credit_risk_ensemble.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble Learning\n",
    "\n",
    "## Initial Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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": 79,
   "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.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the CSV and Perform Basic Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data\n",
    "file_path = Path('LoanStats_2019Q1.csv')\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "# Drop the null rows\n",
    "df = df.dropna()\n",
    "\n",
    "# Preview data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>home_ownership_MORTGAGE</th>\n",
       "      <th>home_ownership_OWN</th>\n",
       "      <th>home_ownership_RENT</th>\n",
       "      <th>verification_status_Source Verified</th>\n",
       "      <th>verification_status_Verified</th>\n",
       "      <th>issue_d_Jan-2019</th>\n",
       "      <th>issue_d_Mar-2019</th>\n",
       "      <th>initial_list_status_w</th>\n",
       "      <th>next_pymnt_d_May-2019</th>\n",
       "      <th>application_type_Joint App</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>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>529.88</td>\n",
       "      <td>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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "    </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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "    </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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 87 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",
       "\n",
       "   delinq_2yrs  inq_last_6mths  open_acc  pub_rec  ...  \\\n",
       "0          0.0             0.0       8.0      0.0  ...   \n",
       "1          0.0             0.0      17.0      1.0  ...   \n",
       "2          0.0             0.0       8.0      0.0  ...   \n",
       "3          0.0             1.0      10.0      1.0  ...   \n",
       "4          0.0             1.0      14.0      0.0  ...   \n",
       "\n",
       "   home_ownership_MORTGAGE  home_ownership_OWN  home_ownership_RENT  \\\n",
       "0                        0                   0                    1   \n",
       "1                        1                   0                    0   \n",
       "2                        1                   0                    0   \n",
       "3                        0                   0                    1   \n",
       "4                        1                   0                    0   \n",
       "\n",
       "   verification_status_Source Verified  verification_status_Verified  \\\n",
       "0                                    1                             0   \n",
       "1                                    0                             1   \n",
       "2                                    0                             1   \n",
       "3                                    0                             1   \n",
       "4                                    0                             0   \n",
       "\n",
       "   issue_d_Jan-2019  issue_d_Mar-2019  initial_list_status_w  \\\n",
       "0                 0                 1                      1   \n",
       "1                 0                 1                      1   \n",
       "2                 0                 1                      1   \n",
       "3                 0                 1                      1   \n",
       "4                 0                 1                      1   \n",
       "\n",
       "   next_pymnt_d_May-2019  application_type_Joint App  \n",
       "0                      1                           0  \n",
       "1                      1                           0  \n",
       "2                      1                           0  \n",
       "3                      1                           0  \n",
       "4                      1                           0  \n",
       "\n",
       "[5 rows x 87 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert string values to binary for 'homeowner' column\n",
    "df = pd.get_dummies(df, columns=[\"home_ownership\", \"verification_status\", \"issue_d\", \"pymnt_plan\", \"initial_list_status\", \"next_pymnt_d\", \"application_type\", \"hardship_flag\", \"debt_settlement_flag\"], drop_first=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split the Data into Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our features\n",
    "X = df.drop(columns=\"loan_status\")\n",
    "\n",
    "# Create our target\n",
    "y = df.loan_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>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>home_ownership_MORTGAGE</th>\n",
       "      <th>home_ownership_OWN</th>\n",
       "      <th>home_ownership_RENT</th>\n",
