th>pct_tl_nvr_dlq</th>\n",
              "      <th>percent_bc_gt_75</th>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "      <th>tax_liens</th>\n",
              "      <th>tot_hi_cred_lim</th>\n",
              "      <th>total_bal_ex_mort</th>\n",
              "      <th>total_bc_limit</th>\n",
              "      <th>total_il_high_credit_limit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3.971700e+04</td>\n",
              "      <td>3.971700e+04</td>\n",
              "      <td>39717.000000</td>\n",
              "      <td>39717.000000</td>\n",
              "      <td>39717.000000</td>\n",
              "      <td>39717.000000</td>\n",
              "      <td>3.971700e+04</td>\n",
              "      <td>39717.000000</td>\n",
              "      <td>39717.000000</td>\n",
              "      <td>39717.000000</td>\n",
              "      <td>14035.000000</td>\n",
              "      <td>2786.000000</td>\n",
              "      <td>39717.000000</td>\n",
              "      <td>39717.000000</td>\n",
              "      <td>39717.000000</td>\n",
              "      <td>39717.000000</td>\n",
              "      <td>39717.000000</td>\n",
              "      <td>39717.000000</td>\n",
              "      <td>39717.000000</td>\n",
              "      <td>39717.000000</td>\n",
              "      <td>39717.000000</td>\n",
              "      <td>39717.000000</td>\n",
              "      <td>39717.000000</td>\n",
              "      <td>39717.000000</td>\n",
              "      <td>39717.000000</td>\n",
              "      <td>39717.000000</td>\n",
              "      <td>39661.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>39717.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>39717.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>39661.0</td>\n",
              "      <td>39717.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>39020.000000</td>\n",
              "      <td>39678.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>6.831319e+05</td>\n",
              "      <td>8.504636e+05</td>\n",
              "      <td>11219.443815</td>\n",
              "      <td>10947.713196</td>\n",
              "      <td>10397.448868</td>\n",
              "      <td>324.561922</td>\n",
              "      <td>6.896893e+04</td>\n",
              "      <td>13.315130</td>\n",
              "      <td>0.146512</td>\n",
              "      <td>0.869200</td>\n",
              "      <td>35.900962</td>\n",
              "      <td>69.698134</td>\n",
              "      <td>9.294408</td>\n",
              "      <td>0.055065</td>\n",
              "      <td>13382.528086</td>\n",
              "      <td>22.088828</td>\n",
              "      <td>51.227887</td>\n",
              "      <td>50.989768</td>\n",
              "      <td>12153.596544</td>\n",
              "      <td>11567.149118</td>\n",
              "      <td>9793.348813</td>\n",
              "      <td>2263.663172</td>\n",
              "      <td>1.363015</td>\n",
              "      <td>95.221624</td>\n",
              "      <td>12.406112</td>\n",
              "      <td>2678.826162</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.043260</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.106941e+05</td>\n",
              "      <td>2.656783e+05</td>\n",
              "      <td>7456.670694</td>\n",
              "      <td>7187.238670</td>\n",
              "      <td>7128.450439</td>\n",
              "      <td>208.874874</td>\n",
              "      <td>6.379377e+04</td>\n",
              "      <td>6.678594</td>\n",
              "      <td>0.491812</td>\n",
              "      <td>1.070219</td>\n",
              "      <td>22.020060</td>\n",
              "      <td>43.822529</td>\n",
              "      <td>4.400282</td>\n",
              "      <td>0.237200</td>\n",
              "      <td>15885.016641</td>\n",
              "      <td>11.401709</td>\n",
              "      <td>375.172839</td>\n",
              "      <td>373.824457</td>\n",
              "      <td>9042.040766</td>\n",
              "      <td>8942.672613</td>\n",
              "      <td>7065.522127</td>\n",
              "      <td>2608.111964</td>\n",
              "      <td>7.289979</td>\n",
              "      <td>688.744771</td>\n",
              "      <td>148.671593</td>\n",
              "      <td>4447.136012</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.204324</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>5.473400e+04</td>\n",
              "      <td>7.069900e+04</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>15.690000</td>\n",
              "      <td>4.000000e+03</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.162210e+05</td>\n",
              "      <td>6.667800e+05</td>\n",
              "      <td>5500.000000</td>\n",
              "      <td>5400.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>167.020000</td>\n",
              "      <td>4.040400e+04</td>\n",
              "      <td>8.170000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3703.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5576.930000</td>\n",
              "      <td>5112.310000</td>\n",
              "      <td>4600.000000</td>\n",
              "      <td>662.180000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>218.680000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6.656650e+05</td>\n",
              "      <td>8.508120e+05</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>9600.000000</td>\n",
              "      <td>8975.000000</td>\n",
              "      <td>280.220000</td>\n",
              "      <td>5.900000e+04</td>\n",
              "      <td>13.400000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8850.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9899.640319</td>\n",
              "      <td>9287.150000</td>\n",
              "      <td>8000.000000</td>\n",
              "      <td>1348.910000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>546.140000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>8.377550e+05</td>\n",
              "      <td>1.047339e+06</td>\n",
              "      <td>15000.000000</td>\n",
              "      <td>15000.000000</td>\n",
              "      <td>14400.000000</td>\n",
              "      <td>430.780000</td>\n",
              "      <td>8.230000e+04</td>\n",
              "      <td>18.600000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>104.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>17058.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>16534.433040</td>\n",
              "      <td>15798.810000</td>\n",
              "      <td>13653.260000</td>\n",
              "      <td>2833.400000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3293.160000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.077501e+06</td>\n",
              "      <td>1.314167e+06</td>\n",
              "      <td>35000.000000</td>\n",
              "      <td>35000.000000</td>\n",
              "      <td>35000.000000</td>\n",
              "      <td>1305.190000</td>\n",
              "      <td>6.000000e+06</td>\n",
              "      <td>29.990000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>129.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>149588.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>6311.470000</td>\n",
              "      <td>6307.370000</td>\n",
              "      <td>58563.679930</td>\n",
              "      <td>58563.680000</td>\n",
              "      <td>35000.020000</td>\n",
              "      <td>23563.680000</td>\n",
              "      <td>180.200000</td>\n",
              "      <td>29623.350000</td>\n",
              "      <td>7002.190000</td>\n",
              "      <td>36115.200000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 87 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3e4f6086-4b95-416e-8ebf-9ace282af4cd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3e4f6086-4b95-416e-8ebf-9ace282af4cd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3e4f6086-4b95-416e-8ebf-9ace282af4cd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                 id     member_id  ...  total_bc_limit  total_il_high_credit_limit\n",
              "count  3.971700e+04  3.971700e+04  ...             0.0                         0.0\n",
              "mean   6.831319e+05  8.504636e+05  ...             NaN                         NaN\n",
              "std    2.106941e+05  2.656783e+05  ...             NaN                         NaN\n",
              "min    5.473400e+04  7.069900e+04  ...             NaN                         NaN\n",
              "25%    5.162210e+05  6.667800e+05  ...             NaN                         NaN\n",
              "50%    6.656650e+05  8.508120e+05  ...             NaN                         NaN\n",
              "75%    8.377550e+05  1.047339e+06  ...             NaN                         NaN\n",
              "max    1.077501e+06  1.314167e+06  ...             NaN                         NaN\n",
              "\n",
              "[8 rows x 87 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "loan.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HsNtm2bCCI8_"
      },
      "source": [
        "**Data Cleaning and missing value handle**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B7PussbDDk2R"
      },
      "source": [
        "Find the percentage of missing values in each column in csv file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gYn2M72FA8CA",
        "outputId": "36321527-e30c-4a4f-947e-146ce2afae15"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id                              0.000000\n",
            "member_id                       0.000000\n",
            "loan_amnt                       0.000000\n",
            "funded_amnt                     0.000000\n",
            "funded_amnt_inv                 0.000000\n",
            "                                 ...    \n",
            "tax_liens                       0.098195\n",
            "tot_hi_cred_lim               100.000000\n",
            "total_bal_ex_mort             100.000000\n",
            "total_bc_limit                100.000000\n",
            "total_il_high_credit_limit    100.000000\n",
            "Length: 111, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "percent_nullvalues = loan.isnull().sum() * 100 / len(loan)\n",
        "print(percent_nullvalues)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t8lvgN_Mo3TT"
      },
      "source": [
        "create a new dataframe which include column name and their null value perncentage"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "58Ip6zJfCsSF",
        "outputId": "9dec2843-092a-431a-8d8c-75dd0bf9656d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                          columns_name  percent_nullvalues\n",
            "id                                                  id                 0.0\n",
            "member_id                                    member_id                 0.0\n",
            "loan_amnt                                    loan_amnt                 0.0\n",
            "funded_amnt                                funded_amnt                 0.0\n",
            "funded_amnt_inv                        funded_amnt_inv                 0.0\n",
            "...                                                ...                 ...\n",
            "tax_liens                                    tax_liens                 0.0\n",
            "tot_hi_cred_lim                        tot_hi_cred_lim               100.0\n",
            "total_bal_ex_mort                    total_bal_ex_mort               100.0\n",
            "total_bc_limit                          total_bc_limit               100.0\n",
            "total_il_high_credit_limit  total_il_high_credit_limit               100.0\n",
            "\n",
            "[111 rows x 2 columns]\n"
          ]
        }
      ],
      "source": [
        "nullvalue_df = pd.DataFrame({'columns_name': loan.columns,\n",
        "                                 'percent_nullvalues': percent_nullvalues.round()})\n",
        "print(nullvalue_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mDgrAlT3oc01"
      },
      "source": [
        "counting the null value which have 100.0% null value coantain  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dp0F27KaFZWn",
        "outputId": "052ac9b4-fbee-4eff-960a-0a0c502cd244"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "54"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "(nullvalue_df['percent_nullvalues'] == 100.0).sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RBrAeKUGqKWD"
      },
      "source": [
        "List of columns having NULL values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WSl80xs0F6ma",
