ANN_loan_borrower_chargeOff.ipynb

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  "metadata": {
    "colab": {
      "name": "ANN loan borrower charge off.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "ru6SjRy2luTK",
        "KVGpT6u9CBnL",
        "t1WSm6l-FZsv"
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      "mount_file_id": "1idluNrKWzFmiRsl1pDapcx5AcApuOwWw",
      "authorship_tag": "ABX9TyO5jAuXOPXHJSl/32Y1jnL5",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shamreen20/Learn-Deep-Learning/blob/master/ANN_loan_borrower_chargeOff.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "toRU5yTF5Td7",
        "colab_type": "text"
      },
      "source": [
        "Data on loans given out with information on whether or not the borrower defaulted (charge-off), can we build a model that can predict whether or not a borrower will pay back their loan? This way in the future when we get a new potential customer we can assess whether or not they are likely to pay back the loan.\n",
        "**Data collection**: From Kaggle.com\n",
        "**Data Analysis**: Get an understanding for which variables are        important, view summary statistics, and\n",
        "visualize the data\n",
        "**Feature Engineering:** Remove or fill any missing data. Remove unnecessary or repetitive features.\n",
        "Convert categorical string features to dummy variables.\n",
        "**Feature Selection:** Train_test_split and normalizing data by MinMax Scaler.\n",
        "**Model Creation:** importing necessary Keras libraries and apply ANN(Artificial Neural Network).\n",
        "**Hyperparameter Tuning:** confusion matrix, classification report."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "43PRlnaJbpfl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "9667f0de-abe5-4fed-90e0-de9b5f88cd45"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RTBmKghWcdAQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "086ed0ff-cc08-4cd8-9654-05564f144d79"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6cZAvWh8fjCZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "3df8fe02-72c0-4eac-c0d2-256d54a7e0fd"
      },
      "source": [
        "df = pd.read_csv(\"/content/drive/My Drive/lending_club_loan_two.csv\")\n",
        "df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>term</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>grade</th>\n",
              "      <th>sub_grade</th>\n",
              "      <th>emp_title</th>\n",
              "      <th>emp_length</th>\n",
              "      <th>home_ownership</th>\n",
              "      <th>annual_inc</th>\n",
              "      <th>verification_status</th>\n",
              "      <th>issue_d</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>purpose</th>\n",
              "      <th>title</th>\n",
              "      <th>dti</th>\n",
              "      <th>earliest_cr_line</th>\n",
              "      <th>open_acc</th>\n",
              "      <th>pub_rec</th>\n",
              "      <th>revol_bal</th>\n",
              "      <th>revol_util</th>\n",
              "      <th>total_acc</th>\n",
              "      <th>initial_list_status</th>\n",
              "      <th>application_type</th>\n",
              "      <th>mort_acc</th>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "      <th>address</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.44</td>\n",
              "      <td>329.48</td>\n",
              "      <td>B</td>\n",
              "      <td>B4</td>\n",
              "      <td>Marketing</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>117000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Jan-2015</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>vacation</td>\n",
              "      <td>Vacation</td>\n",
              "      <td>26.24</td>\n",
              "      <td>Jun-1990</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>36369.0</td>\n",
              "      <td>41.8</td>\n",
              "      <td>25.0</td>\n",
              "      <td>w</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0174 Michelle Gateway\\r\\nMendozaberg, OK 22690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.99</td>\n",
              "      <td>265.68</td>\n",
              "      <td>B</td>\n",
              "      <td>B5</td>\n",
              "      <td>Credit analyst</td>\n",
              "      <td>4 years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>65000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Jan-2015</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>22.05</td>\n",
              "      <td>Jul-2004</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20131.0</td>\n",
              "      <td>53.3</td>\n",
              "      <td>27.0</td>\n",
              "      <td>f</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1076 Carney Fort Apt. 347\\r\\nLoganmouth, SD 05113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15600.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>10.49</td>\n",
              "      <td>506.97</td>\n",
              "      <td>B</td>\n",
              "      <td>B3</td>\n",
              "      <td>Statistician</td>\n",
              "      <td>&lt; 1 year</td>\n",
              "      <td>RENT</td>\n",
              "      <td>43057.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Jan-2015</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>Credit card refinancing</td>\n",
              "      <td>12.79</td>\n",
              "      <td>Aug-2007</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11987.0</td>\n",
              "      <td>92.2</td>\n",
              "      <td>26.0</td>\n",
              "      <td>f</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>87025 Mark Dale Apt. 269\\r\\nNew Sabrina, WV 05113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7200.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>6.49</td>\n",
              "      <td>220.65</td>\n",
              "      <td>A</td>\n",
              "      <td>A2</td>\n",
              "      <td>Client Advocate</td>\n",
              "      <td>6 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>54000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Nov-2014</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>Credit card refinancing</td>\n",
              "      <td>2.60</td>\n",
              "      <td>Sep-2006</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5472.0</td>\n",
              "      <td>21.5</td>\n",
              "      <td>13.0</td>\n",
              "      <td>f</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>823 Reid Ford\\r\\nDelacruzside, MA 00813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24375.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>17.27</td>\n",
              "      <td>609.33</td>\n",
              "      <td>C</td>\n",
              "      <td>C5</td>\n",
              "      <td>Destiny Management Inc.</td>\n",
              "      <td>9 years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>55000.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>Apr-2013</td>\n",
              "      <td>Charged Off</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>Credit Card Refinance</td>\n",
              "      <td>33.95</td>\n",
              "      <td>Mar-1999</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24584.0</td>\n",
              "      <td>69.8</td>\n",
              "      <td>43.0</td>\n",
              "      <td>f</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>679 Luna Roads\\r\\nGreggshire, VA 11650</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   loan_amnt  ...                                            address\n",
              "0    10000.0  ...     0174 Michelle Gateway\\r\\nMendozaberg, OK 22690\n",
              "1     8000.0  ...  1076 Carney Fort Apt. 347\\r\\nLoganmouth, SD 05113\n",
              "2    15600.0  ...  87025 Mark Dale Apt. 269\\r\\nNew Sabrina, WV 05113\n",
              "3     7200.0  ...            823 Reid Ford\\r\\nDelacruzside, MA 00813\n",
              "4    24375.0  ...             679 Luna Roads\\r\\nGreggshire, VA 11650\n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rHMO9NY4gCYN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "outputId": "8a942e20-c5c1-4286-b068-7eae297b37f7"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 396030 entries, 0 to 396029\n",
            "Data columns (total 27 columns):\n",
            " #   Column                Non-Null Count   Dtype  \n",
            "---  ------                --------------   -----  \n",
            " 0   loan_amnt             396030 non-null  float64\n",
            " 1   term                  396030 non-null  object \n",
            " 2   int_rate              396030 non-null  float64\n",
            " 3   installment           396030 non-null  float64\n",
            " 4   grade                 396030 non-null  object \n",
            " 5   sub_grade             396030 non-null  object \n",
            " 6   emp_title             373103 non-null  object \n",
            " 7   emp_length            377729 non-null  object \n",
            " 8   home_ownership        396030 non-null  object \n",
            " 9   annual_inc            396030 non-null  float64\n",
            " 10  verification_status   396030 non-null  object \n",
            " 11  issue_d               396030 non-null  object \n",
            " 12  loan_status           396030 non-null  object \n",
            " 13  purpose               396030 non-null  object \n",
            " 14  title                 394275 non-null  object \n",
            " 15  dti                   396030 non-null  float64\n",
            " 16  earliest_cr_line      396030 non-null  object \n",
            " 17  open_acc              396030 non-null  float64\n",
            " 18  pub_rec               396030 non-null  float64\n",
            " 19  revol_bal             396030 non-null  float64\n",
            " 20  revol_util            395754 non-null  float64\n",
            " 21  total_acc             396030 non-null  float64\n",
            " 22  initial_list_status   396030 non-null  object \n",
            " 23  application_type      396030 non-null  object \n",
            " 24  mort_acc              358235 non-null  float64\n",
            " 25  pub_rec_bankruptcies  395495 non-null  float64\n",
            " 26  address               396030 non-null  object \n",
            "dtypes: float64(12), object(15)\n",
            "memory usage: 81.6+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QtRMYZNBgrJO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "1dd1fba9-3a2c-4f88-a9a6-ae5c6f09f571"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>annual_inc</th>\n",
              "      <th>dti</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>mort_acc</th>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>396030.000000</td>\n",
              "      <td>396030.000000</td>\n",
              "      <td>396030.000000</td>\n",
              "      <td>3.960300e+05</td>\n",
              "      <td>396030.000000</td>\n",
              "      <td>396030.000000</td>\n",
              "      <td>396030.000000</td>\n",
              "      <td>3.960300e+05</td>\n",
              "      <td>395754.000000</td>\n",
              "      <td>396030.000000</td>\n",
              "      <td>358235.000000</td>\n",
              "      <td>395495.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>14113.888089</td>\n",
              "      <td>13.639400</td>\n",
              "      <td>431.849698</td>\n",
              "      <td>7.420318e+04</td>\n",
              "      <td>17.379514</td>\n",
              "      <td>11.311153</td>\n",
              "      <td>0.178191</td>\n",
              "      <td>1.584454e+04</td>\n",
              "      <td>53.791749</td>\n",
              "      <td>25.414744</td>\n",
              "      <td>1.813991</td>\n",
              "      <td>0.121648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8357.441341</td>\n",
              "      <td>4.472157</td>\n",
              "      <td>250.727790</td>\n",
              "      <td>6.163762e+04</td>\n",
              "      <td>18.019092</td>\n",
              "      <td>5.137649</td>\n",
              "      <td>0.530671</td>\n",
              "      <td>2.059184e+04</td>\n",
              "      <td>24.452193</td>\n",
              "      <td>11.886991</td>\n",
              "      <td>2.147930</td>\n",
              "      <td>0.356174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>500.000000</td>\n",
              "      <td>5.320000</td>\n",
              "      <td>16.080000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>8000.000000</td>\n",
              "      <td>10.490000</td>\n",
              "      <td>250.330000</td>\n",
              "      <td>4.500000e+04</td>\n",
              "      <td>11.280000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.025000e+03</td>\n",
              "      <td>35.800000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>12000.000000</td>\n",
              "      <td>13.330000</td>\n",
              "      <td>375.430000</td>\n",
              "      <td>6.400000e+04</td>\n",
              "      <td>16.910000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.118100e+04</td>\n",
              "      <td>54.800000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>20000.000000</td>\n",
              "      <td>16.490000</td>\n",
              "      <td>567.300000</td>\n",
              "      <td>9.000000e+04</td>\n",
              "      <td>22.980000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.962000e+04</td>\n",
              "      <td>72.900000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>40000.000000</td>\n",
              "      <td>30.990000</td>\n",
