credit_risk_resampling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Credit Risk Resampling Techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the CSV and Perform Basic Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\n",
    "    \"loan_amnt\", \"int_rate\", \"installment\", \"home_ownership\",\n",
    "    \"annual_inc\", \"verification_status\", \"issue_d\", \"loan_status\",\n",
    "    \"pymnt_plan\", \"dti\", \"delinq_2yrs\", \"inq_last_6mths\",\n",
    "    \"open_acc\", \"pub_rec\", \"revol_bal\", \"total_acc\",\n",
    "    \"initial_list_status\", \"out_prncp\", \"out_prncp_inv\", \"total_pymnt\",\n",
    "    \"total_pymnt_inv\", \"total_rec_prncp\", \"total_rec_int\", \"total_rec_late_fee\",\n",
    "    \"recoveries\", \"collection_recovery_fee\", \"last_pymnt_amnt\", \"next_pymnt_d\",\n",
    "    \"collections_12_mths_ex_med\", \"policy_code\", \"application_type\", \"acc_now_delinq\",\n",
    "    \"tot_coll_amt\", \"tot_cur_bal\", \"open_acc_6m\", \"open_act_il\",\n",
    "    \"open_il_12m\", \"open_il_24m\", \"mths_since_rcnt_il\", \"total_bal_il\",\n",
    "    \"il_util\", \"open_rv_12m\", \"open_rv_24m\", \"max_bal_bc\",\n",
    "    \"all_util\", \"total_rev_hi_lim\", \"inq_fi\", \"total_cu_tl\",\n",
    "    \"inq_last_12m\", \"acc_open_past_24mths\", \"avg_cur_bal\", \"bc_open_to_buy\",\n",
    "    \"bc_util\", \"chargeoff_within_12_mths\", \"delinq_amnt\", \"mo_sin_old_il_acct\",\n",
    "    \"mo_sin_old_rev_tl_op\", \"mo_sin_rcnt_rev_tl_op\", \"mo_sin_rcnt_tl\", \"mort_acc\",\n",
    "    \"mths_since_recent_bc\", \"mths_since_recent_inq\", \"num_accts_ever_120_pd\", \"num_actv_bc_tl\",\n",
    "    \"num_actv_rev_tl\", \"num_bc_sats\", \"num_bc_tl\", \"num_il_tl\",\n",
    "    \"num_op_rev_tl\", \"num_rev_accts\", \"num_rev_tl_bal_gt_0\",\n",
    "    \"num_sats\", \"num_tl_120dpd_2m\", \"num_tl_30dpd\", \"num_tl_90g_dpd_24m\",\n",
    "    \"num_tl_op_past_12m\", \"pct_tl_nvr_dlq\", \"percent_bc_gt_75\", \"pub_rec_bankruptcies\",\n",
    "    \"tax_liens\", \"tot_hi_cred_lim\", \"total_bal_ex_mort\", \"total_bc_limit\",\n",
    "    \"total_il_high_credit_limit\", \"hardship_flag\", \"debt_settlement_flag\"\n",
    "]\n",
    "\n",
    "target = [\"loan_status\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>pymnt_plan</th>\n",
       "      <th>dti</th>\n",
       "      <th>...</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>20000.0</td>\n",
       "      <td>17.19%</td>\n",
       "      <td>499.10</td>\n",
       "      <td>RENT</td>\n",
       "      <td>47000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>Issued</td>\n",
       "      <td>n</td>\n",
       "      <td>14.02</td>\n",
       "      <td>...</td>\n",
       "      <td>98.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75824.0</td>\n",
       "      <td>31546.0</td>\n",
       "      <td>33800.0</td>\n",
       "      <td>21524.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21225.0</td>\n",
       "      <td>14.74%</td>\n",
       "      <td>502.05</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>Issued</td>\n",
       "      <td>n</td>\n",
       "      <td>16.80</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>747075.0</td>\n",
       "      <td>209426.0</td>\n",
       "      <td>53500.0</td>\n",
       "      <td>128175.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>17.97%</td>\n",
       "      <td>180.69</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>62000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>Issued</td>\n",
       "      <td>n</td>\n",
       "      <td>19.82</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>255738.0</td>\n",
       "      <td>31615.0</td>\n",
       "      <td>9400.0</td>\n",
       "      <td>39938.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>8.19%</td>\n",
       "      <td>628.49</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>Issued</td>\n",
       "      <td>n</td>\n",
       "      <td>22.66</td>\n",
       "      <td>...</td>\n",
       "      <td>95.7</td>\n",
       "      <td>22.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>448069.0</td>\n",
       "      <td>84744.0</td>\n",
       "      <td>49400.0</td>\n",
       "      <td>105180.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12000.0</td>\n",
       "      <td>15.57%</td>\n",
       "      <td>289.09</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>49000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>Issued</td>\n",
       "      <td>n</td>\n",
       "      <td>13.47</td>\n",
       "      <td>...</td>\n",
       "      <td>86.1</td>\n",
       "      <td>14.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>189260.0</td>\n",
       "      <td>106025.0</td>\n",
       "      <td>24400.0</td>\n",