       "      <th>verification_status_Source Verified</th>\n",
       "      <th>verification_status_Verified</th>\n",
       "      <th>issue_d_Jan-2019</th>\n",
       "      <th>issue_d_Mar-2019</th>\n",
       "      <th>initial_list_status_w</th>\n",
       "      <th>next_pymnt_d_May-2019</th>\n",
       "      <th>application_type_Joint App</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>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "    </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>0.526309</td>\n",
       "      <td>0.106747</td>\n",
       "      <td>0.357659</td>\n",
       "      <td>0.373992</td>\n",
       "      <td>0.148001</td>\n",
       "      <td>0.451066</td>\n",
       "      <td>0.177238</td>\n",
       "      <td>0.876121</td>\n",
       "      <td>0.616839</td>\n",
       "      <td>0.139660</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>0.499311</td>\n",
       "      <td>0.308793</td>\n",
       "      <td>0.479314</td>\n",
       "      <td>0.483865</td>\n",
       "      <td>0.355104</td>\n",
       "      <td>0.497603</td>\n",
       "      <td>0.381873</td>\n",
       "      <td>0.329446</td>\n",
       "      <td>0.486161</td>\n",
       "      <td>0.346637</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</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>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</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>1.000000</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>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 86 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  ...  home_ownership_MORTGAGE  home_ownership_OWN  \\\n",
       "count   68817.000000  ...             68817.000000        68817.000000   \n",
       "mean    17604.142828  ...                 0.526309            0.106747   \n",
       "std     21835.880400  ...                 0.499311            0.308793   \n",
       "min         0.000000  ...                 0.000000            0.000000   \n",
       "25%      6293.000000  ...                 0.000000            0.000000   \n",
       "50%     12068.000000  ...                 1.000000            0.000000   \n",
       "75%     21735.000000  ...                 1.000000            0.000000   \n",
       "max    587191.000000  ...                 1.000000            1.000000   \n",
       "\n",
       "       home_ownership_RENT  verification_status_Source Verified  \\\n",
       "count         68817.000000                         68817.000000   \n",
       "mean              0.357659                             0.373992   \n",
       "std               0.479314                             0.483865   \n",
       "min               0.000000                             0.000000   \n",
       "25%               0.000000                             0.000000   \n",
       "50%               0.000000                             0.000000   \n",
       "75%               1.000000                             1.000000   \n",
       "max               1.000000                             1.000000   \n",
       "\n",
       "       verification_status_Verified  issue_d_Jan-2019  issue_d_Mar-2019  \\\n",
       "count                  68817.000000      68817.000000      68817.000000   \n",
       "mean                       0.148001          0.451066          0.177238   \n",
       "std                        0.355104          0.497603          0.381873   \n",
       "min                        0.000000          0.000000          0.000000   \n",
       "25%                        0.000000          0.000000          0.000000   \n",
       "50%                        0.000000          0.000000          0.000000   \n",
       "75%                        0.000000          1.000000          0.000000   \n",
       "max                        1.000000          1.000000          1.000000   \n",
       "\n",
       "       initial_list_status_w  next_pymnt_d_May-2019  \\\n",
       "count           68817.000000           68817.000000   \n",
       "mean                0.876121               0.616839   \n",
       "std                 0.329446               0.486161   \n",
       "min                 0.000000               0.000000   \n",
       "25%                 1.000000               0.000000   \n",
       "50%                 1.000000               1.000000   \n",
       "75%                 1.000000               1.000000   \n",
       "max                 1.000000               1.000000   \n",
       "\n",
       "       application_type_Joint App  \n",
       "count                68817.000000  \n",
       "mean                     0.139660  \n",
       "std                      0.346637  \n",
       "min                      0.000000  \n",
       "25%                      0.000000  \n",
