credit_risk_ensemble.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from imblearn.metrics import classification_report_imbalanced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the CSV and Perform Basic Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://help.lendingclub.com/hc/en-us/articles/215488038-What-do-the-different-Note-statuses-mean-\n",
    "\n",
    "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": [],
   "source": [
    "# Load the data\n",
    "file_path = Path('resources/LoanStats_2019Q1.csv')\n",
    "df = pd.read_csv(file_path, skiprows=1)[:-2]\n",
    "df = df.loc[:, columns].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loan_amnt                     float64\n",
      "int_rate                       object\n",
      "installment                   float64\n",
      "home_ownership                 object\n",
      "annual_inc                    float64\n",
      "verification_status            object\n",
      "issue_d                        object\n",
      "loan_status                    object\n",
      "pymnt_plan                     object\n",
      "dti                           float64\n",
      "delinq_2yrs                   float64\n",
      "inq_last_6mths                float64\n",
      "open_acc                      float64\n",
      "pub_rec                       float64\n",
      "revol_bal                     float64\n",
      "total_acc                     float64\n",
      "initial_list_status            object\n",
      "out_prncp                     float64\n",
      "out_prncp_inv                 float64\n",
      "total_pymnt                   float64\n",
      "total_pymnt_inv               float64\n",
      "total_rec_prncp               float64\n",
      "total_rec_int                 float64\n",
      "total_rec_late_fee            float64\n",
      "recoveries                    float64\n",
      "collection_recovery_fee       float64\n",
      "last_pymnt_amnt               float64\n",
      "next_pymnt_d                   object\n",
      "collections_12_mths_ex_med    float64\n",
      "policy_code                   float64\n",
      "application_type               object\n",
      "acc_now_delinq                float64\n",
      "tot_coll_amt                  float64\n",
      "tot_cur_bal                   float64\n",
      "open_acc_6m                   float64\n",
      "open_act_il                   float64\n",
      "open_il_12m                   float64\n",
      "open_il_24m                   float64\n",
      "mths_since_rcnt_il            float64\n",
      "total_bal_il                  float64\n",
      "il_util                       float64\n",
      "open_rv_12m                   float64\n",
      "open_rv_24m                   float64\n",
      "max_bal_bc                    float64\n",
      "all_util                      float64\n",
      "total_rev_hi_lim              float64\n",
      "inq_fi                        float64\n",
      "total_cu_tl                   float64\n",
      "inq_last_12m                  float64\n",
      "acc_open_past_24mths          float64\n",
      "avg_cur_bal                   float64\n",
      "bc_open_to_buy                float64\n",
      "bc_util                       float64\n",
      "chargeoff_within_12_mths      float64\n",
      "delinq_amnt                   float64\n",
      "mo_sin_old_il_acct            float64\n",
      "mo_sin_old_rev_tl_op          float64\n",
      "mo_sin_rcnt_rev_tl_op         float64\n",
      "mo_sin_rcnt_tl                float64\n",
      "mort_acc                      float64\n",
      "mths_since_recent_bc          float64\n",
      "mths_since_recent_inq         float64\n",
      "num_accts_ever_120_pd         float64\n",
      "num_actv_bc_tl                float64\n",
      "num_actv_rev_tl               float64\n",
      "num_bc_sats                   float64\n",
      "num_bc_tl                     float64\n",
      "num_il_tl                     float64\n",
      "num_op_rev_tl                 float64\n",
      "num_rev_accts                 float64\n",
      "num_rev_tl_bal_gt_0           float64\n",
      "num_sats                      float64\n",
      "num_tl_120dpd_2m              float64\n",
      "num_tl_30dpd                  float64\n",
      "num_tl_90g_dpd_24m            float64\n",
      "num_tl_op_past_12m            float64\n",
      "pct_tl_nvr_dlq                float64\n",
      "percent_bc_gt_75              float64\n",
      "pub_rec_bankruptcies          float64\n",
      "tax_liens                     float64\n",
      "tot_hi_cred_lim               float64\n",
      "total_bal_ex_mort             float64\n",
      "total_bc_limit                float64\n",
      "total_il_high_credit_limit    float64\n",
      "hardship_flag                  object\n",
      "debt_settlement_flag           object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
    "    print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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>...</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>...</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>...</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>...</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>...</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>...</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",
