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": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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\n",
    "from sklearn.metrics import plot_confusion_matrix"
   ]
  },
  {
   "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": [
    {
     "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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()\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",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split the Data into Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our features\n",
    "X = pd.get_dummies(df.drop(columns = ['loan_status']))\n",
    "\n",
    "# Create our target\n",
    "y = pd.DataFrame(df['loan_status'])"
   ]
  },
  {
   "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>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>issue_d_Mar-2019</th>\n",
       "      <th>pymnt_plan_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>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",
       "    </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>68817.000000</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>68817.0</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>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>0.177238</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>0.860340</td>\n",
       "      <td>0.139660</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>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>0.381873</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>0.346637</td>\n",
       "      <td>0.346637</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>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>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>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>0.000000</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",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>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>0.000000</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>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>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>0.000000</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>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>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>1.000000</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>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 95 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  ...  issue_d_Mar-2019  pymnt_plan_n  \\\n",
       "count   68817.000000  ...      68817.000000       68817.0   \n",
       "mean    17604.142828  ...          0.177238           1.0   \n",
       "std     21835.880400  ...          0.381873           0.0   \n",
       "min         0.000000  ...          0.000000           1.0   \n",
       "25%      6293.000000  ...          0.000000           1.0   \n",
       "50%     12068.000000  ...          0.000000           1.0   \n",
       "75%     21735.000000  ...          0.000000           1.0   \n",
       "max    587191.000000  ...          1.000000           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  application_type_Individual  \\\n",
       "count           68817.000000                 68817.000000   \n",
       "mean                0.616839                     0.860340   \n",
       "std                 0.486161                     0.346637   \n",
       "min                 0.000000                     0.000000   \n",
       "25%                 0.000000                     1.000000   \n",
       "50%                 1.000000                     1.000000   \n",
       "75%                 1.000000                     1.000000   \n",
       "max                 1.000000                     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",
       "[8 rows x 95 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "low_risk     68470\n",
       "high_risk      347\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the balance of our target values\n",
    "y['loan_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51612, 1)"
      ]
     },
     "execution_count": 9,
     "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,\n",
    "                                                    y,\n",
    "                                                    random_state = 1,\n",
    "                                                    stratify = y)\n",
    "\n",
    "y_train.shape"
   ]
  },
  {
   "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": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resample the training data with the BalancedRandomForestClassifier\n",
    "from imblearn.ensemble import BalancedRandomForestClassifier\n",
    "\n",
    "rf_model = BalancedRandomForestClassifier(n_estimators = 100, random_state = 1)\n",
    "rf_model = rf_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7877672625306695"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "\n",
    "rf_y_pred = rf_model.predict(X_test)\n",
    "balanced_accuracy_score(y_test, rf_y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   58,    29],\n",
       "       [ 1560, 15558]], dtype=int64)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "rf_cm = confusion_matrix(y_test, rf_y_pred )\n",
    "rf_cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot confusion matrix\n",
    "fig = plot_confusion_matrix(rf_model, X_test, rf_y_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.04      0.67      0.91      0.07      0.78      0.59        87\n",
      "   low_risk       1.00      0.91      0.67      0.95      0.78      0.62     17118\n",
      "\n",
      "avg / total       0.99      0.91      0.67      0.95      0.78      0.62     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "print(classification_report_imbalanced(y_test, rf_y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.07376667607601396, 'total_rec_prncp'),\n",
       " (0.06390324452717588, 'total_rec_int'),\n",
       " (0.06073336071656837, 'total_pymnt_inv'),\n",
       " (0.05811195697921674, 'total_pymnt'),\n",