        "outputId": "4bc1dbea-af5d-49bc-9e68-53b1f4a0154a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['mths_since_last_major_derog', 'annual_inc_joint', 'dti_joint', 'verification_status_joint', 'tot_coll_amt', 'tot_cur_bal', 'open_acc_6m', 'open_il_6m', 'open_il_12m', 'open_il_24m', 'mths_since_rcnt_il', 'total_bal_il', 'il_util', 'open_rv_12m', 'open_rv_24m', 'max_bal_bc', 'all_util', 'total_rev_hi_lim', 'inq_fi', 'total_cu_tl', 'inq_last_12m', 'acc_open_past_24mths', 'avg_cur_bal', 'bc_open_to_buy', 'bc_util', 'mo_sin_old_il_acct', 'mo_sin_old_rev_tl_op', 'mo_sin_rcnt_rev_tl_op', 'mo_sin_rcnt_tl', 'mort_acc', 'mths_since_recent_bc', 'mths_since_recent_bc_dlq', 'mths_since_recent_inq', 'mths_since_recent_revol_delinq', 'num_accts_ever_120_pd', 'num_actv_bc_tl', 'num_actv_rev_tl', 'num_bc_sats', 'num_bc_tl', 'num_il_tl', 'num_op_rev_tl', 'num_rev_accts', 'num_rev_tl_bal_gt_0', 'num_sats', 'num_tl_120dpd_2m', 'num_tl_30dpd', 'num_tl_90g_dpd_24m', 'num_tl_op_past_12m', 'pct_tl_nvr_dlq', 'percent_bc_gt_75', 'tot_hi_cred_lim', 'total_bal_ex_mort', 'total_bc_limit', 'total_il_high_credit_limit']\n"
          ]
        }
      ],
      "source": [
        "nullvalue_df_columns = nullvalue_df[(nullvalue_df['percent_nullvalues'] == 100.00)].columns_name.tolist()\n",
        "print(nullvalue_df_columns)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kwPGcRivr2jP"
      },
      "source": [
        "Confirming the list which is equal to above count and it must be same"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3NPbxMcDqse6",
        "outputId": "f3f7ce0b-b941-48fc-bacb-996df1b4e745"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "54\n"
          ]
        }
      ],
      "source": [
        "print(len(nullvalue_df_columns))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gVncfRLlr05H",
        "outputId": "c5126b35-fb60-4abe-a2ed-bca04889c275"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            id  member_id  ...  pub_rec_bankruptcies  tax_liens\n",
            "0      1077501    1296599  ...                   0.0        0.0\n",
            "1      1077430    1314167  ...                   0.0        0.0\n",
            "2      1077175    1313524  ...                   0.0        0.0\n",
            "3      1076863    1277178  ...                   0.0        0.0\n",
            "4      1075358    1311748  ...                   0.0        0.0\n",
            "...        ...        ...  ...                   ...        ...\n",
            "39712    92187      92174  ...                   NaN        NaN\n",
            "39713    90665      90607  ...                   NaN        NaN\n",
            "39714    90395      90390  ...                   NaN        NaN\n",
            "39715    90376      89243  ...                   NaN        NaN\n",
            "39716    87023      86999  ...                   NaN        NaN\n",
            "\n",
            "[39717 rows x 57 columns]\n"
          ]
        }
      ],
      "source": [
        "inp1 = loan.drop(nullvalue_df_columns, axis=1)\n",
        "print(inp1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8OZIIrwovB-L"
      },
      "source": [
        "check the null values again"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SNkkwGCdtgGw",
        "outputId": "fd06111d-9619-4feb-a157-af44471a2355"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                0\n",
              "member_id                         0\n",
              "loan_amnt                         0\n",
              "funded_amnt                       0\n",
              "funded_amnt_inv                   0\n",
              "term                              0\n",
              "int_rate                          0\n",
              "installment                       0\n",
              "grade                             0\n",
              "sub_grade                         0\n",
              "emp_title                      2459\n",
              "emp_length                     1075\n",
              "home_ownership                    0\n",
              "annual_inc                        0\n",
              "verification_status               0\n",
              "issue_d                           0\n",
              "loan_status                       0\n",
              "pymnt_plan                        0\n",
              "url                               0\n",
              "desc                          12940\n",
              "purpose                           0\n",
              "title                            11\n",
              "zip_code                          0\n",
              "addr_state                        0\n",
              "dti                               0\n",
              "delinq_2yrs                       0\n",
              "earliest_cr_line                  0\n",
              "inq_last_6mths                    0\n",
              "mths_since_last_delinq        25682\n",
              "mths_since_last_record        36931\n",
              "open_acc                          0\n",
              "pub_rec                           0\n",
              "revol_bal                         0\n",
              "revol_util                       50\n",
              "total_acc                         0\n",
              "initial_list_status               0\n",
              "out_prncp                         0\n",
              "out_prncp_inv                     0\n",
              "total_pymnt                       0\n",
              "total_pymnt_inv                   0\n",
              "total_rec_prncp                   0\n",
              "total_rec_int                     0\n",
              "total_rec_late_fee                0\n",
              "recoveries                        0\n",
              "collection_recovery_fee           0\n",
              "last_pymnt_d                     71\n",
              "last_pymnt_amnt                   0\n",
              "next_pymnt_d                  38577\n",
              "last_credit_pull_d                2\n",
              "collections_12_mths_ex_med       56\n",
              "policy_code                       0\n",
              "application_type                  0\n",
              "acc_now_delinq                    0\n",
              "chargeoff_within_12_mths         56\n",
              "delinq_amnt                       0\n",
              "pub_rec_bankruptcies            697\n",
              "tax_liens                        39\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "inp1.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zSvqcaee1m2T"
      },
      "source": [
        "Now we will do same process to remove a null columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "neoempJOtrLg",
        "outputId": "c2ffa971-30f1-49de-f3e3-419eb26b6944"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id                             0.000000\n",
            "member_id                      0.000000\n",
            "loan_amnt                      0.000000\n",
            "funded_amnt                    0.000000\n",
            "funded_amnt_inv                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                      6.191303\n",
            "emp_length                     2.706650\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",
            "pymnt_plan                     0.000000\n",
            "url                            0.000000\n",
            "desc                          32.580507\n",
            "purpose                        0.000000\n",
            "title                          0.027696\n",
            "zip_code                       0.000000\n",
            "addr_state                     0.000000\n",
            "dti                            0.000000\n",
            "delinq_2yrs                    0.000000\n",
            "earliest_cr_line               0.000000\n",
            "inq_last_6mths                 0.000000\n",
            "mths_since_last_delinq        64.662487\n",
            "mths_since_last_record        92.985372\n",
            "open_acc                       0.000000\n",
            "pub_rec                        0.000000\n",
            "revol_bal                      0.000000\n",
            "revol_util                     0.125891\n",
            "total_acc                      0.000000\n",
            "initial_list_status            0.000000\n",
            "out_prncp                      0.000000\n",
            "out_prncp_inv                  0.000000\n",
            "total_pymnt                    0.000000\n",
            "total_pymnt_inv                0.000000\n",
            "total_rec_prncp                0.000000\n",
            "total_rec_int                  0.000000\n",
            "total_rec_late_fee             0.000000\n",
            "recoveries                     0.000000\n",
            "collection_recovery_fee        0.000000\n",
            "last_pymnt_d                   0.178765\n",
            "last_pymnt_amnt                0.000000\n",
            "next_pymnt_d                  97.129693\n",
            "last_credit_pull_d             0.005036\n",
            "collections_12_mths_ex_med     0.140998\n",
            "policy_code                    0.000000\n",
            "application_type               0.000000\n",
            "acc_now_delinq                 0.000000\n",
            "chargeoff_within_12_mths       0.140998\n",
            "delinq_amnt                    0.000000\n",
            "pub_rec_bankruptcies           1.754916\n",
            "tax_liens                      0.098195\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "per2 = inp1.isnull().sum() * 100 / len(loan)\n",
        "print(per2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q8enThBezXAn",
        "outputId": "d3ec3472-413a-485d-a0b5-609415f2a1fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                      new_columns_name  new_percent_nullvalues\n",
            "id                                                  id                    0.00\n",
            "member_id                                    member_id                    0.00\n",
            "loan_amnt                                    loan_amnt                    0.00\n",
            "funded_amnt                                funded_amnt                    0.00\n",
            "funded_amnt_inv                        funded_amnt_inv                    0.00\n",
            "term                                              term                    0.00\n",
            "int_rate                                      int_rate                    0.00\n",
            "installment                                installment                    0.00\n",
            "grade                                            grade                    0.00\n",
            "sub_grade                                    sub_grade                    0.00\n",
            "emp_title                                    emp_title                    6.19\n",
            "emp_length                                  emp_length                    2.71\n",
            "home_ownership                          home_ownership                    0.00\n",
            "annual_inc                                  annual_inc                    0.00\n",
            "verification_status                verification_status                    0.00\n",
            "issue_d                                        issue_d                    0.00\n",
            "loan_status                                loan_status                    0.00\n",
            "pymnt_plan                                  pymnt_plan                    0.00\n",
            "url                                                url                    0.00\n",
            "desc                                              desc                   32.58\n",
            "purpose                                        purpose                    0.00\n",
            "title                                            title                    0.03\n",
            "zip_code                                      zip_code                    0.00\n",
            "addr_state                                  addr_state                    0.00\n",
            "dti                                                dti                    0.00\n",
            "delinq_2yrs                                delinq_2yrs                    0.00\n",
            "earliest_cr_line                      earliest_cr_line                    0.00\n",
            "inq_last_6mths                          inq_last_6mths                    0.00\n",
            "mths_since_last_delinq          mths_since_last_delinq                   64.66\n",
            "mths_since_last_record          mths_since_last_record                   92.99\n",
            "open_acc                                      open_acc                    0.00\n",
            "pub_rec                                        pub_rec                    0.00\n",
            "revol_bal                                    revol_bal                    0.00\n",
            "revol_util                                  revol_util                    0.13\n",