              "      <td>1533.810000</td>\n",
              "      <td>8.706582e+06</td>\n",
              "      <td>9999.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>86.000000</td>\n",
              "      <td>1.743266e+06</td>\n",
              "      <td>892.300000</td>\n",
              "      <td>151.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           loan_amnt       int_rate  ...       mort_acc  pub_rec_bankruptcies\n",
              "count  396030.000000  396030.000000  ...  358235.000000         395495.000000\n",
              "mean    14113.888089      13.639400  ...       1.813991              0.121648\n",
              "std      8357.441341       4.472157  ...       2.147930              0.356174\n",
              "min       500.000000       5.320000  ...       0.000000              0.000000\n",
              "25%      8000.000000      10.490000  ...       0.000000              0.000000\n",
              "50%     12000.000000      13.330000  ...       1.000000              0.000000\n",
              "75%     20000.000000      16.490000  ...       3.000000              0.000000\n",
              "max     40000.000000      30.990000  ...      34.000000              8.000000\n",
              "\n",
              "[8 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KzcpRGETmtqm",
        "colab_type": "text"
      },
      "source": [
        "#Exploratory Data analysis\n",
        "Get an understanding for which variables are important, view summary statistics, and visualize the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bYh904t8mRe5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "9d4471c4-b746-4ca9-cb76-1aa498bd796e"
      },
      "source": [
        "sns.countplot(x=\"loan_status\",data=df)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe528796160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zJHG53c4n34I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "6e951e63-68f5-44ce-9675-e453a2a89e9f"
      },
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.distplot(df.loan_amnt,bins=50,rug=True,kde=False)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe5286c3240>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bfww7IKPnvej",
        "colab_type": "text"
      },
      "source": [
        "correlation between the continuous feature variables. Calculate the correlation between all continuous numeric variables using .corr() method."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yGCLJS2jpbjw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "cc60f569-db80-496e-a2ca-dd2bdd2ebe5b"
      },
      "source": [
        " corr = df.corr()\n",
        " corr"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>annual_inc</th>\n",
              "      <th>dti</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>mort_acc</th>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>loan_amnt</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.168921</td>\n",
              "      <td>0.953929</td>\n",
              "      <td>0.336887</td>\n",
              "      <td>0.016636</td>\n",
              "      <td>0.198556</td>\n",
              "      <td>-0.077779</td>\n",
              "      <td>0.328320</td>\n",
              "      <td>0.099911</td>\n",
              "      <td>0.223886</td>\n",
              "      <td>0.222315</td>\n",
              "      <td>-0.106539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>int_rate</th>\n",
              "      <td>0.168921</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.162758</td>\n",
              "      <td>-0.056771</td>\n",
              "      <td>0.079038</td>\n",
              "      <td>0.011649</td>\n",
              "      <td>0.060986</td>\n",
              "      <td>-0.011280</td>\n",
              "      <td>0.293659</td>\n",
              "      <td>-0.036404</td>\n",
              "      <td>-0.082583</td>\n",
              "      <td>0.057450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>installment</th>\n",
              "      <td>0.953929</td>\n",
              "      <td>0.162758</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.330381</td>\n",
              "      <td>0.015786</td>\n",
              "      <td>0.188973</td>\n",
              "      <td>-0.067892</td>\n",
              "      <td>0.316455</td>\n",
              "      <td>0.123915</td>\n",
              "      <td>0.202430</td>\n",
              "      <td>0.193694</td>\n",
              "      <td>-0.098628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>annual_inc</th>\n",
              "      <td>0.336887</td>\n",
              "      <td>-0.056771</td>\n",
              "      <td>0.330381</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.081685</td>\n",
              "      <td>0.136150</td>\n",
              "      <td>-0.013720</td>\n",
              "      <td>0.299773</td>\n",
              "      <td>0.027871</td>\n",
              "      <td>0.193023</td>\n",
              "      <td>0.236320</td>\n",
              "      <td>-0.050162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dti</th>\n",
              "      <td>0.016636</td>\n",
              "      <td>0.079038</td>\n",
              "      <td>0.015786</td>\n",
              "      <td>-0.081685</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.136181</td>\n",
              "      <td>-0.017639</td>\n",
              "      <td>0.063571</td>\n",
              "      <td>0.088375</td>\n",
              "      <td>0.102128</td>\n",
              "      <td>-0.025439</td>\n",
              "      <td>-0.014558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>open_acc</th>\n",
              "      <td>0.198556</td>\n",
              "      <td>0.011649</td>\n",
              "      <td>0.188973</td>\n",
              "      <td>0.136150</td>\n",
              "      <td>0.136181</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.018392</td>\n",
              "      <td>0.221192</td>\n",
              "      <td>-0.131420</td>\n",
              "      <td>0.680728</td>\n",
              "      <td>0.109205</td>\n",
              "      <td>-0.027732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pub_rec</th>\n",
              "      <td>-0.077779</td>\n",
              "      <td>0.060986</td>\n",
              "      <td>-0.067892</td>\n",
              "      <td>-0.013720</td>\n",
              "      <td>-0.017639</td>\n",
              "      <td>-0.018392</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.101664</td>\n",
              "      <td>-0.075910</td>\n",
              "      <td>0.019723</td>\n",
              "      <td>0.011552</td>\n",
              "      <td>0.699408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>revol_bal</th>\n",
              "      <td>0.328320</td>\n",
              "      <td>-0.011280</td>\n",
              "      <td>0.316455</td>\n",
              "      <td>0.299773</td>\n",
              "      <td>0.063571</td>\n",
              "      <td>0.221192</td>\n",
              "      <td>-0.101664</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.226346</td>\n",
              "      <td>0.191616</td>\n",
              "      <td>0.194925</td>\n",
              "      <td>-0.124532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>revol_util</th>\n",
              "      <td>0.099911</td>\n",
              "      <td>0.293659</td>\n",
              "      <td>0.123915</td>\n",
              "      <td>0.027871</td>\n",
              "      <td>0.088375</td>\n",
              "      <td>-0.131420</td>\n",
              "      <td>-0.075910</td>\n",
              "      <td>0.226346</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.104273</td>\n",
              "      <td>0.007514</td>\n",
              "      <td>-0.086751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_acc</th>\n",
              "      <td>0.223886</td>\n",
              "      <td>-0.036404</td>\n",
              "      <td>0.202430</td>\n",
              "      <td>0.193023</td>\n",
              "      <td>0.102128</td>\n",
              "      <td>0.680728</td>\n",
              "      <td>0.019723</td>\n",
              "      <td>0.191616</td>\n",
              "      <td>-0.104273</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.381072</td>\n",
              "      <td>0.042035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mort_acc</th>\n",
              "      <td>0.222315</td>\n",
              "      <td>-0.082583</td>\n",
              "      <td>0.193694</td>\n",
              "      <td>0.236320</td>\n",
              "      <td>-0.025439</td>\n",
              "      <td>0.109205</td>\n",
              "      <td>0.011552</td>\n",
              "      <td>0.194925</td>\n",
              "      <td>0.007514</td>\n",
              "      <td>0.381072</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.027239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "      <td>-0.106539</td>\n",
              "      <td>0.057450</td>\n",
              "      <td>-0.098628</td>\n",
              "      <td>-0.050162</td>\n",
              "      <td>-0.014558</td>\n",
              "      <td>-0.027732</td>\n",
              "      <td>0.699408</td>\n",
              "      <td>-0.124532</td>\n",
              "      <td>-0.086751</td>\n",
              "      <td>0.042035</td>\n",
              "      <td>0.027239</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      loan_amnt  int_rate  ...  mort_acc  pub_rec_bankruptcies\n",
              "loan_amnt              1.000000  0.168921  ...  0.222315             -0.106539\n",
              "int_rate               0.168921  1.000000  ... -0.082583              0.057450\n",
              "installment            0.953929  0.162758  ...  0.193694             -0.098628\n",
              "annual_inc             0.336887 -0.056771  ...  0.236320             -0.050162\n",
              "dti                    0.016636  0.079038  ... -0.025439             -0.014558\n",
              "open_acc               0.198556  0.011649  ...  0.109205             -0.027732\n",
              "pub_rec               -0.077779  0.060986  ...  0.011552              0.699408\n",
              "revol_bal              0.328320 -0.011280  ...  0.194925             -0.124532\n",
              "revol_util             0.099911  0.293659  ...  0.007514             -0.086751\n",
              "total_acc              0.223886 -0.036404  ...  0.381072              0.042035\n",
              "mort_acc               0.222315 -0.082583  ...  1.000000              0.027239\n",
              "pub_rec_bankruptcies  -0.106539  0.057450  ...  0.027239              1.000000\n",
              "\n",
              "[12 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u_Y7Xra8sxly",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "outputId": "f5959949-fab3-44bf-f4c4-fc52af54ed08"
      },
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "sns.heatmap(corr,annot=True)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe5286c3978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-VI0c2zFqdbb",
        "colab_type": "text"
      },
      "source": [
        "You should have noticed almost perfect correlation with the \"installment\" feature. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "98MxUE8nvm7D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "eb9827ce-1243-4dcb-9272-fa36e5d9fac4"
      },
      "source": [
        "df.installment"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         329.48\n",
              "1         265.68\n",
              "2         506.97\n",
              "3         220.65\n",
              "4         609.33\n",
              "           ...  \n",
              "396025    217.38\n",
              "396026    700.42\n",
              "396027    161.32\n",
              "396028    503.02\n",
              "396029     67.98\n",
              "Name: installment, Length: 396030, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1YWFgd6hyRTR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "a8a49499-d3e9-454b-ce93-97384b0f58d7"
      },
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.scatterplot(y=\"loan_amnt\",x=\"installment\",data=df)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe517e0fc18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0b_1_z1AqoWb",
        "colab_type": "text"
      },
      "source": [
        "Calculate the summary statistics for the loan amount, grouped by the loan_status."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xDVcjTcFypWx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "5840e8fb-e741-4d9d-c6d0-e52c431bda12"
      },
      "source": [
        "df.groupby(\"loan_status\")[\"loan_amnt\"].describe()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_status</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Charged Off</th>\n",
              "      <td>77673.0</td>\n",
              "      <td>15126.300967</td>\n",
              "      <td>8505.090557</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>8525.0</td>\n",
              "      <td>14000.0</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>40000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fully Paid</th>\n",
              "      <td>318357.0</td>\n",
              "      <td>13866.878771</td>\n",
              "      <td>8302.319699</td>\n",
              "      <td>500.0</td>\n",
              "      <td>7500.0</td>\n",
              "      <td>12000.0</td>\n",
              "      <td>19225.0</td>\n",
              "      <td>40000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                count          mean          std  ...      50%      75%      max\n",
              "loan_status                                       ...                           \n",
              "Charged Off   77673.0  15126.300967  8505.090557  ...  14000.0  20000.0  40000.0\n",
