credit_risk_resampling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Credit Risk Resampling Techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the CSV and Perform Basic Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\n",
    "    \"loan_amnt\", \"int_rate\", \"installment\", \"home_ownership\",\n",
    "    \"annual_inc\", \"verification_status\", \"issue_d\", \"loan_status\",\n",
    "    \"pymnt_plan\", \"dti\", \"delinq_2yrs\", \"inq_last_6mths\",\n",
    "    \"open_acc\", \"pub_rec\", \"revol_bal\", \"total_acc\",\n",
    "    \"initial_list_status\", \"out_prncp\", \"out_prncp_inv\", \"total_pymnt\",\n",
    "    \"total_pymnt_inv\", \"total_rec_prncp\", \"total_rec_int\", \"total_rec_late_fee\",\n",
    "    \"recoveries\", \"collection_recovery_fee\", \"last_pymnt_amnt\", \"next_pymnt_d\",\n",
    "    \"collections_12_mths_ex_med\", \"policy_code\", \"application_type\", \"acc_now_delinq\",\n",
    "    \"tot_coll_amt\", \"tot_cur_bal\", \"open_acc_6m\", \"open_act_il\",\n",
    "    \"open_il_12m\", \"open_il_24m\", \"mths_since_rcnt_il\", \"total_bal_il\",\n",
    "    \"il_util\", \"open_rv_12m\", \"open_rv_24m\", \"max_bal_bc\",\n",
    "    \"all_util\", \"total_rev_hi_lim\", \"inq_fi\", \"total_cu_tl\",\n",
    "    \"inq_last_12m\", \"acc_open_past_24mths\", \"avg_cur_bal\", \"bc_open_to_buy\",\n",
    "    \"bc_util\", \"chargeoff_within_12_mths\", \"delinq_amnt\", \"mo_sin_old_il_acct\",\n",
    "    \"mo_sin_old_rev_tl_op\", \"mo_sin_rcnt_rev_tl_op\", \"mo_sin_rcnt_tl\", \"mort_acc\",\n",
    "    \"mths_since_recent_bc\", \"mths_since_recent_inq\", \"num_accts_ever_120_pd\", \"num_actv_bc_tl\",\n",
    "    \"num_actv_rev_tl\", \"num_bc_sats\", \"num_bc_tl\", \"num_il_tl\",\n",
    "    \"num_op_rev_tl\", \"num_rev_accts\", \"num_rev_tl_bal_gt_0\",\n",
    "    \"num_sats\", \"num_tl_120dpd_2m\", \"num_tl_30dpd\", \"num_tl_90g_dpd_24m\",\n",
    "    \"num_tl_op_past_12m\", \"pct_tl_nvr_dlq\", \"percent_bc_gt_75\", \"pub_rec_bankruptcies\",\n",
    "    \"tax_liens\", \"tot_hi_cred_lim\", \"total_bal_ex_mort\", \"total_bc_limit\",\n",
    "    \"total_il_high_credit_limit\", \"hardship_flag\", \"debt_settlement_flag\"\n",
    "]\n",
    "\n",
    "target = [\"loan_status\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>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>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>next_pymnt_d</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>policy_code</th>\n",
       "      <th>application_type</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <th>tot_cur_bal</th>\n",
       "      <th>open_acc_6m</th>\n",
       "      <th>open_act_il</th>\n",
       "      <th>open_il_12m</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>mths_since_rcnt_il</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>max_bal_bc</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>total_cu_tl</th>\n",
       "      <th>inq_last_12m</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>avg_cur_bal</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>bc_util</th>\n",
       "      <th>chargeoff_within_12_mths</th>\n",
       "      <th>delinq_amnt</th>\n",
       "      <th>mo_sin_old_il_acct</th>\n",
       "      <th>mo_sin_old_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_tl</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>mths_since_recent_bc</th>\n",
       "      <th>mths_since_recent_inq</th>\n",
       "      <th>num_accts_ever_120_pd</th>\n",
       "      <th>num_actv_bc_tl</th>\n",
       "      <th>num_actv_rev_tl</th>\n",
       "      <th>num_bc_sats</th>\n",
       "      <th>num_bc_tl</th>\n",
       "      <th>num_il_tl</th>\n",
       "      <th>num_op_rev_tl</th>\n",
       "      <th>num_rev_accts</th>\n",
       "      <th>num_rev_tl_bal_gt_0</th>\n",
       "      <th>num_sats</th>\n",
       "      <th>num_tl_120dpd_2m</th>\n",
       "      <th>num_tl_30dpd</th>\n",
       "      <th>num_tl_90g_dpd_24m</th>\n",
       "      <th>num_tl_op_past_12m</th>\n",
       "      <th>pct_tl_nvr_dlq</th>\n",
       "      <th>percent_bc_gt_75</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "      <th>tot_hi_cred_lim</th>\n",
       "      <th>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "      <th>total_il_high_credit_limit</th>\n",
       "      <th>hardship_flag</th>\n",
       "      <th>debt_settlement_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10500.0</td>\n",
       "      <td>0.1719</td>\n",
       "      <td>375.35</td>\n",
       "      <td>RENT</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>27.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1609.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>w</td>\n",
       "      <td>10204.87</td>\n",
       "      <td>10204.87</td>\n",
       "      <td>355.29</td>\n",
       "      <td>355.29</td>\n",
       "      <td>295.13</td>\n",
       "      <td>60.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>375.35</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>38199.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>36590.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1553.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4775.0</td>\n",
       "      <td>447.0</td>\n",
       "      <td>77.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>85.7</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65687.0</td>\n",
       "      <td>38199.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>61987.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25000.0</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>929.09</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>20.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18368.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>w</td>\n",
       "      <td>24293.13</td>\n",
       "      <td>24293.13</td>\n",
       "      <td>873.53</td>\n",
       "      <td>873.53</td>\n",
       "      <td>706.87</td>\n",
       "      <td>166.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>929.09</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>228119.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42273.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5197.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>50400.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13419.0</td>\n",
       "      <td>29502.0</td>\n",
       "      <td>28.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>91.2</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>271427.0</td>\n",
       "      <td>60641.0</td>\n",
       "      <td>41200.0</td>\n",
       "      <td>49197.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>529.88</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>56000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>24.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13247.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>w</td>\n",
       "      <td>19636.79</td>\n",
       "      <td>19636.79</td>\n",
       "      <td>485.44</td>\n",
       "      <td>485.44</td>\n",
       "      <td>363.21</td>\n",
       "      <td>122.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>529.88</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45684.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32437.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2810.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5711.0</td>\n",
       "      <td>2839.0</td>\n",
       "      <td>62.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>66.7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60644.0</td>\n",
       "      <td>45684.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>43144.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.1640</td>\n",
       "      <td>353.55</td>\n",
       "      <td>RENT</td>\n",
       "      <td>92000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>31.44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17996.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>w</td>\n",
       "      <td>9719.34</td>\n",
       "      <td>9719.34</td>\n",
       "      <td>335.33</td>\n",
       "      <td>335.33</td>\n",
       "      <td>280.66</td>\n",
       "      <td>54.67</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>353.55</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68784.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>50788.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6360.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6878.0</td>\n",
       "      <td>4899.0</td>\n",
       "      <td>75.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99506.0</td>\n",
       "      <td>68784.0</td>\n",
       "      <td>19700.0</td>\n",
       "      <td>76506.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22000.0</td>\n",
       "      <td>0.1474</td>\n",
       "      <td>520.39</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>18.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9091.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>w</td>\n",
       "      <td>21614.73</td>\n",
       "      <td>21614.73</td>\n",
       "      <td>484.36</td>\n",
       "      <td>484.36</td>\n",
       "      <td>385.27</td>\n",
       "      <td>99.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>520.39</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>177745.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16828.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>43100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13673.0</td>\n",
       "      <td>19699.0</td>\n",
       "      <td>28.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>219750.0</td>\n",
       "      <td>25919.0</td>\n",
       "      <td>27600.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  int_rate  installment home_ownership  annual_inc  \\\n",
       "0    10500.0    0.1719       375.35           RENT     66000.0   \n",
       "1    25000.0    0.2000       929.09       MORTGAGE    105000.0   \n",
       "2    20000.0    0.2000       529.88       MORTGAGE     56000.0   \n",
       "3    10000.0    0.1640       353.55           RENT     92000.0   \n",
       "4    22000.0    0.1474       520.39       MORTGAGE     52000.0   \n",
       "\n",
       "  verification_status   issue_d loan_status pymnt_plan    dti  delinq_2yrs  \\\n",
       "0     Source Verified  Mar-2019    low_risk          n  27.24          0.0   \n",
       "1            Verified  Mar-2019    low_risk          n  20.23          0.0   \n",
       "2            Verified  Mar-2019    low_risk          n  24.26          0.0   \n",
       "3            Verified  Mar-2019    low_risk          n  31.44          0.0   \n",
       "4        Not Verified  Mar-2019    low_risk          n  18.76          0.0   \n",
       "\n",
       "   inq_last_6mths  open_acc  pub_rec  revol_bal  total_acc  \\\n",
       "0             0.0       8.0      0.0     1609.0       14.0   \n",
       "1             0.0      17.0      1.0    18368.0       34.0   \n",
       "2             0.0       8.0      0.0    13247.0       21.0   \n",
       "3             1.0      10.0      1.0    17996.0       23.0   \n",
       "4             1.0      14.0      0.0     9091.0       28.0   \n",
       "\n",
       "  initial_list_status  out_prncp  out_prncp_inv  total_pymnt  total_pymnt_inv  \\\n",
       "0                   w   10204.87       10204.87       355.29           355.29   \n",
       "1                   w   24293.13       24293.13       873.53           873.53   \n",
       "2                   w   19636.79       19636.79       485.44           485.44   \n",
       "3                   w    9719.34        9719.34       335.33           335.33   \n",
       "4                   w   21614.73       21614.73       484.36           484.36   \n",
       "\n",
       "   total_rec_prncp  total_rec_int  total_rec_late_fee  recoveries  \\\n",
       "0           295.13          60.16                 0.0         0.0   \n",
       "1           706.87         166.66                 0.0         0.0   \n",
       "2           363.21         122.23                 0.0         0.0   \n",