            "total_acc                                    total_acc                    0.00\n",
            "initial_list_status                initial_list_status                    0.00\n",
            "out_prncp                                    out_prncp                    0.00\n",
            "out_prncp_inv                            out_prncp_inv                    0.00\n",
            "total_pymnt                                total_pymnt                    0.00\n",
            "total_pymnt_inv                        total_pymnt_inv                    0.00\n",
            "total_rec_prncp                        total_rec_prncp                    0.00\n",
            "total_rec_int                            total_rec_int                    0.00\n",
            "total_rec_late_fee                  total_rec_late_fee                    0.00\n",
            "recoveries                                  recoveries                    0.00\n",
            "collection_recovery_fee        collection_recovery_fee                    0.00\n",
            "last_pymnt_d                              last_pymnt_d                    0.18\n",
            "last_pymnt_amnt                        last_pymnt_amnt                    0.00\n",
            "next_pymnt_d                              next_pymnt_d                   97.13\n",
            "last_credit_pull_d                  last_credit_pull_d                    0.01\n",
            "collections_12_mths_ex_med  collections_12_mths_ex_med                    0.14\n",
            "policy_code                                policy_code                    0.00\n",
            "application_type                      application_type                    0.00\n",
            "acc_now_delinq                          acc_now_delinq                    0.00\n",
            "chargeoff_within_12_mths      chargeoff_within_12_mths                    0.14\n",
            "delinq_amnt                                delinq_amnt                    0.00\n",
            "pub_rec_bankruptcies              pub_rec_bankruptcies                    1.75\n",
            "tax_liens                                    tax_liens                    0.10\n"
          ]
        }
      ],
      "source": [
        "nullvalue2 = pd.DataFrame({'new_columns_name': inp1.columns,\n",
        "                                 'new_percent_nullvalues': per2.round(2)})\n",
        "print(nullvalue2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fGjAkvuvz6Ke",
        "outputId": "f7b3187d-70b0-46eb-d873-77f015a19e3d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "(nullvalue2['new_percent_nullvalues'] > 0.00).sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4fHKTied0OY-",
        "outputId": "3f167a5b-1c42-4a46-f6dc-ef6d9151183c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['emp_title', 'emp_length', 'desc', 'title', 'mths_since_last_delinq', 'mths_since_last_record', 'revol_util', 'last_pymnt_d', 'next_pymnt_d', 'last_credit_pull_d', 'collections_12_mths_ex_med', 'chargeoff_within_12_mths', 'pub_rec_bankruptcies', 'tax_liens']\n"
          ]
        }
      ],
      "source": [
        "nullvalue_new_columns = nullvalue2[(nullvalue2['new_percent_nullvalues'] > 0.00)].new_columns_name.tolist()\n",
        "print(nullvalue_new_columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HY6L66yh1OmP",
        "outputId": "a31c6981-62db-4e5c-9979-dd9ef9521a59"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14\n"
          ]
        }
      ],
      "source": [
        "print(len(nullvalue_new_columns))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "All null values columns removed and assigned into new variable"
      ],
      "metadata": {
        "id": "DkQ95C1LnhSZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uCSGh-XO1jI1",
        "outputId": "7a0e0878-7e5f-484c-cf33-4bbd4ebb6fcc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            id  member_id  ...  acc_now_delinq  delinq_amnt\n",
            "0      1077501    1296599  ...               0            0\n",
            "1      1077430    1314167  ...               0            0\n",
            "2      1077175    1313524  ...               0            0\n",
            "3      1076863    1277178  ...               0            0\n",
            "4      1075358    1311748  ...               0            0\n",
            "...        ...        ...  ...             ...          ...\n",
            "39712    92187      92174  ...               0            0\n",
            "39713    90665      90607  ...               0            0\n",
            "39714    90395      90390  ...               0            0\n",
            "39715    90376      89243  ...               0            0\n",
            "39716    87023      86999  ...               0            0\n",
            "\n",
            "[39717 rows x 43 columns]\n"
          ]
        }
      ],
      "source": [
        "inp2 = inp1.drop(nullvalue_new_columns, axis=1)\n",
        "print(inp2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HdSIdwNL17h3",
        "outputId": "3f0ddc9e-c168-4e79-d05a-e73ce0d20fb7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(39717, 43)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "inp2.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now the new data set does not contain any null values"
      ],
      "metadata": {
        "id": "b0aLQzwYnw5n"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7wQ0dGxu2Kyz",
        "outputId": "e6012d30-c2d5-40ac-9dd6-51d1ec9c2bfe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                         0\n",
              "member_id                  0\n",
              "loan_amnt                  0\n",
              "funded_amnt                0\n",
              "funded_amnt_inv            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",
              "pymnt_plan                 0\n",
              "url                        0\n",
              "purpose                    0\n",
              "zip_code                   0\n",
              "addr_state                 0\n",
              "dti                        0\n",
              "delinq_2yrs                0\n",
              "earliest_cr_line           0\n",
              "inq_last_6mths             0\n",
              "open_acc                   0\n",
              "pub_rec                    0\n",
              "revol_bal                  0\n",
              "total_acc                  0\n",
              "initial_list_status        0\n",
              "out_prncp                  0\n",
              "out_prncp_inv              0\n",
              "total_pymnt                0\n",
              "total_pymnt_inv            0\n",
              "total_rec_prncp            0\n",
              "total_rec_int              0\n",
              "total_rec_late_fee         0\n",
              "recoveries                 0\n",
              "collection_recovery_fee    0\n",
              "last_pymnt_amnt            0\n",
              "policy_code                0\n",
              "application_type           0\n",
              "acc_now_delinq             0\n",
              "delinq_amnt                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "inp2.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QLx7FvCW2ZwV",
        "outputId": "fa26877f-2b4b-4552-d612-242f87b787c2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 39717 entries, 0 to 39716\n",
            "Data columns (total 43 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   id                       39717 non-null  int64  \n",
            " 1   member_id                39717 non-null  int64  \n",
            " 2   loan_amnt                39717 non-null  int64  \n",
            " 3   funded_amnt              39717 non-null  int64  \n",
            " 4   funded_amnt_inv          39717 non-null  float64\n",
            " 5   term                     39717 non-null  object \n",
            " 6   int_rate                 39717 non-null  object \n",
            " 7   installment              39717 non-null  float64\n",
            " 8   grade                    39717 non-null  object \n",
            " 9   sub_grade                39717 non-null  object \n",
            " 10  home_ownership           39717 non-null  object \n",
            " 11  annual_inc               39717 non-null  float64\n",
            " 12  verification_status      39717 non-null  object \n",
            " 13  issue_d                  39717 non-null  object \n",
            " 14  loan_status              39717 non-null  object \n",
            " 15  pymnt_plan               39717 non-null  object \n",
            " 16  url                      39717 non-null  object \n",
            " 17  purpose                  39717 non-null  object \n",
            " 18  zip_code                 39717 non-null  object \n",
            " 19  addr_state               39717 non-null  object \n",
            " 20  dti                      39717 non-null  float64\n",
            " 21  delinq_2yrs              39717 non-null  int64  \n",
            " 22  earliest_cr_line         39717 non-null  object \n",
            " 23  inq_last_6mths           39717 non-null  int64  \n",
            " 24  open_acc                 39717 non-null  int64  \n",
            " 25  pub_rec                  39717 non-null  int64  \n",
            " 26  revol_bal                39717 non-null  int64  \n",
            " 27  total_acc                39717 non-null  int64  \n",
            " 28  initial_list_status      39717 non-null  object \n",
            " 29  out_prncp                39717 non-null  float64\n",
            " 30  out_prncp_inv            39717 non-null  float64\n",
            " 31  total_pymnt              39717 non-null  float64\n",
            " 32  total_pymnt_inv          39717 non-null  float64\n",
            " 33  total_rec_prncp          39717 non-null  float64\n",
            " 34  total_rec_int            39717 non-null  float64\n",
            " 35  total_rec_late_fee       39717 non-null  float64\n",
            " 36  recoveries               39717 non-null  float64\n",
            " 37  collection_recovery_fee  39717 non-null  float64\n",
            " 38  last_pymnt_amnt          39717 non-null  float64\n",
            " 39  policy_code              39717 non-null  int64  \n",
            " 40  application_type         39717 non-null  object \n",
            " 41  acc_now_delinq           39717 non-null  int64  \n",
            " 42  delinq_amnt              39717 non-null  int64  \n",
            "dtypes: float64(14), int64(13), object(16)\n",
            "memory usage: 13.0+ MB\n"
          ]
        }
      ],
      "source": [
        "inp2.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "NJucFSnT3TvI",
        "outputId": "c5508880-bc22-415f-8070-ac5c65f71f1e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-6aa9eb1f-ad8c-4231-8197-a34d4ba37889\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>member_id</th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>funded_amnt</th>\n",
              "      <th>funded_amnt_inv</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>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>pymnt_plan</th>\n",
              "      <th>url</th>\n",
              "      <th>purpose</th>\n",
              "      <th>zip_code</th>\n",
              "      <th>addr_state</th>\n",
              "      <th>dti</th>\n",
              "      <th>delinq_2yrs</th>\n",
              "      <th>earliest_cr_line</th>\n",
              "      <th>inq_last_6mths</th>\n",
              "      <th>open_acc</th>\n",
              "      <th>pub_rec</th>\n",
              "      <th>revol_bal</th>\n",
              "      <th>total_acc</th>\n",
              "      <th>initial_list_status</th>\n",
              "      <th>out_prncp</th>\n",
              "      <th>out_prncp_inv</th>\n",
              "      <th>total_pymnt</th>\n",
              "      <th>total_pymnt_inv</th>\n",
              "      <th>total_rec_prncp</th>\n",
              "      <th>total_rec_int</th>\n",
              "      <th>total_rec_late_fee</th>\n",
              "      <th>recoveries</th>\n",
              "      <th>collection_recovery_fee</th>\n",
              "      <th>last_pymnt_amnt</th>\n",
              "      <th>policy_code</th>\n",
              "      <th>application_type</th>\n",
              "      <th>acc_now_delinq</th>\n",
              "      <th>delinq_amnt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1077501</td>\n",
              "      <td>1296599</td>\n",
              "      <td>5000</td>\n",
              "      <td>5000</td>\n",
              "      <td>4975.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>10.65%</td>\n",
              "      <td>162.87</td>\n",
              "      <td>B</td>\n",
              "      <td>B2</td>\n",
              "      <td>RENT</td>\n",