              "Fully Paid   318357.0  13866.878771  8302.319699  ...  12000.0  19225.0  40000.0\n",
              "\n",
              "[2 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_W6J_zZ3rl_a",
        "colab_type": "text"
      },
      "source": [
        "Let's explore the Grade and SubGrade columns that LendingClub attributes to the loans. What are the unique possible grades and subgrades?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HUmIdQijz_-D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "41d68a9d-e926-4136-c1b6-da372bc2beab"
      },
      "source": [
        "df.grade.unique()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['B', 'A', 'C', 'E', 'D', 'F', 'G'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ekzmsb9M52rA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "81aac857-9f43-4a87-a9ca-138d6d0a32f0"
      },
      "source": [
        "df.sub_grade.unique()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['B4', 'B5', 'B3', 'A2', 'C5', 'C3', 'A1', 'B2', 'C1', 'A5', 'E4',\n",
              "       'A4', 'A3', 'D1', 'C2', 'B1', 'D3', 'D5', 'D2', 'E1', 'E2', 'E5',\n",
              "       'F4', 'E3', 'D4', 'G1', 'F5', 'G2', 'C4', 'F1', 'F3', 'G5', 'G4',\n",
              "       'F2', 'G3'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NEoN1QJYtFpi",
        "colab_type": "text"
      },
      "source": [
        "Create a countplot per grade. Set the hue to the loan_status label."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mkqg_x_t55GD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "fdf29d98-20d9-421f-b65a-530e8a6e8bf9"
      },
      "source": [
        "sns.countplot(x=\"grade\", data= df,hue=\"loan_status\")"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe51659a208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t6-dXeEU64iv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "outputId": "c2b814bc-bfb3-4f19-857b-1dcde88eab30"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['loan_amnt', 'term', 'int_rate', 'installment', 'grade', 'sub_grade',\n",
              "       'emp_title', 'emp_length', 'home_ownership', 'annual_inc',\n",
              "       'verification_status', 'issue_d', 'loan_status', 'purpose', 'title',\n",
              "       'dti', 'earliest_cr_line', 'open_acc', 'pub_rec', 'revol_bal',\n",
              "       'revol_util', 'total_acc', 'initial_list_status', 'application_type',\n",
              "       'mort_acc', 'pub_rec_bankruptcies', 'address'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i0zPvYFeuDoc",
        "colab_type": "text"
      },
      "source": [
        "Display a count plot per subgrade\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6usnTUU57A9s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "8ac05588-8cef-4bcf-e1a7-ada448bb4fce"
      },
      "source": [
        "plt.figure(figsize=(15,6))\n",
        "subgrade_order = sorted(df['sub_grade'].unique())\n",
        "sns.countplot(x=\"sub_grade\", data=df,order=subgrade_order, palette=\"coolwarm\")"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe516524c18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FYLSu50auo1v",
        "colab_type": "text"
      },
      "source": [
        " Explore both all loans made per subgrade as well being separated based on the loan_status"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HXfRg_i51vRJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "e4458d74-aca5-4c2d-eeae-e3b311cb6d00"
      },
      "source": [
        "plt.figure(figsize=(15,6))\n",
        "subgrade_order = sorted(df['sub_grade'].unique())\n",
        "sns.countplot(x=\"sub_grade\", data=df, order=subgrade_order, hue=\"loan_status\")"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe5164c1518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQ2Sg2Gwv2fa",
        "colab_type": "text"
      },
      "source": [
        " It looks like F and G subgrades don't get paid back that often. Isloate those and recreate the countplot just for those subgrades."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Zh-aPIC7QmW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "b8f3cbfc-bff1-4205-e25e-f75f738408c4"
      },
      "source": [
        "f_and_g = df[(df[\"grade\"]=='F') | (df[\"grade\"]=='G')]\n",
        "plt.figure(figsize=(15,6))\n",
        "subgrade_order = sorted(f_and_g[\"sub_grade\"].unique())\n",
        "sns.countplot(x=\"sub_grade\", data= df,order=subgrade_order, hue=\"loan_status\")"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe51635d978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a3tBYv2a9oqv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "2524977d-a0fd-4114-e918-40618220481e"
      },
      "source": [
        "df.head(2)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>term</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>grade</th>\n",
              "      <th>sub_grade</th>\n",
              "      <th>emp_title</th>\n",
              "      <th>emp_length</th>\n",
              "      <th>home_ownership</th>\n",
              "      <th>annual_inc</th>\n",
              "      <th>verification_status</th>\n",
              "      <th>issue_d</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>purpose</th>\n",
              "      <th>title</th>\n",
              "      <th>dti</th>\n",
              "      <th>earliest_cr_line</th>\n",
              "      <th>open_acc</th>\n",
              "      <th>pub_rec</th>\n",
              "      <th>revol_bal</th>\n",
              "      <th>revol_util</th>\n",
              "      <th>total_acc</th>\n",
              "      <th>initial_list_status</th>\n",
              "      <th>application_type</th>\n",
              "      <th>mort_acc</th>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "      <th>address</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.44</td>\n",
              "      <td>329.48</td>\n",
              "      <td>B</td>\n",
              "      <td>B4</td>\n",
              "      <td>Marketing</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>117000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Jan-2015</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>vacation</td>\n",
              "      <td>Vacation</td>\n",
              "      <td>26.24</td>\n",
              "      <td>Jun-1990</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>36369.0</td>\n",
              "      <td>41.8</td>\n",
              "      <td>25.0</td>\n",
              "      <td>w</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0174 Michelle Gateway\\r\\nMendozaberg, OK 22690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.99</td>\n",
              "      <td>265.68</td>\n",
              "      <td>B</td>\n",
              "      <td>B5</td>\n",
              "      <td>Credit analyst</td>\n",
              "      <td>4 years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>65000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Jan-2015</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>22.05</td>\n",
              "      <td>Jul-2004</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20131.0</td>\n",
              "      <td>53.3</td>\n",
              "      <td>27.0</td>\n",
              "      <td>f</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1076 Carney Fort Apt. 347\\r\\nLoganmouth, SD 05113</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   loan_amnt  ...                                            address\n",
              "0    10000.0  ...     0174 Michelle Gateway\\r\\nMendozaberg, OK 22690\n",
              "1     8000.0  ...  1076 Carney Fort Apt. 347\\r\\nLoganmouth, SD 05113\n",
              "\n",
              "[2 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_b8TbEHbBShb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e79fa79b-7da3-4fed-81e1-47b7e723fb2c"
      },
      "source": [
        "df.loan_status.unique()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Fully Paid', 'Charged Off'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pO545Hryx0Ma",
        "colab_type": "text"
      },
      "source": [
        "Create a new column called 'load_repaid' which will contain a 1 if the loan status was \"Fully Paid\" and a 0 if it was \"Charged Off\"."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AKLxDrsRDSq_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df[\"loan_repaid\"] = df[\"loan_status\"].map({\"Fully Paid\":1, \"Charged Off\":0})"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bcv10V4kHpdx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "a4be9ac6-d28f-408c-a6fa-b9b4744cf8d4"
      },
      "source": [
        "df[[\"loan_repaid\",\"loan_status\"]]"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loan_repaid</th>\n",
              "      <th>loan_status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Fully Paid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Fully Paid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Fully Paid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>Fully Paid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>Charged Off</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396025</th>\n",
              "      <td>1</td>\n",
              "      <td>Fully Paid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396026</th>\n",
              "      <td>1</td>\n",
              "      <td>Fully Paid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396027</th>\n",
              "      <td>1</td>\n",
              "      <td>Fully Paid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396028</th>\n",
              "      <td>1</td>\n",
              "      <td>Fully Paid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396029</th>\n",
              "      <td>1</td>\n",
              "      <td>Fully Paid</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>396030 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        loan_repaid  loan_status\n",
              "0                 1   Fully Paid\n",
              "1                 1   Fully Paid\n",
              "2                 1   Fully Paid\n",
              "3                 1   Fully Paid\n",
              "4                 0  Charged Off\n",
              "...             ...          ...\n",
              "396025            1   Fully Paid\n",
              "396026            1   Fully Paid\n",
              "396027            1   Fully Paid\n",
              "396028            1   Fully Paid\n",
              "396029            1   Fully Paid\n",
              "\n",
              "[396030 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r7G792a-Qrvs",
        "colab_type": "text"
      },
      "source": [
        "#Data PreProcessing\n",
        "Remove or fill any missing data. Remove unnecessary or repetitive features. Convert categorical string features to dummy variables.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1W0T49WdHxle",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "695716a8-6080-4fc7-db60-7cfc5ab3eccb"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>term</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>grade</th>\n",
              "      <th>sub_grade</th>\n",
              "      <th>emp_title</th>\n",
              "      <th>emp_length</th>\n",
              "      <th>home_ownership</th>\n",
              "      <th>annual_inc</th>\n",
              "      <th>verification_status</th>\n",
              "      <th>issue_d</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>purpose</th>\n",
              "      <th>title</th>\n",
              "      <th>dti</th>\n",
              "      <th>earliest_cr_line</th>\n",
              "      <th>open_acc</th>\n",
              "      <th>pub_rec</th>\n",
              "      <th>revol_bal</th>\n",
              "      <th>revol_util</th>\n",
              "      <th>total_acc</th>\n",
              "      <th>initial_list_status</th>\n",
              "      <th>application_type</th>\n",
              "      <th>mort_acc</th>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "      <th>address</th>\n",
              "      <th>loan_repaid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.44</td>\n",
              "      <td>329.48</td>\n",
              "      <td>B</td>\n",
              "      <td>B4</td>\n",
              "      <td>Marketing</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>117000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Jan-2015</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>vacation</td>\n",
              "      <td>Vacation</td>\n",
              "      <td>26.24</td>\n",
              "      <td>Jun-1990</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>36369.0</td>\n",
              "      <td>41.8</td>\n",
              "      <td>25.0</td>\n",
              "      <td>w</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0174 Michelle Gateway\\r\\nMendozaberg, OK 22690</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.99</td>\n",
              "      <td>265.68</td>\n",
              "      <td>B</td>\n",
              "      <td>B5</td>\n",
              "      <td>Credit analyst</td>\n",
              "      <td>4 years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>65000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Jan-2015</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>22.05</td>\n",
              "      <td>Jul-2004</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20131.0</td>\n",
              "      <td>53.3</td>\n",
              "      <td>27.0</td>\n",
              "      <td>f</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1076 Carney Fort Apt. 347\\r\\nLoganmouth, SD 05113</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15600.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>10.49</td>\n",