       "3           280.66          54.67                 0.0         0.0   \n",
       "4           385.27          99.09                 0.0         0.0   \n",
       "\n",
       "   collection_recovery_fee  last_pymnt_amnt next_pymnt_d  \\\n",
       "0                      0.0           375.35     May-2019   \n",
       "1                      0.0           929.09     May-2019   \n",
       "2                      0.0           529.88     May-2019   \n",
       "3                      0.0           353.55     May-2019   \n",
       "4                      0.0           520.39     May-2019   \n",
       "\n",
       "   collections_12_mths_ex_med  policy_code application_type  acc_now_delinq  \\\n",
       "0                         0.0          1.0       Individual             0.0   \n",
       "1                         0.0          1.0       Individual             0.0   \n",
       "2                         0.0          1.0       Individual             0.0   \n",
       "3                         0.0          1.0       Individual             0.0   \n",
       "4                         0.0          1.0       Individual             0.0   \n",
       "\n",
       "   tot_coll_amt  tot_cur_bal  open_acc_6m  open_act_il  open_il_12m  \\\n",
       "0         178.0      38199.0          3.0          4.0          2.0   \n",
       "1         165.0     228119.0          3.0          3.0          2.0   \n",
       "2           0.0      45684.0          2.0          3.0          2.0   \n",
       "3           0.0      68784.0          0.0          4.0          1.0   \n",
       "4           0.0     177745.0          0.0          1.0          0.0   \n",
       "\n",
       "   open_il_24m  mths_since_rcnt_il  total_bal_il  il_util  open_rv_12m  \\\n",
       "0          4.0                 3.0       36590.0     59.0          1.0   \n",
       "1          3.0                 4.0       42273.0     86.0          4.0   \n",
       "2          3.0                 4.0       32437.0     75.0          2.0   \n",
       "3          3.0                 9.0       50788.0     66.0          0.0   \n",
       "4          2.0                14.0       16828.0     84.0          1.0   \n",
       "\n",
       "   open_rv_24m  max_bal_bc  all_util  total_rev_hi_lim  inq_fi  total_cu_tl  \\\n",
       "0          2.0      1553.0      58.0            3700.0     1.0          0.0   \n",
       "1          8.0      5197.0      61.0           50400.0     4.0          9.0   \n",
       "2          3.0      2810.0      75.0           17500.0     0.0          4.0   \n",
       "3          1.0      6360.0      69.0           23000.0     3.0          5.0   \n",
       "4          1.0      3600.0      41.0           43100.0     2.0          0.0   \n",
       "\n",
       "   inq_last_12m  acc_open_past_24mths  avg_cur_bal  bc_open_to_buy  bc_util  \\\n",
       "0           4.0                   6.0       4775.0           447.0     77.7   \n",
       "1           3.0                  13.0      13419.0         29502.0     28.4   \n",
       "2           1.0                   6.0       5711.0          2839.0     62.1   \n",
       "3           4.0                   4.0       6878.0          4899.0     75.1   \n",
       "4           2.0                   4.0      13673.0         19699.0     28.6   \n",
       "\n",
       "   chargeoff_within_12_mths  delinq_amnt  mo_sin_old_il_acct  \\\n",
       "0                       0.0          0.0                93.0   \n",
       "1                       0.0          0.0                87.0   \n",
       "2                       0.0          0.0               159.0   \n",
       "3                       0.0          0.0               100.0   \n",
       "4                       0.0          0.0                67.0   \n",
       "\n",
       "   mo_sin_old_rev_tl_op  mo_sin_rcnt_rev_tl_op  mo_sin_rcnt_tl  mort_acc  \\\n",
       "0                  96.0                    3.0             3.0       0.0   \n",
       "1                 286.0                    1.0             1.0       2.0   \n",
       "2                 156.0                    7.0             4.0       0.0   \n",
       "3                  89.0                   24.0             9.0       0.0   \n",
       "4                 138.0                   11.0            11.0       4.0   \n",
       "\n",
       "   mths_since_recent_bc  mths_since_recent_inq  num_accts_ever_120_pd  \\\n",
       "0                  20.0                    1.0                    0.0   \n",
       "1                   1.0                   11.0                    3.0   \n",
       "2                   7.0                    7.0                    6.0   \n",
       "3                  34.0                    0.0                    0.0   \n",
       "4                  11.0                    0.0                    0.0   \n",
       "\n",
       "   num_actv_bc_tl  num_actv_rev_tl  num_bc_sats  num_bc_tl  num_il_tl  \\\n",
       "0             1.0              2.0          1.0        1.0       10.0   \n",
       "1             7.0             11.0          8.0       13.0       14.0   \n",
       "2             2.0              5.0          2.0        7.0        8.0   \n",
       "3             4.0              6.0          4.0        4.0       17.0   \n",
       "4             4.0              6.0          7.0       11.0        6.0   \n",
       "\n",
       "   num_op_rev_tl  num_rev_accts  num_rev_tl_bal_gt_0  num_sats  \\\n",
       "0            3.0            3.0                  2.0       8.0   \n",
       "1           13.0           18.0                 11.0      17.0   \n",
       "2            5.0           13.0                  5.0       8.0   \n",
       "3            6.0            6.0                  6.0      10.0   \n",
       "4           12.0           18.0                  6.0      14.0   \n",
       "\n",
       "   num_tl_120dpd_2m  num_tl_30dpd  num_tl_90g_dpd_24m  num_tl_op_past_12m  \\\n",
       "0               0.0           0.0                 0.0                 3.0   \n",
       "1               0.0           0.0                 0.0                 6.0   \n",
       "2               0.0           0.0                 0.0                 4.0   \n",
       "3               0.0           0.0                 0.0                 1.0   \n",
       "4               0.0           0.0                 0.0                 1.0   \n",
       "\n",
       "   pct_tl_nvr_dlq  percent_bc_gt_75  pub_rec_bankruptcies  tax_liens  \\\n",
       "0            85.7             100.0                   0.0        0.0   \n",
       "1            91.2              50.0                   1.0        0.0   \n",
       "2            66.7              50.0                   0.0        0.0   \n",
       "3           100.0              50.0                   1.0        0.0   \n",
       "4           100.0               0.0                   0.0        0.0   \n",
       "\n",
       "   tot_hi_cred_lim  total_bal_ex_mort  total_bc_limit  \\\n",
       "0          65687.0            38199.0          2000.0   \n",
       "1         271427.0            60641.0         41200.0   \n",
       "2          60644.0            45684.0          7500.0   \n",
       "3          99506.0            68784.0         19700.0   \n",
       "4         219750.0            25919.0         27600.0   \n",
       "\n",
       "   total_il_high_credit_limit hardship_flag debt_settlement_flag  \n",
       "0                     61987.0             N                    N  \n",
       "1                     49197.0             N                    N  \n",
       "2                     43144.0             N                    N  \n",
       "3                     76506.0             N                    N  \n",
       "4                     20000.0             N                    N  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data\n",
    "file_path = Path('LoanStats_2019Q1.csv')\n",
    "df = pd.read_csv(file_path, skiprows=1)[:-2]\n",
    "df = df.loc[:, columns].copy()\n",
    "\n",
    "# Drop the null columns where all values are null\n",
    "df = df.dropna(axis='columns', how='all')\n",
    "\n",
    "# Drop the null rows\n",
    "df = df.dropna()\n",
    "\n",
    "# Remove the `Issued` loan status\n",
    "issued_mask = df['loan_status'] != 'Issued'\n",
    "df = df.loc[issued_mask]\n",
    "\n",
    "# convert interest rate to numerical\n",
    "df['int_rate'] = df['int_rate'].str.replace('%', '')\n",
    "df['int_rate'] = df['int_rate'].astype('float') / 100\n",
    "\n",
    "\n",
    "# Convert the target column values to low_risk and high_risk based on their values\n",
    "x = {'Current': 'low_risk'}   \n",
    "df = df.replace(x)\n",
    "\n",
    "x = dict.fromkeys(['Late (31-120 days)', 'Late (16-30 days)', 'Default', 'In Grace Period'], 'high_risk')    \n",
    "df = df.replace(x)\n",
    "\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "\n",
    "# View all columns\n",
    "pd.set_option('max_columns', None)\n",
    "\n",
    "# View dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encode the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['RENT' 'MORTGAGE' 'OWN' 'ANY']\n",
      "['Source Verified' 'Verified' 'Not Verified']\n",
      "['low_risk' 'high_risk']\n",
      "['n']\n",
      "['w' 'f']\n",
      "['Individual' 'Joint App']\n",
      "['N']\n",
      "['N']\n",
      "['Mar-2019' 'Feb-2019' 'Jan-2019']\n",
      "['May-2019' 'Apr-2019']\n"
     ]
    }
   ],
   "source": [
    "# View unique values for text columns.\n",
    "# This may be helpful for interpreting dummy values later on\n",
    "print(df[\"home_ownership\"].unique())\n",
    "print(df[\"verification_status\"].unique())\n",
    "print(df[\"loan_status\"].unique())\n",
    "print(df[\"pymnt_plan\"].unique())\n",
    "print(df[\"initial_list_status\"].unique())\n",
    "print(df[\"application_type\"].unique())\n",
    "print(df[\"hardship_flag\"].unique())\n",
    "print(df[\"debt_settlement_flag\"].unique())\n",
    "print(df[\"issue_d\"].unique())\n",
    "print(df[\"next_pymnt_d\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</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>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>next_pymnt_d</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>policy_code</th>\n",
       "      <th>application_type</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <th>tot_cur_bal</th>\n",
       "      <th>open_acc_6m</th>\n",
       "      <th>open_act_il</th>\n",
       "      <th>open_il_12m</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>mths_since_rcnt_il</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>max_bal_bc</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>total_cu_tl</th>\n",
       "      <th>inq_last_12m</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>avg_cur_bal</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>bc_util</th>\n",
       "      <th>chargeoff_within_12_mths</th>\n",
       "      <th>delinq_amnt</th>\n",
       "      <th>mo_sin_old_il_acct</th>\n",
       "      <th>mo_sin_old_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_tl</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>mths_since_recent_bc</th>\n",
       "      <th>mths_since_recent_inq</th>\n",
       "      <th>num_accts_ever_120_pd</th>\n",
       "      <th>num_actv_bc_tl</th>\n",
       "      <th>num_actv_rev_tl</th>\n",
       "      <th>num_bc_sats</th>\n",
       "      <th>num_bc_tl</th>\n",
       "      <th>num_il_tl</th>\n",
       "      <th>num_op_rev_tl</th>\n",
       "      <th>num_rev_accts</th>\n",
       "      <th>num_rev_tl_bal_gt_0</th>\n",
       "      <th>num_sats</th>\n",
       "      <th>num_tl_120dpd_2m</th>\n",
       "      <th>num_tl_30dpd</th>\n",
       "      <th>num_tl_90g_dpd_24m</th>\n",
       "      <th>num_tl_op_past_12m</th>\n",
       "      <th>pct_tl_nvr_dlq</th>\n",
       "      <th>percent_bc_gt_75</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "      <th>tot_hi_cred_lim</th>\n",
       "      <th>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "      <th>total_il_high_credit_limit</th>\n",
