07: Statistical_Thinking_Data_Analysis_and_Segmentation.ipynb

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  "metadata": {
    "colab": {
      "name": "07: StatisticalThinking - Data Analysis and Segmentation.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OnkgywPmax-s"
      },
      "source": [
        "Video walkthrough of the code : https://www.youtube.com/watch?v=sN6coxYCMQE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TnU_4f65k8K0"
      },
      "source": [
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "warnings.simplefilter(action='ignore', category=Warning)\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jb8ZYaI52s4E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2c3462f-520e-4472-bc1c-688186d2b2e6"
      },
      "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": "3cx3vP5-2srd"
      },
      "source": [
        "loan_full_df = pd.read_csv('/content/drive/MyDrive/dataset/accepted_2007_to_2018Q4.csv_2.gz', compression='gzip', error_bad_lines=False)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q6OrI7zKb8pY",
        "outputId": "068c1efe-3e38-4bce-ca36-a53352a5e897"
      },
      "source": [
        "loan_full_df.shape[0]"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2260701"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3bkJC-733RFL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fca9b477-47b0-449f-cb4d-0959b468a4e4"
      },
      "source": [
        "# take a subset of the dataset to avoid the colab crash\n",
        "loan_full_df = loan_full_df[0:1000000]\n",
        "loan_full_df.shape[0]"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RAYMSQGX3Q_d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "outputId": "1c8b4ee1-bee1-4436-cf7a-7fb116ceff4a"
      },
      "source": [
        "loan_full_df.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>member_id</th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>funded_amnt</th>\n",
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              "      <th>loan_status</th>\n",
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              "      <th>title</th>\n",
              "      <th>zip_code</th>\n",
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              "      <th>total_acc</th>\n",
              "      <th>initial_list_status</th>\n",
              "      <th>out_prncp</th>\n",
              "      <th>out_prncp_inv</th>\n",
              "      <th>...</th>\n",
              "      <th>tot_hi_cred_lim</th>\n",
              "      <th>total_bal_ex_mort</th>\n",
              "      <th>total_bc_limit</th>\n",
              "      <th>total_il_high_credit_limit</th>\n",
              "      <th>revol_bal_joint</th>\n",
              "      <th>sec_app_fico_range_low</th>\n",
              "      <th>sec_app_fico_range_high</th>\n",
              "      <th>sec_app_earliest_cr_line</th>\n",
              "      <th>sec_app_inq_last_6mths</th>\n",
              "      <th>sec_app_mort_acc</th>\n",
              "      <th>sec_app_open_acc</th>\n",
              "      <th>sec_app_revol_util</th>\n",
              "      <th>sec_app_open_act_il</th>\n",
              "      <th>sec_app_num_rev_accts</th>\n",
              "      <th>sec_app_chargeoff_within_12_mths</th>\n",
              "      <th>sec_app_collections_12_mths_ex_med</th>\n",
              "      <th>sec_app_mths_since_last_major_derog</th>\n",
              "      <th>hardship_flag</th>\n",
              "      <th>hardship_type</th>\n",
              "      <th>hardship_reason</th>\n",
              "      <th>hardship_status</th>\n",
              "      <th>deferral_term</th>\n",
              "      <th>hardship_amount</th>\n",
              "      <th>hardship_start_date</th>\n",
              "      <th>hardship_end_date</th>\n",
              "      <th>payment_plan_start_date</th>\n",
              "      <th>hardship_length</th>\n",
              "      <th>hardship_dpd</th>\n",
              "      <th>hardship_loan_status</th>\n",
              "      <th>orig_projected_additional_accrued_interest</th>\n",
              "      <th>hardship_payoff_balance_amount</th>\n",
              "      <th>hardship_last_payment_amount</th>\n",
              "      <th>disbursement_method</th>\n",
              "      <th>debt_settlement_flag</th>\n",
              "      <th>debt_settlement_flag_date</th>\n",
              "      <th>settlement_status</th>\n",
              "      <th>settlement_date</th>\n",
              "      <th>settlement_amount</th>\n",
              "      <th>settlement_percentage</th>\n",
              "      <th>settlement_term</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>68407277</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3600.0</td>\n",
              "      <td>3600.0</td>\n",
              "      <td>3600.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>13.99</td>\n",
              "      <td>123.03</td>\n",
              "      <td>C</td>\n",
              "      <td>C4</td>\n",
              "      <td>leadman</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>55000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Dec-2015</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>190xx</td>\n",
              "      <td>PA</td>\n",
              "      <td>5.91</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Aug-2003</td>\n",
              "      <td>675.0</td>\n",
              "      <td>679.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>30.0</td>\n",
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              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2765.0</td>\n",
              "      <td>29.7</td>\n",
              "      <td>13.0</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
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              "      <td>7746.0</td>\n",
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              "      <td>13734.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cash</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>68355089</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24700.0</td>\n",
              "      <td>24700.0</td>\n",
              "      <td>24700.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.99</td>\n",
              "      <td>820.28</td>\n",
              "      <td>C</td>\n",
              "      <td>C1</td>\n",
              "      <td>Engineer</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>65000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Dec-2015</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>715.0</td>\n",
              "      <td>719.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>39475.0</td>\n",
              "      <td>79300.0</td>\n",
              "      <td>24667.0</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cash</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>68341763</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>10.78</td>\n",
              "      <td>432.66</td>\n",
              "      <td>B</td>\n",
              "      <td>B4</td>\n",
              "      <td>truck driver</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>63000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Dec-2015</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>home_improvement</td>\n",
              "      <td>NaN</td>\n",
              "      <td>605xx</td>\n",
              "      <td>IL</td>\n",
              "      <td>10.78</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Aug-2000</td>\n",
              "      <td>695.0</td>\n",
              "      <td>699.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7869.0</td>\n",
              "      <td>56.2</td>\n",
              "      <td>18.0</td>\n",
              "      <td>w</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>218418.0</td>\n",
              "      <td>18696.0</td>\n",
              "      <td>6200.0</td>\n",
              "      <td>14877.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cash</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>66310712</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35000.0</td>\n",
              "      <td>35000.0</td>\n",
              "      <td>35000.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>14.85</td>\n",
              "      <td>829.90</td>\n",
              "      <td>C</td>\n",
              "      <td>C5</td>\n",