              "      <td>506.97</td>\n",
              "      <td>B</td>\n",
              "      <td>B3</td>\n",
              "      <td>Statistician</td>\n",
              "      <td>&lt; 1 year</td>\n",
              "      <td>RENT</td>\n",
              "      <td>43057.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Jan-2015</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>Credit card refinancing</td>\n",
              "      <td>12.79</td>\n",
              "      <td>Aug-2007</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11987.0</td>\n",
              "      <td>92.2</td>\n",
              "      <td>26.0</td>\n",
              "      <td>f</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>87025 Mark Dale Apt. 269\\r\\nNew Sabrina, WV 05113</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7200.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>6.49</td>\n",
              "      <td>220.65</td>\n",
              "      <td>A</td>\n",
              "      <td>A2</td>\n",
              "      <td>Client Advocate</td>\n",
              "      <td>6 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>54000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Nov-2014</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>Credit card refinancing</td>\n",
              "      <td>2.60</td>\n",
              "      <td>Sep-2006</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5472.0</td>\n",
              "      <td>21.5</td>\n",
              "      <td>13.0</td>\n",
              "      <td>f</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>823 Reid Ford\\r\\nDelacruzside, MA 00813</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24375.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>17.27</td>\n",
              "      <td>609.33</td>\n",
              "      <td>C</td>\n",
              "      <td>C5</td>\n",
              "      <td>Destiny Management Inc.</td>\n",
              "      <td>9 years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>55000.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>Apr-2013</td>\n",
              "      <td>Charged Off</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>Credit Card Refinance</td>\n",
              "      <td>33.95</td>\n",
              "      <td>Mar-1999</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24584.0</td>\n",
              "      <td>69.8</td>\n",
              "      <td>43.0</td>\n",
              "      <td>f</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>679 Luna Roads\\r\\nGreggshire, VA 11650</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   loan_amnt  ... loan_repaid\n",
              "0    10000.0  ...           1\n",
              "1     8000.0  ...           1\n",
              "2    15600.0  ...           1\n",
              "3     7200.0  ...           1\n",
              "4    24375.0  ...           0\n",
              "\n",
              "[5 rows x 28 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3fAry2K3z0nz",
        "colab_type": "text"
      },
      "source": [
        "#Missing Data\n",
        "Create a Series that displays the total count of missing values per column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cCW07RppQ0tx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "9a2f596f-281b-48ac-9ee3-78e64a380135"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_amnt                   0\n",
              "term                        0\n",
              "int_rate                    0\n",
              "installment                 0\n",
              "grade                       0\n",
              "sub_grade                   0\n",
              "emp_title               22927\n",
              "emp_length              18301\n",
              "home_ownership              0\n",
              "annual_inc                  0\n",
              "verification_status         0\n",
              "issue_d                     0\n",
              "loan_status                 0\n",
              "purpose                     0\n",
              "title                    1755\n",
              "dti                         0\n",
              "earliest_cr_line            0\n",
              "open_acc                    0\n",
              "pub_rec                     0\n",
              "revol_bal                   0\n",
              "revol_util                276\n",
              "total_acc                   0\n",
              "initial_list_status         0\n",
              "application_type            0\n",
              "mort_acc                37795\n",
              "pub_rec_bankruptcies      535\n",
              "address                     0\n",
              "loan_repaid                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5AIGyQyK0PMQ",
        "colab_type": "text"
      },
      "source": [
        " Convert this Series to be in term of percentage of the total DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpxhbUe2Wzwb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "1cf34c79-54e6-4ff5-9af2-79adf228bbd0"
      },
      "source": [
        "100* df.isnull().sum()/len(df)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_amnt               0.000000\n",
              "term                    0.000000\n",
              "int_rate                0.000000\n",
              "installment             0.000000\n",
              "grade                   0.000000\n",
              "sub_grade               0.000000\n",
              "emp_title               5.789208\n",
              "emp_length              4.621115\n",
              "home_ownership          0.000000\n",
              "annual_inc              0.000000\n",
              "verification_status     0.000000\n",
              "issue_d                 0.000000\n",
              "loan_status             0.000000\n",
              "purpose                 0.000000\n",
              "title                   0.443148\n",
              "dti                     0.000000\n",
              "earliest_cr_line        0.000000\n",
              "open_acc                0.000000\n",
              "pub_rec                 0.000000\n",
              "revol_bal               0.000000\n",
              "revol_util              0.069692\n",
              "total_acc               0.000000\n",
              "initial_list_status     0.000000\n",
              "application_type        0.000000\n",
              "mort_acc                9.543469\n",
              "pub_rec_bankruptcies    0.135091\n",
              "address                 0.000000\n",
              "loan_repaid             0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JID6pb4QXpU-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "1b1551d7-90a6-42ff-97ba-5441ca4949aa"
      },
      "source": [
        "df.emp_title.value_counts()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Teacher                   4389\n",
              "Manager                   4250\n",
              "Registered Nurse          1856\n",
              "RN                        1846\n",
              "Supervisor                1830\n",
              "                          ... \n",
              "Cardno TBE                   1\n",
              "Dual Language Teacher        1\n",
              "Omega Office Systems         1\n",
              "ITW Switches                 1\n",
              "STAAR Surgical Company       1\n",
              "Name: emp_title, Length: 173105, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ExSidNSb5Fhl",
        "colab_type": "text"
      },
      "source": [
        "There are soo many emp_title, we can't get data for soo many many title. So, drop the emp_title column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V6kVGB5GZErr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.drop(\"emp_title\", axis =1, inplace=True)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gtZnc2kdaQma",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "outputId": "1c39d35f-d361-4218-c577-78afe33453c3"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 396030 entries, 0 to 396029\n",
            "Data columns (total 27 columns):\n",
            " #   Column                Non-Null Count   Dtype  \n",
            "---  ------                --------------   -----  \n",
            " 0   loan_amnt             396030 non-null  float64\n",
            " 1   term                  396030 non-null  object \n",
            " 2   int_rate              396030 non-null  float64\n",
            " 3   installment           396030 non-null  float64\n",
            " 4   grade                 396030 non-null  object \n",
            " 5   sub_grade             396030 non-null  object \n",
            " 6   emp_length            377729 non-null  object \n",
            " 7   home_ownership        396030 non-null  object \n",
            " 8   annual_inc            396030 non-null  float64\n",
            " 9   verification_status   396030 non-null  object \n",
            " 10  issue_d               396030 non-null  object \n",
            " 11  loan_status           396030 non-null  object \n",
            " 12  purpose               396030 non-null  object \n",
            " 13  title                 394275 non-null  object \n",
            " 14  dti                   396030 non-null  float64\n",
            " 15  earliest_cr_line      396030 non-null  object \n",
            " 16  open_acc              396030 non-null  float64\n",
            " 17  pub_rec               396030 non-null  float64\n",
            " 18  revol_bal             396030 non-null  float64\n",
            " 19  revol_util            395754 non-null  float64\n",
            " 20  total_acc             396030 non-null  float64\n",
            " 21  initial_list_status   396030 non-null  object \n",
            " 22  application_type      396030 non-null  object \n",
            " 23  mort_acc              358235 non-null  float64\n",
            " 24  pub_rec_bankruptcies  395495 non-null  float64\n",
            " 25  address               396030 non-null  object \n",
            " 26  loan_repaid           396030 non-null  int64  \n",
            "dtypes: float64(12), int64(1), object(14)\n",
            "memory usage: 81.6+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4KUMcvdY55-E",
        "colab_type": "text"
      },
      "source": [
        "explore the emp_length column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2yaIXStHaWTQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "e0af1978-ab02-4458-8617-3ecac6df1bac"
      },
      "source": [
        "df.emp_length.unique()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['10+ years', '4 years', '< 1 year', '6 years', '9 years',\n",
              "       '2 years', '3 years', '8 years', '7 years', '5 years', '1 year',\n",
              "       nan], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ptYH3Vmr5_36",
        "colab_type": "text"
      },
      "source": [
        " Create a count plot of the emp_length feature column. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s0gUQi44aoa_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "outputId": "d80a5380-2244-4f69-9fa5-9b023ef36aa9"
      },
      "source": [
        "emp_length_order = [\"< 1 year\",\n",
        "                    \"1 year\",\n",
        "                    \"2 years\",\n",
        "                    \"3 years\",\n",
        "                    \"4 years\",\n",
        "                    \"5 years\",\n",
        "                    \"6 years\",\n",
        "                    \"7 years\",\n",
        "                    \"8 years\",\n",
        "                    \"9 years\",\n",
        "                    \"10+ years\"]\n",
        "plt.figure(figsize=(14,5))\n",
        "sns.countplot(x=\"emp_length\", data=df, order=emp_length_order)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe5162a1438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FIo-tiu3CQGO",
        "colab_type": "text"
      },
      "source": [
        " Plot out the countplot with a hue separating Fully Paid vs Charged Off"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ECKgn89rc46t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "7b4eeeac-ccc4-494f-e3cf-cf2a197e1249"
      },
      "source": [
        "plt.figure(figsize=(12,4))\n",
        "\n",
        "sns.countplot(x='emp_length',data=df,order=emp_length_order, hue=\"loan_status\")"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe5286e7ac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H5PiCWdpCotA",
        "colab_type": "text"
      },
      "source": [
        "This still doesn't really inform us if there is a strong relationship between employment length and being charged off, what we want is the percentage of charge offs per category. Essentially informing us what percent of people per employment category didn't pay back their loan."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9E2n3UlzH0hJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "emp_length_order = [\"< 1 year\",\n",
        "                    \"1 year\",\n",
        "                    \"2 years\",\n",
        "                    \"3 years\",\n",
        "                    \"4 years\",\n",
        "                    \"5 years\",\n",
        "                    \"6 years\",\n",
        "                    \"7 years\",\n",
        "                    \"8 years\",\n",
        "                    \"9 years\",\n",
        "                    \"10+ years\"]"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kg9GtVUmA4v7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "222e73d5-dd41-426b-d1e6-4597240bfac4"
      },
      "source": [
        "emp_fp = df[df[\"loan_status\"]==\"Fully Paid\"].groupby(\"emp_length\").count()[\"loan_status\"]\n",
        "emp_fp"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "emp_length\n",