       "      <td>68860.0</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    20000.0   17.19%       499.10           RENT     47000.0   \n",
       "1    21225.0   14.74%       502.05       MORTGAGE    225000.0   \n",
       "2     5000.0   17.97%       180.69       MORTGAGE     62000.0   \n",
       "3    20000.0    8.19%       628.49       MORTGAGE    200000.0   \n",
       "4    12000.0   15.57%       289.09       MORTGAGE     49000.0   \n",
       "\n",
       "  verification_status   issue_d loan_status pymnt_plan    dti  ...  \\\n",
       "0     Source Verified  Mar-2019      Issued          n  14.02  ...   \n",
       "1        Not Verified  Mar-2019      Issued          n  16.80  ...   \n",
       "2        Not Verified  Mar-2019      Issued          n  19.82  ...   \n",
       "3        Not Verified  Mar-2019      Issued          n  22.66  ...   \n",
       "4     Source Verified  Mar-2019      Issued          n  13.47  ...   \n",
       "\n",
       "   pct_tl_nvr_dlq  percent_bc_gt_75  pub_rec_bankruptcies  tax_liens  \\\n",
       "0            98.0              12.5                   0.0        0.0   \n",
       "1           100.0              50.0                   1.0        0.0   \n",
       "2            66.7              50.0                   0.0        0.0   \n",
       "3            95.7              22.2                   0.0        0.0   \n",
       "4            86.1              14.3                   0.0        0.0   \n",
       "\n",
       "   tot_hi_cred_lim  total_bal_ex_mort total_bc_limit  \\\n",
       "0          75824.0            31546.0        33800.0   \n",
       "1         747075.0           209426.0        53500.0   \n",
       "2         255738.0            31615.0         9400.0   \n",
       "3         448069.0            84744.0        49400.0   \n",
       "4         189260.0           106025.0        24400.0   \n",
       "\n",
       "   total_il_high_credit_limit  hardship_flag  debt_settlement_flag  \n",
       "0                     21524.0              N                     N  \n",
       "1                    128175.0              N                     N  \n",
       "2                     39938.0              N                     N  \n",
       "3                    105180.0              N                     N  \n",
       "4                     68860.0              N                     N  \n",
       "\n",
       "[5 rows x 86 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define the data path\n",
    "file_path = Path('LoanStats_2019Q1.csv')\n",
    "\n",
    "# load the data - \"skiprows=1)[:-2]\" skips the first rows and the last two ones.\n",
    "df = pd.read_csv(file_path, skiprows=1)[:-2]\n",
    "\n",
    "# .loc is locating all the columns and all the rows that are also assigned to the variable column, and making a copy of it, so now we have a dataframe with the skipped rows that are not actual data.\n",
    "df = df.loc[:, columns].copy()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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>20000.0</td>\n",
       "      <td>17.19%</td>\n",
       "      <td>499.10</td>\n",
       "      <td>RENT</td>\n",
       "      <td>47000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>Issued</td>\n",
       "      <td>n</td>\n",
       "      <td>14.02</td>\n",
       "      <td>...</td>\n",
       "      <td>98.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75824.0</td>\n",
       "      <td>31546.0</td>\n",
       "      <td>33800.0</td>\n",
       "      <td>21524.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21225.0</td>\n",
       "      <td>14.74%</td>\n",
       "      <td>502.05</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>Issued</td>\n",
       "      <td>n</td>\n",
       "      <td>16.80</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>747075.0</td>\n",
       "      <td>209426.0</td>\n",
       "      <td>53500.0</td>\n",
       "      <td>128175.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 86 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt int_rate  installment home_ownership  annual_inc  \\\n",
       "0    20000.0   17.19%       499.10           RENT     47000.0   \n",
       "1    21225.0   14.74%       502.05       MORTGAGE    225000.0   \n",
       "\n",
       "  verification_status   issue_d loan_status pymnt_plan    dti  ...  \\\n",
       "0     Source Verified  Mar-2019      Issued          n  14.02  ...   \n",
       "1        Not Verified  Mar-2019      Issued          n  16.80  ...   \n",
       "\n",
       "   pct_tl_nvr_dlq  percent_bc_gt_75  pub_rec_bankruptcies  tax_liens  \\\n",
       "0            98.0              12.5                   0.0        0.0   \n",
       "1           100.0              50.0                   1.0        0.0   \n",
       "\n",
       "   tot_hi_cred_lim  total_bal_ex_mort total_bc_limit  \\\n",