              "      <td>24000.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>860xx</td>\n",
              "      <td>AZ</td>\n",
              "      <td>27.65</td>\n",
              "      <td>0</td>\n",
              "      <td>Jan-85</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>13648</td>\n",
              "      <td>9</td>\n",
              "      <td>f</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>5863.155187</td>\n",
              "      <td>5833.84</td>\n",
              "      <td>5000.00</td>\n",
              "      <td>863.16</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>171.62</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1077430</td>\n",
              "      <td>1314167</td>\n",
              "      <td>2500</td>\n",
              "      <td>2500</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>15.27%</td>\n",
              "      <td>59.83</td>\n",
              "      <td>C</td>\n",
              "      <td>C4</td>\n",
              "      <td>RENT</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Charged Off</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>car</td>\n",
              "      <td>309xx</td>\n",
              "      <td>GA</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0</td>\n",
              "      <td>Apr-99</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1687</td>\n",
              "      <td>4</td>\n",
              "      <td>f</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1008.710000</td>\n",
              "      <td>1008.71</td>\n",
              "      <td>456.46</td>\n",
              "      <td>435.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>117.08</td>\n",
              "      <td>1.11</td>\n",
              "      <td>119.66</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1077175</td>\n",
              "      <td>1313524</td>\n",
              "      <td>2400</td>\n",
              "      <td>2400</td>\n",
              "      <td>2400.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>15.96%</td>\n",
              "      <td>84.33</td>\n",
              "      <td>C</td>\n",
              "      <td>C5</td>\n",
              "      <td>RENT</td>\n",
              "      <td>12252.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>small_business</td>\n",
              "      <td>606xx</td>\n",
              "      <td>IL</td>\n",
              "      <td>8.72</td>\n",
              "      <td>0</td>\n",
              "      <td>Nov-01</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2956</td>\n",
              "      <td>10</td>\n",
              "      <td>f</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3005.666844</td>\n",
              "      <td>3005.67</td>\n",
              "      <td>2400.00</td>\n",
              "      <td>605.67</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>649.91</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1076863</td>\n",
              "      <td>1277178</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>13.49%</td>\n",
              "      <td>339.31</td>\n",
              "      <td>C</td>\n",
              "      <td>C1</td>\n",
              "      <td>RENT</td>\n",
              "      <td>49200.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>other</td>\n",
              "      <td>917xx</td>\n",
              "      <td>CA</td>\n",
              "      <td>20.00</td>\n",
              "      <td>0</td>\n",
              "      <td>Feb-96</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>5598</td>\n",
              "      <td>37</td>\n",
              "      <td>f</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>12231.890000</td>\n",
              "      <td>12231.89</td>\n",
              "      <td>10000.00</td>\n",
              "      <td>2214.92</td>\n",
              "      <td>16.97</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>357.48</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1075358</td>\n",
              "      <td>1311748</td>\n",
              "      <td>3000</td>\n",
              "      <td>3000</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>12.69%</td>\n",
              "      <td>67.79</td>\n",
              "      <td>B</td>\n",
              "      <td>B5</td>\n",
              "      <td>RENT</td>\n",
              "      <td>80000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>other</td>\n",
              "      <td>972xx</td>\n",
              "      <td>OR</td>\n",
              "      <td>17.94</td>\n",
              "      <td>0</td>\n",
              "      <td>Jan-96</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>27783</td>\n",
              "      <td>38</td>\n",
              "      <td>f</td>\n",
              "      <td>524.06</td>\n",
              "      <td>524.06</td>\n",
              "      <td>3513.330000</td>\n",
              "      <td>3513.33</td>\n",
              "      <td>2475.94</td>\n",
              "      <td>1037.39</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>67.79</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1075269</td>\n",
              "      <td>1311441</td>\n",
              "      <td>5000</td>\n",
              "      <td>5000</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>7.90%</td>\n",
              "      <td>156.46</td>\n",
              "      <td>A</td>\n",
              "      <td>A4</td>\n",
              "      <td>RENT</td>\n",
              "      <td>36000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>wedding</td>\n",
              "      <td>852xx</td>\n",
              "      <td>AZ</td>\n",
              "      <td>11.20</td>\n",
              "      <td>0</td>\n",
              "      <td>Nov-04</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>7963</td>\n",
              "      <td>12</td>\n",
              "      <td>f</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>5632.210000</td>\n",
              "      <td>5632.21</td>\n",
              "      <td>5000.00</td>\n",
              "      <td>632.21</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>161.03</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1069639</td>\n",
              "      <td>1304742</td>\n",
              "      <td>7000</td>\n",
              "      <td>7000</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>15.96%</td>\n",
              "      <td>170.08</td>\n",
              "      <td>C</td>\n",
              "      <td>C5</td>\n",
              "      <td>RENT</td>\n",
              "      <td>47004.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>280xx</td>\n",
              "      <td>NC</td>\n",
              "      <td>23.51</td>\n",
              "      <td>0</td>\n",
              "      <td>Jul-05</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>17726</td>\n",
              "      <td>11</td>\n",
              "      <td>f</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>10110.840000</td>\n",
              "      <td>10110.84</td>\n",
              "      <td>6985.61</td>\n",
              "      <td>3125.23</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1313.76</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  "
            ],
            "text/plain": [
              "        id  member_id  loan_amnt  ...  application_type  acc_now_delinq delinq_amnt\n",
              "0  1077501    1296599       5000  ...        INDIVIDUAL               0           0\n",
              "1  1077430    1314167       2500  ...        INDIVIDUAL               0           0\n",
              "2  1077175    1313524       2400  ...        INDIVIDUAL               0           0\n",
              "3  1076863    1277178      10000  ...        INDIVIDUAL               0           0\n",
              "4  1075358    1311748       3000  ...        INDIVIDUAL               0           0\n",
              "5  1075269    1311441       5000  ...        INDIVIDUAL               0           0\n",
              "6  1069639    1304742       7000  ...        INDIVIDUAL               0           0\n",
              "\n",
              "[7 rows x 43 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "inp2.head(7)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NW92EdH0-GnQ"
      },
      "source": [
        "Count the value of loan_status because we don't need the current status because these candidates are not labelled as 'defaulted'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MbRWuZKI98FH",
        "outputId": "c2895fc6-24ce-4d9c-9231-6b27066b4e49"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Fully Paid     32950\n",
              "Charged Off     5627\n",
              "Current         1140\n",
              "Name: loan_status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "inp2['loan_status'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "dEfVXbFe5jL_",
        "outputId": "f4613f9c-c84c-4f9e-db47-b30c7a2c5e0e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-6bee9df5-15ac-4a04-a679-600a29f941b7\">\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>member_id</th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>funded_amnt</th>\n",
              "      <th>funded_amnt_inv</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>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>pymnt_plan</th>\n",
              "      <th>url</th>\n",
              "      <th>purpose</th>\n",
              "      <th>zip_code</th>\n",
              "      <th>addr_state</th>\n",
              "      <th>dti</th>\n",
              "      <th>delinq_2yrs</th>\n",
              "      <th>earliest_cr_line</th>\n",
              "      <th>inq_last_6mths</th>\n",
              "      <th>open_acc</th>\n",
              "      <th>pub_rec</th>\n",
              "      <th>revol_bal</th>\n",
              "      <th>total_acc</th>\n",
              "      <th>initial_list_status</th>\n",
              "      <th>out_prncp</th>\n",
              "      <th>out_prncp_inv</th>\n",
              "      <th>total_pymnt</th>\n",
              "      <th>total_pymnt_inv</th>\n",
              "      <th>total_rec_prncp</th>\n",
              "      <th>total_rec_int</th>\n",
              "      <th>total_rec_late_fee</th>\n",
              "      <th>recoveries</th>\n",
              "      <th>collection_recovery_fee</th>\n",
              "      <th>last_pymnt_amnt</th>\n",
              "      <th>policy_code</th>\n",
              "      <th>application_type</th>\n",
              "      <th>acc_now_delinq</th>\n",
              "      <th>delinq_amnt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1075358</td>\n",
              "      <td>1311748</td>\n",
              "      <td>3000</td>\n",
              "      <td>3000</td>\n",
              "      <td>3000.00000</td>\n",
              "      <td>60 months</td>\n",
              "      <td>12.69%</td>\n",
              "      <td>67.79</td>\n",
              "      <td>B</td>\n",
              "      <td>B5</td>\n",
              "      <td>RENT</td>\n",
              "      <td>80000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>other</td>\n",
              "      <td>972xx</td>\n",
              "      <td>OR</td>\n",
              "      <td>17.94</td>\n",
              "      <td>0</td>\n",
              "      <td>Jan-96</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>27783</td>\n",
              "      <td>38</td>\n",
              "      <td>f</td>\n",
              "      <td>524.06</td>\n",
              "      <td>524.06</td>\n",
              "      <td>3513.33</td>\n",
              "      <td>3513.33</td>\n",
              "      <td>2475.94</td>\n",
              "      <td>1037.39</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>67.79</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>1065420</td>\n",
              "      <td>1299514</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000</td>\n",
              "      <td>9975.00000</td>\n",
              "      <td>60 months</td>\n",
              "      <td>15.96%</td>\n",
              "      <td>242.97</td>\n",
              "      <td>C</td>\n",
              "      <td>C5</td>\n",
              "      <td>RENT</td>\n",
              "      <td>29120.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>330xx</td>\n",
              "      <td>FL</td>\n",
              "      <td>22.83</td>\n",
              "      <td>0</td>\n",
              "      <td>Feb-97</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>16158</td>\n",
              "      <td>31</td>\n",
              "      <td>f</td>\n",
              "      <td>1849.10</td>\n",
              "      <td>1844.43</td>\n",
              "      <td>12594.24</td>\n",
              "      <td>12562.85</td>\n",
              "      <td>8150.89</td>\n",
              "      <td>4443.35</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>242.97</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>1069346</td>\n",