       "      <th>hardship_flag</th>\n",
       "      <th>debt_settlement_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10500.0</td>\n",
       "      <td>0.1719</td>\n",
       "      <td>375.35</td>\n",
       "      <td>3</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1609.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10204.87</td>\n",
       "      <td>10204.87</td>\n",
       "      <td>355.29</td>\n",
       "      <td>355.29</td>\n",
       "      <td>295.13</td>\n",
       "      <td>60.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>375.35</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>38199.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>36590.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1553.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4775.0</td>\n",
       "      <td>447.0</td>\n",
       "      <td>77.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>85.7</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65687.0</td>\n",
       "      <td>38199.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>61987.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25000.0</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>929.09</td>\n",
       "      <td>1</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18368.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1</td>\n",
       "      <td>24293.13</td>\n",
       "      <td>24293.13</td>\n",
       "      <td>873.53</td>\n",
       "      <td>873.53</td>\n",
       "      <td>706.87</td>\n",
       "      <td>166.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>929.09</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>228119.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42273.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5197.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>50400.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13419.0</td>\n",
       "      <td>29502.0</td>\n",
       "      <td>28.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>91.2</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>271427.0</td>\n",
       "      <td>60641.0</td>\n",
       "      <td>41200.0</td>\n",
       "      <td>49197.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>529.88</td>\n",
       "      <td>1</td>\n",
       "      <td>56000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13247.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>19636.79</td>\n",
       "      <td>19636.79</td>\n",
       "      <td>485.44</td>\n",
       "      <td>485.44</td>\n",
       "      <td>363.21</td>\n",
       "      <td>122.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>529.88</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45684.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32437.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2810.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5711.0</td>\n",
       "      <td>2839.0</td>\n",
       "      <td>62.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>66.7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60644.0</td>\n",
       "      <td>45684.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>43144.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.1640</td>\n",
       "      <td>353.55</td>\n",
       "      <td>3</td>\n",
       "      <td>92000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>31.44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17996.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9719.34</td>\n",
       "      <td>9719.34</td>\n",
       "      <td>335.33</td>\n",
       "      <td>335.33</td>\n",
       "      <td>280.66</td>\n",
       "      <td>54.67</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>353.55</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68784.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>50788.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6360.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6878.0</td>\n",
       "      <td>4899.0</td>\n",
       "      <td>75.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99506.0</td>\n",
       "      <td>68784.0</td>\n",
       "      <td>19700.0</td>\n",
       "      <td>76506.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22000.0</td>\n",
       "      <td>0.1474</td>\n",
       "      <td>520.39</td>\n",
       "      <td>1</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9091.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>21614.73</td>\n",
       "      <td>21614.73</td>\n",
       "      <td>484.36</td>\n",
       "      <td>484.36</td>\n",
       "      <td>385.27</td>\n",
       "      <td>99.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>520.39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>177745.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16828.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>43100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13673.0</td>\n",
       "      <td>19699.0</td>\n",
       "      <td>28.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>219750.0</td>\n",
       "      <td>25919.0</td>\n",
       "      <td>27600.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68812</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.1502</td>\n",
       "      <td>346.76</td>\n",
       "      <td>3</td>\n",
       "      <td>26000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9.60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2684.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9326.86</td>\n",
       "      <td>9326.86</td>\n",
       "      <td>1019.42</td>\n",
       "      <td>1019.42</td>\n",
       "      <td>673.14</td>\n",
       "      <td>346.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>346.76</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>6798.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>4114.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>977.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>755.0</td>\n",
       "      <td>9677.0</td>\n",
       "      <td>14.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20625.0</td>\n",
       "      <td>6798.0</td>\n",
       "      <td>11300.0</td>\n",
       "      <td>5425.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68813</th>\n",
       "      <td>12000.0</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>368.37</td>\n",
       "      <td>3</td>\n",
       "      <td>63000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13314.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>11706.42</td>\n",
       "      <td>11706.42</td>\n",
       "      <td>1086.93</td>\n",
       "      <td>1086.93</td>\n",
       "      <td>293.58</td>\n",
       "      <td>793.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>368.37</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60350.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>47036.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1177.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7544.0</td>\n",
       "      <td>11387.0</td>\n",
       "      <td>15.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>96.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87939.0</td>\n",
       "      <td>60350.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>62939.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68814</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.1992</td>\n",
       "      <td>185.62</td>\n",
       "      <td>1</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3715.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4666.78</td>\n",
       "      <td>4666.78</td>\n",
       "      <td>571.33</td>\n",
       "      <td>571.33</td>\n",
       "      <td>333.22</td>\n",
       "      <td>238.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>185.62</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18611.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14896.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3722.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30592.0</td>\n",
       "      <td>18611.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>18492.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68815</th>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0646</td>\n",
       "      <td>1225.24</td>\n",
       "      <td>1</td>\n",
       "      <td>520000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59529.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
       "      <td>36953.94</td>\n",
       "      <td>36953.94</td>\n",
       "      <td>3661.36</td>\n",
       "      <td>3661.36</td>\n",
       "      <td>3046.06</td>\n",
       "      <td>615.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1225.24</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>864480.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>27151.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>38479.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>111100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>41166.0</td>\n",
       "      <td>41467.0</td>\n",
       "      <td>34.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>98.2</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1033574.0</td>\n",
       "      <td>95958.0</td>\n",
       "      <td>100800.0</td>\n",
       "      <td>78634.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68816</th>\n",
       "      <td>16000.0</td>\n",
       "      <td>0.1131</td>\n",
       "      <td>350.36</td>\n",
       "      <td>1</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.02</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11882.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>15395.66</td>\n",
       "      <td>15395.66</td>\n",
       "      <td>1041.03</td>\n",
       "      <td>1041.03</td>\n",
       "      <td>604.34</td>\n",
       "      <td>436.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>350.36</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>225413.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>62953.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5568.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18784.0</td>\n",
       "      <td>11705.0</td>\n",
       "      <td>49.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>94.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>251486.0</td>\n",
       "      <td>74835.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>63090.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68817 rows × 86 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       loan_amnt  int_rate  installment  home_ownership  annual_inc  \\\n",
       "0        10500.0    0.1719       375.35               3     66000.0   \n",
       "1        25000.0    0.2000       929.09               1    105000.0   \n",
       "2        20000.0    0.2000       529.88               1     56000.0   \n",
       "3        10000.0    0.1640       353.55               3     92000.0   \n",
       "4        22000.0    0.1474       520.39               1     52000.0   \n",
       "...          ...       ...          ...             ...         ...   \n",
       "68812    10000.0    0.1502       346.76               3     26000.0   \n",
       "68813    12000.0    0.2727       368.37               3     63000.0   \n",
       "68814     5000.0    0.1992       185.62               1     52000.0   \n",
       "68815    40000.0    0.0646      1225.24               1    520000.0   \n",
       "68816    16000.0    0.1131       350.36               1     72000.0   \n",
       "\n",
       "       verification_status  issue_d  loan_status  pymnt_plan    dti  \\\n",
       "0                        1        2            1           0  27.24   \n",
       "1                        2        2            1           0  20.23   \n",
       "2                        2        2            1           0  24.26   \n",
       "3                        2        2            1           0  31.44   \n",
       "4                        0        2            1           0  18.76   \n",
       "...                    ...      ...          ...         ...    ...   \n",