              "      <td>Information Systems Officer</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>110000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-2015</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>076xx</td>\n",
              "      <td>NJ</td>\n",
              "      <td>17.06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Sep-2008</td>\n",
              "      <td>785.0</td>\n",
              "      <td>789.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7802.0</td>\n",
              "      <td>11.6</td>\n",
              "      <td>17.0</td>\n",
              "      <td>w</td>\n",
              "      <td>15897.65</td>\n",
              "      <td>15897.65</td>\n",
              "      <td>...</td>\n",
              "      <td>381215.0</td>\n",
              "      <td>52226.0</td>\n",
              "      <td>62500.0</td>\n",
              "      <td>18000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cash</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>68476807</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10400.0</td>\n",
              "      <td>10400.0</td>\n",
              "      <td>10400.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>22.45</td>\n",
              "      <td>289.91</td>\n",
              "      <td>F</td>\n",
              "      <td>F1</td>\n",
              "      <td>Contract Specialist</td>\n",
              "      <td>3 years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>104433.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-2015</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>major_purchase</td>\n",
              "      <td>Major purchase</td>\n",
              "      <td>174xx</td>\n",
              "      <td>PA</td>\n",
              "      <td>25.37</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Jun-1998</td>\n",
              "      <td>695.0</td>\n",
              "      <td>699.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21929.0</td>\n",
              "      <td>64.5</td>\n",
              "      <td>35.0</td>\n",
              "      <td>w</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>439570.0</td>\n",
              "      <td>95768.0</td>\n",
              "      <td>20300.0</td>\n",
              "      <td>88097.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cash</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 151 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         id  member_id  ...  settlement_percentage  settlement_term\n",
              "0  68407277        NaN  ...                    NaN              NaN\n",
              "1  68355089        NaN  ...                    NaN              NaN\n",
              "2  68341763        NaN  ...                    NaN              NaN\n",
              "3  66310712        NaN  ...                    NaN              NaN\n",
              "4  68476807        NaN  ...                    NaN              NaN\n",
              "\n",
              "[5 rows x 151 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RX6yrhQi6hdJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd1fb731-840f-489a-e100-f08e47f0b215"
      },
      "source": [
        "loan_full_df['loan_status'].value_counts()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Fully Paid            450285\n",
              "Current               411982\n",
              "Charged Off           121189\n",
              "Late (31-120 days)     10475\n",
              "In Grace Period         3997\n",
              "Late (16-30 days)       2040\n",
              "Default                   20\n",
              "Name: loan_status, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cVve-WrP5y9X"
      },
      "source": [
        "loan_int_df=loan_full_df[loan_full_df['loan_status']!='Current']\n",
        "loan_int_df=loan_int_df[loan_full_df['loan_status']!='Late (31-120 days)']\n",
        "loan_int_df=loan_int_df[loan_full_df['loan_status']!='In Grace Period']\n",
        "loan_int_df=loan_int_df[loan_full_df['loan_status']!='Late (16-30 days)']\n",
        "loan_int_df['loan_status']=loan_int_df['loan_status'].replace('Charged Off','Default')\n",
        "loan_int_df['loan_status']=loan_int_df['loan_status'].replace('Fully Paid','Good')"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRlGJ8Hj5yyP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9985e82-761c-4707-e07f-ff71669e328b"
      },
      "source": [
        "loan_int_df['loan_status'].value_counts()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Good       450285\n",
              "Default    121209\n",
              "Name: loan_status, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vHvU8y-oAuR3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2decc2c6-35de-47b1-a9a8-5e7f0e947b82"
      },
      "source": [
        "loan_int_df['last_fico_range_low'].min()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8HdrGx1xAwHn"
      },
      "source": [
        "loan_int_df['last_fico_range_low']=loan_int_df.apply(lambda x: x['fico_range_low'] if x['last_fico_range_low']==0 else x['last_fico_range_low'],axis=1)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OgZnrghW-b09",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac7a96a2-f327-419f-c47a-56ac72dc6839"
      },
      "source": [
        "loan_int_df['last_fico_range_low'].value_counts().sort_index()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "500.0    3385\n",
              "505.0    3671\n",
              "510.0    4074\n",
              "515.0    4270\n",
              "520.0    4898\n",
              "         ... \n",
              "825.0    1349\n",
              "830.0     803\n",
              "835.0     409\n",
              "840.0     218\n",
              "845.0     114\n",
              "Name: last_fico_range_low, Length: 70, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "llHI5PsedmST",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62360d20-9168-4438-b630-d083dbbbb459"
      },
      "source": [
        "pd.cut(loan_int_df['last_fico_range_low'],5)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         (499.655, 569.0]\n",
              "1           (638.0, 707.0]\n",
              "2           (638.0, 707.0]\n",
              "4           (638.0, 707.0]\n",
              "5           (707.0, 776.0]\n",
              "                ...       \n",
              "999993      (707.0, 776.0]\n",
              "999994      (638.0, 707.0]\n",
              "999996    (499.655, 569.0]\n",
              "999997      (569.0, 638.0]\n",
              "999998    (499.655, 569.0]\n",
              "Name: last_fico_range_low, Length: 571506, dtype: category\n",
              "Categories (5, interval[float64]): [(499.655, 569.0] < (569.0, 638.0] < (638.0, 707.0] < (707.0, 776.0] <\n",
              "                                    (776.0, 845.0]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CNQqinfa3Q1U"
      },
      "source": [
        "loan_int_df['fico_buckets']=pd.cut(loan_int_df['last_fico_range_low'],5,labels=['High Risky','Risky','Low Risk','Good','Excellent'])"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tSZ-1q856dQ6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cfa8ae7f-2d44-4bca-ad37-f86ad0057667"
      },
      "source": [
        "loan_int_df['fico_buckets'].value_counts()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Low Risk      208275\n",
              "Good          174166\n",
              "Risky          74774\n",
              "High Risky     71702\n",
              "Excellent      42577\n",
              "Name: fico_buckets, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X05JOrJd6dEL"
      },
      "source": [
        "loan_status_df=loan_int_df[['loan_status','fico_buckets']].groupby(['loan_status','fico_buckets']).agg({'fico_buckets':'count'})"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EEysVBJ8JvKe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "outputId": "d8ff3844-9a5f-4241-b7be-084caa478491"
      },
      "source": [
        "loan_status_df.rename({'fico_buckets':'count'},axis='columns').reset_index()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loan_status</th>\n",
              "      <th>fico_buckets</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Default</td>\n",
              "      <td>High Risky</td>\n",
              "      <td>58537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Default</td>\n",