              "      <td>1304237</td>\n",
              "      <td>12500</td>\n",
              "      <td>12500</td>\n",
              "      <td>12475.00000</td>\n",
              "      <td>60 months</td>\n",
              "      <td>12.69%</td>\n",
              "      <td>282.44</td>\n",
              "      <td>B</td>\n",
              "      <td>B5</td>\n",
              "      <td>RENT</td>\n",
              "      <td>27000.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>604xx</td>\n",
              "      <td>IL</td>\n",
              "      <td>16.04</td>\n",
              "      <td>0</td>\n",
              "      <td>Dec-01</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>10143</td>\n",
              "      <td>24</td>\n",
              "      <td>f</td>\n",
              "      <td>2181.42</td>\n",
              "      <td>2177.03</td>\n",
              "      <td>14636.30</td>\n",
              "      <td>14607.08</td>\n",
              "      <td>10318.58</td>\n",
              "      <td>4317.72</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>282.44</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>1063958</td>\n",
              "      <td>1297940</td>\n",
              "      <td>14000</td>\n",
              "      <td>14000</td>\n",
              "      <td>13975.00000</td>\n",
              "      <td>60 months</td>\n",
              "      <td>17.27%</td>\n",
              "      <td>349.98</td>\n",
              "      <td>D</td>\n",
              "      <td>D3</td>\n",
              "      <td>RENT</td>\n",
              "      <td>28000.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>other</td>\n",
              "      <td>992xx</td>\n",
              "      <td>WA</td>\n",
              "      <td>6.34</td>\n",
              "      <td>0</td>\n",
              "      <td>Jul-05</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>6986</td>\n",
              "      <td>10</td>\n",
              "      <td>f</td>\n",
              "      <td>2637.34</td>\n",
              "      <td>2632.59</td>\n",
              "      <td>18176.96</td>\n",
              "      <td>18144.59</td>\n",
              "      <td>11362.67</td>\n",
              "      <td>6814.29</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>349.98</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>1068575</td>\n",
              "      <td>1303001</td>\n",
              "      <td>15300</td>\n",
              "      <td>15300</td>\n",
              "      <td>15275.00000</td>\n",
              "      <td>60 months</td>\n",
              "      <td>22.06%</td>\n",
              "      <td>423.10</td>\n",
              "      <td>F</td>\n",
              "      <td>F4</td>\n",
              "      <td>RENT</td>\n",
              "      <td>85000.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>226xx</td>\n",
              "      <td>VA</td>\n",
              "      <td>20.58</td>\n",
              "      <td>0</td>\n",
              "      <td>Feb-84</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>26524</td>\n",
              "      <td>27</td>\n",
              "      <td>f</td>\n",
              "      <td>3125.79</td>\n",
              "      <td>3120.68</td>\n",
              "      <td>21988.20</td>\n",
              "      <td>21952.28</td>\n",
              "      <td>12174.21</td>\n",
              "      <td>9813.99</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>423.10</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15972</th>\n",
              "      <td>725556</td>\n",
              "      <td>920821</td>\n",
              "      <td>10400</td>\n",
              "      <td>10400</td>\n",
              "      <td>10400.00000</td>\n",
              "      <td>60 months</td>\n",
              "      <td>6.00%</td>\n",
              "      <td>218.99</td>\n",
              "      <td>E</td>\n",
              "      <td>E3</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>29000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>May-11</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>799xx</td>\n",
              "      <td>TX</td>\n",
              "      <td>10.84</td>\n",
              "      <td>0</td>\n",
              "      <td>Nov-02</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>8696</td>\n",
              "      <td>15</td>\n",
              "      <td>f</td>\n",
              "      <td>238.82</td>\n",
              "      <td>238.82</td>\n",
              "      <td>13886.92</td>\n",
              "      <td>13886.92</td>\n",
              "      <td>10161.18</td>\n",
              "      <td>3725.74</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>218.99</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15981</th>\n",
              "      <td>738420</td>\n",
              "      <td>935790</td>\n",
              "      <td>6000</td>\n",
              "      <td>6000</td>\n",
              "      <td>6000.00000</td>\n",
              "      <td>60 months</td>\n",
              "      <td>10.37%</td>\n",
              "      <td>128.58</td>\n",
              "      <td>B</td>\n",
              "      <td>B3</td>\n",
              "      <td>RENT</td>\n",
              "      <td>24000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Apr-11</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>car</td>\n",
              "      <td>142xx</td>\n",
              "      <td>NY</td>\n",
              "      <td>11.05</td>\n",
              "      <td>0</td>\n",
              "      <td>Oct-05</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>f</td>\n",
              "      <td>10.26</td>\n",
              "      <td>10.26</td>\n",
              "      <td>7701.15</td>\n",
              "      <td>7701.15</td>\n",
              "      <td>5989.74</td>\n",
              "      <td>1711.41</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>128.58</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16361</th>\n",
              "      <td>733695</td>\n",
              "      <td>930086</td>\n",
              "      <td>19200</td>\n",
              "      <td>19200</td>\n",
              "      <td>18847.76292</td>\n",
              "      <td>60 months</td>\n",
              "      <td>10.74%</td>\n",
              "      <td>414.97</td>\n",
              "      <td>B</td>\n",
              "      <td>B4</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>100000.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>Apr-11</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>small_business</td>\n",
              "      <td>071xx</td>\n",
              "      <td>NJ</td>\n",
              "      <td>4.43</td>\n",
              "      <td>0</td>\n",
              "      <td>Jan-91</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>f</td>\n",
              "      <td>19.12</td>\n",
              "      <td>19.09</td>\n",
              "      <td>24894.24</td>\n",
              "      <td>24310.75</td>\n",
              "      <td>19180.88</td>\n",
              "      <td>5713.36</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>414.97</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16609</th>\n",
              "      <td>728422</td>\n",
              "      <td>924106</td>\n",
              "      <td>9350</td>\n",
              "      <td>9350</td>\n",
              "      <td>9350.00000</td>\n",
              "      <td>60 months</td>\n",
              "      <td>10.00%</td>\n",
              "      <td>198.66</td>\n",
              "      <td>B</td>\n",
              "      <td>B2</td>\n",
              "      <td>RENT</td>\n",
              "      <td>66000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Apr-11</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>car</td>\n",
              "      <td>103xx</td>\n",
              "      <td>NY</td>\n",
              "      <td>4.84</td>\n",
              "      <td>0</td>\n",
              "      <td>Dec-01</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>88</td>\n",
              "      <td>7</td>\n",
              "      <td>f</td>\n",
              "      <td>13.28</td>\n",
              "      <td>13.28</td>\n",
              "      <td>11906.14</td>\n",
              "      <td>11906.14</td>\n",
              "      <td>9336.72</td>\n",
              "      <td>2569.42</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>198.66</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18215</th>\n",
              "      <td>702171</td>\n",
              "      <td>894029</td>\n",
              "      <td>9000</td>\n",
              "      <td>9000</td>\n",
              "      <td>9000.00000</td>\n",
              "      <td>60 months</td>\n",
              "      <td>13.06%</td>\n",
              "      <td>205.06</td>\n",
              "      <td>C</td>\n",
              "      <td>C2</td>\n",
              "      <td>RENT</td>\n",
              "      <td>44137.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Mar-11</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>906xx</td>\n",
              "      <td>CA</td>\n",
              "      <td>5.74</td>\n",
              "      <td>0</td>\n",
              "      <td>Apr-01</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>7556</td>\n",
              "      <td>20</td>\n",
              "      <td>f</td>\n",
              "      <td>79.24</td>\n",
              "      <td>79.24</td>\n",
              "      <td>12179.38</td>\n",
              "      <td>12179.38</td>\n",
              "      <td>8920.76</td>\n",
              "      <td>3243.62</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>205.06</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1140 rows × 43 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6bee9df5-15ac-4a04-a679-600a29f941b7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6bee9df5-15ac-4a04-a679-600a29f941b7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "            id  member_id  ...  acc_now_delinq  delinq_amnt\n",
              "4      1075358    1311748  ...               0            0\n",
              "32     1065420    1299514  ...               0            0\n",
              "39     1069346    1304237  ...               0            0\n",
              "86     1063958    1297940  ...               0            0\n",
              "95     1068575    1303001  ...               0            0\n",
              "...        ...        ...  ...             ...          ...\n",
              "15972   725556     920821  ...               0            0\n",
              "15981   738420     935790  ...               0            0\n",
              "16361   733695     930086  ...               0            0\n",
              "16609   728422     924106  ...               0            0\n",
              "18215   702171     894029  ...               0            0\n",
              "\n",
              "[1140 rows x 43 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "inp2[inp2['loan_status'] == \"Current\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gl8EZIMM7W_F",
        "outputId": "c4379770-f389-47ff-ad4e-1f87df0029aa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(38577, 43)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "inp2 = inp2[inp2['loan_status'] != \"Current\"]\n",
        "inp2.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we removed 'Loan_status' value is current because we don't need that"
      ],
      "metadata": {
        "id": "RIrSXqHCoSCg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDc6nAF48oyl",
        "outputId": "6da70e57-b1a1-4024-f7c5-50c49c280d21"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Fully Paid     32950\n",
              "Charged Off     5627\n",
              "Name: loan_status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "inp2['loan_status'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZNUAYOtA_SRz"
      },
      "source": [
        "Here we remove the current loan status raw"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-6XCsOOp_Nuw",
        "outputId": "83b2c17d-1ef9-4427-f6cf-8d1df7b7d204"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.000000     36582\n",
              "15.000000      245\n",
              "15.000000       58\n",
              "30.000000       52\n",
              "15.000000       47\n",
              "             ...  \n",
              "44.840000        1\n",
              "16.010000        1\n",
              "29.914662        1\n",
              "23.820000        1\n",
              "17.165009        1\n",
              "Name: total_rec_late_fee, Length: 1320, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "inp2['total_rec_late_fee'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EANFfvpIAWaW",
        "outputId": "0031c5eb-5e37-4841-e9c3-3138e8c74fb0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    38577\n",