       "<p>5 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           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  ...  \\\n",
       "0     Source Verified  Mar-2019    low_risk          n  27.24  ...   \n",
       "1            Verified  Mar-2019    low_risk          n  20.23  ...   \n",
       "2            Verified  Mar-2019    low_risk          n  24.26  ...   \n",
       "3            Verified  Mar-2019    low_risk          n  31.44  ...   \n",
       "4        Not Verified  Mar-2019    low_risk          n  18.76  ...   \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  \n",
       "\n",
       "[5 rows x 86 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 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",
    "# 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",
    "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",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split the Data into Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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>annual_inc</th>\n",
       "      <th>loan_status</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>...</th>\n",
       "      <th>pymnt_plan_n</th>\n",
       "      <th>application_type_Individual</th>\n",
       "      <th>application_type_Joint App</th>\n",
       "      <th>hardship_flag_N</th>\n",
       "      <th>debt_settlement_flag_N</th>\n",
       "      <th>initial_list_status_f</th>\n",
       "      <th>initial_list_status_w</th>\n",
       "      <th>next_pymnt_d_Apr-2019</th>\n",
       "      <th>next_pymnt_d_May-2019</th>\n",
       "      <th>issue_d_num</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>66000.0</td>\n",
       "      <td>low_risk</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>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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>105000.0</td>\n",
       "      <td>low_risk</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>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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>56000.0</td>\n",
       "      <td>low_risk</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>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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>92000.0</td>\n",
       "      <td>low_risk</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>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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>52000.0</td>\n",
       "      <td>low_risk</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>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25000.0</td>\n",
       "      <td>0.1797</td>\n",
       "      <td>634.43</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>18.49</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20400.0</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>540.48</td>\n",
       "      <td>51315.0</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>25.63</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.1557</td>\n",
       "      <td>349.46</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14000.0</td>\n",
       "      <td>0.0881</td>\n",
       "      <td>443.96</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>10.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.1474</td>\n",
       "      <td>345.39</td>\n",
       "      <td>63295.0</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>39.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  int_rate  installment  annual_inc loan_status    dti  \\\n",
       "0    10500.0    0.1719       375.35     66000.0    low_risk  27.24   \n",
       "1    25000.0    0.2000       929.09    105000.0    low_risk  20.23   \n",
       "2    20000.0    0.2000       529.88     56000.0    low_risk  24.26   \n",
       "3    10000.0    0.1640       353.55     92000.0    low_risk  31.44   \n",
       "4    22000.0    0.1474       520.39     52000.0    low_risk  18.76   \n",
       "5    25000.0    0.1797       634.43     90000.0    low_risk  18.49   \n",
       "6    20400.0    0.2000       540.48     51315.0    low_risk  25.63   \n",
       "7    10000.0    0.1557       349.46     66000.0    low_risk   9.00   \n",
       "8    14000.0    0.0881       443.96     45000.0    low_risk  10.24   \n",
       "9    10000.0    0.1474       345.39     63295.0    low_risk  39.34   \n",
       "\n",
       "   delinq_2yrs  inq_last_6mths  open_acc  pub_rec  ...  pymnt_plan_n  \\\n",
       "0          0.0             0.0       8.0      0.0  ...             1   \n",
       "1          0.0             0.0      17.0      1.0  ...             1   \n",
       "2          0.0             0.0       8.0      0.0  ...             1   \n",
       "3          0.0             1.0      10.0      1.0  ...             1   \n",
       "4          0.0             1.0      14.0      0.0  ...             1   \n",
       "5          1.0             1.0      14.0      0.0  ...             1   \n",
       "6          0.0             2.0       8.0      1.0  ...             1   \n",
       "7          0.0             0.0      11.0      0.0  ...             1   \n",
       "8          0.0             1.0       9.0      0.0  ...             1   \n",
       "9          0.0             0.0      27.0      0.0  ...             1   \n",
       "\n",