       "0          75824.0            31546.0        33800.0   \n",
       "1         747075.0           209426.0        53500.0   \n",
       "\n",
       "   total_il_high_credit_limit  hardship_flag  debt_settlement_flag  \n",
       "0                     21524.0              N                     N  \n",
       "1                    128175.0              N                     N  \n",
       "\n",
       "[2 rows x 86 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first we are dropping the columns that are completely empty, then we look at the rows and drop the rows that have at least one null value.\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",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>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>93</th>\n",
       "      <td>10500.0</td>\n",
       "      <td>17.19%</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>Current</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>99</th>\n",
       "      <td>25000.0</td>\n",
       "      <td>20.00%</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>Current</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 86 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    loan_amnt int_rate  installment home_ownership  annual_inc  \\\n",
       "93    10500.0   17.19%       375.35           RENT     66000.0   \n",
       "99    25000.0   20.00%       929.09       MORTGAGE    105000.0   \n",
       "\n",
       "   verification_status   issue_d loan_status pymnt_plan    dti  ...  \\\n",
       "93     Source Verified  Mar-2019     Current          n  27.24  ...   \n",
       "99            Verified  Mar-2019     Current          n  20.23  ...   \n",
       "\n",
       "    pct_tl_nvr_dlq  percent_bc_gt_75  pub_rec_bankruptcies  tax_liens  \\\n",
       "93            85.7             100.0                   0.0        0.0   \n",
       "99            91.2              50.0                   1.0        0.0   \n",
       "\n",
       "    tot_hi_cred_lim  total_bal_ex_mort total_bc_limit  \\\n",
       "93          65687.0            38199.0         2000.0   \n",
       "99         271427.0            60641.0        41200.0   \n",
       "\n",
       "    total_il_high_credit_limit  hardship_flag  debt_settlement_flag  \n",
       "93                     61987.0              N                     N  \n",
       "99                     49197.0              N                     N  \n",
       "\n",
       "[2 rows x 86 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the `Issued` loan status (we only want to look at loans that haven't been issued already, so we are filtering on the 'loan_status' column to display only the ones values that are not equal to 'issued').\n",
    "issued_mask = df['loan_status'] != 'Issued'\n",
    "\n",
    "df = df.loc[issued_mask]\n",
    "\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt                     float64\n",
       "int_rate                       object\n",
       "installment                   float64\n",
       "home_ownership                 object\n",
       "annual_inc                    float64\n",
       "                               ...   \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",
       "Length: 86, dtype: object"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check datatype - we can see that it is an object.\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert interest rate to numerical - we are replacing the '%' by a nothing '',\n",
    "# and using .astype to convert the object to a numeric type 'float' and divide the results by 100.\n",
    "df['int_rate'] = df['int_rate'].str.replace('%', '')\n",
    "df['int_rate'] = df['int_rate'].astype('float') / 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt                     float64\n",
       "int_rate                      float64\n",
       "installment                   float64\n",
       "home_ownership                 object\n",
       "annual_inc                    float64\n",
       "                               ...   \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",
       "Length: 86, dtype: object"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check datatype - now we see that it tis a float, ans that's how we need it to be.\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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",
       "        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",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 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",
       "\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",
       "\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",
       "\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",
       "\n",
       "   total_il_high_credit_limit  hardship_flag  debt_settlement_flag  \n",