              "      <td>Risky</td>\n",
              "      <td>37735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Default</td>\n",
              "      <td>Low Risk</td>\n",
              "      <td>21309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Default</td>\n",
              "      <td>Good</td>\n",
              "      <td>3315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Default</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Good</td>\n",
              "      <td>High Risky</td>\n",
              "      <td>13165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Good</td>\n",
              "      <td>Risky</td>\n",
              "      <td>37039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Good</td>\n",
              "      <td>Low Risk</td>\n",
              "      <td>186966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Good</td>\n",
              "      <td>Good</td>\n",
              "      <td>170851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Good</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>42264</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  loan_status fico_buckets   count\n",
              "0     Default   High Risky   58537\n",
              "1     Default        Risky   37735\n",
              "2     Default     Low Risk   21309\n",
              "3     Default         Good    3315\n",
              "4     Default    Excellent     313\n",
              "5        Good   High Risky   13165\n",
              "6        Good        Risky   37039\n",
              "7        Good     Low Risk  186966\n",
              "8        Good         Good  170851\n",
              "9        Good    Excellent   42264"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vIkRNo5KJuh1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "a3d5c762-e1dd-48e7-be77-e1152003a81a"
      },
      "source": [
        "sns.barplot(x=\"fico_buckets\", y=\"count\", hue=\"loan_status\", data=loan_status_df.rename({'fico_buckets':'count'},axis='columns').reset_index())"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcf5eeb2a50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "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": "JBgXw46CWtJ9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "846ea69b-bded-4d8f-fb3a-a7107442c083"
      },
      "source": [
        "pd.qcut(loan_int_df['int_rate'],5)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         (13.33, 16.55]\n",
              "1         (10.99, 13.33]\n",
              "2          (8.18, 10.99]\n",
              "4         (16.55, 30.99]\n",
              "5         (13.33, 16.55]\n",
              "               ...      \n",
              "999993     (8.18, 10.99]\n",
              "999994    (13.33, 16.55]\n",
              "999996    (16.55, 30.99]\n",
              "999997    (16.55, 30.99]\n",
              "999998    (16.55, 30.99]\n",
              "Name: int_rate, Length: 571506, dtype: category\n",
              "Categories (5, interval[float64]): [(5.308999999999999, 8.18] < (8.18, 10.99] < (10.99, 13.33] < (13.33, 16.55] <\n",
              "                                    (16.55, 30.99]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "URnE0Tnz6cUC"
      },
      "source": [
        "loan_int_df['rate_buckets']=pd.qcut(loan_int_df['int_rate'],5,labels=['Low','Medium','High','Very High','ForgetIt'])"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6gz6DFwV6cJx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8469074-25a8-4524-ad4c-859651be4094"
      },
      "source": [
        "loan_int_df['rate_buckets'].value_counts()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Low          121007\n",
              "High         120515\n",
              "Very High    111893\n",
              "ForgetIt     109591\n",
              "Medium       108488\n",
              "Name: rate_buckets, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9DxJjm5P7Vp6"
      },
      "source": [
        "loan_rate_df=loan_int_df[['loan_status','rate_buckets']].groupby(['loan_status','rate_buckets']).agg({'rate_buckets':'count'})"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yS5FA2og7VnG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "outputId": "eb2056cf-b593-449e-b0dd-5bdda64ce692"
      },
      "source": [
        "loan_rate_df.rename({'rate_buckets':'count'},axis='columns').reset_index()"
      ],
      "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_status</th>\n",
              "      <th>rate_buckets</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Default</td>\n",
              "      <td>Low</td>\n",
              "      <td>8035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Default</td>\n",
              "      <td>Medium</td>\n",
              "      <td>14877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Default</td>\n",
              "      <td>High</td>\n",
              "      <td>23965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Default</td>\n",
              "      <td>Very High</td>\n",
              "      <td>30709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Default</td>\n",
              "      <td>ForgetIt</td>\n",
              "      <td>43623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Good</td>\n",
              "      <td>Low</td>\n",
              "      <td>112972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Good</td>\n",
              "      <td>Medium</td>\n",
              "      <td>93611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Good</td>\n",
              "      <td>High</td>\n",
              "      <td>96550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Good</td>\n",
              "      <td>Very High</td>\n",
              "      <td>81184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Good</td>\n",
              "      <td>ForgetIt</td>\n",
              "      <td>65968</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  loan_status rate_buckets   count\n",
              "0     Default          Low    8035\n",
              "1     Default       Medium   14877\n",
              "2     Default         High   23965\n",
              "3     Default    Very High   30709\n",
              "4     Default     ForgetIt   43623\n",
              "5        Good          Low  112972\n",
              "6        Good       Medium   93611\n",
              "7        Good         High   96550\n",
              "8        Good    Very High   81184\n",
              "9        Good     ForgetIt   65968"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cGo50cv77Vgj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "c02cb9c8-053f-4b7c-eb8b-e3ed3bd5d4d0"
      },
      "source": [
        "sns.barplot(x=\"rate_buckets\", y=\"count\", hue=\"loan_status\", data=loan_rate_df.rename({'rate_buckets':'count'},axis='columns').reset_index())"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fceb1452c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kYmZmmXNyMTOzzDm5mJlZ5pxczMwsc04uZmaWOScXMzPLnJOLmZllzsnFzMwy5+RiZmaZc3IxM7PMObmYmVnmnFzMzCxzTi5mZpY5JxczM8uck4uZmWXOycXMzDLn5GJmZplzcjEzs8y1KHcDzMys8V67trpkdR3y/YWNXqeoMxdJTxQTMzMzg22cuUhqDewFHCCpPaC0aF+gYxO3zczMdlLb6hb7BvBN4DPAXDYnl/eAnzVhu8zMbCe21eQSET8BfiJpdET8tERtMjOznVxRF/Qj4qeSvgBU5a8TEROaqF1mZrYTKyq5SPoV8A/AfGBTCgfg5GJmZlso9lbkfkC3iIimbIyZme0aiv0R5QvAp7OqVFI7SQ9K+quklyT9L0mfkvS4pMXpvX0qK0m3SloiaYGkPnnbGZbKL5Y0LC/eV9LCtM6tklSoHWZm1jSKTS4HAC9KmiZpSu1rB+r9CfCHiDgM6Am8BFwFPBERXYAn0jzASUCX9LoAGAcg6VPA1cCRwBHA1bUJKZUZmbfeoB1oq5mZNVKx3WLXZFWhpP2AAcBwgIhYD6yXNBg4NhW7B5gBXAkMBiakLrlZ6aznoFT28YhYlbb7ODBI0gxg34iYleITgCHA77PaBzMz27pi7xb7Y4Z1dgZWAHdJ6knu9zOXAR0i4s1U5u9AhzTdEXg9b/2aFNtavKZAfAuSLiB3NsQhhxyy/XtkZmafUOzwL+9Lei+91knaJOm97ayzBdAHGBcRvYEP2NwFBkA6S2nymwciYnxE9IuIfpWVlU1dnZnZbqOo5BIR+0TEvhGxL9AG+Arwn9tZZw1QExHPpfkHySWbt1J3F+l9eVr+BnBw3vqdUmxr8U4F4mZmViKNHnI/ch4FTtyeCiPi78Drkj6fQicALwJTgNo7voYBk9P0FODcdNfYUcDq1H02DRgoqX26kD8QmJaWvSfpqHSX2Ll52zIzsxIo9keU/5o3uwe5372s24F6RwP3SmoFLAVGpO0+IOk84FXgtFR2KnAysAT4MJUlIlZJ+iEwO5W7tvbiPnAxcDe5s6zf44v5ZmYlVezdYl/Om94ILCN3F9d2iYj55BJUfScUKBvAqAa2cydwZ4H4HKD79rbPzMx2TLF3i41o6oaYmdmuo9i7xTpJekTS8vR6SFKnba9pZma7o2Iv6N9F7sL6Z9LrNylmZma2hWKTS2VE3BURG9PrbsA/DDEzs4KKTS4rJZ0tqSK9zgZWNmXDzMxs51Vscvk6uVuD/w68CXyVNDaYmZlZfcXeinwtMCwi3oG6EYlvIZd0zMzMPqHYM5cetYkFcj9gBHo3TZPMzGxnV2xy2SPvWSm1Zy7FnvWYmdluptgE8WPgWUm/TvNfA65rmiaZmdnOrthf6E+QNAc4PoX+NSJebLpmmZnZzqzorq2UTJxQzMxsmxo95L6Zmdm2OLmYmVnmnFzMzCxzTi5mZpY5JxczM8uck4uZmWXOycXMzDLn5GJmZplzcjEzs8w5uZiZWeacXMzMLHNOLmZmljk/k8XMLCN9r5hQsroe2adkVW0Xn7mYmVnmnFzMzCxzZUsukiok/UXSb9N8Z0nPSVoiaZKkVim+Z5pfkpZX5W3jOyn+N0kn5sUHpdgSSVeVet/MzHZ35TxzuQx4KW/+RmBsRPwj8A5wXoqfB7yT4mNTOSR1A4YChwODgP9MCasCuA04CegGnJHKmplZiZQluUjqBHwJ+GWaF7lHKD+YitwDDEnTg9M8afkJqfxgYGJEfBQRrwBLgCPSa0lELI2I9cDEVNbMzEqkXGcu/wF8G/g4ze8PvBsRG9N8DdAxTXcEXgdIy1en8nXxeus0FDczsxIpeXKR9C/A8oiYW+q6C7TlAklzJM1ZsWJFuZtjZrbLKMeZy9HAKZKWkeuyOh74CdBOUu3vbjoBb6TpN4CDAdLy/YCV+fF66zQU30JEjI+IfhHRr7Kycsf3zMzMgDIkl4j4TkR0iogqchfkn4yIs4DpwFdTsWHA5DQ9Jc2Tlj8ZEZHiQ9PdZJ2BLsCfgdlAl3T3WatUx5QS7JqZmSXN6Rf6VwITJf0I+AtwR4rfAfxK0hJgFblkQUQskvQA8CKwERgVEZsAJF0CTAMqgDsjYlFJ98TMbDdX1uQSETOAGWl6Kbk7veqXWQd8rYH1rwOuKxCfCkzNsKlmZtYI/oW+mZllzsnFzMwy5+RiZmaZc3IxM7PMObmYmVnmnFzMzCxzTi5mZpY5JxczM8uck4uZmWXOycXMzDLn5GJmZplzcjEzs8w1p1GRzWwn1PeKCSWr65F9SlaV7SCfuZiZWeacXMzMLHNOLmZmljknFzMzy5yTi5mZZc7JxczMMufkYmZmmXNyMTOzzDm5mJlZ5pxczMwsc04uZmaWOY8tZrYdPJ6W2db5zMXMzDLn5GJmZplzcjEzs8yVPLlIOljSdEkvSlok6bIU/5SkxyUtTu/tU1ySbpW0RNICSX3ytjUslV8saVhevK+khWmdWyWp1PtpZrY7K8eZy0bg/0REN+AoYJSkbsBVwBMR0QV4Is0DnAR0Sa8LgHGQS0bA1cCRwBHA1bUJKZUZmbfeoBLsl5mZJSVPLhHxZkTMS9PvAy8BHYHBwD2p2D3AkDQ9GJgQObOAdpIOAk4EHo+IVRHxDvA4MCgt2zciZkVEABPytmVmZiVQ1msukqqA3sBzQIeIeDMt+jvQIU13BF7PW60mxbYWrykQL1T/BZLmSJqzYsWKHdoXMzPbrGzJRVJb4CHgmxHxXv6ydMYRTd2GiBgfEf0iol9lZWVTV2dmttsoS3KR1JJcYrk3Ih5O4bdSlxbpfXmKvwEcnLd6pxTbWrxTgbiZmZVIOe4WE3AH8FJE/HveoilA7R1fw4DJefFz011jRwGrU/fZNGCgpPbpQv5AYFpa9p6ko1Jd5+Zty8zMSqAcw78cDZwDLJQ0P8W+C9wAPCDpPOBV4LS0bCpwMrAE+BAYARARqyT9EJidyl0bEavS9MXA3UAb4PfpZWZmJVLy5BIRTwMN/e7khALlAxjVwLbuBO4sEJ8DdN+BZpqZ2Q7wL/TNzCxzTi5mZpY5JxczM8uck4uZmWXODwuzovkBWWZWLJ+5mJlZ5pxczMwsc04uZmaWOScXMzPLnJOLmZllzsnFzMwy5+RiZmaZc3IxM7PMObmYmVnmnFzMzCxzTi5mZpY5jy1WhFKNqeXxtMxsV+EzFzMzy5yTi5mZZc7JxczMMufkYmZmmXNyMTOzzDm5mJlZ5pxczMwsc04uZmaWOScXMzPLnJOLmZllzsnFzMwyt8smF0mDJP1N0hJJV5W7PWZmu5NdMrlIqgBuA04CugFnSOpW3laZme0+dsnkAhwBLImIpRGxHpgIDC5zm8zMdhuKiHK3IXOSvgoMiojz0/w5wJERcUm9chcAF6TZzwN/K2lDt3QA8HaZ29Bc+Fhs5mOxmY/FZs3lWBwaEZX1g7v181wiYjwwvtztqCVpTkT0K3c7mgMfi818LDbzsdisuR+LXbVb7A3g4Lz5TilmZmYlsKsml9lAF0mdJbUChgJTytwmM7Pdxi7ZLRYRGyVdAkwDKoA7I2JRmZtVjGbTRdcM+Fhs5mOxmY/FZs36WOySF/TNzKy8dtVuMTMzKyMnFzMzy5yTSwlJWlPuNjQlSSHp/+XNt5C0QtJvG7mdGZL6pempktpl3dbmoP7nQdJwST9L0xdKOncb69eVby4kTZd0Yr3YNyWNy7ieu9Pv2fJja9L7ZyQ9WMQ2yvL/UdImSfPzXlVNWFeVpDPz5o+t/f+Ypr/QVHXvkhf0rWw+ALpLahMRa4F/ZgdvAY+IkzNp2U4mIn5e7jZsp/vJ3Z05LS82FPh2MStLqoiITTvSgIj4b+Cr2yxYPmsjoldjV5LUIiI2NnK1KuBM4L4Cy44F1gDPNLYtxfCZS5lJ6iVplqQFkh6R1F7SgZLmpuU90xnBIWn+/0vaq7yt3qqpwJfS9Bnk/tgAIGlvSXdK+rOkv0ganOJtJE2U9JKkR4A2eessk3RA+gb2Ql78cknXpOkZksZKmpO20V/Sw5IWS/pRCfY5c5KukXR5mu6fPh/zJd2cfxyAz0j6Q9rXm8rU3HwPAl9KPwEgfSv/DPCUpIGSnpU0T9KvJbVNZZZJulHSPOCq9E5a1iV/vhj5nxVJe0l6QNKL6f/Xc7VnxWn5dZKeT/8HO+zozm+vQn8HUnyGpP+QNAe4rKHPgqSKND87Lf9G2vQNwDGp/Ji8+qqAC4ExadkxWe+Tk0v5TQCujIgewELg6ohYDrSWtC9wDDCH3AfkUGB5RHxYvuZu00RgqKTWQA/gubxl3wOejIgjgOOAmyXtDVwEfBgRXYGrgb7bUe/69GvlnwOTgVFAd2C4pP23e2+aVpv87hHg2gbK3QV8I33brf+tvhdwOlANnC7p4Porl1JErAL+TG7QWMidtTwA7A/8X+CLEdGH3Gf6W3mrroyIPhFxHbBaUu03+xHk9r+Qm+sdv0IuBt6JiG7Av/HJz9bewKyI6AnMBEY2Zl93QP6/+yMptsXfgbzyrSKiX0T8mIY/C+cBqyOiP9AfGCmpM3AV8FRE9IqIsbWFI2IZuf8rY9Oyp7LeSXeLlZGk/YB2EfHHFLoH+HWafgY4GhgAXA8MAgRk/iHIUkQsSN+KziB3FpNvIHBK7TdyoDVwCLl9vDVv/QXbUXXtj2QXAosi4k0ASUvJjdawcju22dQ+0T0iaTjwieE8lLvetE9EPJtC9wH/klfkiYhYncq+CBwKvN6UjS5CbdfY5PR+HnAUuRHK/yQJoBXwbN46k/KmfwmMkPQtconziAbquSIi6q6tqPA1lP8N/AQgIl6o99laD9ReD5xLrhu3FOr/u2/t7wCkY7ONz8JAoIc2X4faD+hCbh/Lwsml+ZpJ7qzlUHL/Sa8EAvhdORtVpCnALeT6dPPPGgR8JSI+MUBo+mOzLRv55Jl263rLP0rvH+dN187vyp/z/H3dRPPY18nAWEl9gL0iYq6kLwOPR8QZDazzQd70Q+S+uT8JzI2IpvpisCE2/9CvuRy7Qj7YdhEEjI6IaZ8ISsc2SYuK4G6xMkrfON/J6+88B6j99vIUcDawOCI+BlYBJwNPl7yhjXcn8IOIWFgvPg0YrZRNJPVO8ZnkLjoiqTu57rT63gIOlLS/pD355Lf3XVZEvAu8L+nIFBpazvYUIyLWANPJfQ5qr7nNAo6W9I9Qd/3tcw2sv47cZ2UcDXeJFetPwGmpzm7kug+blW38Hcgvt7XPwjTgIkktASR9LnU5vw/s00DVW1u2w5xcSmsvSTV5r28Bw8j1HS8g139+LdT1iYrcH17IJZV3I+KdMrS7USKiJiJuLbDoh0BLYIGkRWkecn9E2kp6idz+zy2wzQ1p2Z+Bx4G/NkXbm6nzgF+k6wp7A6vL3J5i3A/0TO9ExApgOHB/+qw/Cxy2lfXvJXfW+dgOtuM/gcrUZfgjYBHN8/gV/DtQQEOfhV8CLwLz0kX+28mdiS0ANqWbFsbU29ZvgFOb6oK+h38xa+YktU1nAyj3yO6DIuKyMjerSaXrcvtFxL/t4HYqgJYRsU7SPwD/BXw+PURwp7MzfRaaax+jmW32JUnfIff/9VVyZwC7rHQH1T8Ax2ewub2A6am7SMDFO2tiSXaaz4LPXMzMLHO+5mJmZplzcjEzs8w5uZiZWeacXMzMLHNOLmYZUm54+e0aWDQN4HhABm2oe2RBEWU/MSS7WVacXMwaSTkN/d/5JrnbX3cWVaTREcyy5ORiVoT0Df9vkiYALwB3KDfE/yJJP0hlLiU3vPx0SdNTrOAw81vxbUkLlXssQe1QKZ94MFb+AI2Srkzln5d0Q70275HW/ZGKHJJd0uGp7vmpXJcdPHS2m/KPKM2K1wUYFhGzJH0qIlalX4A/IalHRNyahvQ5LiLeTl1ctcPMfyDpSnLDzDc0tAfkhk2vVu4plP/BVsZQk3QSMBg4MiI+lPSpvMUtyA2h8kJEXCfpgrTt/mlstj9JeozckOyXR8S/pG3+FPhJRNyr3DNZKrbrSNluz8nFrHivRsSsNH1a+oPdAjiI3HDy9R8VsK1h5gu5P+997NYKAl8E7qp9vk96lkqt24EH0vNRoPgh2Z8FviepE2TSm1oAAAFoSURBVPBwRCzeRhvMCnK3mFnxPgBQ7iFMlwMnpIc7/Y4tHwEAueFGHk8PY+oVEd0i4rxt1BEFpuseN5Cu9bQqoq3PAMcp99C22raMzmtL54jYYlDIiLgPOAVYC0yVlMUQLLYbcnIxa7x9ySWa1co9GvekvGX5w5gXPcx8ntPz3mvPcpax+QmKp5AbWRpyo0OPqL07rV632B3kHtb2gKQWFDkku6TPAkvTqNaTKfz4A7NtcreYWSNFxPOS/kJu2P/XyT0zpNZ44A+S/jsijlPu6ZL3p+sckLsG8/JWNt8+Dbv+EbmneQL8Apgs6XngD6QzqIj4g3KPA54jaT25ZPLdvHb+u3JPOfwVcBa5O8PmKddHtwIYQt6Q7MDdwJ7AOZI2AH8n9xRUs0bzwJVmZpY5d4uZmVnm3C1mVmLpeSWd64WvrP/8c7OdmbvFzMwsc+4WMzOzzDm5mJlZ5pxczMwsc04uZmaWuf8BANOyYkyKq2UAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "elFQf2BjCuQG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 846
        },
        "outputId": "fccb462b-852b-42a5-f4a1-734f87f12814"
      },
      "source": [
        "loan_int_df[['loan_status','rate_buckets','fico_buckets']].groupby(['fico_buckets','rate_buckets']).agg({'loan_status':'count'})"
      ],
      "execution_count": 25,
      "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></th>\n",
              "      <th>loan_status</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fico_buckets</th>\n",
              "      <th>rate_buckets</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">High Risky</th>\n",
              "      <th>Low</th>\n",
              "      <td>4554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Medium</th>\n",
              "      <td>8894</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>High</th>\n",
              "      <td>15166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Very High</th>\n",
              "      <td>18681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ForgetIt</th>\n",
              "      <td>24407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">Risky</th>\n",
              "      <th>Low</th>\n",
              "      <td>6751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Medium</th>\n",
              "      <td>10841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>High</th>\n",
              "      <td>17419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Very High</th>\n",
              "      <td>18104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ForgetIt</th>\n",
              "      <td>21659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">Low Risk</th>\n",
              "      <th>Low</th>\n",
              "      <td>30899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Medium</th>\n",
              "      <td>40479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>High</th>\n",
              "      <td>48580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Very High</th>\n",
              "      <td>45259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ForgetIt</th>\n",
              "      <td>43058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">Good</th>\n",
              "      <th>Low</th>\n",
              "      <td>55376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Medium</th>\n",
              "      <td>39973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>High</th>\n",
              "      <td>33904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Very High</th>\n",
              "      <td>26420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ForgetIt</th>\n",
              "      <td>18493</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">Excellent</th>\n",
              "      <th>Low</th>\n",
              "      <td>23427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Medium</th>\n",
              "      <td>8301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>High</th>\n",
              "      <td>5446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Very High</th>\n",
              "      <td>3429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ForgetIt</th>\n",
              "      <td>1974</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           loan_status\n",