       "   application_type_Individual  application_type_Joint App  hardship_flag_N  \\\n",
       "0                            1                           0                1   \n",
       "1                            1                           0                1   \n",
       "2                            1                           0                1   \n",
       "3                            1                           0                1   \n",
       "4                            1                           0                1   \n",
       "5                            1                           0                1   \n",
       "6                            1                           0                1   \n",
       "7                            1                           0                1   \n",
       "8                            1                           0                1   \n",
       "9                            1                           0                1   \n",
       "\n",
       "   debt_settlement_flag_N  initial_list_status_f  initial_list_status_w  \\\n",
       "0                       1                      0                      1   \n",
       "1                       1                      0                      1   \n",
       "2                       1                      0                      1   \n",
       "3                       1                      0                      1   \n",
       "4                       1                      0                      1   \n",
       "5                       1                      0                      1   \n",
       "6                       1                      0                      1   \n",
       "7                       1                      0                      1   \n",
       "8                       1                      1                      0   \n",
       "9                       1                      0                      1   \n",
       "\n",
       "   next_pymnt_d_Apr-2019  next_pymnt_d_May-2019  issue_d_num  \n",
       "0                      0                      1            3  \n",
       "1                      0                      1            3  \n",
       "2                      0                      1            3  \n",
       "3                      0                      1            3  \n",
       "4                      0                      1            3  \n",
       "5                      0                      1            3  \n",
       "6                      0                      1            3  \n",
       "7                      0                      1            3  \n",
       "8                      0                      1            3  \n",
       "9                      0                      1            3  \n",
       "\n",
       "[10 rows x 94 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bin_encode = pd.get_dummies(df, columns=[\"home_ownership\",\n",
    "                                            \"verification_status\",\n",
    "                                            \"pymnt_plan\",\n",
    "                                            \"application_type\",\n",
    "                                            \"hardship_flag\",\n",
    "                                            \"debt_settlement_flag\",\n",
    "                                            \"initial_list_status\",\n",
    "                                            \"next_pymnt_d\"])\n",
    "\n",
    "months_num = {\n",
    "    'Jan-2019': 1,\n",
    "    'Feb-2019': 2,\n",
    "    'Mar-2019':3\n",
    "}\n",
    "\n",
    "# Months' and Loan Status condition encoded using the dictionary values\n",
    "df_bin_encode[\"issue_d_num\"] = df_bin_encode[\"issue_d\"].apply(lambda x: months_num[x])\n",
    "# Drop the issue_d columns\n",
    "df_bin_encode = df_bin_encode.drop([\"issue_d\"], axis=1)\n",
    "\n",
    "df_bin_encode.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our features\n",
    "X = df_bin_encode.drop(columns=\"loan_status\")\n",
    "\n",
    "# Create our target\n",
    "y = pd.DataFrame(df_bin_encode[\"loan_status\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>annual_inc</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>...</th>\n",
       "      <th>pymnt_plan_n</th>\n",
       "      <th>application_type_Individual</th>\n",
       "      <th>application_type_Joint App</th>\n",
       "      <th>hardship_flag_N</th>\n",
       "      <th>debt_settlement_flag_N</th>\n",
       "      <th>initial_list_status_f</th>\n",
       "      <th>initial_list_status_w</th>\n",
       "      <th>next_pymnt_d_Apr-2019</th>\n",
       "      <th>next_pymnt_d_May-2019</th>\n",
       "      <th>issue_d_num</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>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>68817.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>68817.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16677.594562</td>\n",
       "      <td>0.127718</td>\n",
       "      <td>480.652863</td>\n",
       "      <td>8.821371e+04</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>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.860340</td>\n",
       "      <td>0.139660</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.123879</td>\n",
       "      <td>0.876121</td>\n",
       "      <td>0.383161</td>\n",
       "      <td>0.616839</td>\n",