       "0                     61987.0              N                     N  \n",
       "1                     49197.0              N                     N  \n",
       "\n",
       "[2 rows x 86 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 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(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split the Data into Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our features (.get_dummies() is converting all strings values into numeric ones,\n",
    "# by removing the whole columns and then adding one column per each new numeric value at the end, \n",
    "# and dropping the 'loan_status' column since this is the one to be used as the target.\n",
    "X = pd.get_dummies(df.drop(columns='loan_status'))\n",
    "\n",
    "# Create our target - the target is the outcome we want to check. \n",
    "# For this porject, we want to check if the loan is more likely going to be be high_rick or low_risk.\n",
    "y = df['loan_status']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate the summary statistics - here we are using '.describe' to display the summary statistic of X, \n",
    "# which represents the features/columns that we are using for analysis and prediction.\n",
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt                      float64\n",
       "int_rate                       float64\n",
       "installment                    float64\n",
       "annual_inc                     float64\n",
       "dti                            float64\n",
       "                                ...   \n",
       "next_pymnt_d_May-2019            uint8\n",
       "application_type_Individual      uint8\n",
       "application_type_Joint App       uint8\n",
       "hardship_flag_N                  uint8\n",
       "debt_settlement_flag_N           uint8\n",
       "Length: 95, dtype: object"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "low_risk     68470\n",
       "high_risk      347\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the balance of our target values - we can see that we have a lot more low_risk values values than high_risk, \n",
    "# which makes sense for a bank to have this natural imbalance.\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51612, 95)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import train_test_split \n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# here we are defining the train and test for both the features 'X' and the target 'y', \n",
    "# then in parenthesis we call the X and y variables and choose which random_state to use \n",
    "# (which is important to be the same one throughout the analysis to ensure that the random numbers are generated in the same order).\n",
    "# The \"stratify=y\" ensures that both the train and test sets have the proportion of examples in each class that is present in the provided “y” array.\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1, stratify= y)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Oversampling\n",
    "\n",
    "In this section, you will compare two oversampling algorithms to determine which algorithm results in the best performance. You will oversample the data using the naive random oversampling algorithm and the SMOTE algorithm. For each algorithm, be sure to complete the folliowing steps:\n",
    "\n",
    "1. View the count of the target classes using `Counter` from the collections library. \n",
    "3. Use the resampled data to train a logistic regression model.\n",
    "3. Calculate the balanced accuracy score from sklearn.metrics.\n",
    "4. Print the confusion matrix from sklearn.metrics.\n",
    "5. Generate a classication report using the `imbalanced_classification_report` from imbalanced-learn.\n",
    "\n",
    "Note: Use a random state of 1 for each sampling algorithm to ensure consistency between tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Random Oversampling\n",
    "\n",
    "One way to fight imbalance data is to generate new samples in the minority classes. The most naive strategy is to generate new samples by randomly sampling with replacement of the currently available samples. The RandomOverSampler offers such a scheme."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'low_risk': 51352, 'high_risk': 51352})"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the training data with the RandomOversampler\n",
    "\n",
    "# implement random oversampling\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "# Here we are declaring the variable ros and assigning it to the RandomOverSamples method with the \"random_state=1\".\n",
    "# The RandomOverSamples method is used to over-sample the minority class by picking samples at random with replacement.\n",
    "ros = RandomOverSampler(random_state=1)\n",
    "\n",