              "fico_buckets rate_buckets             \n",
              "High Risky   Low                  4554\n",
              "             Medium               8894\n",
              "             High                15166\n",
              "             Very High           18681\n",
              "             ForgetIt            24407\n",
              "Risky        Low                  6751\n",
              "             Medium              10841\n",
              "             High                17419\n",
              "             Very High           18104\n",
              "             ForgetIt            21659\n",
              "Low Risk     Low                 30899\n",
              "             Medium              40479\n",
              "             High                48580\n",
              "             Very High           45259\n",
              "             ForgetIt            43058\n",
              "Good         Low                 55376\n",
              "             Medium              39973\n",
              "             High                33904\n",
              "             Very High           26420\n",
              "             ForgetIt            18493\n",
              "Excellent    Low                 23427\n",
              "             Medium               8301\n",
              "             High                 5446\n",
              "             Very High            3429\n",
              "             ForgetIt             1974"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yjw2Vs8ACuCq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        },
        "outputId": "12122fab-9d81-4ba8-e01a-52c4280944fc"
      },
      "source": [
        "pd.crosstab(index=loan_int_df['fico_buckets'],columns=loan_int_df['rate_buckets'], margins=True)"
      ],
      "execution_count": 26,
      "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>rate_buckets</th>\n",
              "      <th>Low</th>\n",
              "      <th>Medium</th>\n",
              "      <th>High</th>\n",
              "      <th>Very High</th>\n",
              "      <th>ForgetIt</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fico_buckets</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>High Risky</th>\n",
              "      <td>4554</td>\n",
              "      <td>8894</td>\n",
              "      <td>15166</td>\n",
              "      <td>18681</td>\n",
              "      <td>24407</td>\n",
              "      <td>71702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Risky</th>\n",
              "      <td>6751</td>\n",
              "      <td>10841</td>\n",
              "      <td>17419</td>\n",
              "      <td>18104</td>\n",
              "      <td>21659</td>\n",
              "      <td>74774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Low Risk</th>\n",
              "      <td>30899</td>\n",
              "      <td>40479</td>\n",
              "      <td>48580</td>\n",
              "      <td>45259</td>\n",
              "      <td>43058</td>\n",
              "      <td>208275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Good</th>\n",
              "      <td>55376</td>\n",
              "      <td>39973</td>\n",
              "      <td>33904</td>\n",
              "      <td>26420</td>\n",
              "      <td>18493</td>\n",
              "      <td>174166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Excellent</th>\n",
              "      <td>23427</td>\n",
              "      <td>8301</td>\n",
              "      <td>5446</td>\n",
              "      <td>3429</td>\n",
              "      <td>1974</td>\n",
              "      <td>42577</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <td>121007</td>\n",
              "      <td>108488</td>\n",
              "      <td>120515</td>\n",
              "      <td>111893</td>\n",
              "      <td>109591</td>\n",
              "      <td>571494</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "rate_buckets     Low  Medium    High  Very High  ForgetIt     All\n",
              "fico_buckets                                                     \n",
              "High Risky      4554    8894   15166      18681     24407   71702\n",
              "Risky           6751   10841   17419      18104     21659   74774\n",
              "Low Risk       30899   40479   48580      45259     43058  208275\n",
              "Good           55376   39973   33904      26420     18493  174166\n",
              "Excellent      23427    8301    5446       3429      1974   42577\n",
              "All           121007  108488  120515     111893    109591  571494"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e9gh2_EboqUr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "5e8400b5-a013-4231-8df2-a42af2b8641e"
      },
      "source": [
        "pd.crosstab(index=loan_int_df['fico_buckets'],columns=loan_int_df['rate_buckets']).apply(lambda x: x/x.sum(), axis=1)"
      ],
      "execution_count": 27,
      "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>rate_buckets</th>\n",
              "      <th>Low</th>\n",
              "      <th>Medium</th>\n",
              "      <th>High</th>\n",
              "      <th>Very High</th>\n",
              "      <th>ForgetIt</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fico_buckets</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>High Risky</th>\n",
              "      <td>0.063513</td>\n",
              "      <td>0.124041</td>\n",
              "      <td>0.211514</td>\n",
              "      <td>0.260537</td>\n",
              "      <td>0.340395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Risky</th>\n",
              "      <td>0.090285</td>\n",
              "      <td>0.144984</td>\n",
              "      <td>0.232955</td>\n",
              "      <td>0.242116</td>\n",
              "      <td>0.289660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Low Risk</th>\n",
              "      <td>0.148357</td>\n",
              "      <td>0.194354</td>\n",
              "      <td>0.233249</td>\n",
              "      <td>0.217304</td>\n",
              "      <td>0.206736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Good</th>\n",
              "      <td>0.317950</td>\n",
              "      <td>0.229511</td>\n",
              "      <td>0.194665</td>\n",
              "      <td>0.151694</td>\n",
              "      <td>0.106180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Excellent</th>\n",
              "      <td>0.550227</td>\n",
              "      <td>0.194964</td>\n",
              "      <td>0.127909</td>\n",
              "      <td>0.080536</td>\n",
              "      <td>0.046363</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "rate_buckets       Low    Medium      High  Very High  ForgetIt\n",
              "fico_buckets                                                   \n",
              "High Risky    0.063513  0.124041  0.211514   0.260537  0.340395\n",
              "Risky         0.090285  0.144984  0.232955   0.242116  0.289660\n",
              "Low Risk      0.148357  0.194354  0.233249   0.217304  0.206736\n",
              "Good          0.317950  0.229511  0.194665   0.151694  0.106180\n",
              "Excellent     0.550227  0.194964  0.127909   0.080536  0.046363"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JHZFLqZrCpVq"
      },
      "source": [
        "fico_rate_ct=pd.crosstab(index=loan_int_df['fico_buckets'],columns=loan_int_df['rate_buckets'])"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ohuhrOrL6V6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "outputId": "07f757dc-0381-45e0-cdd2-a32d734206aa"
      },
      "source": [
        "fico_rate_ct.plot.bar()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fceb161fa50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "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": "mEw9PSF9E4Ez"
      },
      "source": [
        "from scipy import stats\n",
        "(chi2, p, dof,_) = stats.chi2_contingency([fico_rate_ct.iloc[0].values,fico_rate_ct.iloc[1].values, fico_rate_ct.iloc[2].values, fico_rate_ct.iloc[3].values, fico_rate_ct.iloc[4].values])"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DQNwYh7OE38K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a5c43d2-1d4d-4ffb-ed51-8431062cf241"
      },
      "source": [
        "print (\"chi2     : \" ,chi2)\n",