       "50%                      0.000000  \n",
       "75%                      0.000000  \n",
       "max                      1.000000  \n",
       "\n",
       "[8 rows x 86 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "low_risk     68470\n",
       "high_risk      347\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the balance of our target values\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the X and y into X_train, X_test, y_train, y_test\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    random_state=1,\n",
    "                                                    stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Pre-Processing\n",
    "\n",
    "Scale the training and testing data using the `StandardScaler` from `sklearn`. Remember that when scaling the data, you only scale the features data (`X_train` and `X_testing`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the StandardScaler instance\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the Standard Scaler with the training data\n",
    "X_scaler = scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the training and testing data\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "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 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. Display 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 only, 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": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resample the training data with the BalancedRandomForestClassifier\n",
    "\n",
    "# fit Random Forest Classifier\n",
    "from imblearn.ensemble import BalancedRandomForestClassifier\n",
    "brf = BalancedRandomForestClassifier(n_estimators=100, random_state=1)\n",
    "brf.fit(X_train, y_train)\n",
    "\n",
    "y_pred_rf = brf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.795829959187949"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "\n",
    "balanced_accuracy_score(y_test, y_pred_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   62,    25],\n",
       "       [ 2071, 15047]])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, y_pred_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.03      0.71      0.88      0.06      0.79      0.62        87\n",
      "   low_risk       1.00      0.88      0.71      0.93      0.79      0.64     17118\n",
      "\n",
      "avg / total       0.99      0.88      0.71      0.93      0.79      0.64     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "print(classification_report_imbalanced(y_test, y_pred_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.07879500304736044, 'total_rec_prncp'),\n",
       " (0.06829645652825876, 'total_rec_int'),\n",
       " (0.06332331273002255, 'total_pymnt_inv'),\n",
       " (0.060702719175672847, 'total_pymnt'),\n",
       " (0.04832507175020008, 'last_pymnt_amnt'),\n",
       " (0.03000950634504879, 'int_rate'),\n",
       " (0.019434519122300684, 'mths_since_recent_inq'),\n",
       " (0.019336147557062193, 'dti'),\n",
       " (0.01830260141700211, 'installment'),\n",
       " (0.01810687139169386, 'issue_d_Jan-2019'),\n",
       " (0.017868317243972903, 'out_prncp_inv'),\n",
       " (0.017744481092540076, 'out_prncp'),\n",
       " (0.017266674348851853, 'mo_sin_old_rev_tl_op'),\n",
       " (0.017100709715639754, 'annual_inc'),\n",
       " (0.016525686456290593, 'max_bal_bc'),\n",
       " (0.01615940678387423, 'tot_cur_bal'),\n",
       " (0.016086658185768037, 'total_bc_limit'),\n",
       " (0.015465081854338305, 'il_util'),\n",
       " (0.015295210530466636, 'tot_hi_cred_lim'),\n",
       " (0.01513099875341602, 'bc_util'),\n",
       " (0.014208860445959953, 'revol_bal'),\n",
       " (0.014131229400594334, 'avg_cur_bal'),\n",
       " (0.01323256898112484, 'loan_amnt'),\n",
       " (0.012879429263832887, 'all_util'),\n",
       " (0.012660236513082953, 'total_bal_ex_mort'),\n",
       " (0.01237941668140317, 'issue_d_Mar-2019'),\n",
       " (0.012303202091248236, 'bc_open_to_buy'),\n",
       " (0.012169026173095324, 'total_bal_il'),\n",
       " (0.01209975666739387, 'total_il_high_credit_limit'),\n",
       " (0.012080181637595106, 'mths_since_recent_bc'),\n",
       " (0.01199556065746669, 'mo_sin_old_il_acct'),\n",
       " (0.011404425246702325, 'mths_since_rcnt_il'),\n",
       " (0.010932049428973856, 'inq_last_12m'),\n",
       " (0.010881788431255828, 'num_rev_accts'),\n",
       " (0.010715432303843322, 'total_rev_hi_lim'),\n",
       " (0.010468606551107892, 'total_acc'),\n",
       " (0.010017949027194955, 'open_acc'),\n",
       " (0.009874341137086302, 'num_actv_rev_tl'),\n",
       " (0.009498379004472549, 'total_cu_tl'),\n",
       " (0.009100493285450376, 'mo_sin_rcnt_rev_tl_op'),\n",
       " (0.009047352432489109, 'mo_sin_rcnt_tl'),\n",
       " (0.008964106730093296, 'num_il_tl'),\n",