    "# To the \"ros\" variable, we apply the \"fit_resample\" method which basically resample's the dataset. \n",
    "X_resampled, y_resampled = ros.fit_resample(X_train, y_train)\n",
    "\n",
    "#Then we count the y_resampled to make sure that now \"low_risk\" and \"high_risk\" have the same amount of values for the oversampling analysis purpose.\n",
    "Counter(y_resampled)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Logistic Regression model using the resampled data\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# create the model - \"Logisticregression\" is a classification algorithm rather than regression algorithm.\n",
    "# Based on a given set of independent variables, it is used to estimate discrete value (0 or 1, yes/no, true/false).\n",
    "# solver='lbfgs' — Stands for Limited-memory Broyden–Fletcher–Goldfarb–Shanno. \n",
    "# It approximates the second derivative matrix updates with gradient evaluations. It stores only the last few updates, so it saves memory.\n",
    "model = LogisticRegression(solver='lbfgs', random_state=1)\n",
    "\n",
    "# Train the model - fitting is equal to training. Then, after it is trained, the model can be used to make predictions, usually with a .predict() method call.\n",
    "model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6456130066757718"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "\n",
    "# Predict - The predict() function accepts only a single argument which is usually the data to be tested, in this case X-test.\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# The balance_accuracy_score is defined as the average of recall obtained on each class.\n",
    "ros_accuracy = balanced_accuracy_score(y_test, y_pred)\n",
    "ros_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "• \"y_pred\" represents the Estimated targets as returned by a classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   53,    34],\n",
       "       [ 5443, 11675]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# define the y_pred\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# generate the confusion matrix by using the y_test and y_pred as the arguments.\n",
    "# The matrix compares the actual target values with those predicted by the machine learning model.\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.01      0.61      0.68      0.02      0.64      0.41        87\n",
      "   low_risk       1.00      0.68      0.61      0.81      0.64      0.42     17118\n",
      "\n",
      "avg / total       0.99      0.68      0.61      0.81      0.64      0.42     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report using \"y_test\" and \"y_pred\" as their arguments.\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explanation of the classification report\n",
    "\n",
    "• Precision will give us the proportion of positive identifications that were indeed correct.\n",
    "\n",
    "• The sensitivity/recall is the ratio tp / (tp + fn) where tp is the number of true positives and fn the number of false negatives. The sensitivity quantifies the ability to avoid false negatives_[1]. In other words, recall will determine the proportion of real positives that were correctly identified.\n",
    "\n",
    "• The specificity is the ratio tn / (tn + fp) where tn is the number of true negatives and fn the number of false negatives. The specificity quantifies the ability to avoid false positives_[1].\n",
    "\n",
    "\n",
    "• F1 Score is a metric that is useful when we need to seek a balance between precision and recall.\n",
    "\n",
    "• geo - The geometric mean (G-mean) is the root of the product of class-wise sensitivity. This measure tries to maximize the accuracy on each of the classes while keeping these accuracies balanced. For binary classification G-mean is the squared root of the product of the sensitivity and specificity. For multi-class problems it is a higher root of the product of sensitivity for each class. The best value is 1 and the worst value is 0. \n",
    "\n",
    "• iba -  index balanced accuracy of the geometric mean.\n",
    "\n",
    "• The support is the number of occurrences of each class in y_true."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SMOTE Oversampling\n",
    "\n",
    "SMOTE (Synthetic Minority Oversampling Technique) works by randomly picking a point from the minority class and computing the k-nearest neighbors for this point. The synthetic points are added between the chosen point and its neighbors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'low_risk': 51352, 'high_risk': 51352})"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the training data with SMOTE\n",
    "from imblearn.over_sampling import SMOTE\n",
    "X_resampled, y_resampled = SMOTE(random_state=1, sampling_strategy='auto').fit_resample(\n",