        "print (\"p-value  : \" ,p)\n",
        "print (\"Degree for Freedom : \" ,dof)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "chi2     :  86145.63053225547\n",
            "p-value  :  0.0\n",
            "Degree for Freedom :  16\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cXXgG2tYEJBV",
        "outputId": "faa0730c-4c1a-4fc6-ac76-deaa521e6c17"
      },
      "source": [
        "loan_int_df.isna().any().sum()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "152"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UJ1lyhiq6TgF"
      },
      "source": [
        "loan_int_df['last_fico_range_low'] = loan_int_df['last_fico_range_low'].fillna(method='bfill')"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kIz1dp916ewb"
      },
      "source": [
        "import math\n",
        "loan_int_df['fico_score']=loan_int_df['last_fico_range_low'].apply(lambda x: int(math.ceil(int(x)/10))*10)"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EsJ8OKUz9ctf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "outputId": "7494d94d-ee53-447a-b154-fe7222ee2d1d"
      },
      "source": [
        "loan_int_df.head()"
      ],
      "execution_count": 45,
      "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>id</th>\n",
              "      <th>member_id</th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>funded_amnt</th>\n",
              "      <th>funded_amnt_inv</th>\n",
              "      <th>term</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>grade</th>\n",
              "      <th>sub_grade</th>\n",
              "      <th>emp_title</th>\n",
              "      <th>emp_length</th>\n",
              "      <th>home_ownership</th>\n",
              "      <th>annual_inc</th>\n",
              "      <th>verification_status</th>\n",
              "      <th>issue_d</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>pymnt_plan</th>\n",
              "      <th>url</th>\n",
              "      <th>desc</th>\n",
              "      <th>purpose</th>\n",
              "      <th>title</th>\n",
              "      <th>zip_code</th>\n",
              "      <th>addr_state</th>\n",
              "      <th>dti</th>\n",
              "      <th>delinq_2yrs</th>\n",
              "      <th>earliest_cr_line</th>\n",
              "      <th>fico_range_low</th>\n",
              "      <th>fico_range_high</th>\n",
              "      <th>inq_last_6mths</th>\n",
              "      <th>mths_since_last_delinq</th>\n",
              "      <th>mths_since_last_record</th>\n",
              "      <th>open_acc</th>\n",
              "      <th>pub_rec</th>\n",
              "      <th>revol_bal</th>\n",
              "      <th>revol_util</th>\n",
              "      <th>total_acc</th>\n",
              "      <th>initial_list_status</th>\n",
              "      <th>out_prncp</th>\n",
              "      <th>out_prncp_inv</th>\n",
              "      <th>...</th>\n",
              "      <th>total_il_high_credit_limit</th>\n",
              "      <th>revol_bal_joint</th>\n",
              "      <th>sec_app_fico_range_low</th>\n",
              "      <th>sec_app_fico_range_high</th>\n",
              "      <th>sec_app_earliest_cr_line</th>\n",
              "      <th>sec_app_inq_last_6mths</th>\n",
              "      <th>sec_app_mort_acc</th>\n",
              "      <th>sec_app_open_acc</th>\n",
              "      <th>sec_app_revol_util</th>\n",
              "      <th>sec_app_open_act_il</th>\n",
              "      <th>sec_app_num_rev_accts</th>\n",
              "      <th>sec_app_chargeoff_within_12_mths</th>\n",
              "      <th>sec_app_collections_12_mths_ex_med</th>\n",
              "      <th>sec_app_mths_since_last_major_derog</th>\n",
              "      <th>hardship_flag</th>\n",
              "      <th>hardship_type</th>\n",
              "      <th>hardship_reason</th>\n",
              "      <th>hardship_status</th>\n",
              "      <th>deferral_term</th>\n",
              "      <th>hardship_amount</th>\n",
              "      <th>hardship_start_date</th>\n",
              "      <th>hardship_end_date</th>\n",
              "      <th>payment_plan_start_date</th>\n",
              "      <th>hardship_length</th>\n",
              "      <th>hardship_dpd</th>\n",
              "      <th>hardship_loan_status</th>\n",
              "      <th>orig_projected_additional_accrued_interest</th>\n",
              "      <th>hardship_payoff_balance_amount</th>\n",
              "      <th>hardship_last_payment_amount</th>\n",
              "      <th>disbursement_method</th>\n",
              "      <th>debt_settlement_flag</th>\n",
              "      <th>debt_settlement_flag_date</th>\n",
              "      <th>settlement_status</th>\n",
              "      <th>settlement_date</th>\n",
              "      <th>settlement_amount</th>\n",
              "      <th>settlement_percentage</th>\n",
              "      <th>settlement_term</th>\n",
              "      <th>fico_buckets</th>\n",
              "      <th>rate_buckets</th>\n",
              "      <th>fico_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>68407277</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3600.0</td>\n",
              "      <td>3600.0</td>\n",
              "      <td>3600.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>13.99</td>\n",
              "      <td>123.03</td>\n",
              "      <td>C</td>\n",
              "      <td>C4</td>\n",
              "      <td>leadman</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>55000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Dec-2015</td>\n",
              "      <td>Good</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>190xx</td>\n",
              "      <td>PA</td>\n",
              "      <td>5.91</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Aug-2003</td>\n",
              "      <td>675.0</td>\n",
              "      <td>679.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2765.0</td>\n",
              "      <td>29.7</td>\n",
              "      <td>13.0</td>\n",
              "      <td>w</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>13734.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cash</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>High Risky</td>\n",
              "      <td>Very High</td>\n",
              "      <td>560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>68355089</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24700.0</td>\n",
              "      <td>24700.0</td>\n",
              "      <td>24700.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.99</td>\n",
              "      <td>820.28</td>\n",
              "      <td>C</td>\n",
              "      <td>C1</td>\n",
              "      <td>Engineer</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>65000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Dec-2015</td>\n",
              "      <td>Good</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>small_business</td>\n",
              "      <td>Business</td>\n",
              "      <td>577xx</td>\n",
              "      <td>SD</td>\n",
              "      <td>16.06</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Dec-1999</td>\n",
              "      <td>715.0</td>\n",
              "      <td>719.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21470.0</td>\n",
              "      <td>19.2</td>\n",
              "      <td>38.0</td>\n",
              "      <td>w</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>24667.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cash</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Low Risk</td>\n",
              "      <td>High</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>68341763</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>10.78</td>\n",
              "      <td>432.66</td>\n",
              "      <td>B</td>\n",
              "      <td>B4</td>\n",
              "      <td>truck driver</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>63000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Dec-2015</td>\n",
              "      <td>Good</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>home_improvement</td>\n",
              "      <td>NaN</td>\n",
              "      <td>605xx</td>\n",
              "      <td>IL</td>\n",
              "      <td>10.78</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Aug-2000</td>\n",