       "      <td>1.726172</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>1.155800e+05</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.346637</td>\n",
       "      <td>0.346637</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.329446</td>\n",
       "      <td>0.329446</td>\n",
       "      <td>0.486161</td>\n",
       "      <td>0.486161</td>\n",
       "      <td>0.743862</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>4.000000e+01</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>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9000.000000</td>\n",
       "      <td>0.088100</td>\n",
       "      <td>265.730000</td>\n",
       "      <td>5.000000e+04</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>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</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>7.300000e+04</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>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</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>1.040000e+05</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>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>40000.000000</td>\n",
       "      <td>0.308400</td>\n",
       "      <td>1676.230000</td>\n",
       "      <td>8.797500e+06</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>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 93 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          loan_amnt      int_rate   installment    annual_inc           dti  \\\n",
       "count  68817.000000  68817.000000  68817.000000  6.881700e+04  68817.000000   \n",
       "mean   16677.594562      0.127718    480.652863  8.821371e+04     21.778153   \n",
       "std    10277.348590      0.048130    288.062432  1.155800e+05     20.199244   \n",
       "min     1000.000000      0.060000     30.890000  4.000000e+01      0.000000   \n",
       "25%     9000.000000      0.088100    265.730000  5.000000e+04     13.890000   \n",
       "50%    15000.000000      0.118000    404.560000  7.300000e+04     19.760000   \n",
       "75%    24000.000000      0.155700    648.100000  1.040000e+05     26.660000   \n",
       "max    40000.000000      0.308400   1676.230000  8.797500e+06    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  ...  pymnt_plan_n  application_type_Individual  \\\n",
       "count   68817.000000  ...       68817.0                 68817.000000   \n",
       "mean    17604.142828  ...           1.0                     0.860340   \n",
       "std     21835.880400  ...           0.0                     0.346637   \n",
       "min         0.000000  ...           1.0                     0.000000   \n",
       "25%      6293.000000  ...           1.0                     1.000000   \n",
       "50%     12068.000000  ...           1.0                     1.000000   \n",
       "75%     21735.000000  ...           1.0                     1.000000   \n",
       "max    587191.000000  ...           1.0                     1.000000   \n",
       "\n",
       "       application_type_Joint App  hardship_flag_N  debt_settlement_flag_N  \\\n",
       "count                68817.000000          68817.0                 68817.0   \n",
       "mean                     0.139660              1.0                     1.0   \n",
       "std                      0.346637              0.0                     0.0   \n",
       "min                      0.000000              1.0                     1.0   \n",
       "25%                      0.000000              1.0                     1.0   \n",
       "50%                      0.000000              1.0                     1.0   \n",
       "75%                      0.000000              1.0                     1.0   \n",
       "max                      1.000000              1.0                     1.0   \n",
       "\n",
       "       initial_list_status_f  initial_list_status_w  next_pymnt_d_Apr-2019  \\\n",
       "count           68817.000000           68817.000000           68817.000000   \n",
       "mean                0.123879               0.876121               0.383161   \n",
       "std                 0.329446               0.329446               0.486161   \n",
       "min                 0.000000               0.000000               0.000000   \n",
       "25%                 0.000000               1.000000               0.000000   \n",
       "50%                 0.000000               1.000000               0.000000   \n",
       "75%                 0.000000               1.000000               1.000000   \n",
       "max                 1.000000               1.000000               1.000000   \n",
       "\n",
       "       next_pymnt_d_May-2019   issue_d_num  \n",
       "count           68817.000000  68817.000000  \n",
       "mean                0.616839      1.726172  \n",
       "std                 0.486161      0.743862  \n",
       "min                 0.000000      1.000000  \n",
       "25%                 0.000000      1.000000  \n",
       "50%                 1.000000      2.000000  \n",
       "75%                 1.000000      2.000000  \n",
       "max                 1.000000      3.000000  \n",
       "\n",
       "[8 rows x 93 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  loan_status\n",
       "0    low_risk\n",
       "1    low_risk\n",
       "2    low_risk\n",