    "    X_train, y_train\n",
    ")\n",
    "# Count the \"y_resmapled\" to make sure both \"high_risk\" and \"low_risk\" now have the same amount of values, which means they are balanced.\n",
    "Counter(y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Logistic Regression model using the resampled data\n",
    "\n",
    "# create the model - \"Logisticregression\" is a classification algorithm rather than regression algorithm.\n",
    "# Based on a given set of independent variables, it is used to estimate discrete value (0 or 1, yes/no, true/false).\n",
    "# solver='lbfgs' — Stands for Limited-memory Broyden–Fletcher–Goldfarb–Shanno. \n",
    "# It approximates the second derivative matrix updates with gradient evaluations. It stores only the last few updates, so it saves memory.\n",
    "model = LogisticRegression(solver='lbfgs', random_state=1)\n",
    "model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6234433606890912"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "y_pred = model.predict(X_test)\n",
    "smote_accuracy = balanced_accuracy_score(y_test, y_pred)\n",
    "smote_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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 high_risk</th>\n",
       "      <th>Predicted low_risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual high_risk</th>\n",
       "      <td>53</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual low_risk</th>\n",
       "      <td>6202</td>\n",
       "      <td>10916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Predicted high_risk  Predicted low_risk\n",
       "Actual high_risk                   53                  34\n",
       "Actual low_risk                  6202               10916"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Create a DataFrame from the confusion matrix.\n",
    "cm_df = pd.DataFrame(\n",
    "    cm, index=[\"Actual high_risk\", \"Actual low_risk\"], columns=[\"Predicted high_risk\", \"Predicted low_risk\"])\n",
    "\n",
    "cm_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.01      0.61      0.64      0.02      0.62      0.39        87\n",
      "   low_risk       1.00      0.64      0.61      0.78      0.62      0.39     17118\n",
      "\n",
      "avg / total       0.99      0.64      0.61      0.77      0.62      0.39     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Undersampling\n",
    "\n",
    "In this section, you will test an undersampling algorithms to determine which algorithm results in the best performance compared to the oversampling algorithms above. You will undersample the data using the Cluster Centroids algorithm and complete the folliowing steps:\n",
    "\n",
    "1. View the count of the target classes using `Counter` from the collections library. \n",
    "3. Use the resampled data to train a logistic regression model.\n",
    "3. Calculate the balanced accuracy score from sklearn.metrics.\n",
    "4. Print the confusion matrix from sklearn.metrics.\n",
    "5. Generate a classication report using the `imbalanced_classification_report` from imbalanced-learn.\n",
    "\n",
    "Note: Use a random state of 1 for each sampling algorithm to ensure consistency between tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'high_risk': 260, 'low_risk': 260})"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the data using the ClusterCentroids resampler\n",
    "# Warning: This is a large dataset, and this step may take some time to complete\n",
    "\n",
    "from imblearn.under_sampling import ClusterCentroids\n",
    "cc = ClusterCentroids(random_state=1)\n",
    "X_resampled, y_resampled = cc.fit_resample(X_train, y_train)\n",
    "Counter(y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Logistic Regression model using the resampled data\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression(solver='lbfgs', random_state=1)\n",
    "model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5177862786097313"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "y_pred = model.predict(X_test)\n",
    "undersampling_accuracy = balanced_accuracy_score(y_test, y_pred)\n",
    "undersampling_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted high_risk</th>\n",
       "      <th>Predicted low_risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual high_risk</th>\n",
       "      <td>50</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual low_risk</th>\n",
       "      <td>9229</td>\n",
       "      <td>7889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Predicted high_risk  Predicted low_risk\n",
       "Actual high_risk                   50                  37\n",
       "Actual low_risk                  9229                7889"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred = model.predict(X_test)\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Create a DataFrame from the confusion matrix.\n",