              "      <td>695.0</td>\n",
              "      <td>699.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7869.0</td>\n",
              "      <td>56.2</td>\n",
              "      <td>18.0</td>\n",
              "      <td>w</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>14877.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cash</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Low Risk</td>\n",
              "      <td>Medium</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>68476807</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10400.0</td>\n",
              "      <td>10400.0</td>\n",
              "      <td>10400.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>22.45</td>\n",
              "      <td>289.91</td>\n",
              "      <td>F</td>\n",
              "      <td>F1</td>\n",
              "      <td>Contract Specialist</td>\n",
              "      <td>3 years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>104433.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-2015</td>\n",
              "      <td>Good</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>major_purchase</td>\n",
              "      <td>Major purchase</td>\n",
              "      <td>174xx</td>\n",
              "      <td>PA</td>\n",
              "      <td>25.37</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Jun-1998</td>\n",
              "      <td>695.0</td>\n",
              "      <td>699.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21929.0</td>\n",
              "      <td>64.5</td>\n",
              "      <td>35.0</td>\n",
              "      <td>w</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>88097.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cash</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Low Risk</td>\n",
              "      <td>ForgetIt</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>68426831</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11950.0</td>\n",
              "      <td>11950.0</td>\n",
              "      <td>11950.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>13.44</td>\n",
              "      <td>405.18</td>\n",
              "      <td>C</td>\n",
              "      <td>C3</td>\n",
              "      <td>Veterinary Tecnician</td>\n",
              "      <td>4 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>34000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-2015</td>\n",
              "      <td>Good</td>\n",
              "      <td>n</td>\n",
              "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>300xx</td>\n",
              "      <td>GA</td>\n",
              "      <td>10.20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Oct-1987</td>\n",
              "      <td>690.0</td>\n",
              "      <td>694.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8822.0</td>\n",
              "      <td>68.4</td>\n",
              "      <td>6.0</td>\n",
              "      <td>w</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cash</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Good</td>\n",
              "      <td>Very High</td>\n",
              "      <td>760</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 154 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         id  member_id  loan_amnt  ...  fico_buckets  rate_buckets fico_score\n",
              "0  68407277        NaN     3600.0  ...    High Risky     Very High        560\n",
              "1  68355089        NaN    24700.0  ...      Low Risk          High        700\n",
              "2  68341763        NaN    20000.0  ...      Low Risk        Medium        700\n",
              "4  68476807        NaN    10400.0  ...      Low Risk      ForgetIt        700\n",
              "5  68426831        NaN    11950.0  ...          Good     Very High        760\n",
              "\n",
              "[5 rows x 154 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0DDN2s-V9co_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a7771d0-29a9-49ce-eb8d-f9a1505b99c2"
      },
      "source": [
        "loan_int_df[loan_int_df['loan_status']=='Default']['fico_score'].value_counts().sort_index()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "500    2997\n",
              "510    6767\n",
              "520    7813\n",
              "530    8672\n",
              "540    9475\n",
              "550    9507\n",
              "560    9099\n",
              "570    8503\n",
              "580    7542\n",
              "590    6448\n",
              "600    5493\n",
              "610    4874\n",
              "620    4184\n",
              "630    3481\n",
              "640    2740\n",
              "650    2165\n",
              "660    3828\n",
              "670    4919\n",
              "680    3658\n",
              "690    2699\n",
              "700    1919\n",
              "710    1435\n",
              "720     956\n",
              "730     568\n",
              "740     420\n",
              "750     283\n",
              "760     219\n",
              "770     161\n",
              "780     129\n",
              "790      88\n",
              "800      70\n",
              "810      50\n",
              "820      25\n",
              "830      13\n",
              "840       6\n",
              "850       3\n",
              "Name: fico_score, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vatFtDxE9cgF"
      },
      "source": [
        "default_df=pd.DataFrame(loan_int_df[loan_int_df['loan_status']=='Default']['fico_score'].value_counts())"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DVLwVLd2-A1f"
      },
      "source": [
        "default_df=default_df.reset_index()"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pibmmzOYESm_"
      },
      "source": [
        "default_df.columns = ['fico_score','default_total']"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aiXgskHxE9Ch",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4fd2b5fa-f8ca-468d-eb88-956050f4786b"
      },
      "source": [
        "default_df"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fico_score</th>\n",
              "      <th>default_total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>8672</td>\n",
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              "      <td>8503</td>\n",
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              "      <td>510</td>\n",
              "      <td>6767</td>\n",
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              "      <td>6448</td>\n",
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              "      <th>9</th>\n",
              "      <td>600</td>\n",
              "      <td>5493</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>670</td>\n",
              "      <td>4919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>610</td>\n",
              "      <td>4874</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>620</td>\n",
              "      <td>4184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>660</td>\n",
              "      <td>3828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>680</td>\n",
              "      <td>3658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>630</td>\n",
              "      <td>3481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>500</td>\n",
              "      <td>2997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>640</td>\n",
              "      <td>2740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>690</td>\n",
              "      <td>2699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>650</td>\n",
              "      <td>2165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>700</td>\n",
              "      <td>1919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>710</td>\n",
              "      <td>1435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>720</td>\n",
              "      <td>956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>730</td>\n",
              "      <td>568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>740</td>\n",
              "      <td>420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>750</td>\n",
              "      <td>283</td>\n",
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              "      <td>760</td>\n",
              "      <td>219</td>\n",