              "1 year        20728\n",
              "10+ years    102826\n",
              "2 years       28903\n",
              "3 years       25483\n",
              "4 years       19344\n",
              "5 years       21403\n",
              "6 years       16898\n",
              "7 years       16764\n",
              "8 years       15339\n",
              "9 years       12244\n",
              "< 1 year      25162\n",
              "Name: loan_status, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1RUpLD52Hspn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "f7b4022d-b065-4548-e4f8-013a60b2be14"
      },
      "source": [
        "emp_co = df[df[\"loan_status\"]==\"Charged Off\"].groupby(\"emp_length\").count()[\"loan_status\"]\n",
        "emp_co"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "emp_length\n",
              "1 year        5154\n",
              "10+ years    23215\n",
              "2 years       6924\n",
              "3 years       6182\n",
              "4 years       4608\n",
              "5 years       5092\n",
              "6 years       3943\n",
              "7 years       4055\n",
              "8 years       3829\n",
              "9 years       3070\n",
              "< 1 year      6563\n",
              "Name: loan_status, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xJi2QApTI_hf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "emp_len = emp_co/emp_fp"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U-C14-YZKVno",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "d8e39bec-3041-43d6-a13a-fd446bc54556"
      },
      "source": [
        "emp_len"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "emp_length\n",
              "1 year       0.248649\n",
              "10+ years    0.225770\n",
              "2 years      0.239560\n",
              "3 years      0.242593\n",
              "4 years      0.238213\n",
              "5 years      0.237911\n",
              "6 years      0.233341\n",
              "7 years      0.241887\n",
              "8 years      0.249625\n",
              "9 years      0.250735\n",
              "< 1 year     0.260830\n",
              "Name: loan_status, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zWB1Vz28LFFP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "d25a6b4d-4432-412a-818d-b9d74c80095d"
      },
      "source": [
        "emp_len.plot(kind=\"bar\")"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe51603ada0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uXQVrj8YC6mb",
        "colab_type": "text"
      },
      "source": [
        " Charge off rates are extremely similar across all employment lengths. Go ahead and drop the emp_length column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oalrsGnOMESp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.drop(\"emp_length\", axis=1, inplace=True)"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8nDCph4zN77O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "outputId": "c10d4102-71a2-4cfc-a9fc-b827db219e80"
      },
      "source": [
        "100* df.isnull().sum()/len(df)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_amnt               0.000000\n",
              "term                    0.000000\n",
              "int_rate                0.000000\n",
              "installment             0.000000\n",
              "grade                   0.000000\n",
              "sub_grade               0.000000\n",
              "home_ownership          0.000000\n",
              "annual_inc              0.000000\n",
              "verification_status     0.000000\n",
              "issue_d                 0.000000\n",
              "loan_status             0.000000\n",
              "purpose                 0.000000\n",
              "title                   0.443148\n",
              "dti                     0.000000\n",
              "earliest_cr_line        0.000000\n",
              "open_acc                0.000000\n",
              "pub_rec                 0.000000\n",
              "revol_bal               0.000000\n",
              "revol_util              0.069692\n",
              "total_acc               0.000000\n",
              "initial_list_status     0.000000\n",
              "application_type        0.000000\n",
              "mort_acc                9.543469\n",
              "pub_rec_bankruptcies    0.135091\n",
              "address                 0.000000\n",
              "loan_repaid             0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "flpwYH4GDEoR",
        "colab_type": "text"
      },
      "source": [
        "explore title column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L_ruoDwGO-lw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6354d2c9-ba85-4928-a9d8-ccbe7bbe2f40"
      },
      "source": [
        "len(df.title.unique())"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "48818"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zooDbG6vPM8H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.drop(\"title\", axis=1,inplace=True)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsewgWQ4PsTW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "outputId": "df331851-6d75-46b2-89ec-00f46d3078dd"
      },
      "source": [
        "100* df.isnull().sum()/len(df)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_amnt               0.000000\n",
              "term                    0.000000\n",
              "int_rate                0.000000\n",
              "installment             0.000000\n",
              "grade                   0.000000\n",
              "sub_grade               0.000000\n",
              "home_ownership          0.000000\n",
              "annual_inc              0.000000\n",
              "verification_status     0.000000\n",
              "issue_d                 0.000000\n",
              "loan_status             0.000000\n",
              "purpose                 0.000000\n",
              "dti                     0.000000\n",
              "earliest_cr_line        0.000000\n",
              "open_acc                0.000000\n",
              "pub_rec                 0.000000\n",
              "revol_bal               0.000000\n",
              "revol_util              0.069692\n",
              "total_acc               0.000000\n",
              "initial_list_status     0.000000\n",
              "application_type        0.000000\n",
              "mort_acc                9.543469\n",
              "pub_rec_bankruptcies    0.135091\n",
              "address                 0.000000\n",
              "loan_repaid             0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FkVGXPGRDUEE",
        "colab_type": "text"
      },
      "source": [
        "Find out what the mort_acc feature represents"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5VdsUoyPuFh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "72dcbb45-8232-47d3-e8e2-632e2b4b2c74"
      },
      "source": [
        "df.mort_acc.head()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.0\n",
              "1    3.0\n",
              "2    0.0\n",
              "3    0.0\n",
              "4    1.0\n",
              "Name: mort_acc, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9hebshm-QW-7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "outputId": "4c836052-b7a2-42ee-b44f-8f399e9406d2"
      },
      "source": [
        "df.mort_acc.value_counts()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0     139777\n",
              "1.0      60416\n",
              "2.0      49948\n",
              "3.0      38049\n",
              "4.0      27887\n",
              "5.0      18194\n",
              "6.0      11069\n",
              "7.0       6052\n",
              "8.0       3121\n",
              "9.0       1656\n",
              "10.0       865\n",
              "11.0       479\n",
              "12.0       264\n",
              "13.0       146\n",
              "14.0       107\n",
              "15.0        61\n",
              "16.0        37\n",
              "17.0        22\n",
              "18.0        18\n",
              "19.0        15\n",
              "20.0        13\n",
              "24.0        10\n",
              "22.0         7\n",
              "21.0         4\n",
              "25.0         4\n",
              "27.0         3\n",
              "23.0         2\n",
              "32.0         2\n",
              "26.0         2\n",
              "31.0         2\n",
              "30.0         1\n",
              "28.0         1\n",
              "34.0         1\n",
              "Name: mort_acc, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KCq5pNS3F13R",
        "colab_type": "text"
      },
      "source": [
        "There are many ways we could deal with this missing data. We could attempt to build a simple model to fill it in, such as a linear model, we could just fill it in based on the mean of the other columns, or you could even bin the columns into categories and then set NaN as its own category. There is no 100% correct approach! Let's review the other columsn to see which most highly correlates to mort_acc"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MJy5dvLwdfmm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "4c2faacb-befc-46af-8856-2d573895d91a"
      },
      "source": [
        "df.corr()[\"mort_acc\"].sort_values()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "int_rate               -0.082583\n",
              "dti                    -0.025439\n",
              "revol_util              0.007514\n",
              "pub_rec                 0.011552\n",
              "pub_rec_bankruptcies    0.027239\n",
              "loan_repaid             0.073111\n",
              "open_acc                0.109205\n",
              "installment             0.193694\n",
              "revol_bal               0.194925\n",
              "loan_amnt               0.222315\n",
              "annual_inc              0.236320\n",
              "total_acc               0.381072\n",
              "mort_acc                1.000000\n",
              "Name: mort_acc, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zAnLByq7eVvQ",
        "colab_type": "text"
      },
      "source": [
        "Looks like the total_acc feature correlates with the mort_acc , Let's try this fillna() approach. We will group the dataframe by the total_acc and calculate the mean value for the mort_acc per total_acc entry."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yOCPamTOeK01",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "846157a8-b61b-4959-e090-256dab1397fb"
      },
      "source": [
        "total_acc_avg = df.groupby(by=\"total_acc\").mean()[\"mort_acc\"]\n",
        "total_acc_avg"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "total_acc\n",
              "2.0      0.000000\n",
              "3.0      0.052023\n",
              "4.0      0.066743\n",
              "5.0      0.103289\n",
              "6.0      0.151293\n",
              "           ...   \n",
              "124.0    1.000000\n",
              "129.0    1.000000\n",
              "135.0    3.000000\n",
              "150.0    2.000000\n",
              "151.0    0.000000\n",
              "Name: mort_acc, Length: 118, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e7NHAszgfzHJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def fill_mort_acc(total_acc,mort_acc):\n",
        "  if np.isnan(mort_acc):\n",
        "        return total_acc_avg[total_acc]\n",
        "  else:\n",
        "        return mort_acc"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yABcrKwmhSrr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['mort_acc'] = df.apply(lambda x: fill_mort_acc(x['total_acc'], x['mort_acc']), axis=1)"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWEsatTch0LT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "outputId": "19c6519a-2e26-40ec-8ba4-ca2fdde2c1e0"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_amnt                 0\n",
              "term                      0\n",
              "int_rate                  0\n",
              "installment               0\n",
              "grade                     0\n",
              "sub_grade                 0\n",
              "home_ownership            0\n",
              "annual_inc                0\n",
              "verification_status       0\n",
              "issue_d                   0\n",
              "loan_status               0\n",
              "purpose                   0\n",
              "dti                       0\n",
              "earliest_cr_line          0\n",
              "open_acc                  0\n",
              "pub_rec                   0\n",
              "revol_bal                 0\n",
              "revol_util              276\n",
              "total_acc                 0\n",
              "initial_list_status       0\n",
              "application_type          0\n",
              "mort_acc                  0\n",
              "pub_rec_bankruptcies    535\n",
              "address                   0\n",
              "loan_repaid               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NMeWzHMWGbYV",
        "colab_type": "text"
      },
      "source": [
        "revol_util and the pub_rec_bankruptcies have missing data points, but they account for less than 0.5% of the total data. Go ahead and remove the rows that are missing those values in those columns with dropna()."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V-FmzQsUjHcH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.dropna()"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DMpJiA7Zj6TT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "outputId": "f179ef0d-2f25-485d-9aa3-955845470436"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_amnt               0\n",
              "term                    0\n",