    "cm_df = pd.DataFrame(\n",
    "    cm, index=[\"Actual high_risk\", \"Actual low_risk\"], columns=[\"Predicted high_risk\", \"Predicted low_risk\"])\n",
    "\n",
    "cm_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.01      0.57      0.46      0.01      0.51      0.27        87\n",
      "   low_risk       1.00      0.46      0.57      0.63      0.51      0.26     17118\n",
      "\n",
      "avg / total       0.99      0.46      0.57      0.63      0.51      0.26     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combination (Over and Under) Sampling\n",
    "\n",
    "In this section, you will test a combination over- and under-sampling algorithm to determine if the algorithm results in the best performance compared to the other sampling algorithms above. You will resample the data using the SMOTEENN algorithm and complete the folliowing steps:\n",
    "\n",
    "1. View the count of the target classes using `Counter` from the collections library. \n",
    "3. Use the resampled data to train a logistic regression model.\n",
    "3. Calculate the balanced accuracy score from sklearn.metrics.\n",
    "4. Print the confusion matrix from sklearn.metrics.\n",
    "5. Generate a classication report using the `imbalanced_classification_report` from imbalanced-learn.\n",
    "\n",
    "Note: Use a random state of 1 for each sampling algorithm to ensure consistency between tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'high_risk': 68458, 'low_risk': 62022})"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the training data with SMOTEENN\n",
    "# Warning: This is a large dataset, and this step may take some time to complete\n",
    "# Count the resampled classes\n",
    "from imblearn.combine import SMOTEENN\n",
    "\n",
    "smote_enn = SMOTEENN(random_state=1)\n",
    "X_resampled, y_resampled = smote_enn.fit_resample(X, y)\n",
    "Counter(y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Logistic Regression model using the resampled data\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression(solver='lbfgs', random_state=1)\n",
    "model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6531287896185101"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "y_pred = model.predict(X_test)\n",
    "smoteenn_accuracy = balanced_accuracy_score(y_test, y_pred)\n",
    "smoteenn_accuracy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\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 high_risk</th>\n",
       "      <th>Predicted low_risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual high_risk</th>\n",
       "      <td>60</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual low_risk</th>\n",
       "      <td>6563</td>\n",
       "      <td>10555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Predicted high_risk  Predicted low_risk\n",
       "Actual high_risk                   60                  27\n",
       "Actual low_risk                  6563               10555"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Create a DataFrame from the confusion matrix.\n",
    "cm_df = pd.DataFrame(\n",
    "    cm, index=[\"Actual high_risk\", \"Actual low_risk\"], columns=[\"Predicted high_risk\", \"Predicted low_risk\"])\n",
    "\n",
    "cm_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.01      0.69      0.62      0.02      0.65      0.43        87\n",
      "   low_risk       1.00      0.62      0.69      0.76      0.65      0.42     17118\n",
      "\n",
      "avg / total       0.99      0.62      0.69      0.76      0.65      0.42     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy Score Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>Model</th>\n",
       "      <th>Balanced Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomOverSampler</td>\n",
       "      <td>0.645613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SMOTE Oversampling</td>\n",
       "      <td>0.623443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CC Undersampling</td>\n",
       "      <td>0.517786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SMOTEENN Combination</td>\n",
       "      <td>0.653129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Model  Balanced Accuracy\n",
       "0     RandomOverSampler           0.645613\n",
       "1    SMOTE Oversampling           0.623443\n",
       "2      CC Undersampling           0.517786\n",
       "3  SMOTEENN Combination           0.653129"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"Model\":[\"RandomOverSampler\",\"SMOTE Oversampling\",\"CC Undersampling\",\"SMOTEENN Combination\"],\n",
    "            \"Balanced Accuracy\":[ros_accuracy, smote_accuracy, undersampling_accuracy, smoteenn_accuracy]})"
   ]
  }
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