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              "      <th>27</th>\n",
              "      <td>770</td>\n",
              "      <td>161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>780</td>\n",
              "      <td>129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>790</td>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>800</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>810</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>820</td>\n",
              "      <td>25</td>\n",
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              "      <th>33</th>\n",
              "      <td>830</td>\n",
              "      <td>13</td>\n",
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              "      <td>850</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    fico_score  default_total\n",
              "0          550           9507\n",
              "1          540           9475\n",
              "2          560           9099\n",
              "3          530           8672\n",
              "4          570           8503\n",
              "5          520           7813\n",
              "6          580           7542\n",
              "7          510           6767\n",
              "8          590           6448\n",
              "9          600           5493\n",
              "10         670           4919\n",
              "11         610           4874\n",
              "12         620           4184\n",
              "13         660           3828\n",
              "14         680           3658\n",
              "15         630           3481\n",
              "16         500           2997\n",
              "17         640           2740\n",
              "18         690           2699\n",
              "19         650           2165\n",
              "20         700           1919\n",
              "21         710           1435\n",
              "22         720            956\n",
              "23         730            568\n",
              "24         740            420\n",
              "25         750            283\n",
              "26         760            219\n",
              "27         770            161\n",
              "28         780            129\n",
              "29         790             88\n",
              "30         800             70\n",
              "31         810             50\n",
              "32         820             25\n",
              "33         830             13\n",
              "34         840              6\n",
              "35         850              3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hPVWi6O5sv9v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "83f0d5fc-af84-4038-e285-cb291f316b15"
      },
      "source": [
        "default_df[['fico_score','default_total']].corr()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fico_score</th>\n",
              "      <th>default_total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>fico_score</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.887156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>default_total</th>\n",
              "      <td>-0.887156</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "               fico_score  default_total\n",
              "fico_score       1.000000      -0.887156\n",
              "default_total   -0.887156       1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "8KU_OG0PnBCF",
        "colab": {
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          "height": 299
        },
        "outputId": "dedd1ff9-d3c0-4e79-d542-56cc312affee"
      },
      "source": [
        "sns.regplot(x='fico_score',y='default_total',data=default_df) "
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcf5ef53750>"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JVtDbMfzoiTg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "a7c69af3-4ee5-4668-d238-a72c2421d5d0"
      },
      "source": [
        "default_df[['fico_score','default_total']].corr('spearman')"
      ],
      "execution_count": 53,
      "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>fico_score</th>\n",
              "      <th>default_total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>fico_score</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.939511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>default_total</th>\n",
              "      <td>-0.939511</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               fico_score  default_total\n",
              "fico_score       1.000000      -0.939511\n",
              "default_total   -0.939511       1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fpQz11T-jZw0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "386ad7a6-5697-4906-f6a2-eb489fe2e14b"
      },
      "source": [
        "!pip install pwlf\n",
        "import pwlf"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pwlf\n",
            "  Downloading https://files.pythonhosted.org/packages/25/db/00b3067ce44edbb097571e336f27398ed0187408a0419ac3f947d6ae8c53/pwlf-2.0.4.tar.gz\n",
            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.7/dist-packages (from pwlf) (1.19.5)\n",
            "Requirement already satisfied: scipy>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from pwlf) (1.4.1)\n",
            "Collecting pyDOE>=0.3.8\n",
            "  Downloading https://files.pythonhosted.org/packages/bc/ac/91fe4c039e2744466621343d3b8af4a485193ed0aab53af5b1db03be0989/pyDOE-0.3.8.zip\n",
            "Building wheels for collected packages: pwlf, pyDOE\n",
            "  Building wheel for pwlf (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pwlf: filename=pwlf-2.0.4-cp37-none-any.whl size=16143 sha256=4551e95423d1e596301d2617515bc3a1bae9c6486810a2e0094f8b168bb4aa9b\n",
            "  Stored in directory: /root/.cache/pip/wheels/aa/b4/ef/2cb67a24a71a0d9dd93a5ba02f46106e9fa34dd2e3d9d8b4a0\n",
            "  Building wheel for pyDOE (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyDOE: filename=pyDOE-0.3.8-cp37-none-any.whl size=18178 sha256=21198da8b6706a724ad5400ae18beba209dd01085da8237632e91d1d4eb4bf92\n",
            "  Stored in directory: /root/.cache/pip/wheels/7c/c8/58/a6493bd415e8ba5735082b5e0c096d7c1f2933077a8ce34544\n",
            "Successfully built pwlf pyDOE\n",
            "Installing collected packages: pyDOE, pwlf\n",
            "Successfully installed pwlf-2.0.4 pyDOE-0.3.8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2EloHNkEujWc"
      },
      "source": [
        "pwlf = pwlf.PiecewiseLinFit(default_df['fico_score'], default_df['default_total'])"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ikbTQkRkvO1K"
      },
      "source": [
        "pwise_model = pwlf.fit(4)"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IK8_WFMKvUEq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "436acbf0-976b-49d9-e8ef-37a39c33bd29"
      },
      "source": [
        "pwlf.fit_breaks"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([500.        , 536.46779027, 619.99999996, 758.94519029,\n",
              "       850.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FCuu8q-wvbc1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9c40f5aa-12a3-41c6-9589-4569aded705c"
      },
      "source": [
        "pwlf.slopes"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([180.64934368, -73.41043634, -29.67871404,  -2.33301867])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "smK1TgHkz_jY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "69eef718-0c82-4e5e-d7cd-c38d89cd3ea3"
      },
      "source": [
        "pwlf.intercepts"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-86472.51517657,  49822.37360737,  22708.70578329,   1954.82180264])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uRhm5XNf0IiI"
      },
      "source": [
        "yHat_piecewise = pwlf.predict(default_df['fico_score'])"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "93x1vOd11SuD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "a4286d0c-4bb0-482a-fed0-a21879bf10c8"
      },
      "source": [
        "plt.figure()\n",
        "plt.plot(default_df['fico_score'], default_df['default_total'], 'o')\n",
        "plt.plot(default_df['fico_score'], yHat_piecewise, '-')\n",
        "plt.show()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I-hf05J49HFC"
      },
      "source": [
        "------------------------------------------"
      ]
    }
  ]
}