              "int_rate                0\n",
              "installment             0\n",
              "grade                   0\n",
              "sub_grade               0\n",
              "home_ownership          0\n",
              "annual_inc              0\n",
              "verification_status     0\n",
              "issue_d                 0\n",
              "loan_status             0\n",
              "purpose                 0\n",
              "dti                     0\n",
              "earliest_cr_line        0\n",
              "open_acc                0\n",
              "pub_rec                 0\n",
              "revol_bal               0\n",
              "revol_util              0\n",
              "total_acc               0\n",
              "initial_list_status     0\n",
              "application_type        0\n",
              "mort_acc                0\n",
              "pub_rec_bankruptcies    0\n",
              "address                 0\n",
              "loan_repaid             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ru6SjRy2luTK",
        "colab_type": "text"
      },
      "source": [
        "#Categorical Variables and Dummy Variables\n",
        "We're done working with the missing data! Now we just need to deal with the string values due to the categorical columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VzWYRIjyj9Db",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "28bc9b45-e462-496c-8227-bbd77605795b"
      },
      "source": [
        "df.select_dtypes(['object']).columns"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['term', 'grade', 'sub_grade', 'home_ownership', 'verification_status',\n",
              "       'issue_d', 'loan_status', 'purpose', 'earliest_cr_line',\n",
              "       'initial_list_status', 'application_type', 'address'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4lBteC3NG__r",
        "colab_type": "text"
      },
      "source": [
        "Convert the term feature into either a 36 or 60 integer numeric data type using .apply() or .map()."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iZp0UxZXmaVB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "e027c314-30ac-4941-8279-f9ade2e3790c"
      },
      "source": [
        "df.term.value_counts()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " 36 months    301247\n",
              " 60 months     93972\n",
              "Name: term, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kk8iuMq9nMDx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "c67f77e6-e882-407c-da79-ca273ad0a067"
      },
      "source": [
        "df['term'] = df['term'].apply(lambda term: int(term[:3]))\n",
        "df['term']"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         36\n",
              "1         36\n",
              "2         36\n",
              "3         36\n",
              "4         60\n",
              "          ..\n",
              "396025    60\n",
              "396026    36\n",
              "396027    36\n",
              "396028    60\n",
              "396029    36\n",
              "Name: term, Length: 395219, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eV2m5z9aofom",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "8918a6d9-ec1b-4e61-b450-2a2ac5dc88fe"
      },
      "source": [
        "df.grade"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         B\n",
              "1         B\n",
              "2         B\n",
              "3         A\n",
              "4         C\n",
              "         ..\n",
              "396025    B\n",
              "396026    C\n",
              "396027    B\n",
              "396028    C\n",
              "396029    C\n",
              "Name: grade, Length: 395219, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "En-Z7oBttcJb",
        "colab_type": "text"
      },
      "source": [
        "Grade is a part of subgrade so, drop this feature"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ydu02uGpSM0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.drop(\"grade\",axis=1,inplace=True)"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WjVPyCB4HHkf",
        "colab_type": "text"
      },
      "source": [
        "Convert the subgrade into dummy variables. Then concatenate these new columns to the original dataframe. Remember to drop the original subgrade column and to add drop_first=True to your get_dummies call."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rPQrcRADtqF5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sub_grade_new = pd.get_dummies(df[\"sub_grade\"], drop_first=True)"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MpblXLVKulWe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "1960d504-cad2-49ae-8a6e-bf16a5485fc6"
      },
      "source": [
        "sub_grade_new"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396025</th>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396026</th>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396027</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396028</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396029</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>395219 rows × 34 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        A2  A3  A4  A5  B1  B2  B3  B4  B5  ...  F2  F3  F4  F5  G1  G2  G3  G4  G5\n",
              "0        0   0   0   0   0   0   0   1   0  ...   0   0   0   0   0   0   0   0   0\n",
              "1        0   0   0   0   0   0   0   0   1  ...   0   0   0   0   0   0   0   0   0\n",
              "2        0   0   0   0   0   0   1   0   0  ...   0   0   0   0   0   0   0   0   0\n",
              "3        1   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   0   0\n",
              "4        0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   0   0\n",
              "...     ..  ..  ..  ..  ..  ..  ..  ..  ..  ...  ..  ..  ..  ..  ..  ..  ..  ..  ..\n",
              "396025   0   0   0   0   0   0   0   1   0  ...   0   0   0   0   0   0   0   0   0\n",
              "396026   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   0   0\n",
              "396027   0   0   0   0   1   0   0   0   0  ...   0   0   0   0   0   0   0   0   0\n",
              "396028   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   0   0\n",
              "396029   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   0   0\n",
              "\n",
              "[395219 rows x 34 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8DEcSDpFuz-U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.concat([df.drop('sub_grade', axis=1),sub_grade_new], axis=1)"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uM05B4PawAHX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "d103df5b-b8a5-4f10-fd8d-a0beed095e15"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['loan_amnt', 'term', 'int_rate', 'installment', 'home_ownership',\n",
              "       'annual_inc', 'verification_status', 'issue_d', 'loan_status',\n",
              "       'purpose', 'dti', 'earliest_cr_line', 'open_acc', 'pub_rec',\n",
              "       'revol_bal', 'revol_util', 'total_acc', 'initial_list_status',\n",
              "       'application_type', 'mort_acc', 'pub_rec_bankruptcies', 'address',\n",
              "       'loan_repaid', 'A2', 'A3', 'A4', 'A5', 'B1', 'B2', 'B3', 'B4', 'B5',\n",
              "       'C1', 'C2', 'C3', 'C4', 'C5', 'D1', 'D2', 'D3', 'D4', 'D5', 'E1', 'E2',\n",
              "       'E3', 'E4', 'E5', 'F1', 'F2', 'F3', 'F4', 'F5', 'G1', 'G2', 'G3', 'G4',\n",
              "       'G5'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "evUMHPVvwCRC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "2e16690f-f211-4cde-dee3-bf0131c87d3d"
      },
      "source": [
        "df.select_dtypes(\"object\").columns"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['home_ownership', 'verification_status', 'issue_d', 'loan_status',\n",
              "       'purpose', 'earliest_cr_line', 'initial_list_status',\n",
              "       'application_type', 'address'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CxyGCZKWH2Qt",
        "colab_type": "text"
      },
      "source": [
        "Convert these columns: ['verification_status', 'application_type','initial_list_status','purpose'] into dummy variables and concatenate them with the original dataframe. Remember to set drop_first=True and to drop the original columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N5emqWU9y0D6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " dummies = pd.get_dummies([\"verification_status\",\"initial_list_status\",\"application_type\",\"purpose\"], drop_first=True)\n",
        " df = df.drop([\"application_type\",\"initial_list_status\",\"verification_status\",\"purpose\"], axis=1)\n",
        " df = pd.concat([df,dummies], axis=1)"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xTnz9NObIJfr",
        "colab_type": "text"
      },
      "source": [
        "Review the value_counts for the home_ownership column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tAV7r5pWwdgL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "4433fb86-aebb-4d30-c97e-55203171dc4e"
      },
      "source": [
        "df.home_ownership.unique()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['RENT', 'MORTGAGE', 'OWN', 'OTHER', 'ANY', 'NONE'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_3ig2gbjI4wA",
        "colab_type": "text"
      },
      "source": [
        " Convert these to dummy variables, but replace NONE and ANY with OTHER, so that we end up with just 4 categories, MORTGAGE, RENT, OWN, OTHER. Then concatenate them with the original dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lRTbCz4dwl3L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df[\"home_ownership\"] = df['home_ownership'].replace([\"NONE\",\"ANY\"],'OTHER')"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RIvRLNz1K0u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "39b69956-4843-404e-e9ea-2e9f032ae288"
      },
      "source": [
        "dummies = pd.get_dummies(df[\"home_ownership\"], drop_first=True)\n",
        "df = pd.concat([df,dummies], axis=1)\n",
        "df.info()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 395219 entries, 0 to 396029\n",
            "Data columns (total 59 columns):\n",
            " #   Column                Non-Null Count   Dtype  \n",
            "---  ------                --------------   -----  \n",
            " 0   loan_amnt             395219 non-null  float64\n",
            " 1   term                  395219 non-null  int64  \n",
            " 2   int_rate              395219 non-null  float64\n",
            " 3   installment           395219 non-null  float64\n",
            " 4   home_ownership        395219 non-null  object \n",
            " 5   annual_inc            395219 non-null  float64\n",
            " 6   issue_d               395219 non-null  object \n",
            " 7   loan_status           395219 non-null  object \n",
            " 8   dti                   395219 non-null  float64\n",
            " 9   earliest_cr_line      395219 non-null  object \n",
            " 10  open_acc              395219 non-null  float64\n",
            " 11  pub_rec               395219 non-null  float64\n",
            " 12  revol_bal             395219 non-null  float64\n",
            " 13  revol_util            395219 non-null  float64\n",
            " 14  total_acc             395219 non-null  float64\n",
            " 15  mort_acc              395219 non-null  float64\n",
            " 16  pub_rec_bankruptcies  395219 non-null  float64\n",
            " 17  address               395219 non-null  object \n",
            " 18  loan_repaid           395219 non-null  int64  \n",
            " 19  A2                    395219 non-null  uint8  \n",
            " 20  A3                    395219 non-null  uint8  \n",
            " 21  A4                    395219 non-null  uint8  \n",
            " 22  A5                    395219 non-null  uint8  \n",
            " 23  B1                    395219 non-null  uint8  \n",
            " 24  B2                    395219 non-null  uint8  \n",
            " 25  B3                    395219 non-null  uint8  \n",
            " 26  B4                    395219 non-null  uint8  \n",
            " 27  B5                    395219 non-null  uint8  \n",
            " 28  C1                    395219 non-null  uint8  \n",
            " 29  C2                    395219 non-null  uint8  \n",
            " 30  C3                    395219 non-null  uint8  \n",
            " 31  C4                    395219 non-null  uint8  \n",
            " 32  C5                    395219 non-null  uint8  \n",
            " 33  D1                    395219 non-null  uint8  \n",
            " 34  D2                    395219 non-null  uint8  \n",
            " 35  D3                    395219 non-null  uint8  \n",
            " 36  D4                    395219 non-null  uint8  \n",
            " 37  D5                    395219 non-null  uint8  \n",
            " 38  E1                    395219 non-null  uint8  \n",
            " 39  E2                    395219 non-null  uint8  \n",
            " 40  E3                    395219 non-null  uint8  \n",
            " 41  E4                    395219 non-null  uint8  \n",
            " 42  E5                    395219 non-null  uint8  \n",
            " 43  F1                    395219 non-null  uint8  \n",
            " 44  F2                    395219 non-null  uint8  \n",
            " 45  F3                    395219 non-null  uint8  \n",
            " 46  F4                    395219 non-null  uint8  \n",
            " 47  F5                    395219 non-null  uint8  \n",
            " 48  G1                    395219 non-null  uint8  \n",
            " 49  G2                    395219 non-null  uint8  \n",