              "Name: acc_now_delinq, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "inp2['acc_now_delinq'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OeJw4-eXAhR-",
        "outputId": "2cabaeb7-1048-4942-8fac-98eeedc0775e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    38577\n",
              "Name: delinq_amnt, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "inp2['delinq_amnt'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zKndTulYBfan",
        "outputId": "b96e7674-d402-4735-fedc-51d893f4fd5c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00      34359\n",
              "10.40         4\n",
              "11.29         4\n",
              "19.20         3\n",
              "13.59         3\n",
              "          ...  \n",
              "240.15        1\n",
              "385.70        1\n",
              "995.74        1\n",
              "176.49        1\n",
              "269.31        1\n",
              "Name: recoveries, Length: 4040, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "inp2['recoveries'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o5QwxY2EBuQj",
        "outputId": "01d63502-29f9-4079-dc38-624828e3d7e5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    36507\n",
              "1     2013\n",
              "2       48\n",
              "3        7\n",
              "4        2\n",
              "Name: pub_rec, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "inp2['pub_rec'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QUyGMM3gCdfr"
      },
      "source": [
        "Remove this column because it is not required"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a9reuLccApmm",
        "outputId": "a3b42d7b-6c2b-4c82-a83c-261b64aad025"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(38577, 40)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "inp2= inp2.drop([\"acc_now_delinq\", \"delinq_amnt\", \"url\"], axis=1)\n",
        "inp2.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "TRcJsnJ_CAH2",
        "outputId": "4badeadd-e08c-41be-995b-82d26c77cbe5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>id</th>\n",
              "      <th>member_id</th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>funded_amnt</th>\n",
              "      <th>funded_amnt_inv</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>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>pymnt_plan</th>\n",
              "      <th>purpose</th>\n",
              "      <th>zip_code</th>\n",
              "      <th>addr_state</th>\n",
              "      <th>dti</th>\n",
              "      <th>delinq_2yrs</th>\n",
              "      <th>earliest_cr_line</th>\n",
              "      <th>inq_last_6mths</th>\n",
              "      <th>open_acc</th>\n",
              "      <th>pub_rec</th>\n",
              "      <th>revol_bal</th>\n",
              "      <th>total_acc</th>\n",
              "      <th>initial_list_status</th>\n",
              "      <th>out_prncp</th>\n",
              "      <th>out_prncp_inv</th>\n",
              "      <th>total_pymnt</th>\n",
              "      <th>total_pymnt_inv</th>\n",
              "      <th>total_rec_prncp</th>\n",
              "      <th>total_rec_int</th>\n",
              "      <th>total_rec_late_fee</th>\n",
              "      <th>recoveries</th>\n",
              "      <th>collection_recovery_fee</th>\n",
              "      <th>last_pymnt_amnt</th>\n",
              "      <th>policy_code</th>\n",
              "      <th>application_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1077501</td>\n",
              "      <td>1296599</td>\n",
              "      <td>5000</td>\n",
              "      <td>5000</td>\n",
              "      <td>4975.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>10.65%</td>\n",
              "      <td>162.87</td>\n",
              "      <td>B</td>\n",
              "      <td>B2</td>\n",
              "      <td>RENT</td>\n",
              "      <td>24000.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>860xx</td>\n",
              "      <td>AZ</td>\n",
              "      <td>27.65</td>\n",
              "      <td>0</td>\n",
              "      <td>Jan-85</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>13648</td>\n",
              "      <td>9</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5863.155187</td>\n",
              "      <td>5833.84</td>\n",
              "      <td>5000.00</td>\n",
              "      <td>863.16</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>171.62</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1077430</td>\n",
              "      <td>1314167</td>\n",
              "      <td>2500</td>\n",
              "      <td>2500</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>15.27%</td>\n",
              "      <td>59.83</td>\n",
              "      <td>C</td>\n",
              "      <td>C4</td>\n",
              "      <td>RENT</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Charged Off</td>\n",
              "      <td>n</td>\n",
              "      <td>car</td>\n",
              "      <td>309xx</td>\n",
              "      <td>GA</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0</td>\n",
              "      <td>Apr-99</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1687</td>\n",
              "      <td>4</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1008.710000</td>\n",
              "      <td>1008.71</td>\n",
              "      <td>456.46</td>\n",
              "      <td>435.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>117.08</td>\n",
              "      <td>1.11</td>\n",
              "      <td>119.66</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1077175</td>\n",
              "      <td>1313524</td>\n",
              "      <td>2400</td>\n",
              "      <td>2400</td>\n",
              "      <td>2400.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>15.96%</td>\n",
              "      <td>84.33</td>\n",
              "      <td>C</td>\n",
              "      <td>C5</td>\n",
              "      <td>RENT</td>\n",
              "      <td>12252.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>small_business</td>\n",
              "      <td>606xx</td>\n",
              "      <td>IL</td>\n",
              "      <td>8.72</td>\n",
              "      <td>0</td>\n",
              "      <td>Nov-01</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2956</td>\n",
              "      <td>10</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3005.666844</td>\n",
              "      <td>3005.67</td>\n",
              "      <td>2400.00</td>\n",
              "      <td>605.67</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>649.91</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1076863</td>\n",
              "      <td>1277178</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>13.49%</td>\n",
              "      <td>339.31</td>\n",
              "      <td>C</td>\n",
              "      <td>C1</td>\n",
              "      <td>RENT</td>\n",
              "      <td>49200.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>other</td>\n",
              "      <td>917xx</td>\n",
              "      <td>CA</td>\n",
              "      <td>20.00</td>\n",
              "      <td>0</td>\n",
              "      <td>Feb-96</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>5598</td>\n",
              "      <td>37</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12231.890000</td>\n",
              "      <td>12231.89</td>\n",
              "      <td>10000.00</td>\n",
              "      <td>2214.92</td>\n",
              "      <td>16.97</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>357.48</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1075269</td>\n",
              "      <td>1311441</td>\n",
              "      <td>5000</td>\n",
              "      <td>5000</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>7.90%</td>\n",
              "      <td>156.46</td>\n",
              "      <td>A</td>\n",
              "      <td>A4</td>\n",
              "      <td>RENT</td>\n",
              "      <td>36000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>wedding</td>\n",
              "      <td>852xx</td>\n",
              "      <td>AZ</td>\n",
              "      <td>11.20</td>\n",
              "      <td>0</td>\n",
              "      <td>Nov-04</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>7963</td>\n",
              "      <td>12</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5632.210000</td>\n",
              "      <td>5632.21</td>\n",
              "      <td>5000.00</td>\n",
              "      <td>632.21</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>161.03</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b46f4186-bdd6-4759-99bc-21707528ffd4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b46f4186-bdd6-4759-99bc-21707528ffd4 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b46f4186-bdd6-4759-99bc-21707528ffd4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        id  member_id  loan_amnt  ...  last_pymnt_amnt  policy_code application_type\n",
              "0  1077501    1296599       5000  ...           171.62            1       INDIVIDUAL\n",
              "1  1077430    1314167       2500  ...           119.66            1       INDIVIDUAL\n",
              "2  1077175    1313524       2400  ...           649.91            1       INDIVIDUAL\n",
              "3  1076863    1277178      10000  ...           357.48            1       INDIVIDUAL\n",
              "5  1075269    1311441       5000  ...           161.03            1       INDIVIDUAL\n",
              "\n",
              "[5 rows x 40 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "inp2.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_lvO63mkCpoc",
        "outputId": "4c35b8d0-fd8f-4938-fa5f-8c567ddd2b32"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10.99%    913\n",
              "11.49%    790\n",
              "7.51%     787\n",
              "13.49%    749\n",
              "7.88%     725\n",
              "         ... \n",
              "17.34%      1\n",
              "22.64%      1\n",
              "14.67%      1\n",
              "17.46%      1\n",
              "16.15%      1\n",
              "Name: int_rate, Length: 370, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "inp2['int_rate'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yEtDMSKNFDdb"
      },
      "source": [
        "Here we remove \"%\" from int_rate because we don't need this for analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nf-7WpIhEP5m",
        "outputId": "295167dd-75bd-4ca9-dbb4-b105a145ea46"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10.99    913\n",
              "11.49    790\n",
              "7.51     787\n",
              "13.49    749\n",
              "7.88     725\n",
              "        ... \n",
              "17.46      1\n",
              "16.71      1\n",
              "21.48      1\n",
              "20.52      1\n",
              "22.64      1\n",
              "Name: int_rate, Length: 370, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "inp2['int_rate'] = inp2['int_rate'].replace({'\\%' : ''},regex = True).astype(float)\n",
        "inp2['int_rate'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "Oq51AdqqEkBe",
        "outputId": "680b7869-15e3-4b18-ca30-172615826f19"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-845637e5-caac-4098-ae05-d422c33edbef\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>member_id</th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>funded_amnt</th>\n",
              "      <th>funded_amnt_inv</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>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>pymnt_plan</th>\n",
              "      <th>purpose</th>\n",
              "      <th>zip_code</th>\n",
              "      <th>addr_state</th>\n",
              "      <th>dti</th>\n",
              "      <th>delinq_2yrs</th>\n",
              "      <th>earliest_cr_line</th>\n",
              "      <th>inq_last_6mths</th>\n",
              "      <th>open_acc</th>\n",
              "      <th>pub_rec</th>\n",
              "      <th>revol_bal</th>\n",
              "      <th>total_acc</th>\n",
              "      <th>initial_list_status</th>\n",
              "      <th>out_prncp</th>\n",
              "      <th>out_prncp_inv</th>\n",
              "      <th>total_pymnt</th>\n",
              "      <th>total_pymnt_inv</th>\n",
              "      <th>total_rec_prncp</th>\n",
              "      <th>total_rec_int</th>\n",
              "      <th>total_rec_late_fee</th>\n",
              "      <th>recoveries</th>\n",
              "      <th>collection_recovery_fee</th>\n",
              "      <th>last_pymnt_amnt</th>\n",
              "      <th>policy_code</th>\n",
              "      <th>application_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1077501</td>\n",