       "68812                    1        1            1           0   9.60   \n",
       "68813                    0        1            1           0  29.07   \n",
       "68814                    1        1            1           0  14.86   \n",
       "68815                    2        1            1           0   9.96   \n",
       "68816                    2        1            1           0   7.02   \n",
       "\n",
       "       delinq_2yrs  inq_last_6mths  open_acc  pub_rec  revol_bal  total_acc  \\\n",
       "0              0.0             0.0       8.0      0.0     1609.0       14.0   \n",
       "1              0.0             0.0      17.0      1.0    18368.0       34.0   \n",
       "2              0.0             0.0       8.0      0.0    13247.0       21.0   \n",
       "3              0.0             1.0      10.0      1.0    17996.0       23.0   \n",
       "4              0.0             1.0      14.0      0.0     9091.0       28.0   \n",
       "...            ...             ...       ...      ...        ...        ...   \n",
       "68812          0.0             0.0       9.0      0.0     2684.0       20.0   \n",
       "68813          0.0             0.0       8.0      0.0    13314.0       26.0   \n",
       "68814          0.0             0.0       5.0      1.0     3715.0       14.0   \n",
       "68815          0.0             1.0      21.0      0.0    59529.0       57.0   \n",
       "68816          2.0             0.0      12.0      1.0    11882.0       39.0   \n",
       "\n",
       "       initial_list_status  out_prncp  out_prncp_inv  total_pymnt  \\\n",
       "0                        1   10204.87       10204.87       355.29   \n",
       "1                        1   24293.13       24293.13       873.53   \n",
       "2                        1   19636.79       19636.79       485.44   \n",
       "3                        1    9719.34        9719.34       335.33   \n",
       "4                        1   21614.73       21614.73       484.36   \n",
       "...                    ...        ...            ...          ...   \n",
       "68812                    1    9326.86        9326.86      1019.42   \n",
       "68813                    1   11706.42       11706.42      1086.93   \n",
       "68814                    1    4666.78        4666.78       571.33   \n",
       "68815                    0   36953.94       36953.94      3661.36   \n",
       "68816                    1   15395.66       15395.66      1041.03   \n",
       "\n",
       "       total_pymnt_inv  total_rec_prncp  total_rec_int  total_rec_late_fee  \\\n",
       "0               355.29           295.13          60.16                 0.0   \n",
       "1               873.53           706.87         166.66                 0.0   \n",
       "2               485.44           363.21         122.23                 0.0   \n",
       "3               335.33           280.66          54.67                 0.0   \n",
       "4               484.36           385.27          99.09                 0.0   \n",
       "...                ...              ...            ...                 ...   \n",
       "68812          1019.42           673.14         346.28                 0.0   \n",
       "68813          1086.93           293.58         793.35                 0.0   \n",
       "68814           571.33           333.22         238.11                 0.0   \n",
       "68815          3661.36          3046.06         615.30                 0.0   \n",
       "68816          1041.03           604.34         436.69                 0.0   \n",
       "\n",
       "       recoveries  collection_recovery_fee  last_pymnt_amnt  next_pymnt_d  \\\n",
       "0             0.0                      0.0           375.35             1   \n",
       "1             0.0                      0.0           929.09             1   \n",
       "2             0.0                      0.0           529.88             1   \n",
       "3             0.0                      0.0           353.55             1   \n",
       "4             0.0                      0.0           520.39             1   \n",
       "...           ...                      ...              ...           ...   \n",
       "68812         0.0                      0.0           346.76             1   \n",
       "68813         0.0                      0.0           368.37             1   \n",
       "68814         0.0                      0.0           185.62             1   \n",
       "68815         0.0                      0.0          1225.24             1   \n",
       "68816         0.0                      0.0           350.36             1   \n",
       "\n",
       "       collections_12_mths_ex_med  policy_code  application_type  \\\n",
       "0                             0.0          1.0                 0   \n",
       "1                             0.0          1.0                 0   \n",
       "2                             0.0          1.0                 0   \n",
       "3                             0.0          1.0                 0   \n",
       "4                             0.0          1.0                 0   \n",
       "...                           ...          ...               ...   \n",
       "68812                         0.0          1.0                 0   \n",
       "68813                         0.0          1.0                 0   \n",
       "68814                         0.0          1.0                 0   \n",
       "68815                         0.0          1.0                 0   \n",
       "68816                         0.0          1.0                 0   \n",
       "\n",
       "       acc_now_delinq  tot_coll_amt  tot_cur_bal  open_acc_6m  open_act_il  \\\n",
       "0                 0.0         178.0      38199.0          3.0          4.0   \n",
       "1                 0.0         165.0     228119.0          3.0          3.0   \n",
       "2                 0.0           0.0      45684.0          2.0          3.0   \n",
       "3                 0.0           0.0      68784.0          0.0          4.0   \n",
       "4                 0.0           0.0     177745.0          0.0          1.0   \n",
       "...               ...           ...          ...          ...          ...   \n",
       "68812             0.0         242.0       6798.0          1.0          3.0   \n",
       "68813             0.0           0.0      60350.0          3.0          4.0   \n",
       "68814             0.0           0.0      18611.0          2.0          1.0   \n",
       "68815             0.0           0.0     864480.0          2.0          3.0   \n",
       "68816             0.0           0.0     225413.0          0.0          2.0   \n",
       "\n",
       "       open_il_12m  open_il_24m  mths_since_rcnt_il  total_bal_il  il_util  \\\n",
       "0              2.0          4.0                 3.0       36590.0     59.0   \n",
       "1              2.0          3.0                 4.0       42273.0     86.0   \n",
       "2              2.0          3.0                 4.0       32437.0     75.0   \n",
       "3              1.0          3.0                 9.0       50788.0     66.0   \n",
       "4              0.0          2.0                14.0       16828.0     84.0   \n",
       "...            ...          ...                 ...           ...      ...   \n",
       "68812          0.0          0.0               160.0        4114.0     76.0   \n",
       "68813          2.0          2.0                 2.0       47036.0     75.0   \n",
       "68814          1.0          1.0                 8.0       14896.0     81.0   \n",
       "68815          0.0          0.0                28.0       27151.0     35.0   \n",
       "68816          0.0          0.0                28.0       62953.0     79.0   \n",
       "\n",
       "       open_rv_12m  open_rv_24m  max_bal_bc  all_util  total_rev_hi_lim  \\\n",
       "0              1.0          2.0      1553.0      58.0            3700.0   \n",
       "1              4.0          8.0      5197.0      61.0           50400.0   \n",
       "2              2.0          3.0      2810.0      75.0           17500.0   \n",
       "3              0.0          1.0      6360.0      69.0           23000.0   \n",
       "4              1.0          1.0      3600.0      41.0           43100.0   \n",
       "...            ...          ...         ...       ...               ...   \n",
       "68812          3.0          6.0       977.0      33.0           15200.0   \n",
       "68813          3.0          5.0      1177.0      69.0           25000.0   \n",
       "68814          2.0          3.0         0.0      61.0           12100.0   \n",
       "68815          3.0          5.0     38479.0      34.0          111100.0   \n",
       "68816          0.0          0.0      5568.0      45.0           32000.0   \n",
       "\n",
       "       inq_fi  total_cu_tl  inq_last_12m  acc_open_past_24mths  avg_cur_bal  \\\n",
       "0         1.0          0.0           4.0                   6.0       4775.0   \n",
       "1         4.0          9.0           3.0                  13.0      13419.0   \n",
       "2         0.0          4.0           1.0                   6.0       5711.0   \n",
       "3         3.0          5.0           4.0                   4.0       6878.0   \n",
       "4         2.0          0.0           2.0                   4.0      13673.0   \n",
       "...       ...          ...           ...                   ...          ...   \n",
       "68812     0.0          1.0           1.0                   6.0        755.0   \n",
       "68813     1.0          0.0           4.0                   7.0       7544.0   \n",
       "68814     0.0          2.0           1.0                   4.0       3722.0   \n",
       "68815     1.0          0.0           3.0                   7.0      41166.0   \n",
       "68816     2.0          2.0           4.0                   1.0      18784.0   \n",
       "\n",
       "       bc_open_to_buy  bc_util  chargeoff_within_12_mths  delinq_amnt  \\\n",
       "0               447.0     77.7                       0.0          0.0   \n",
       "1             29502.0     28.4                       0.0          0.0   \n",
       "2              2839.0     62.1                       0.0          0.0   \n",
       "3              4899.0     75.1                       0.0          0.0   \n",
       "4             19699.0     28.6                       0.0          0.0   \n",
       "...               ...      ...                       ...          ...   \n",
       "68812          9677.0     14.4                       0.0          0.0   \n",
       "68813         11387.0     15.7                       0.0          0.0   \n",
       "68814          3600.0      0.0                       0.0          0.0   \n",
       "68815         41467.0     34.3                       0.0          0.0   \n",
       "68816         11705.0     49.1                       2.0          0.0   \n",
       "\n",
       "       mo_sin_old_il_acct  mo_sin_old_rev_tl_op  mo_sin_rcnt_rev_tl_op  \\\n",
       "0                    93.0                  96.0                    3.0   \n",
       "1                    87.0                 286.0                    1.0   \n",
       "2                   159.0                 156.0                    7.0   \n",
       "3                   100.0                  89.0                   24.0   \n",
       "4                    67.0                 138.0                   11.0   \n",
       "...                   ...                   ...                    ...   \n",
       "68812               185.0                  17.0                    4.0   \n",
       "68813               164.0                 107.0                    1.0   \n",
       "68814               197.0                  91.0                    0.0   \n",
       "68815               147.0                 146.0                    2.0   \n",
       "68816               159.0                 129.0                   25.0   \n",
       "\n",
       "       mo_sin_rcnt_tl  mort_acc  mths_since_recent_bc  mths_since_recent_inq  \\\n",