       " (0.04951778391272079, 'last_pymnt_amnt'),\n",
       " (0.02458051789018817, 'int_rate'),\n",
       " (0.020398879691407974, 'out_prncp'),\n",
       " (0.018625883307901298, 'dti'),\n",
       " (0.018378884967316288, 'max_bal_bc'),\n",
       " (0.017480030880564042, 'issue_d_Jan-2019'),\n",
       " (0.01698703048268034, 'installment'),\n",
       " (0.01661321625916749, 'annual_inc'),\n",
       " (0.01623198470200134, 'bc_util'),\n",
       " (0.015821923028819504, 'mths_since_recent_inq'),\n",
       " (0.015385686591272854, 'total_bc_limit'),\n",
       " (0.015337070167127763, 'out_prncp_inv'),\n",
       " (0.01506649550412446, 'avg_cur_bal'),\n",
       " (0.014926455495755147, 'issue_d_Mar-2019'),\n",
       " (0.014924489307566175, 'revol_bal'),\n",
       " (0.014583210727445232, 'mo_sin_old_rev_tl_op'),\n",
       " (0.01428231366275729, 'total_bal_ex_mort'),\n",
       " (0.014238473969302518, 'tot_cur_bal'),\n",
       " (0.014067369111282934, 'mo_sin_old_il_acct'),\n",
       " (0.01351439379612201, 'total_bal_il'),\n",
       " (0.013343855077273033, 'bc_open_to_buy'),\n",
       " (0.013138998568932102, 'il_util'),\n",
       " (0.013106005248709657, 'tot_hi_cred_lim'),\n",
       " (0.013009300843392654, 'all_util'),\n",
       " (0.012104219411815444, 'total_il_high_credit_limit'),\n",
       " (0.011927748948209041, 'num_rev_accts'),\n",
       " (0.011194718062040365, 'total_rev_hi_lim'),\n",
       " (0.01072808547266412, 'mths_since_recent_bc'),\n",
       " (0.010485766778776592, 'pct_tl_nvr_dlq'),\n",
       " (0.010480039757813501, 'total_acc'),\n",
       " (0.010433852907121713, 'mths_since_rcnt_il'),\n",
       " (0.010171477424921242, 'mo_sin_rcnt_rev_tl_op'),\n",
       " (0.00990081847673844, 'num_rev_tl_bal_gt_0'),\n",
       " (0.00982912014219909, 'loan_amnt'),\n",
       " (0.009709211384642156, 'inq_last_12m'),\n",
       " (0.00939406635676521, 'num_bc_tl'),\n",
       " (0.009390960836832822, 'num_op_rev_tl'),\n",
       " (0.008883434647669785, 'mo_sin_rcnt_tl'),\n",
       " (0.008287412625195438, 'num_actv_bc_tl'),\n",
       " (0.00794880573688952, 'total_cu_tl'),\n",
       " (0.007944596002325902, 'percent_bc_gt_75'),\n",
       " (0.007841727903546316, 'num_sats'),\n",
       " (0.007813875400824535, 'num_actv_rev_tl'),\n",
       " (0.007760323250042998, 'open_acc'),\n",
       " (0.007420667106687749, 'acc_open_past_24mths'),\n",
       " (0.007404967411706728, 'inq_fi'),\n",
       " (0.007339507170175945, 'num_il_tl'),\n",
       " (0.006876404000725424, 'open_il_24m'),\n",
       " (0.00678542812487817, 'num_bc_sats'),\n",
       " (0.006660043732852357, 'open_act_il'),\n",
       " (0.006599809671624734, 'open_rv_24m'),\n",
       " (0.006393774120528892, 'mort_acc'),\n",
       " (0.006391381020548387, 'total_rec_late_fee'),\n",
       " (0.006065825838862652, 'num_tl_op_past_12m'),\n",
       " (0.006004776142662084, 'open_acc_6m'),\n",
       " (0.005920335354841418, 'next_pymnt_d_Apr-2019'),\n",
       " (0.005413306654546768, 'open_rv_12m'),\n",
       " (0.005248840731650208, 'open_il_12m'),\n",
       " (0.00487120814476765, 'issue_d_Feb-2019'),\n",
       " (0.004802823221798599, 'next_pymnt_d_May-2019'),\n",
       " (0.0047562588132075015, 'inq_last_6mths'),\n",
       " (0.0045238834578884635, 'delinq_2yrs'),\n",
       " (0.0039029173953907185, 'num_accts_ever_120_pd'),\n",
       " (0.003789677267237295, 'tot_coll_amt'),\n",
       " (0.0020853638216694215, 'home_ownership_OWN'),\n",
       " (0.0020060097354201176, 'home_ownership_MORTGAGE'),\n",
       " (0.001984067551288744, 'verification_status_Source Verified'),\n",
       " (0.0018789153640443002, 'verification_status_Verified'),\n",
       " (0.0018544555674736187, 'verification_status_Not Verified'),\n",
       " (0.0016995259802440514, 'home_ownership_RENT'),\n",
       " (0.001520907850775936, 'pub_rec_bankruptcies'),\n",
       " (0.0014185754855487824, 'pub_rec'),\n",
       " (0.0013879548937552864, 'initial_list_status_f'),\n",
       " (0.001239058844214324, 'application_type_Individual'),\n",
       " (0.0011046122443753368, 'num_tl_90g_dpd_24m'),\n",
       " (0.000805030988421211, 'initial_list_status_w'),\n",
       " (0.000710297252940571, 'application_type_Joint App'),\n",
       " (0.0006949062181389056, 'collections_12_mths_ex_med'),\n",
       " (0.00013272979926763346, '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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List the features sorted in descending order by feature importance\n",
    "sorted(zip(rf_model.feature_importances_, X.columns), reverse = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Easy Ensemble AdaBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the EasyEnsembleClassifier\n",
    "from imblearn.ensemble import EasyEnsembleClassifier\n",
    "\n",
    "ee_model = EasyEnsembleClassifier(n_estimators = 100, random_state = 1)\n",
    "ee_model = ee_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.925427358175101"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "ee_y_pred = ee_model.predict(X_test)\n",
    "balanced_accuracy_score(y_test, ee_y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   79,     8],\n",
       "       [  979, 16139]], dtype=int64)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "ee_cm = confusion_matrix(y_test, ee_y_pred )\n",
    "ee_cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot confusion matrix\n",
    "fig = plot_confusion_matrix(ee_model, X_test, rf_y_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.07      0.91      0.94      0.14      0.93      0.85        87\n",
      "   low_risk       1.00      0.94      0.91      0.97      0.93      0.86     17118\n",
      "\n",
      "avg / total       0.99      0.94      0.91      0.97      0.93      0.86     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "print(classification_report_imbalanced(y_test, ee_y_pred))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mlenv",
   "language": "python",
   "name": "mlenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}