              "      <td>1296599</td>\n",
              "      <td>5000</td>\n",
              "      <td>5000</td>\n",
              "      <td>4975.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>10.65</td>\n",
              "      <td>162.87</td>\n",
              "      <td>B</td>\n",
              "      <td>B2</td>\n",
              "      <td>RENT</td>\n",
              "      <td>24000.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>860xx</td>\n",
              "      <td>AZ</td>\n",
              "      <td>27.65</td>\n",
              "      <td>0</td>\n",
              "      <td>Jan-85</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>13648</td>\n",
              "      <td>9</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5863.155187</td>\n",
              "      <td>5833.84</td>\n",
              "      <td>5000.00</td>\n",
              "      <td>863.16</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>171.62</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1077430</td>\n",
              "      <td>1314167</td>\n",
              "      <td>2500</td>\n",
              "      <td>2500</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>15.27</td>\n",
              "      <td>59.83</td>\n",
              "      <td>C</td>\n",
              "      <td>C4</td>\n",
              "      <td>RENT</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Charged Off</td>\n",
              "      <td>n</td>\n",
              "      <td>car</td>\n",
              "      <td>309xx</td>\n",
              "      <td>GA</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0</td>\n",
              "      <td>Apr-99</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1687</td>\n",
              "      <td>4</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1008.710000</td>\n",
              "      <td>1008.71</td>\n",
              "      <td>456.46</td>\n",
              "      <td>435.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>117.08</td>\n",
              "      <td>1.11</td>\n",
              "      <td>119.66</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1077175</td>\n",
              "      <td>1313524</td>\n",
              "      <td>2400</td>\n",
              "      <td>2400</td>\n",
              "      <td>2400.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>15.96</td>\n",
              "      <td>84.33</td>\n",
              "      <td>C</td>\n",
              "      <td>C5</td>\n",
              "      <td>RENT</td>\n",
              "      <td>12252.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>small_business</td>\n",
              "      <td>606xx</td>\n",
              "      <td>IL</td>\n",
              "      <td>8.72</td>\n",
              "      <td>0</td>\n",
              "      <td>Nov-01</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2956</td>\n",
              "      <td>10</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3005.666844</td>\n",
              "      <td>3005.67</td>\n",
              "      <td>2400.00</td>\n",
              "      <td>605.67</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>649.91</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1076863</td>\n",
              "      <td>1277178</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>13.49</td>\n",
              "      <td>339.31</td>\n",
              "      <td>C</td>\n",
              "      <td>C1</td>\n",
              "      <td>RENT</td>\n",
              "      <td>49200.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>other</td>\n",
              "      <td>917xx</td>\n",
              "      <td>CA</td>\n",
              "      <td>20.00</td>\n",
              "      <td>0</td>\n",
              "      <td>Feb-96</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>5598</td>\n",
              "      <td>37</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12231.890000</td>\n",
              "      <td>12231.89</td>\n",
              "      <td>10000.00</td>\n",
              "      <td>2214.92</td>\n",
              "      <td>16.97</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>357.48</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1075269</td>\n",
              "      <td>1311441</td>\n",
              "      <td>5000</td>\n",
              "      <td>5000</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>7.90</td>\n",
              "      <td>156.46</td>\n",
              "      <td>A</td>\n",
              "      <td>A4</td>\n",
              "      <td>RENT</td>\n",
              "      <td>36000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>wedding</td>\n",
              "      <td>852xx</td>\n",
              "      <td>AZ</td>\n",
              "      <td>11.20</td>\n",
              "      <td>0</td>\n",
              "      <td>Nov-04</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>7963</td>\n",
              "      <td>12</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5632.210000</td>\n",
              "      <td>5632.21</td>\n",
              "      <td>5000.00</td>\n",
              "      <td>632.21</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>161.03</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1069639</td>\n",
              "      <td>1304742</td>\n",
              "      <td>7000</td>\n",
              "      <td>7000</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>15.96</td>\n",
              "      <td>170.08</td>\n",
              "      <td>C</td>\n",
              "      <td>C5</td>\n",
              "      <td>RENT</td>\n",
              "      <td>47004.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>280xx</td>\n",
              "      <td>NC</td>\n",
              "      <td>23.51</td>\n",
              "      <td>0</td>\n",
              "      <td>Jul-05</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>17726</td>\n",
              "      <td>11</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10110.840000</td>\n",
              "      <td>10110.84</td>\n",
              "      <td>6985.61</td>\n",
              "      <td>3125.23</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1313.76</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1072053</td>\n",
              "      <td>1288686</td>\n",
              "      <td>3000</td>\n",
              "      <td>3000</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>18.64</td>\n",
              "      <td>109.43</td>\n",
              "      <td>E</td>\n",
              "      <td>E1</td>\n",
              "      <td>RENT</td>\n",
              "      <td>48000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>car</td>\n",
              "      <td>900xx</td>\n",
              "      <td>CA</td>\n",
              "      <td>5.35</td>\n",
              "      <td>0</td>\n",
              "      <td>Jan-07</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>8221</td>\n",
              "      <td>4</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3939.135294</td>\n",
              "      <td>3939.14</td>\n",
              "      <td>3000.00</td>\n",
              "      <td>939.14</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>111.34</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-845637e5-caac-4098-ae05-d422c33edbef')\"\n",
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              "  "
            ],
            "text/plain": [
              "        id  member_id  loan_amnt  ...  last_pymnt_amnt  policy_code application_type\n",
              "0  1077501    1296599       5000  ...           171.62            1       INDIVIDUAL\n",
              "1  1077430    1314167       2500  ...           119.66            1       INDIVIDUAL\n",
              "2  1077175    1313524       2400  ...           649.91            1       INDIVIDUAL\n",
              "3  1076863    1277178      10000  ...           357.48            1       INDIVIDUAL\n",
              "5  1075269    1311441       5000  ...           161.03            1       INDIVIDUAL\n",
              "6  1069639    1304742       7000  ...          1313.76            1       INDIVIDUAL\n",
              "7  1072053    1288686       3000  ...           111.34            1       INDIVIDUAL\n",
              "\n",
              "[7 rows x 40 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "inp2.head(7)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yc9rxJy9HhEv",
        "outputId": "94ecc49e-d02e-40f6-81bb-1c025787711f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(38577, 40)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "inp2.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Univariate Analysis**"
      ],
      "metadata": {
        "id": "f3vQGQcOo6pg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "id": "VfAKY2Mu3Bf3",
        "outputId": "56a45a57-edb3-4f07-bd95-0e01cc00e61b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.boxplot( y=inp2[\"loan_amnt\"] )\n",
        "plt.ylabel('Loan Amount')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here the loan amount varies from 0 to 35,000 and 25% is 5000, 50% is nearly equal to 10000 and 75% is 15000 and the outliers is above 30000"
      ],
      "metadata": {
        "id": "JtOeMoHDpNe1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "t3oOdLGF3Rxm",
        "outputId": "4d143695-87b8-4253-edf8-5fefbe43a2a6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "inp2.hist(column='loan_amnt', grid= True);\n",
        "plt.xlabel('Loan Amount')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here the histogram show that most of the loan amount is 5000"
      ],
      "metadata": {
        "id": "XCVsEovhp8za"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "VbxZ02Cn6lJX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "6988eddd-88ae-437f-e16d-ad1c1a203498"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "inp2['loan_status'].value_counts().plot.bar()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "here bar plot show the loan_status is mostly fully paid"
      ],
      "metadata": {
        "id": "Mdx39ocPq3oB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inp2['grade'].value_counts().plot.bar()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        },
        "id": "QPlbvjkLBF7Q",
        "outputId": "c649e111-08d5-442e-bce0-18d9f7f805fd"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "here bar plot show the grade is majorly is 'B'"
      ],
      "metadata": {
        "id": "6Vfuje-QrOc5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inp2['purpose'].value_counts().plot.bar()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "Gwn3Ihl4bAna",
        "outputId": "cb295e47-1095-469a-9f4b-9fe9cfe7e019"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "here bar plot show the purpose is majorly people take a loan for  'debt_consolidation'"
      ],
      "metadata": {
        "id": "GjMBU1m5rdlx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(inp2['loan_amnt'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "ocJm0GNfe9eg",
        "outputId": "261d1c02-304f-4c59-bd45-40b7cad24458"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(inp2['annual_inc'], bins=10)\n",
        "plt.xlabel('Annual Inc')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "mc7W5JtxfI9I",
        "outputId": "f2fe2152-83f5-42e8-8282-72e3a6dd5f9b"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "here we observ the major people annual income in between 0 to 1"
      ],
      "metadata": {
        "id": "ZlNDEialr6dL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=inp2['annual_inc'])\n",
        "plt.xlabel('annual_inc')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "pEDsTo84M6dG",
        "outputId": "6f8ea2b3-6d5a-4a89-cb75-345e18491205"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cggCAJByCAIAkBBgAkhBgAEhCgAEgyf8DYTcVFTIM6fIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here only 5 people annual_inc is above 1.5"
      ],
      "metadata": {
        "id": "yThN8nBLM-dw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Segmented Univariate Analysis"
      ],
      "metadata": {
        "id": "Tk_wtetEWOAG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='purpose', hue=\"loan_status\", data=inp2)\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "WRhvMs2RgIPZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "outputId": "054a8b0c-a647-421b-fc3f-96610c7b7f23"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "here we can so that fully paid and charged off both are take the loan for 'debt_consolidation' purpose"
      ],
      "metadata": {
        "id": "R4c6mroUsTF9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot( y=\"loan_amnt\", x='loan_status' , data=inp2)\n",
        "plt.ylabel('Loan Amount Data')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "3x31baQjWsf0",