       "0                 3.0       0.0                  20.0                    1.0   \n",
       "1                 1.0       2.0                   1.0                   11.0   \n",
       "2                 4.0       0.0                   7.0                    7.0   \n",
       "3                 9.0       0.0                  34.0                    0.0   \n",
       "4                11.0       4.0                  11.0                    0.0   \n",
       "...               ...       ...                   ...                    ...   \n",
       "68812             4.0       0.0                   4.0                    3.0   \n",
       "68813             1.0       0.0                   1.0                    2.0   \n",
       "68814             0.0       1.0                   0.0                   10.0   \n",
       "68815             2.0       9.0                   3.0                    3.0   \n",
       "68816            24.0       3.0                  25.0                    1.0   \n",
       "\n",
       "       num_accts_ever_120_pd  num_actv_bc_tl  num_actv_rev_tl  num_bc_sats  \\\n",
       "0                        0.0             1.0              2.0          1.0   \n",
       "1                        3.0             7.0             11.0          8.0   \n",
       "2                        6.0             2.0              5.0          2.0   \n",
       "3                        0.0             4.0              6.0          4.0   \n",
       "4                        0.0             4.0              6.0          7.0   \n",
       "...                      ...             ...              ...          ...   \n",
       "68812                    4.0             3.0              5.0          3.0   \n",
       "68813                    0.0             3.0              4.0          3.0   \n",
       "68814                    0.0             0.0              2.0          2.0   \n",
       "68815                    0.0             4.0              5.0          8.0   \n",
       "68816                    2.0             4.0              5.0          5.0   \n",
       "\n",
       "       num_bc_tl  num_il_tl  num_op_rev_tl  num_rev_accts  \\\n",
       "0            1.0       10.0            3.0            3.0   \n",
       "1           13.0       14.0           13.0           18.0   \n",
       "2            7.0        8.0            5.0           13.0   \n",
       "3            4.0       17.0            6.0            6.0   \n",
       "4           11.0        6.0           12.0           18.0   \n",
       "...          ...        ...            ...            ...   \n",
       "68812        3.0       14.0            6.0            6.0   \n",
       "68813        4.0       18.0            4.0            8.0   \n",
       "68814        2.0        8.0            4.0            5.0   \n",
       "68815       16.0       10.0           14.0           36.0   \n",
       "68816        9.0       20.0            9.0           16.0   \n",
       "\n",
       "       num_rev_tl_bal_gt_0  num_sats  num_tl_120dpd_2m  num_tl_30dpd  \\\n",
       "0                      2.0       8.0               0.0           0.0   \n",
       "1                     11.0      17.0               0.0           0.0   \n",
       "2                      5.0       8.0               0.0           0.0   \n",
       "3                      6.0      10.0               0.0           0.0   \n",
       "4                      6.0      14.0               0.0           0.0   \n",
       "...                    ...       ...               ...           ...   \n",
       "68812                  5.0       9.0               0.0           0.0   \n",
       "68813                  4.0       8.0               0.0           0.0   \n",
       "68814                  2.0       5.0               0.0           0.0   \n",
       "68815                  5.0      21.0               0.0           0.0   \n",
       "68816                  5.0      12.0               0.0           0.0   \n",
       "\n",
       "       num_tl_90g_dpd_24m  num_tl_op_past_12m  pct_tl_nvr_dlq  \\\n",
       "0                     0.0                 3.0            85.7   \n",
       "1                     0.0                 6.0            91.2   \n",
       "2                     0.0                 4.0            66.7   \n",
       "3                     0.0                 1.0           100.0   \n",
       "4                     0.0                 1.0           100.0   \n",
       "...                   ...                 ...             ...   \n",
       "68812                 0.0                 3.0            80.0   \n",
       "68813                 0.0                 5.0            96.2   \n",
       "68814                 0.0                 3.0           100.0   \n",
       "68815                 0.0                 3.0            98.2   \n",
       "68816                 2.0                 0.0            94.3   \n",
       "\n",
       "       percent_bc_gt_75  pub_rec_bankruptcies  tax_liens  tot_hi_cred_lim  \\\n",
       "0                 100.0                   0.0        0.0          65687.0   \n",
       "1                  50.0                   1.0        0.0         271427.0   \n",
       "2                  50.0                   0.0        0.0          60644.0   \n",
       "3                  50.0                   1.0        0.0          99506.0   \n",
       "4                   0.0                   0.0        0.0         219750.0   \n",
       "...                 ...                   ...        ...              ...   \n",
       "68812               0.0                   0.0        0.0          20625.0   \n",
       "68813               0.0                   0.0        0.0          87939.0   \n",
       "68814               0.0                   1.0        0.0          30592.0   \n",
       "68815              12.5                   0.0        0.0        1033574.0   \n",
       "68816               0.0                   1.0        0.0         251486.0   \n",
       "\n",
       "       total_bal_ex_mort  total_bc_limit  total_il_high_credit_limit  \\\n",
       "0                38199.0          2000.0                     61987.0   \n",
       "1                60641.0         41200.0                     49197.0   \n",
       "2                45684.0          7500.0                     43144.0   \n",
       "3                68784.0         19700.0                     76506.0   \n",
       "4                25919.0         27600.0                     20000.0   \n",
       "...                  ...             ...                         ...   \n",
       "68812             6798.0         11300.0                      5425.0   \n",
       "68813            60350.0         13500.0                     62939.0   \n",
       "68814            18611.0          3600.0                     18492.0   \n",
       "68815            95958.0        100800.0                     78634.0   \n",
       "68816            74835.0         23000.0                     63090.0   \n",
       "\n",
       "       hardship_flag  debt_settlement_flag  \n",
       "0                  0                     0  \n",
       "1                  0                     0  \n",
       "2                  0                     0  \n",
       "3                  0                     0  \n",
       "4                  0                     0  \n",
       "...              ...                   ...  \n",
       "68812              0                     0  \n",
       "68813              0                     0  \n",
       "68814              0                     0  \n",
       "68815              0                     0  \n",
       "68816              0                     0  \n",
       "\n",
       "[68817 rows x 86 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import dependencies and prepare to encode text to numerical values\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "df2 = df.copy()\n",
    "\n",
    "# Create a list of columns to encode\n",
    "textColumns = [\"home_ownership\", \"verification_status\", \"loan_status\", \"issue_d\", \"next_pymnt_d\",\n",
    "              \"pymnt_plan\", \"initial_list_status\", \"application_type\",\n",
    "              \"hardship_flag\", \"debt_settlement_flag\"]\n",
    "\n",
    "# For loop to encode text columns to numerical values\n",
    "for textColumn in textColumns:\n",
    "    df2[textColumn] = le.fit_transform(df2[textColumn])\n",
    "\n",
    "# Display dataframe\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split the Data into Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our features\n",
    "x_cols = [i for i in df2.columns if i not in ('loan_status')]\n",
    "X = df2[x_cols]\n",
    "\n",
    "# Create our target\n",
    "y = df2['loan_status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>pymnt_plan</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>next_pymnt_d</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>policy_code</th>\n",
       "      <th>application_type</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <th>tot_cur_bal</th>\n",
       "      <th>open_acc_6m</th>\n",
       "      <th>open_act_il</th>\n",
       "      <th>open_il_12m</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>mths_since_rcnt_il</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>max_bal_bc</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>total_cu_tl</th>\n",
       "      <th>inq_last_12m</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>avg_cur_bal</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>bc_util</th>\n",
       "      <th>chargeoff_within_12_mths</th>\n",
       "      <th>delinq_amnt</th>\n",
       "      <th>mo_sin_old_il_acct</th>\n",
       "      <th>mo_sin_old_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_tl</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>mths_since_recent_bc</th>\n",
       "      <th>mths_since_recent_inq</th>\n",
       "      <th>num_accts_ever_120_pd</th>\n",
       "      <th>num_actv_bc_tl</th>\n",
       "      <th>num_actv_rev_tl</th>\n",
       "      <th>num_bc_sats</th>\n",
       "      <th>num_bc_tl</th>\n",
       "      <th>num_il_tl</th>\n",
       "      <th>num_op_rev_tl</th>\n",
       "      <th>num_rev_accts</th>\n",
       "      <th>num_rev_tl_bal_gt_0</th>\n",
       "      <th>num_sats</th>\n",
       "      <th>num_tl_120dpd_2m</th>\n",
       "      <th>num_tl_30dpd</th>\n",
       "      <th>num_tl_90g_dpd_24m</th>\n",
       "      <th>num_tl_op_past_12m</th>\n",
       "      <th>pct_tl_nvr_dlq</th>\n",
       "      <th>percent_bc_gt_75</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "      <th>tot_hi_cred_lim</th>\n",
       "      <th>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "      <th>total_il_high_credit_limit</th>\n",
       "      <th>hardship_flag</th>\n",
       "      <th>debt_settlement_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>6.881700e+04</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.0</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.0</td>\n",
       "      <td>68817.0</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.0</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.0</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>6.881700e+04</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>6.881700e+04</td>\n",
       "      <td>68817.00000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.0</td>\n",
       "      <td>68817.0</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.0</td>\n",
       "      <td>6.881700e+04</td>\n",
       "      <td>6.881700e+04</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>6.881700e+04</td>\n",
       "      <td>68817.0</td>\n",
       "      <td>68817.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16677.594562</td>\n",
       "      <td>0.127718</td>\n",
       "      <td>480.652863</td>\n",
       "      <td>1.812779</td>\n",
       "      <td>8.821371e+04</td>\n",
       "      <td>0.669994</td>\n",
       "      <td>0.805542</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.778153</td>\n",
       "      <td>0.217766</td>\n",