            " 50  G3                    395219 non-null  uint8  \n",
            " 51  G4                    395219 non-null  uint8  \n",
            " 52  G5                    395219 non-null  uint8  \n",
            " 53  initial_list_status   4 non-null       float64\n",
            " 54  purpose               4 non-null       float64\n",
            " 55  verification_status   4 non-null       float64\n",
            " 56  OTHER                 395219 non-null  uint8  \n",
            " 57  OWN                   395219 non-null  uint8  \n",
            " 58  RENT                  395219 non-null  uint8  \n",
            "dtypes: float64(15), int64(2), object(5), uint8(37)\n",
            "memory usage: 83.3+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p9qNeTp619E7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "5cf1cfde-508a-40db-c9ef-40a2f5a226cb"
      },
      "source": [
        " df = df.drop(\"home_ownership\", axis=1)\n",
        " df.info()"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 395219 entries, 0 to 396029\n",
            "Data columns (total 58 columns):\n",
            " #   Column                Non-Null Count   Dtype  \n",
            "---  ------                --------------   -----  \n",
            " 0   loan_amnt             395219 non-null  float64\n",
            " 1   term                  395219 non-null  int64  \n",
            " 2   int_rate              395219 non-null  float64\n",
            " 3   installment           395219 non-null  float64\n",
            " 4   annual_inc            395219 non-null  float64\n",
            " 5   issue_d               395219 non-null  object \n",
            " 6   loan_status           395219 non-null  object \n",
            " 7   dti                   395219 non-null  float64\n",
            " 8   earliest_cr_line      395219 non-null  object \n",
            " 9   open_acc              395219 non-null  float64\n",
            " 10  pub_rec               395219 non-null  float64\n",
            " 11  revol_bal             395219 non-null  float64\n",
            " 12  revol_util            395219 non-null  float64\n",
            " 13  total_acc             395219 non-null  float64\n",
            " 14  mort_acc              395219 non-null  float64\n",
            " 15  pub_rec_bankruptcies  395219 non-null  float64\n",
            " 16  address               395219 non-null  object \n",
            " 17  loan_repaid           395219 non-null  int64  \n",
            " 18  A2                    395219 non-null  uint8  \n",
            " 19  A3                    395219 non-null  uint8  \n",
            " 20  A4                    395219 non-null  uint8  \n",
            " 21  A5                    395219 non-null  uint8  \n",
            " 22  B1                    395219 non-null  uint8  \n",
            " 23  B2                    395219 non-null  uint8  \n",
            " 24  B3                    395219 non-null  uint8  \n",
            " 25  B4                    395219 non-null  uint8  \n",
            " 26  B5                    395219 non-null  uint8  \n",
            " 27  C1                    395219 non-null  uint8  \n",
            " 28  C2                    395219 non-null  uint8  \n",
            " 29  C3                    395219 non-null  uint8  \n",
            " 30  C4                    395219 non-null  uint8  \n",
            " 31  C5                    395219 non-null  uint8  \n",
            " 32  D1                    395219 non-null  uint8  \n",
            " 33  D2                    395219 non-null  uint8  \n",
            " 34  D3                    395219 non-null  uint8  \n",
            " 35  D4                    395219 non-null  uint8  \n",
            " 36  D5                    395219 non-null  uint8  \n",
            " 37  E1                    395219 non-null  uint8  \n",
            " 38  E2                    395219 non-null  uint8  \n",
            " 39  E3                    395219 non-null  uint8  \n",
            " 40  E4                    395219 non-null  uint8  \n",
            " 41  E5                    395219 non-null  uint8  \n",
            " 42  F1                    395219 non-null  uint8  \n",
            " 43  F2                    395219 non-null  uint8  \n",
            " 44  F3                    395219 non-null  uint8  \n",
            " 45  F4                    395219 non-null  uint8  \n",
            " 46  F5                    395219 non-null  uint8  \n",
            " 47  G1                    395219 non-null  uint8  \n",
            " 48  G2                    395219 non-null  uint8  \n",
            " 49  G3                    395219 non-null  uint8  \n",
            " 50  G4                    395219 non-null  uint8  \n",
            " 51  G5                    395219 non-null  uint8  \n",
            " 52  initial_list_status   4 non-null       float64\n",
            " 53  purpose               4 non-null       float64\n",
            " 54  verification_status   4 non-null       float64\n",
            " 55  OTHER                 395219 non-null  uint8  \n",
            " 56  OWN                   395219 non-null  uint8  \n",
            " 57  RENT                  395219 non-null  uint8  \n",
            "dtypes: float64(15), int64(2), object(4), uint8(37)\n",
            "memory usage: 80.3+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M0ZaiIt1IReT",
        "colab_type": "text"
      },
      "source": [
        "Explore the address column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DNV_umbE2pXy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "8b9c78d2-e127-4a8a-a726-b563a80a688e"
      },
      "source": [
        "df.address[0]"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'0174 Michelle Gateway\\r\\nMendozaberg, OK 22690'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cx7Pfon-IYUn",
        "colab_type": "text"
      },
      "source": [
        "Take the last 5 digit is address zip code"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sOwqTOXv1l6Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "954b4522-59f3-4cd2-ac9a-8bc1de1358a0"
      },
      "source": [
        "df[\"code\"] = df[\"address\"].apply(lambda address: address[-5:])\n",
        "df[\"code\"]"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         22690\n",
              "1         05113\n",
              "2         05113\n",
              "3         00813\n",
              "4         11650\n",
              "          ...  \n",
              "396025    30723\n",
              "396026    05113\n",
              "396027    70466\n",
              "396028    29597\n",
              "396029    48052\n",
              "Name: code, Length: 395219, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VUu6rKd82g0L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dummies = pd.get_dummies(df['code'],drop_first=True)\n",
        "df = df.drop(['code','address'],axis=1)\n",
        "df = pd.concat([df,dummies],axis=1)"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VYaAblmK-5rV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "b214b0a5-cb18-437e-d24f-01a3e16f772e"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 395219 entries, 0 to 396029\n",
            "Data columns (total 66 columns):\n",
            " #   Column                Non-Null Count   Dtype  \n",
            "---  ------                --------------   -----  \n",
            " 0   loan_amnt             395219 non-null  float64\n",
            " 1   term                  395219 non-null  int64  \n",
            " 2   int_rate              395219 non-null  float64\n",
            " 3   installment           395219 non-null  float64\n",
            " 4   annual_inc            395219 non-null  float64\n",
            " 5   issue_d               395219 non-null  object \n",
            " 6   loan_status           395219 non-null  object \n",
            " 7   dti                   395219 non-null  float64\n",
            " 8   earliest_cr_line      395219 non-null  object \n",
            " 9   open_acc              395219 non-null  float64\n",
            " 10  pub_rec               395219 non-null  float64\n",
            " 11  revol_bal             395219 non-null  float64\n",
            " 12  revol_util            395219 non-null  float64\n",
            " 13  total_acc             395219 non-null  float64\n",
            " 14  mort_acc              395219 non-null  float64\n",
            " 15  pub_rec_bankruptcies  395219 non-null  float64\n",
            " 16  loan_repaid           395219 non-null  int64  \n",
            " 17  A2                    395219 non-null  uint8  \n",
            " 18  A3                    395219 non-null  uint8  \n",
            " 19  A4                    395219 non-null  uint8  \n",
            " 20  A5                    395219 non-null  uint8  \n",
            " 21  B1                    395219 non-null  uint8  \n",
            " 22  B2                    395219 non-null  uint8  \n",
            " 23  B3                    395219 non-null  uint8  \n",
            " 24  B4                    395219 non-null  uint8  \n",
            " 25  B5                    395219 non-null  uint8  \n",
            " 26  C1                    395219 non-null  uint8  \n",
            " 27  C2                    395219 non-null  uint8  \n",
            " 28  C3                    395219 non-null  uint8  \n",
            " 29  C4                    395219 non-null  uint8  \n",
            " 30  C5                    395219 non-null  uint8  \n",
            " 31  D1                    395219 non-null  uint8  \n",
            " 32  D2                    395219 non-null  uint8  \n",
            " 33  D3                    395219 non-null  uint8  \n",
            " 34  D4                    395219 non-null  uint8  \n",
            " 35  D5                    395219 non-null  uint8  \n",
            " 36  E1                    395219 non-null  uint8  \n",
            " 37  E2                    395219 non-null  uint8  \n",
            " 38  E3                    395219 non-null  uint8  \n",
            " 39  E4                    395219 non-null  uint8  \n",
            " 40  E5                    395219 non-null  uint8  \n",
            " 41  F1                    395219 non-null  uint8  \n",
            " 42  F2                    395219 non-null  uint8  \n",
            " 43  F3                    395219 non-null  uint8  \n",
            " 44  F4                    395219 non-null  uint8  \n",
            " 45  F5                    395219 non-null  uint8  \n",
            " 46  G1                    395219 non-null  uint8  \n",
            " 47  G2                    395219 non-null  uint8  \n",
            " 48  G3                    395219 non-null  uint8  \n",
            " 49  G4                    395219 non-null  uint8  \n",
            " 50  G5                    395219 non-null  uint8  \n",
            " 51  initial_list_status   4 non-null       float64\n",
            " 52  purpose               4 non-null       float64\n",
            " 53  verification_status   4 non-null       float64\n",
            " 54  OTHER                 395219 non-null  uint8  \n",
            " 55  OWN                   395219 non-null  uint8  \n",
            " 56  RENT                  395219 non-null  uint8  \n",
            " 57  05113                 395219 non-null  uint8  \n",
            " 58  11650                 395219 non-null  uint8  \n",
            " 59  22690                 395219 non-null  uint8  \n",
            " 60  29597                 395219 non-null  uint8  \n",
            " 61  30723                 395219 non-null  uint8  \n",
            " 62  48052                 395219 non-null  uint8  \n",
            " 63  70466                 395219 non-null  uint8  \n",
            " 64  86630                 395219 non-null  uint8  \n",
            " 65  93700                 395219 non-null  uint8  \n",
            "dtypes: float64(15), int64(2), object(3), uint8(46)\n",
            "memory usage: 90.7+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uFwP1KiQAzVq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "d6df4cbc-6407-499b-c33c-cf44e5bf2f91"
      },
      "source": [
        "df.select_dtypes(\"object\").columns"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['issue_d', 'loan_status', 'earliest_cr_line'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T8Tu6X87BUUI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "8a6abbfc-3e39-421a-d29e-79033dd6ef95"
      },
      "source": [
        "df.issue_d.head(2)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    Jan-2015\n",
              "1    Jan-2015\n",
              "Name: issue_d, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YfGEM1GSBbnf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "429da21f-8e84-4362-88c1-f516b85b710a"
      },
      "source": [
        "df.earliest_cr_line.head(2)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    Jun-1990\n",
              "1    Jul-2004\n",
              "Name: earliest_cr_line, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vOQ5ulOgBlBy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.drop([\"issue_d\",\"earliest_cr_line\"], axis=1)\n"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OIyMrMKEBwWX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "c4377920-34bd-417b-b3b1-d9d85aac022b"
      },
      "source": [
        "df.head(2)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>term</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>annual_inc</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>dti</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>mort_acc</th>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "      <th>loan_repaid</th>\n",
              "      <th>A2</th>\n",
              "      <th>A3</th>\n",