        "outputId": "bcb6e3c1-a012-46a5-d335-f79442a7897f"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "here we show both loan_status mean value is all most equal but outliers is different for fully paid is above 30000 and charged off above 32000"
      ],
      "metadata": {
        "id": "cMb5NLZqsw3q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inp2['term_months_digit'] = inp2['term'].str.rstrip('months')\n",
        "del inp2['term']"
      ],
      "metadata": {
        "id": "cm4C8FoSW9B-"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "here we need the term column and it also have months so we remove the term column and store months digits in new column"
      ],
      "metadata": {
        "id": "N9XIVAh0tkcl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inp2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "OCRjBvJObr19",
        "outputId": "5a3cdfc3-1339-4b54-be01-e9e3fb9665da"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f1004149-e5cd-4376-9311-6e4756cd72f5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>member_id</th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>funded_amnt</th>\n",
              "      <th>funded_amnt_inv</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>grade</th>\n",
              "      <th>sub_grade</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>pymnt_plan</th>\n",
              "      <th>purpose</th>\n",
              "      <th>zip_code</th>\n",
              "      <th>addr_state</th>\n",
              "      <th>dti</th>\n",
              "      <th>delinq_2yrs</th>\n",
              "      <th>earliest_cr_line</th>\n",
              "      <th>inq_last_6mths</th>\n",
              "      <th>open_acc</th>\n",
              "      <th>pub_rec</th>\n",
              "      <th>revol_bal</th>\n",
              "      <th>total_acc</th>\n",
              "      <th>initial_list_status</th>\n",
              "      <th>out_prncp</th>\n",
              "      <th>out_prncp_inv</th>\n",
              "      <th>total_pymnt</th>\n",
              "      <th>total_pymnt_inv</th>\n",
              "      <th>total_rec_prncp</th>\n",
              "      <th>total_rec_int</th>\n",
              "      <th>total_rec_late_fee</th>\n",
              "      <th>recoveries</th>\n",
              "      <th>collection_recovery_fee</th>\n",
              "      <th>last_pymnt_amnt</th>\n",
              "      <th>policy_code</th>\n",
              "      <th>application_type</th>\n",
              "      <th>term_months_digit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1077501</td>\n",
              "      <td>1296599</td>\n",
              "      <td>5000</td>\n",
              "      <td>5000</td>\n",
              "      <td>4975.0</td>\n",
              "      <td>10.65</td>\n",
              "      <td>162.87</td>\n",
              "      <td>B</td>\n",
              "      <td>B2</td>\n",
              "      <td>RENT</td>\n",
              "      <td>24000.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>860xx</td>\n",
              "      <td>AZ</td>\n",
              "      <td>27.65</td>\n",
              "      <td>0</td>\n",
              "      <td>Jan-85</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>13648</td>\n",
              "      <td>9</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5863.155187</td>\n",
              "      <td>5833.84</td>\n",
              "      <td>5000.00</td>\n",
              "      <td>863.16</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>171.62</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1077430</td>\n",
              "      <td>1314167</td>\n",
              "      <td>2500</td>\n",
              "      <td>2500</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>15.27</td>\n",
              "      <td>59.83</td>\n",
              "      <td>C</td>\n",
              "      <td>C4</td>\n",
              "      <td>RENT</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Charged Off</td>\n",
              "      <td>n</td>\n",
              "      <td>car</td>\n",
              "      <td>309xx</td>\n",
              "      <td>GA</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0</td>\n",
              "      <td>Apr-99</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1687</td>\n",
              "      <td>4</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1008.710000</td>\n",
              "      <td>1008.71</td>\n",
              "      <td>456.46</td>\n",
              "      <td>435.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>117.08</td>\n",
              "      <td>1.11</td>\n",
              "      <td>119.66</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1077175</td>\n",
              "      <td>1313524</td>\n",
              "      <td>2400</td>\n",
              "      <td>2400</td>\n",
              "      <td>2400.0</td>\n",
              "      <td>15.96</td>\n",
              "      <td>84.33</td>\n",
              "      <td>C</td>\n",
              "      <td>C5</td>\n",
              "      <td>RENT</td>\n",
              "      <td>12252.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>small_business</td>\n",
              "      <td>606xx</td>\n",
              "      <td>IL</td>\n",
              "      <td>8.72</td>\n",
              "      <td>0</td>\n",
              "      <td>Nov-01</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2956</td>\n",
              "      <td>10</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3005.666844</td>\n",
              "      <td>3005.67</td>\n",
              "      <td>2400.00</td>\n",
              "      <td>605.67</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>649.91</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1076863</td>\n",
              "      <td>1277178</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>13.49</td>\n",
              "      <td>339.31</td>\n",
              "      <td>C</td>\n",
              "      <td>C1</td>\n",
              "      <td>RENT</td>\n",
              "      <td>49200.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>other</td>\n",
              "      <td>917xx</td>\n",
              "      <td>CA</td>\n",
              "      <td>20.00</td>\n",
              "      <td>0</td>\n",
              "      <td>Feb-96</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>5598</td>\n",
              "      <td>37</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12231.890000</td>\n",
              "      <td>12231.89</td>\n",
              "      <td>10000.00</td>\n",
              "      <td>2214.92</td>\n",
              "      <td>16.97</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>357.48</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1075269</td>\n",
              "      <td>1311441</td>\n",
              "      <td>5000</td>\n",
              "      <td>5000</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>7.90</td>\n",
              "      <td>156.46</td>\n",
              "      <td>A</td>\n",
              "      <td>A4</td>\n",
              "      <td>RENT</td>\n",
              "      <td>36000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-11</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>wedding</td>\n",
              "      <td>852xx</td>\n",
              "      <td>AZ</td>\n",
              "      <td>11.20</td>\n",
              "      <td>0</td>\n",
              "      <td>Nov-04</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>7963</td>\n",
              "      <td>12</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5632.210000</td>\n",
              "      <td>5632.21</td>\n",
              "      <td>5000.00</td>\n",
              "      <td>632.21</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>161.03</td>\n",
              "      <td>1</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f1004149-e5cd-4376-9311-6e4756cd72f5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f1004149-e5cd-4376-9311-6e4756cd72f5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f1004149-e5cd-4376-9311-6e4756cd72f5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        id  member_id  ...  application_type  term_months_digit\n",
              "0  1077501    1296599  ...        INDIVIDUAL                36 \n",
              "1  1077430    1314167  ...        INDIVIDUAL                60 \n",
              "2  1077175    1313524  ...        INDIVIDUAL                36 \n",
              "3  1076863    1277178  ...        INDIVIDUAL                36 \n",
              "5  1075269    1311441  ...        INDIVIDUAL                36 \n",
              "\n",
              "[5 rows x 40 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "sns.countplot( x=\"term_months_digit\", hue='purpose', data=inp2)\n",
        "plt.xlabel('Loan purpose in Months')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "1Ku66tiAbur2",
        "outputId": "6907d66d-9814-4fea-c472-095e608535c6"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we can see mostly loan generte in 36 months"
      ],
      "metadata": {
        "id": "FKjTVFgnCytk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "sns.countplot( x=\"grade\", hue='loan_status', data=inp2)\n",
        "plt.xlabel('grades')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "AajO7i0_c_zL",
        "outputId": "8e596814-ec85-46cd-c030-de6e24d198da"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fully paid or charged off both are mostly preffer grades (B,A,C)"
      ],
      "metadata": {
        "id": "nG1CuZmiDN_J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='home_ownership',hue='loan_status', data=inp2)\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "id": "15IcC1QicH8N",
        "outputId": "e25c0460-9e47-416d-ef7d-d262fabf2c28"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Rented or mortgage home people is taking more loan"
      ],
      "metadata": {
        "id": "7nsDlumnErx3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='term_months_digit',hue='loan_status', data=inp2)\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "WSIhZGxlEN6M",
        "outputId": "9abf7e62-636b-4287-d079-96744ae1ad1e"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bivariate Plots"
      ],
      "metadata": {
        "id": "Vi62vezNcnJn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(inp2.corr(), cmap=\"Greens\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        },
        "id": "oUkajVIqcTgC",
        "outputId": "f1ff832a-8e06-48ef-eec5-57a4f9340efe"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "g = inp2[inp2['loan_status']=='Fully Paid'].groupby('addr_state')['loan_status'].count().reset_index()\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.barplot(y='addr_state', x='loan_status', data=g)\n",
        "plt.xlabel('Count of loan status to be defaulter')\n",
        "plt.ylabel('State')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "GYPVlRJseLqw",
        "outputId": "357397b1-a993-4016-8d5b-bfcd69adef26"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "CA \"add_state\" peopel are high defulters"
      ],
      "metadata": {
        "id": "qpzjwo-nGHOj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inp2['funded_amnt'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eVAuV4uEeXY9",
        "outputId": "2695d3c5-e841-4be3-d666-602248e17598"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    38577.000000\n",
              "mean     10784.058506\n",
              "std       7090.306027\n",
              "min        500.000000\n",
              "25%       5200.000000\n",
              "50%       9550.000000\n",
              "75%      15000.000000\n",
              "max      35000.000000\n",
              "Name: funded_amnt, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inp2_avg = inp2[inp2['funded_amnt'] <= 7090].groupby(['loan_status'])['funded_amnt'].count().reset_index()"
      ],
      "metadata": {
        "id": "TpqMcTy1f6Yg"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x='loan_status', y='funded_amnt', data=inp2_avg)\n",
        "plt.title('Loan Amount granted less than 7090')\n",
        "plt.xlabel('Loan Status')\n",
        "plt.ylabel('Loan Amount Granted')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "FcOGf-XvgcPp",
        "outputId": "b453d83e-ca58-47b7-b2cb-6b805066d21f"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inp2_avg = inp2[inp2['funded_amnt'] >= 7090].groupby(['loan_status'])['funded_amnt'].count().reset_index()"
      ],
      "metadata": {
        "id": "Ob-doFECguHw"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x='loan_status', y='funded_amnt', data=inp2_avg)\n",
        "plt.title('Loan Amount granted greter than 7090')\n",
        "plt.xlabel('Loan Status')\n",
        "plt.ylabel('Loan Amount Granted')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "eTLWnmaAg7dO",
        "outputId": "0c482c87-1368-4ff3-dbaf-a89bf3a57f14"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Loan Data Set.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}