       "      <td>0.497697</td>\n",
       "      <td>12.587340</td>\n",
       "      <td>0.126030</td>\n",
       "      <td>17604.142828</td>\n",
       "      <td>24.666347</td>\n",
       "      <td>0.876121</td>\n",
       "      <td>16018.845833</td>\n",
       "      <td>16015.919519</td>\n",
       "      <td>970.639473</td>\n",
       "      <td>970.431151</td>\n",
       "      <td>658.748750</td>\n",
       "      <td>311.855687</td>\n",
       "      <td>0.035072</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>517.202218</td>\n",
       "      <td>0.616839</td>\n",
       "      <td>0.016188</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.139660</td>\n",
       "      <td>0.0</td>\n",
       "      <td>188.664298</td>\n",
       "      <td>1.634976e+05</td>\n",
       "      <td>1.023047</td>\n",
       "      <td>3.290481</td>\n",
       "      <td>0.843730</td>\n",
       "      <td>1.909644</td>\n",
       "      <td>14.303152</td>\n",
       "      <td>4.324044e+04</td>\n",
       "      <td>69.21191</td>\n",
       "      <td>1.240042</td>\n",
       "      <td>2.622245</td>\n",
       "      <td>6297.491710</td>\n",
       "      <td>55.702472</td>\n",
       "      <td>42431.895360</td>\n",
       "      <td>1.382071</td>\n",
       "      <td>1.687795</td>\n",
       "      <td>2.189023</td>\n",
       "      <td>4.827572</td>\n",
       "      <td>14142.936193</td>\n",
       "      <td>16991.852667</td>\n",
       "      <td>48.330667</td>\n",
       "      <td>0.006830</td>\n",
       "      <td>2.347065</td>\n",
       "      <td>125.468605</td>\n",
       "      <td>175.136841</td>\n",
       "      <td>14.294215</td>\n",
       "      <td>7.179781</td>\n",
       "      <td>1.441882</td>\n",
       "      <td>24.714954</td>\n",
       "      <td>7.688028</td>\n",
       "      <td>0.472485</td>\n",
       "      <td>3.806123</td>\n",
       "      <td>5.648038</td>\n",
       "      <td>5.135156</td>\n",
       "      <td>7.414403</td>\n",
       "      <td>9.500153</td>\n",
       "      <td>8.630818</td>\n",
       "      <td>13.517663</td>\n",
       "      <td>5.653036</td>\n",
       "      <td>12.571995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.052138</td>\n",
       "      <td>2.219423</td>\n",
       "      <td>95.057627</td>\n",
       "      <td>30.626217</td>\n",
       "      <td>0.125972</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.100332e+05</td>\n",
       "      <td>6.133843e+04</td>\n",
       "      <td>29734.128558</td>\n",
       "      <td>5.572240e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10277.348590</td>\n",
       "      <td>0.048130</td>\n",
       "      <td>288.062432</td>\n",
       "      <td>0.941313</td>\n",
       "      <td>1.155800e+05</td>\n",
       "      <td>0.719105</td>\n",
       "      <td>0.714932</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.199244</td>\n",
       "      <td>0.718367</td>\n",
       "      <td>0.758122</td>\n",
       "      <td>6.022869</td>\n",
       "      <td>0.336797</td>\n",
       "      <td>21835.880400</td>\n",
       "      <td>12.318917</td>\n",
       "      <td>0.329446</td>\n",
       "      <td>10002.695495</td>\n",
       "      <td>10004.985940</td>\n",
       "      <td>1229.563301</td>\n",
       "      <td>1229.569864</td>\n",
       "      <td>1126.237377</td>\n",
       "      <td>279.786954</td>\n",
       "      <td>1.086720</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>816.021053</td>\n",
       "      <td>0.486161</td>\n",
       "      <td>0.134774</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.346637</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1808.457195</td>\n",
       "      <td>1.723699e+05</td>\n",
       "      <td>1.176782</td>\n",
       "      <td>2.965579</td>\n",
       "      <td>0.970543</td>\n",
       "      <td>1.544994</td>\n",
       "      <td>13.972619</td>\n",
       "      <td>4.956130e+04</td>\n",
       "      <td>22.33711</td>\n",
       "      <td>1.471745</td>\n",
       "      <td>2.502608</td>\n",
       "      <td>5702.281757</td>\n",
       "      <td>18.495480</td>\n",
       "      <td>37166.229316</td>\n",
       "      <td>1.543220</td>\n",
       "      <td>2.852664</td>\n",
       "      <td>2.396186</td>\n",
       "      <td>3.145377</td>\n",
       "      <td>15863.878993</td>\n",
       "      <td>20447.276820</td>\n",
       "      <td>28.106287</td>\n",
       "      <td>0.087327</td>\n",
       "      <td>301.312407</td>\n",
       "      <td>56.968853</td>\n",
       "      <td>99.149467</td>\n",
       "      <td>17.417118</td>\n",
       "      <td>6.303066</td>\n",
       "      <td>1.747777</td>\n",
       "      <td>32.130889</td>\n",
       "      <td>6.034861</td>\n",
       "      <td>1.410819</td>\n",
       "      <td>2.464504</td>\n",
       "      <td>3.470941</td>\n",
       "      <td>3.230651</td>\n",
       "      <td>4.581371</td>\n",
       "      <td>7.573846</td>\n",
       "      <td>5.056994</td>\n",
       "      <td>8.026158</td>\n",
       "      <td>3.472679</td>\n",
       "      <td>6.021318</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.390633</td>\n",
       "      <td>1.897432</td>\n",
       "      <td>8.326426</td>\n",
       "      <td>33.631463</td>\n",
       "      <td>0.336732</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.928088e+05</td>\n",
       "      <td>5.738798e+04</td>\n",
       "      <td>26795.394232</td>\n",
       "      <td>5.095845e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>30.890000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.350000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.600000e+03</td>\n",
       "      <td>2.350000e+02</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.270000e+02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9000.000000</td>\n",
       "      <td>0.088100</td>\n",
       "      <td>265.730000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.890000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6293.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8578.800000</td>\n",
       "      <td>8576.500000</td>\n",
       "      <td>403.760000</td>\n",
       "      <td>403.550000</td>\n",
       "      <td>250.370000</td>\n",
       "      <td>115.440000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>267.110000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.570000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.422300e+04</td>\n",
       "      <td>56.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2558.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>19200.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3505.000000</td>\n",
       "      <td>3644.000000</td>\n",
       "      <td>25.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.697700e+04</td>\n",
       "      <td>2.650300e+04</td>\n",
       "      <td>11600.000000</td>\n",
       "      <td>2.288000e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15000.000000</td>\n",
       "      <td>0.118000</td>\n",
       "      <td>404.560000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.300000e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.760000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12068.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14067.750000</td>\n",
       "      <td>14061.030000</td>\n",
       "      <td>710.790000</td>\n",
       "      <td>710.790000</td>\n",
       "      <td>449.890000</td>\n",
       "      <td>225.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>408.520000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.960600e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.862600e+04</td>\n",
       "      <td>71.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4874.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>32800.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8261.000000</td>\n",
       "      <td>10239.000000</td>\n",
       "      <td>46.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>157.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.467100e+05</td>\n",
       "      <td>4.535700e+04</td>\n",
       "      <td>22100.000000</td>\n",
       "      <td>4.200000e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24000.000000</td>\n",
       "      <td>0.155700</td>\n",
       "      <td>648.100000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.040000e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.660000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21735.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>22860.700000</td>\n",
       "      <td>22860.490000</td>\n",
       "      <td>1214.600000</td>\n",
       "      <td>1214.590000</td>\n",
       "      <td>762.170000</td>\n",
       "      <td>415.710000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>652.580000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.472060e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>5.406500e+04</td>\n",
       "      <td>85.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8321.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>54100.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>19859.000000</td>\n",
       "      <td>22806.000000</td>\n",
       "      <td>71.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>226.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.036400e+05</td>\n",
       "      <td>7.657000e+04</td>\n",
       "      <td>39300.000000</td>\n",
       "      <td>7.249900e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>40000.000000</td>\n",
       "      <td>0.308400</td>\n",
       "      <td>1676.230000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.797500e+06</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>587191.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>41418.981165</td>\n",
       "      <td>41418.980000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>2735.400000</td>\n",
       "      <td>75.240000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40500.340000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159199.000000</td>\n",
       "      <td>3.140082e+06</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>389.000000</td>\n",
       "      <td>1.260281e+06</td>\n",
       "      <td>313.00000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>207484.000000</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>759500.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>448583.000000</td>\n",
       "      <td>506507.000000</td>\n",
       "      <td>169.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>65000.000000</td>\n",
       "      <td>606.000000</td>\n",
       "      <td>902.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.292782e+06</td>\n",
       "      <td>1.295455e+06</td>\n",
       "      <td>509400.000000</td>\n",
       "      <td>1.426964e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          loan_amnt      int_rate   installment  home_ownership    annual_inc  \\\n",
       "count  68817.000000  68817.000000  68817.000000    68817.000000  6.881700e+04   \n",
       "mean   16677.594562      0.127718    480.652863        1.812779  8.821371e+04   \n",
       "std    10277.348590      0.048130    288.062432        0.941313  1.155800e+05   \n",
       "min     1000.000000      0.060000     30.890000        0.000000  4.000000e+01   \n",
       "25%     9000.000000      0.088100    265.730000        1.000000  5.000000e+04   \n",
       "50%    15000.000000      0.118000    404.560000        1.000000  7.300000e+04   \n",
       "75%    24000.000000      0.155700    648.100000        3.000000  1.040000e+05   \n",
       "max    40000.000000      0.308400   1676.230000        3.000000  8.797500e+06   \n",
       "\n",
       "       verification_status       issue_d  pymnt_plan           dti  \\\n",
       "count         68817.000000  68817.000000     68817.0  68817.000000   \n",
       "mean              0.669994      0.805542         0.0     21.778153   \n",
       "std               0.719105      0.714932         0.0     20.199244   \n",