              "      <th>A4</th>\n",
              "      <th>A5</th>\n",
              "      <th>B1</th>\n",
              "      <th>B2</th>\n",
              "      <th>B3</th>\n",
              "      <th>B4</th>\n",
              "      <th>B5</th>\n",
              "      <th>C1</th>\n",
              "      <th>C2</th>\n",
              "      <th>C3</th>\n",
              "      <th>C4</th>\n",
              "      <th>C5</th>\n",
              "      <th>D1</th>\n",
              "      <th>D2</th>\n",
              "      <th>D3</th>\n",
              "      <th>D4</th>\n",
              "      <th>D5</th>\n",
              "      <th>E1</th>\n",
              "      <th>E2</th>\n",
              "      <th>E3</th>\n",
              "      <th>E4</th>\n",
              "      <th>E5</th>\n",
              "      <th>F1</th>\n",
              "      <th>F2</th>\n",
              "      <th>F3</th>\n",
              "      <th>F4</th>\n",
              "      <th>F5</th>\n",
              "      <th>G1</th>\n",
              "      <th>G2</th>\n",
              "      <th>G3</th>\n",
              "      <th>G4</th>\n",
              "      <th>G5</th>\n",
              "      <th>initial_list_status</th>\n",
              "      <th>purpose</th>\n",
              "      <th>verification_status</th>\n",
              "      <th>OTHER</th>\n",
              "      <th>OWN</th>\n",
              "      <th>RENT</th>\n",
              "      <th>05113</th>\n",
              "      <th>11650</th>\n",
              "      <th>22690</th>\n",
              "      <th>29597</th>\n",
              "      <th>30723</th>\n",
              "      <th>48052</th>\n",
              "      <th>70466</th>\n",
              "      <th>86630</th>\n",
              "      <th>93700</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>36</td>\n",
              "      <td>11.44</td>\n",
              "      <td>329.48</td>\n",
              "      <td>117000.0</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>26.24</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>36369.0</td>\n",
              "      <td>41.8</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8000.0</td>\n",
              "      <td>36</td>\n",
              "      <td>11.99</td>\n",
              "      <td>265.68</td>\n",
              "      <td>65000.0</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>22.05</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20131.0</td>\n",
              "      <td>53.3</td>\n",
              "      <td>27.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   loan_amnt  term  int_rate  installment  ...  48052 70466  86630  93700\n",
              "0    10000.0    36     11.44       329.48  ...      0     0      0      0\n",
              "1     8000.0    36     11.99       265.68  ...      0     0      0      0\n",
              "\n",
              "[2 rows x 64 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KVGpT6u9CBnL",
        "colab_type": "text"
      },
      "source": [
        "# Train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o1j9EIqcClf3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "81b9133f-2387-44e7-94b3-eefc922e0e5b"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['loan_amnt', 'term', 'int_rate', 'installment', 'annual_inc',\n",
              "       'loan_status', 'dti', 'open_acc', 'pub_rec', 'revol_bal', 'revol_util',\n",
              "       'total_acc', 'mort_acc', 'pub_rec_bankruptcies', 'loan_repaid', 'A2',\n",
              "       'A3', 'A4', 'A5', 'B1', 'B2', 'B3', 'B4', 'B5', 'C1', 'C2', 'C3', 'C4',\n",
              "       'C5', 'D1', 'D2', 'D3', 'D4', 'D5', 'E1', 'E2', 'E3', 'E4', 'E5', 'F1',\n",
              "       'F2', 'F3', 'F4', 'F5', 'G1', 'G2', 'G3', 'G4', 'G5',\n",
              "       'initial_list_status', 'purpose', 'verification_status', 'OTHER', 'OWN',\n",
              "       'RENT', '05113', '11650', '22690', '29597', '30723', '48052', '70466',\n",
              "       '86630', '93700'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3B6vjBX1JDuX",
        "colab_type": "text"
      },
      "source": [
        "drop loan status column because there are already loan_repaid column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rZpXep7dDMML",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.drop(\"loan_status\", axis=True, inplace=True)"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PgSf3LosB6nR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lch3x_oHCO3g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = df.drop('loan_repaid',axis=1).values"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NW8PjgO_ENyh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "9b7320ee-e76b-40b4-8ce3-daf8e0cedea6"
      },
      "source": [
        "X"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.000e+04, 3.600e+01, 1.144e+01, ..., 0.000e+00, 0.000e+00,\n",
              "        0.000e+00],\n",
              "       [8.000e+03, 3.600e+01, 1.199e+01, ..., 0.000e+00, 0.000e+00,\n",
              "        0.000e+00],\n",
              "       [1.560e+04, 3.600e+01, 1.049e+01, ..., 0.000e+00, 0.000e+00,\n",
              "        0.000e+00],\n",
              "       ...,\n",
              "       [5.000e+03, 3.600e+01, 9.990e+00, ..., 1.000e+00, 0.000e+00,\n",
              "        0.000e+00],\n",
              "       [2.100e+04, 6.000e+01, 1.531e+01, ..., 0.000e+00, 0.000e+00,\n",
              "        0.000e+00],\n",
              "       [2.000e+03, 3.600e+01, 1.361e+01, ..., 0.000e+00, 0.000e+00,\n",
              "        0.000e+00]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HmuBUOLLEOcB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = df['loan_repaid'].values"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O-4u5wF9JZmQ",
        "colab_type": "text"
      },
      "source": [
        "Set X and y variables to the .values of the features and label."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EJcStbDmEZDE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2, random_state=101)"
      ],
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t1WSm6l-FZsv",
        "colab_type": "text"
      },
      "source": [
        "# Normalizing the data\n",
        "Use a MinMaxScaler to normalize the feature data X_train and X_test. Recall we don't want data leakge from the test set so we only fit on the X_train data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p1BmWrsoFGgx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4sEAHbpKFsmn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scalar = MinMaxScaler()"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YugjVgQKFyMl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = scalar.fit_transform(X_train)\n",
        "X_test = scalar.transform(X_test)"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C3zhszNtGNO-",
        "colab_type": "text"
      },
      "source": [
        "#Creating the model\n",
        "Run the cell below to import the necessary Keras functions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "197eVkdAGJlv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense,Activation,Dropout\n",
        "from tensorflow.keras.constraints import max_norm "
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eygb1tVcHVh6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = Sequential()"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i7nCB7OUKg4E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "baeb349b-6472-4b7a-eb02-f07c0ae55201"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(316175, 62)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "224sAWETKmxA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f24b6171-caf2-4115-fc3f-a657fc9bf455"
      },
      "source": [
        "X_test.shape"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(79044, 62)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ecDgu-PMKy2m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# input layer\n",
        "model.add(Dense(16, activation=\"relu\"))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "# hidden layer\n",
        "model.add(Dense(32, activation=\"relu\"))\n",
        "model.add(Dropout(0.2))\n",
        "# hidden layer\n",
        "model.add(Dense(64, activation=\"relu\"))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "# output layer\n",
        "model.add(Dense(units=1,activation='sigmoid'))\n",
        "\n",
        "# Compile model\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam')"
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dohiye31KIMG",
        "colab_type": "text"
      },
      "source": [
        "Fit the model to the training data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvknV0BDL0gK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 908
        },
        "outputId": "655130eb-c910-490c-ffce-720f7b89cd0c"
      },
      "source": [
        "model.fit(x=X_train, y=y_train, epochs=24, batch_size=256, validation_data=(X_test,y_test))"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/24\n",
            "1236/1236 [==============================] - 5s 4ms/step - loss: 0.5773 - val_loss: 0.5165\n",
            "Epoch 2/24\n",
            "1236/1236 [==============================] - 4s 4ms/step - loss: 0.5005 - val_loss: 0.4982\n",
            "Epoch 3/24\n",
            "1236/1236 [==============================] - 4s 4ms/step - loss: 0.4945 - val_loss: 0.4978\n",
            "Epoch 4/24\n",
            "1236/1236 [==============================] - 4s 4ms/step - loss: 0.4943 - val_loss: 0.4978\n",
            "Epoch 5/24\n",
            "1236/1236 [==============================] - 4s 4ms/step - loss: 0.4943 - val_loss: 0.4978\n",
            "Epoch 6/24\n",
            "1236/1236 [==============================] - 4s 4ms/step - loss: 0.4943 - val_loss: 0.4978\n",
            "Epoch 7/24\n",
            "1236/1236 [==============================] - 4s 4ms/step - loss: 0.4943 - val_loss: 0.4978\n",
            "Epoch 8/24\n",
            "1236/1236 [==============================] - 4s 4ms/step - loss: 0.4943 - val_loss: 0.4978\n",
            "Epoch 9/24\n",
            "1236/1236 [==============================] - 4s 4ms/step - loss: 0.4943 - val_loss: 0.4978\n",
            "Epoch 10/24\n",
            "1236/1236 [==============================] - 4s 4ms/step - loss: 0.4943 - val_loss: 0.4978\n",
            "Epoch 11/24\n",
            "1236/1236 [==============================] - 4s 4ms/step - loss: 0.4943 - val_loss: 0.4978\n",
            "Epoch 12/24\n",
            "1236/1236 [==============================] - 4s 4ms/step - loss: 0.4943 - val_loss: 0.4978\n",
            "Epoch 13/24\n",
            "1236/1236 [==============================] - 5s 4ms/step - loss: 0.4943 - val_loss: 0.4978\n",
            "Epoch 14/24\n",
            "1236/1236 [==============================] - 4s 4ms/step - loss: 0.4943 - val_loss: 0.4978\n",
            "Epoch 15/24\n",
            "1236/1236 [==============================] - 4s 4ms/step - loss: 0.4943 - val_loss: 0.4978\n",
            "Epoch 16/24\n",
            "1236/1236 [==============================] - 4s 4ms/step - loss: 0.4943 - val_loss: 0.4977\n",
            "Epoch 17/24\n",
            "1236/1236 [==============================] - 4s 4ms/step - loss: 0.4943 - val_loss: 0.4978\n",
            "Epoch 18/24\n",
            "1236/1236 [==============================] - 5s 4ms/step - loss: 0.4943 - val_loss: 0.4978\n",
            "Epoch 19/24\n",
            "1236/1236 [==============================] - 4s 4ms/step - loss: 0.4943 - val_loss: 0.4978\n",
            "Epoch 20/24\n",
            "1236/1236 [==============================] - 4s 4ms/step - loss: 0.4943 - val_loss: 0.4978\n",
            "Epoch 21/24\n",
            "1236/1236 [==============================] - 4s 4ms/step - loss: 0.4943 - val_loss: 0.4978\n",
            "Epoch 22/24\n",
            "1236/1236 [==============================] - 4s 4ms/step - loss: 0.4943 - val_loss: 0.4978\n",
            "Epoch 23/24\n",
            "1236/1236 [==============================] - 4s 4ms/step - loss: 0.4943 - val_loss: 0.4978\n",
            "Epoch 24/24\n",
            "1236/1236 [==============================] - 4s 4ms/step - loss: 0.4943 - val_loss: 0.4978\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fe4d05d9400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sQPJI42SKSWe",
        "colab_type": "text"
      },
      "source": [
        "#Evaluating Model Performance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1LgWCQ7mMZmO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import classification_report,confusion_matrix"
      ],
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z3uQzMsdN3qt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "outputId": "db543c7a-9360-4688-de92-92a6d6b31d7d"
      },
      "source": [
        "predictions = model.predict_classes(X_test)"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-94-bc83193b8b59>:1: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
            "Instructions for updating:\n",
            "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M0j9o41XN69u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "5bed10f6-2dbf-4726-f574-36b93b948ee9"
      },
      "source": [
        "print(classification_report(y_test,predictions))"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00     15658\n",
            "           1       0.80      1.00      0.89     63386\n",
            "\n",
            "    accuracy                           0.80     79044\n",
            "   macro avg       0.40      0.50      0.45     79044\n",
            "weighted avg       0.64      0.80      0.71     79044\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ftO92-osOA0D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 95,
      "outputs": []
    }
  ]
}