       "3    low_risk\n",
       "4    low_risk\n",
       "5    low_risk\n",
       "6    low_risk\n",
       "7    low_risk\n",
       "8    low_risk\n",
       "9    low_risk"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "low_risk     68470\n",
       "high_risk      347\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the balance of our target values\n",
    "y['loan_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'loan_status': 1})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\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": [
    "# Ensemble Learners\n",
    "\n",
    "In this section, you will compare two ensemble algorithms to determine which algorithm results in the best performance. You will train a Balanced Random Forest Classifier and an Easy Ensemble AdaBoost classifier . For each algorithm, be sure to complete the folliowing steps:\n",
    "\n",
    "1. Train the model using the training data. \n",
    "2. Calculate the balanced accuracy score from sklearn.metrics.\n",
    "3. Print the confusion matrix from sklearn.metrics.\n",
    "4. Generate a classication report using the `imbalanced_classification_report` from imbalanced-learn.\n",
    "5. For the Balanced Random Forest Classifier onely, print the feature importance sorted in descending order (most important feature to least important) along with the feature score\n",
    "\n",
    "Note: Use a random state of 1 for each algorithm to ensure consistency between tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Balanced Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resample the training data with the BalancedRandomForestClassifier\n",
    "# Create a random forest classifier\n",
    "from imblearn.ensemble import BalancedRandomForestClassifier\n",
    "brf_model = BalancedRandomForestClassifier(n_estimators=100, random_state=1)\n",
    "\n",
    "# Fitting the model\n",
    "brf_model = brf_model.fit(X_train, y_train)\n",
    "\n",
    "# Making predictions using the testing data\n",
    "predictions = brf_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "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_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7615432438940808"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the balanced accuracy score\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
    "\n",
    "y_pred = brf_model.predict(X_test)\n",
    "balanced_accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "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>Predicted 0</th>\n",
       "      <th>Predicted 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual 0</th>\n",
       "      <td>66</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>2230</td>\n",
       "      <td>14874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Predicted 0  Predicted 1\n",
       "Actual 0           66           35\n",
       "Actual 1         2230        14874"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "# Calculating the confusion matrix\n",
    "cm = confusion_matrix(y_test, predictions)\n",
    "\n",
    "# Create a DataFrame from the confusion matrix\n",
    "cm_df = pd.DataFrame(cm, index=[\"Actual 0\", \"Actual 1\"], columns=[\"Predicted 0\", \"Predicted 1\"])\n",
    "\n",
    "cm_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.03      0.65      0.87      0.06      0.75      0.56       101\n",
      "   low_risk       1.00      0.87      0.65      0.93      0.75      0.58     17104\n",
      "\n",
      "avg / total       0.99      0.87      0.65      0.92      0.75      0.58     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.07632202168694453, 'total_rec_prncp'),\n",
       " (0.0581069804038416, 'total_rec_int'),\n",
       " (0.05648159451080443, 'last_pymnt_amnt'),\n",
       " (0.0558149776088619, 'total_pymnt_inv'),\n",
       " (0.049686879758249744, 'total_pymnt'),\n",
       " (0.029667796551397632, 'int_rate'),\n",
       " (0.02620107532604457, 'issue_d_num'),\n",
       " (0.02113992088194085, 'mths_since_recent_inq'),\n",
       " (0.01860519442003772, 'dti'),\n",
       " (0.01759332369508704, 'installment'),\n",
       " (0.017393211072182172, 'total_bc_limit'),\n",
       " (0.016367590098591393, 'mo_sin_old_rev_tl_op'),\n",
       " (0.015770712149697937, 'out_prncp_inv'),\n",
       " (0.015651655514843432, 'out_prncp'),\n",
       " (0.015527891193631304, 'revol_bal'),\n",
       " (0.015358776583806324, 'mths_since_rcnt_il'),\n",
       " (0.014093972547545098, 'max_bal_bc'),\n",
       " (0.01402280161041999, 'il_util'),\n",
       " (0.01398865152425114, 'annual_inc'),\n",
       " (0.01392787032246454, 'avg_cur_bal'),\n",
       " (0.013915806563159938, 'bc_open_to_buy'),\n",
       " (0.013715993096666892, 'total_bal_ex_mort'),\n",
       " (0.013522474946434404, 'tot_hi_cred_lim'),\n",
       " (0.013455588046686973, 'all_util'),\n",
       " (0.013130284693849262, 'mths_since_recent_bc'),\n",
       " (0.013087499555045892, 'total_il_high_credit_limit'),\n",
       " (0.01267897064619703, 'tot_cur_bal'),\n",
       " (0.012642674037025303, 'total_rev_hi_lim'),\n",
       " (0.012586738755947182, 'mo_sin_old_il_acct'),\n",
       " (0.012505913560796151, 'loan_amnt'),\n",
       " (0.012038662342559299, 'bc_util'),\n",
       " (0.011989528833459882, 'total_acc'),\n",
       " (0.011950568835725981, 'total_bal_il'),\n",