       "min               0.000000      0.000000         0.0      0.000000   \n",
       "25%               0.000000      0.000000         0.0     13.890000   \n",
       "50%               1.000000      1.000000         0.0     19.760000   \n",
       "75%               1.000000      1.000000         0.0     26.660000   \n",
       "max               2.000000      2.000000         0.0    999.000000   \n",
       "\n",
       "        delinq_2yrs  inq_last_6mths      open_acc       pub_rec  \\\n",
       "count  68817.000000    68817.000000  68817.000000  68817.000000   \n",
       "mean       0.217766        0.497697     12.587340      0.126030   \n",
       "std        0.718367        0.758122      6.022869      0.336797   \n",
       "min        0.000000        0.000000      2.000000      0.000000   \n",
       "25%        0.000000        0.000000      8.000000      0.000000   \n",
       "50%        0.000000        0.000000     11.000000      0.000000   \n",
       "75%        0.000000        1.000000     16.000000      0.000000   \n",
       "max       18.000000        5.000000     72.000000      4.000000   \n",
       "\n",
       "           revol_bal     total_acc  initial_list_status     out_prncp  \\\n",
       "count   68817.000000  68817.000000         68817.000000  68817.000000   \n",
       "mean    17604.142828     24.666347             0.876121  16018.845833   \n",
       "std     21835.880400     12.318917             0.329446  10002.695495   \n",
       "min         0.000000      3.000000             0.000000      0.000000   \n",
       "25%      6293.000000     16.000000             1.000000   8578.800000   \n",
       "50%     12068.000000     23.000000             1.000000  14067.750000   \n",
       "75%     21735.000000     31.000000             1.000000  22860.700000   \n",
       "max    587191.000000    164.000000             1.000000  40000.000000   \n",
       "\n",
       "       out_prncp_inv   total_pymnt  total_pymnt_inv  total_rec_prncp  \\\n",
       "count   68817.000000  68817.000000     68817.000000     68817.000000   \n",
       "mean    16015.919519    970.639473       970.431151       658.748750   \n",
       "std     10004.985940   1229.563301      1229.569864      1126.237377   \n",
       "min         0.000000      0.000000         0.000000         0.000000   \n",
       "25%      8576.500000    403.760000       403.550000       250.370000   \n",
       "50%     14061.030000    710.790000       710.790000       449.890000   \n",
       "75%     22860.490000   1214.600000      1214.590000       762.170000   \n",
       "max     40000.000000  41418.981165     41418.980000     40000.000000   \n",
       "\n",
       "       total_rec_int  total_rec_late_fee  recoveries  collection_recovery_fee  \\\n",
       "count   68817.000000        68817.000000     68817.0                  68817.0   \n",
       "mean      311.855687            0.035072         0.0                      0.0   \n",
       "std       279.786954            1.086720         0.0                      0.0   \n",
       "min         0.000000            0.000000         0.0                      0.0   \n",
       "25%       115.440000            0.000000         0.0                      0.0   \n",
       "50%       225.250000            0.000000         0.0                      0.0   \n",
       "75%       415.710000            0.000000         0.0                      0.0   \n",
       "max      2735.400000           75.240000         0.0                      0.0   \n",
       "\n",
       "       last_pymnt_amnt  next_pymnt_d  collections_12_mths_ex_med  policy_code  \\\n",
       "count     68817.000000  68817.000000                68817.000000      68817.0   \n",
       "mean        517.202218      0.616839                    0.016188          1.0   \n",
       "std         816.021053      0.486161                    0.134774          0.0   \n",
       "min           0.000000      0.000000                    0.000000          1.0   \n",
       "25%         267.110000      0.000000                    0.000000          1.0   \n",
       "50%         408.520000      1.000000                    0.000000          1.0   \n",
       "75%         652.580000      1.000000                    0.000000          1.0   \n",
       "max       40500.340000      1.000000                    4.000000          1.0   \n",
       "\n",
       "       application_type  acc_now_delinq   tot_coll_amt   tot_cur_bal  \\\n",
       "count      68817.000000         68817.0   68817.000000  6.881700e+04   \n",
       "mean           0.139660             0.0     188.664298  1.634976e+05   \n",
       "std            0.346637             0.0    1808.457195  1.723699e+05   \n",
       "min            0.000000             0.0       0.000000  2.350000e+02   \n",
       "25%            0.000000             0.0       0.000000  3.570000e+04   \n",
       "50%            0.000000             0.0       0.000000  9.960600e+04   \n",
       "75%            0.000000             0.0       0.000000  2.472060e+05   \n",
       "max            1.000000             0.0  159199.000000  3.140082e+06   \n",
       "\n",
       "        open_acc_6m   open_act_il   open_il_12m   open_il_24m  \\\n",
       "count  68817.000000  68817.000000  68817.000000  68817.000000   \n",
       "mean       1.023047      3.290481      0.843730      1.909644   \n",
       "std        1.176782      2.965579      0.970543      1.544994   \n",
       "min        0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      2.000000      0.000000      1.000000   \n",
       "50%        1.000000      2.000000      1.000000      2.000000   \n",
       "75%        2.000000      4.000000      1.000000      3.000000   \n",
       "max       14.000000     63.000000      6.000000     22.000000   \n",
       "\n",
       "       mths_since_rcnt_il  total_bal_il      il_util   open_rv_12m  \\\n",
       "count        68817.000000  6.881700e+04  68817.00000  68817.000000   \n",
       "mean            14.303152  4.324044e+04     69.21191      1.240042   \n",
       "std             13.972619  4.956130e+04     22.33711      1.471745   \n",
       "min              0.000000  1.000000e+00      0.00000      0.000000   \n",
       "25%              6.000000  1.422300e+04     56.00000      0.000000   \n",
       "50%             11.000000  2.862600e+04     71.00000      1.000000   \n",
       "75%             18.000000  5.406500e+04     85.00000      2.000000   \n",
       "max            389.000000  1.260281e+06    313.00000     18.000000   \n",
       "\n",
       "        open_rv_24m     max_bal_bc      all_util  total_rev_hi_lim  \\\n",
       "count  68817.000000   68817.000000  68817.000000      68817.000000   \n",
       "mean       2.622245    6297.491710     55.702472      42431.895360   \n",
       "std        2.502608    5702.281757     18.495480      37166.229316   \n",
       "min        0.000000       0.000000      1.000000        200.000000   \n",
       "25%        1.000000    2558.000000     43.000000      19200.000000   \n",
       "50%        2.000000    4874.000000     56.000000      32800.000000   \n",
       "75%        4.000000    8321.000000     68.000000      54100.000000   \n",
       "max       44.000000  207484.000000    216.000000     759500.000000   \n",
       "\n",
       "             inq_fi   total_cu_tl  inq_last_12m  acc_open_past_24mths  \\\n",
       "count  68817.000000  68817.000000  68817.000000          68817.000000   \n",
       "mean       1.382071      1.687795      2.189023              4.827572   \n",
       "std        1.543220      2.852664      2.396186              3.145377   \n",
       "min        0.000000      0.000000      0.000000              0.000000   \n",
       "25%        0.000000      0.000000      1.000000              3.000000   \n",
       "50%        1.000000      1.000000      2.000000              4.000000   \n",
       "75%        2.000000      2.000000      3.000000              6.000000   \n",
       "max       29.000000     70.000000     42.000000             45.000000   \n",
       "\n",
       "         avg_cur_bal  bc_open_to_buy       bc_util  chargeoff_within_12_mths  \\\n",
       "count   68817.000000    68817.000000  68817.000000              68817.000000   \n",
       "mean    14142.936193    16991.852667     48.330667                  0.006830   \n",
       "std     15863.878993    20447.276820     28.106287                  0.087327   \n",
       "min        20.000000        0.000000      0.000000                  0.000000   \n",
       "25%      3505.000000     3644.000000     25.400000                  0.000000   \n",
       "50%      8261.000000    10239.000000     46.400000                  0.000000   \n",
       "75%     19859.000000    22806.000000     71.100000                  0.000000   \n",
       "max    448583.000000   506507.000000    169.500000                  2.000000   \n",
       "\n",
       "        delinq_amnt  mo_sin_old_il_acct  mo_sin_old_rev_tl_op  \\\n",
       "count  68817.000000        68817.000000          68817.000000   \n",
       "mean       2.347065          125.468605            175.136841   \n",
       "std      301.312407           56.968853             99.149467   \n",
       "min        0.000000            0.000000              5.000000   \n",
       "25%        0.000000           87.000000            101.000000   \n",
       "50%        0.000000          132.000000            157.000000   \n",
       "75%        0.000000          159.000000            226.000000   \n",
       "max    65000.000000          606.000000            902.000000   \n",
       "\n",
       "       mo_sin_rcnt_rev_tl_op  mo_sin_rcnt_tl      mort_acc  \\\n",
       "count           68817.000000    68817.000000  68817.000000   \n",
       "mean               14.294215        7.179781      1.441882   \n",
       "std                17.417118        6.303066      1.747777   \n",
       "min                 0.000000        0.000000      0.000000   \n",
       "25%                 4.000000        3.000000      0.000000   \n",
       "50%                 9.000000        5.000000      1.000000   \n",
       "75%                19.000000       10.000000      2.000000   \n",
       "max               564.000000      170.000000     20.000000   \n",
       "\n",
       "       mths_since_recent_bc  mths_since_recent_inq  num_accts_ever_120_pd  \\\n",
       "count          68817.000000           68817.000000           68817.000000   \n",
       "mean              24.714954               7.688028               0.472485   \n",
       "std               32.130889               6.034861               1.410819   \n",
       "min                0.000000               0.000000               0.000000   \n",
       "25%                6.000000               3.000000               0.000000   \n",
       "50%               15.000000               6.000000               0.000000   \n",
       "75%               31.000000              12.000000               0.000000   \n",
       "max              564.000000              24.000000              34.000000   \n",
       "\n",
       "       num_actv_bc_tl  num_actv_rev_tl   num_bc_sats     num_bc_tl  \\\n",
       "count    68817.000000     68817.000000  68817.000000  68817.000000   \n",
       "mean         3.806123         5.648038      5.135156      7.414403   \n",
       "std          2.464504         3.470941      3.230651      4.581371   \n",
       "min          0.000000         0.000000      0.000000      1.000000   \n",
       "25%          2.000000         3.000000      3.000000      4.000000   \n",
       "50%          3.000000         5.000000      4.000000      6.000000   \n",
       "75%          5.000000         7.000000      7.000000     10.000000   \n",
       "max         26.000000        43.000000     43.000000     60.000000   \n",
       "\n",
       "          num_il_tl  num_op_rev_tl  num_rev_accts  num_rev_tl_bal_gt_0  \\\n",
       "count  68817.000000   68817.000000   68817.000000         68817.000000   \n",
       "mean       9.500153       8.630818      13.517663             5.653036   \n",
       "std        7.573846       5.056994       8.026158             3.472679   \n",