       " (0.008723705919088482, 'num_op_rev_tl'),\n",
       " (0.008255306272904285, 'num_actv_bc_tl'),\n",
       " (0.008104471012786076, 'num_bc_sats'),\n",
       " (0.007970865797266325, 'num_sats'),\n",
       " (0.00774183011019435, 'acc_open_past_24mths'),\n",
       " (0.007732374993800439, 'num_tl_op_past_12m'),\n",
       " (0.0073688096073390545, 'pct_tl_nvr_dlq'),\n",
       " (0.007221009875305122, 'open_act_il'),\n",
       " (0.007179505025886921, 'num_rev_tl_bal_gt_0'),\n",
       " (0.007135251173933502, 'mort_acc'),\n",
       " (0.006984562920466658, 'num_bc_tl'),\n",
       " (0.00650399234353768, 'open_il_24m'),\n",
       " (0.006135882920333717, 'percent_bc_gt_75'),\n",
       " (0.005167972351022501, 'total_rec_late_fee'),\n",
       " (0.005162332471058333, 'open_acc_6m'),\n",
       " (0.0051042171194592085, 'open_il_12m'),\n",
       " (0.004987953452469955, 'open_rv_24m'),\n",
       " (0.004938053812449165, 'inq_fi'),\n",
       " (0.004765137984347149, 'inq_last_6mths'),\n",
       " (0.003461230580142148, 'open_rv_12m'),\n",
       " (0.0029652145497967106, 'next_pymnt_d_May-2019'),\n",
       " (0.002935840857976709, 'delinq_2yrs'),\n",
       " (0.0028533775359237956, 'num_accts_ever_120_pd'),\n",
       " (0.0027005577685376265, 'application_type_Joint App'),\n",
       " (0.0026889984701502674, 'tot_coll_amt'),\n",
       " (0.0021716812883856487, 'verification_status_Verified'),\n",
       " (0.0019184203714863275, 'home_ownership_MORTGAGE'),\n",
       " (0.0019019863466120518, 'home_ownership_OWN'),\n",
       " (0.0016829792138755057, 'verification_status_Source Verified'),\n",
       " (0.0016054411771549578, 'pub_rec'),\n",
       " (0.001566696028740402, 'pub_rec_bankruptcies'),\n",
       " (0.0015347586070784517, 'home_ownership_RENT'),\n",
       " (0.0010527959070002451, 'initial_list_status_w'),\n",
       " (0.0006098903871734477, 'num_tl_90g_dpd_24m'),\n",
       " (0.0005158840718607699, 'collections_12_mths_ex_med'),\n",
       " (0.00033118555320792853, 'chargeoff_within_12_mths'),\n",
       " (0.0, 'tax_liens'),\n",
       " (0.0, 'recoveries'),\n",
       " (0.0, 'policy_code'),\n",
       " (0.0, 'num_tl_30dpd'),\n",
       " (0.0, 'num_tl_120dpd_2m'),\n",
       " (0.0, 'delinq_amnt'),\n",
       " (0.0, 'collection_recovery_fee'),\n",
       " (0.0, 'acc_now_delinq')]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List the features sorted in descending order by feature importance\n",
    "sorted(zip(brf.feature_importances_, X.columns), reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Easy Ensemble Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the Classifier\n",
    "from sklearn.datasets import make_classification\n",
    "from imblearn.ensemble import EasyEnsembleClassifier \n",
    "eec = EasyEnsembleClassifier(n_estimators=100, random_state=1)\n",
    "eec.fit(X_train, y_train)\n",
    "y_pred = eec.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9263912558266958"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "\n",
    "balanced_accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   79,     8],\n",
       "       [  946, 16172]])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.08      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",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Questions\n",
    "\n",
    "### 1. Which model had the best balanced accuracy score?\n",
    "\n",
    "   The balanced accuracy scores were as follows - \n",
    "\n",
    "   * Balanced Random Forest Classifier: 0.795829959187949\n",
    "   * Easy Ensemble Classifier: 0.9263912558266958\n",
    "   \n",
    "   Easy Ensemble Classifier had the best balanced accuracy score.\n",
    "\n",
    "### 2. Which model had the best recall score?\n",
    "\n",
    "   The recall scores were as follows - \n",
    "\n",
    "   * Balanced Random Forest Classifier: high risk: 0.71, low risk: 0.88, average: 0.88\n",
    "   * Easy Ensemble Classifier: high risk: 0.91, low risk: 0.94, average: 0.94\n",
    "   \n",
    "   Easy Ensemble Classifier had the best recall score.\n",
    "\n",
    "### 3. Which model had the best geometric mean score?\n",
    "\n",
    "   The geometric mean scores were as follows - \n",
    "\n",
    "   * Balanced Random Forest Classifier: high risk: 0.79, low risk: 0.79, average: 0.79\n",
    "   * Easy Ensemble Classifier: high risk: 0.93, low risk: 0.93, average: 0.93\n",
    "   \n",
    "   Easy Ensemble Classifier had the best geometric mean score.\n",
    "\n",
    "### 4. What are the top three features?\n",
    "\n",
    "   The top three features are: \n",
    "   \n",
    "   * total_rec_prncp\n",
    "   * total_rec_int\n",
    "   * total_pymnt_in\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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