       " (0.0112913630290341, 'pct_tl_nvr_dlq'),\n",
       " (0.010766791440864745, 'mo_sin_rcnt_rev_tl_op'),\n",
       " (0.010449390514013916, 'inq_last_12m'),\n",
       " (0.010186821450947863, 'num_sats'),\n",
       " (0.009931190549145638, 'num_rev_accts'),\n",
       " (0.009916867903460419, 'total_rec_late_fee'),\n",
       " (0.009469657830254446, 'num_rev_tl_bal_gt_0'),\n",
       " (0.009064476986568771, 'num_bc_tl'),\n",
       " (0.008987522085713788, 'num_op_rev_tl'),\n",
       " (0.008987175274159315, 'num_il_tl'),\n",
       " (0.008690878088153095, 'num_actv_rev_tl'),\n",
       " (0.008662985864348214, 'inq_fi'),\n",
       " (0.008600269649289328, 'mo_sin_rcnt_tl'),\n",
       " (0.008549376600640562, 'acc_open_past_24mths'),\n",
       " (0.008496151229255892, 'open_il_24m'),\n",
       " (0.008137842471710642, 'open_acc'),\n",
       " (0.007756597817136973, 'total_cu_tl'),\n",
       " (0.007395408372616129, 'num_tl_op_past_12m'),\n",
       " (0.007167076215698184, 'num_bc_sats'),\n",
       " (0.00715715120263663, 'num_actv_bc_tl'),\n",
       " (0.007030042773562192, 'open_act_il'),\n",
       " (0.006877011762792854, 'percent_bc_gt_75'),\n",
       " (0.0066055968150619425, 'open_acc_6m'),\n",
       " (0.006157251882351968, 'inq_last_6mths'),\n",
       " (0.006021495324632839, 'open_rv_24m'),\n",
       " (0.005557746430652588, 'open_il_12m'),\n",
       " (0.004999194458544024, 'open_rv_12m'),\n",
       " (0.004808231886201055, 'num_accts_ever_120_pd'),\n",
       " (0.004656707299537489, 'delinq_2yrs'),\n",
       " (0.004379785203676273, 'tot_coll_amt'),\n",
       " (0.004287174044576357, 'next_pymnt_d_May-2019'),\n",
       " (0.004283485672791835, 'mort_acc'),\n",
       " (0.0025880378183191486, 'next_pymnt_d_Apr-2019'),\n",
       " (0.002511792005912107, 'application_type_Individual'),\n",
       " (0.002400727630140666, 'home_ownership_MORTGAGE'),\n",
       " (0.002343143336376409, 'application_type_Joint App'),\n",
       " (0.002102316122891845, 'home_ownership_RENT'),\n",
       " (0.001835277810134352, 'home_ownership_OWN'),\n",
       " (0.0018201843291709794, 'initial_list_status_f'),\n",
       " (0.0017958810060539716, 'pub_rec'),\n",
       " (0.0015580746632612675, 'pub_rec_bankruptcies'),\n",
       " (0.001531119419232297, 'verification_status_Not Verified'),\n",
       " (0.0014586164946636877, 'verification_status_Verified'),\n",
       " (0.001285479768650014, 'num_tl_90g_dpd_24m'),\n",
       " (0.0009528564547884995, 'initial_list_status_w'),\n",
       " (0.0009311712871603981, 'verification_status_Source Verified'),\n",
       " (0.00036599839154190955, 'collections_12_mths_ex_med'),\n",
       " (0.0002470039574747757, 'home_ownership_ANY'),\n",
       " (0.0, 'tax_liens'),\n",
       " (0.0, 'recoveries'),\n",
       " (0.0, 'pymnt_plan_n'),\n",
       " (0.0, 'policy_code'),\n",
       " (0.0, 'num_tl_30dpd'),\n",
       " (0.0, 'num_tl_120dpd_2m'),\n",
       " (0.0, 'hardship_flag_N'),\n",
       " (0.0, 'delinq_amnt'),\n",
       " (0.0, 'debt_settlement_flag_N'),\n",
       " (0.0, 'collection_recovery_fee'),\n",
       " (0.0, 'chargeoff_within_12_mths'),\n",
       " (0.0, 'acc_now_delinq')]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List the features sorted in descending order by feature importance\n",
    "# Calculate feature importance in the Random Forest model\n",
    "importances = brf_model.feature_importances_\n",
    "sorted(zip(brf_model.feature_importances_, X.columns), reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Easy Ensemble AdaBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the EasyEnsembleClassifier\n",
    "from imblearn.ensemble import EasyEnsembleClassifier\n",
    "eec_model = EasyEnsembleClassifier(n_estimators=100, random_state=1)\n",
    "\n",
    "# Fitting the model\n",
    "eec_model = eec_model.fit(X_train, y_train)\n",
    "\n",
    "# Making predictions using the testing data\n",
    "predictions = eec_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9319231677611166"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "y_pred = eec_model.predict(X_test)\n",
    "balanced_accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "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>Predicted 0</th>\n",
       "      <th>Predicted 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual 0</th>\n",
       "      <td>93</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>974</td>\n",
       "      <td>16130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Predicted 0  Predicted 1\n",
       "Actual 0           93            8\n",
       "Actual 1          974        16130"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "cm = confusion_matrix(y_test, predictions)\n",
    "\n",
    "# Create a DataFrame from the confusion matrix\n",
    "cm_df = pd.DataFrame(cm, index=[\"Actual 0\", \"Actual 1\"], columns=[\"Predicted 0\", \"Predicted 1\"])\n",
    "\n",
    "cm_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.09      0.92      0.94      0.16      0.93      0.87       101\n",
      "   low_risk       1.00      0.94      0.92      0.97      0.93      0.87     17104\n",
      "\n",
      "avg / total       0.99      0.94      0.92      0.97      0.93      0.87     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  }
 ],
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