       "min        1.000000       1.000000       2.000000             0.000000   \n",
       "25%        4.000000       5.000000       8.000000             3.000000   \n",
       "50%        7.000000       8.000000      12.000000             5.000000   \n",
       "75%       12.000000      11.000000      17.000000             7.000000   \n",
       "max      149.000000      70.000000      98.000000            43.000000   \n",
       "\n",
       "           num_sats  num_tl_120dpd_2m  num_tl_30dpd  num_tl_90g_dpd_24m  \\\n",
       "count  68817.000000           68817.0       68817.0        68817.000000   \n",
       "mean      12.571995               0.0           0.0            0.052138   \n",
       "std        6.021318               0.0           0.0            0.390633   \n",
       "min        2.000000               0.0           0.0            0.000000   \n",
       "25%        8.000000               0.0           0.0            0.000000   \n",
       "50%       11.000000               0.0           0.0            0.000000   \n",
       "75%       16.000000               0.0           0.0            0.000000   \n",
       "max       72.000000               0.0           0.0           18.000000   \n",
       "\n",
       "       num_tl_op_past_12m  pct_tl_nvr_dlq  percent_bc_gt_75  \\\n",
       "count        68817.000000    68817.000000      68817.000000   \n",
       "mean             2.219423       95.057627         30.626217   \n",
       "std              1.897432        8.326426         33.631463   \n",
       "min              0.000000       20.000000          0.000000   \n",
       "25%              1.000000       93.000000          0.000000   \n",
       "50%              2.000000      100.000000         20.000000   \n",
       "75%              3.000000      100.000000         50.000000   \n",
       "max             19.000000      100.000000        100.000000   \n",
       "\n",
       "       pub_rec_bankruptcies  tax_liens  tot_hi_cred_lim  total_bal_ex_mort  \\\n",
       "count          68817.000000    68817.0     6.881700e+04       6.881700e+04   \n",
       "mean               0.125972        0.0     2.100332e+05       6.133843e+04   \n",
       "std                0.336732        0.0     1.928088e+05       5.738798e+04   \n",
       "min                0.000000        0.0     3.600000e+03       2.350000e+02   \n",
       "25%                0.000000        0.0     6.697700e+04       2.650300e+04   \n",
       "50%                0.000000        0.0     1.467100e+05       4.535700e+04   \n",
       "75%                0.000000        0.0     3.036400e+05       7.657000e+04   \n",
       "max                4.000000        0.0     3.292782e+06       1.295455e+06   \n",
       "\n",
       "       total_bc_limit  total_il_high_credit_limit  hardship_flag  \\\n",
       "count    68817.000000                6.881700e+04        68817.0   \n",
       "mean     29734.128558                5.572240e+04            0.0   \n",
       "std      26795.394232                5.095845e+04            0.0   \n",
       "min        100.000000                1.270000e+02            0.0   \n",
       "25%      11600.000000                2.288000e+04            0.0   \n",
       "50%      22100.000000                4.200000e+04            0.0   \n",
       "75%      39300.000000                7.249900e+04            0.0   \n",
       "max     509400.000000                1.426964e+06            0.0   \n",
       "\n",
       "       debt_settlement_flag  \n",
       "count               68817.0  \n",
       "mean                    0.0  \n",
       "std                     0.0  \n",
       "min                     0.0  \n",
       "25%                     0.0  \n",
       "50%                     0.0  \n",
       "75%                     0.0  \n",
       "max                     0.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    68470\n",
       "0      347\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the balance of our target values\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1: 51366, 0: 246})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n",
    "Counter(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Oversampling\n",
    "\n",
    "In this section, you will compare two oversampling algorithms to determine which algorithm results in the best performance. You will oversample the data using the naive random oversampling algorithm and the SMOTE algorithm. For each algorithm, be sure to complete the folliowing steps:\n",
    "\n",
    "1. View the count of the target classes using `Counter` from the collections library. \n",
    "3. Use the resampled data to train a logistic regression model.\n",
    "3. Calculate the balanced accuracy score from sklearn.metrics.\n",
    "4. Print the confusion matrix from sklearn.metrics.\n",
    "5. Generate a classication report using the `imbalanced_classification_report` from imbalanced-learn.\n",
    "\n",
    "Note: Use a random state of 1 for each sampling algorithm to ensure consistency between tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Random Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1: 51366, 0: 51366})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the training data with the RandomOversampler\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "ros = RandomOverSampler(random_state=1)\n",
    "X_resampled, y_resampled = ros.fit_resample(X_train, y_train)\n",
    "Counter(y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Logistic Regression model using the resampled data\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression(solver='lbfgs', random_state=1)\n",
    "model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6568523140901554"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "balanced_accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   71,    30],\n",
       "       [ 6658, 10446]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.01      0.70      0.61      0.02      0.66      0.43       101\n",
      "          1       1.00      0.61      0.70      0.76      0.66      0.43     17104\n",
      "\n",
      "avg / total       0.99      0.61      0.70      0.75      0.66      0.43     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SMOTE Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1: 51366, 0: 51366})"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the training data with SMOTE\n",
    "from imblearn.over_sampling import SMOTE\n",
    "X_resampled, y_resampled = SMOTE(random_state=1, sampling_strategy='auto').fit_resample(\n",
    "    X_train, y_train\n",
    ")\n",
    "Counter(y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Logistic Regression model using the resampled data\n",
    "model = LogisticRegression(solver='lbfgs', random_state=1)\n",
    "model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.645700965091832"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "y_pred = model.predict(X_test)\n",
    "balanced_accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   61,    40],\n",
       "       [ 5346, 11758]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.01      0.60      0.69      0.02      0.64      0.41       101\n",
      "          1       1.00      0.69      0.60      0.81      0.64      0.42     17104\n",
      "\n",
      "avg / total       0.99      0.69      0.60      0.81      0.64      0.42     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Undersampling\n",
    "\n",
    "In this section, you will test an undersampling algorithms to determine which algorithm results in the best performance compared to the oversampling algorithms above. You will undersample the data using the Cluster Centroids algorithm and complete the folliowing steps:\n",
    "\n",
    "1. View the count of the target classes using `Counter` from the collections library. \n",
    "3. Use the resampled data to train a logistic regression model.\n",
    "3. Calculate the balanced accuracy score from sklearn.metrics.\n",
    "4. Print the confusion matrix from sklearn.metrics.\n",
    "5. Generate a classication report using the `imbalanced_classification_report` from imbalanced-learn.\n",
    "\n",
    "Note: Use a random state of 1 for each sampling algorithm to ensure consistency between tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 246, 1: 246})"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the data using the ClusterCentroids resampler\n",
    "# Warning: This is a large dataset, and this step may take some time to complete\n",
    "from imblearn.under_sampling import ClusterCentroids\n",
    "cc = ClusterCentroids(random_state=1)\n",
    "X_resampled, y_resampled = cc.fit_resample(X_train, y_train)\n",
    "Counter(y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=78)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=78)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(random_state=78)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Logistic Regression model using the resampled data\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression(solver='lbfgs', random_state=78)\n",
    "model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5478033625392473"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create predictions\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Calculated the balanced accuracy score\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "balanced_accuracy_score(y_test, y_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   70,    31],\n",
       "       [10219,  6885]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.01      0.69      0.40      0.01      0.53      0.29       101\n",
      "          1       1.00      0.40      0.69      0.57      0.53      0.27     17104\n",
      "\n",
      "avg / total       0.99      0.40      0.69      0.57      0.53      0.27     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combination (Over and Under) Sampling\n",
    "\n",
    "In this section, you will test a combination over- and under-sampling algorithm to determine if the algorithm results in the best performance compared to the other sampling algorithms above. You will resample the data using the SMOTEENN algorithm and complete the folliowing steps:\n",
    "\n",
    "1. View the count of the target classes using `Counter` from the collections library. \n",
    "3. Use the resampled data to train a logistic regression model.\n",
    "3. Calculate the balanced accuracy score from sklearn.metrics.\n",
    "4. Print the confusion matrix from sklearn.metrics.\n",
    "5. Generate a classication report using the `imbalanced_classification_report` from imbalanced-learn.\n",
    "\n",
    "Note: Use a random state of 1 for each sampling algorithm to ensure consistency between tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resample the training data with SMOTEENN\n",
    "# Warning: This is a large dataset, and this step may take some time to complete\n",
    "from imblearn.combine import SMOTEENN\n",
    "smote_enn = SMOTEENN(random_state=0)\n",
    "X_resampled, y_resampled = smote_enn.fit_resample(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Logistic Regression model using the resampled data\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression(solver='lbfgs', random_state=1)\n",
    "model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6547093378654985"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create predictions\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Calculated the balanced accuracy score\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "balanced_accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   73,    28],\n",
       "       [ 7070, 10034]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.01      0.72      0.59      0.02      0.65      0.43       101\n",
      "          1       1.00      0.59      0.72      0.74      0.65      0.42     17104\n",
      "\n",
      "avg / total       0.99      0.59      0.72      0.73      0.65      0.42     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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