credit_risk_resampling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Credit Risk Resampling Techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the CSV and Perform Basic Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\n",
    "    \"loan_amnt\", \"int_rate\", \"installment\", #\"home_ownership\",\n",
    "    #\"annual_inc\", \"verification_status\", \"issue_d\", \n",
    "    \"loan_status\",\n",
    "    #\"pymnt_plan\", \n",
    "    \"dti\", \"delinq_2yrs\", \"inq_last_6mths\",\n",
    "    \"open_acc\", \"pub_rec\", \"revol_bal\", \"total_acc\",\n",
    "    #\"initial_list_status\", \n",
    "    \"out_prncp\", #\"out_prncp_inv\",\n",
    "    \"total_pymnt\", #\"total_pymnt_inv\", \n",
    "    \"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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>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>revol_bal</th>\n",
       "      <th>...</th>\n",
       "      <th>num_tl_90g_dpd_24m</th>\n",
       "      <th>num_tl_op_past_12m</th>\n",
       "      <th>pct_tl_nvr_dlq</th>\n",
       "      <th>percent_bc_gt_75</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "      <th>tot_hi_cred_lim</th>\n",
       "      <th>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "      <th>total_il_high_credit_limit</th>\n",
       "    </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>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>1609.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>85.7</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65687.0</td>\n",
       "      <td>38199.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>61987.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25000.0</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>929.09</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>18368.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>91.2</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>271427.0</td>\n",
       "      <td>60641.0</td>\n",
       "      <td>41200.0</td>\n",
       "      <td>49197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>529.88</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>13247.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>66.7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60644.0</td>\n",
       "      <td>45684.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>43144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.1640</td>\n",
       "      <td>353.55</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>17996.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99506.0</td>\n",
       "      <td>68784.0</td>\n",
       "      <td>19700.0</td>\n",
       "      <td>76506.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22000.0</td>\n",
       "      <td>0.1474</td>\n",
       "      <td>520.39</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>9091.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>219750.0</td>\n",
       "      <td>25919.0</td>\n",
       "      <td>27600.0</td>\n",
       "      <td>20000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  int_rate  installment loan_status    dti  delinq_2yrs  \\\n",
       "0    10500.0    0.1719       375.35    low_risk  27.24          0.0   \n",
       "1    25000.0    0.2000       929.09    low_risk  20.23          0.0   \n",
       "2    20000.0    0.2000       529.88    low_risk  24.26          0.0   \n",
       "3    10000.0    0.1640       353.55    low_risk  31.44          0.0   \n",
       "4    22000.0    0.1474       520.39    low_risk  18.76          0.0   \n",
       "\n",
       "   inq_last_6mths  open_acc  pub_rec  revol_bal  ...  num_tl_90g_dpd_24m  \\\n",
       "0             0.0       8.0      0.0     1609.0  ...                 0.0   \n",
       "1             0.0      17.0      1.0    18368.0  ...                 0.0   \n",
       "2             0.0       8.0      0.0    13247.0  ...                 0.0   \n",
       "3             1.0      10.0      1.0    17996.0  ...                 0.0   \n",
       "4             1.0      14.0      0.0     9091.0  ...                 0.0   \n",
       "\n",
       "   num_tl_op_past_12m  pct_tl_nvr_dlq  percent_bc_gt_75  pub_rec_bankruptcies  \\\n",
       "0                 3.0            85.7             100.0                   0.0   \n",
       "1                 6.0            91.2              50.0                   1.0   \n",
       "2                 4.0            66.7              50.0                   0.0   \n",
       "3                 1.0           100.0              50.0                   1.0   \n",
       "4                 1.0           100.0               0.0                   0.0   \n",
       "\n",
       "   tax_liens  tot_hi_cred_lim  total_bal_ex_mort  total_bc_limit  \\\n",
       "0        0.0          65687.0            38199.0          2000.0   \n",
       "1        0.0         271427.0            60641.0         41200.0   \n",
       "2        0.0          60644.0            45684.0          7500.0   \n",
       "3        0.0          99506.0            68784.0         19700.0   \n",
       "4        0.0         219750.0            25919.0         27600.0   \n",
       "\n",
       "   total_il_high_credit_limit  \n",
       "0                     61987.0  \n",
       "1                     49197.0  \n",
       "2                     43144.0  \n",
       "3                     76506.0  \n",
       "4                     20000.0  \n",
       "\n",
       "[5 rows x 70 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data\n",
    "file_path = Path('./LoanStats_2019Q1.csv')\n",
    "df = pd.read_csv(file_path, skiprows=1)[:-2]\n",
    "df = df.loc[:, columns].copy()\n",
    "\n",
    "# Drop the null columns where all values are null\n",
    "df = df.dropna(axis='columns', how='all')\n",
    "\n",
    "# Drop the null rows\n",
    "df = df.dropna()\n",
    "\n",
    "# Remove the `Issued` loan status\n",
    "# removed fully paid, or charged off also\n",
    "issued_mask = df['loan_status'] != 'Issued'\n",
    "df = df.loc[issued_mask]\n",
    "fp_mask= df['loan_status'] != 'Fully Paid'\n",
    "df = df.loc[fp_mask]\n",
    "co_mask = df['loan_status'] != 'Charged Off'\n",
    "df = df.loc[co_mask]\n",
    "\n",
    "# convert interest rate to numerical\n",
    "df['int_rate'] = df['int_rate'].str.replace('%', '')\n",
    "df['int_rate'] = df['int_rate'].astype('float') / 100\n",
    "\n",
    "# Convert the target column values to low_risk and high_risk based on their values\n",
    "x = {'Current': 'low_risk'}   \n",
    "df = df.replace(x)\n",
    "\n",
    "x = dict.fromkeys(['Late (31-120 days)', 'Late (16-30 days)', 'Default', 'In Grace Period'], 'high_risk')    \n",
    "df = df.replace(x)\n",
    "\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split the Data into Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our features\n",
    "X = df.copy()\n",
    "X = X.drop(\"loan_status\", axis=1)\n",
    "X.head() \n",
    "\n",
    "# Create our target\n",
    "# using label encoder technique\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "df2 = df.copy()\n",
    "\n",
    "df2['loan_status'] = le.fit_transform(df2['loan_status'])\n",
    "from pandas import DataFrame\n",
    "y = DataFrame(df2[\"loan_status\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>...</th>\n",
       "      <th>num_tl_90g_dpd_24m</th>\n",
       "      <th>num_tl_op_past_12m</th>\n",
       "      <th>pct_tl_nvr_dlq</th>\n",
       "      <th>percent_bc_gt_75</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "      <th>tot_hi_cred_lim</th>\n",
       "      <th>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "      <th>total_il_high_credit_limit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-5.13</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>-5.81</td>\n",
       "      <td>-9.45</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-8.29</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-9.95</td>\n",
       "      <td>-8.63</td>\n",
       "      <td>...</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-6.84</td>\n",
       "      <td>6.43</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-9.62</td>\n",
       "      <td>-9.41</td>\n",
       "      <td>-9.93</td>\n",
       "      <td>-9.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.31</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.92</td>\n",
       "      <td>-9.59</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-5.71</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-9.37</td>\n",
       "      <td>-6.15</td>\n",
       "      <td>...</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-3.68</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-8.37</td>\n",
       "      <td>-9.07</td>\n",
       "      <td>-8.39</td>\n",
       "      <td>-9.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.26</td>\n",
       "      <td>1.27</td>\n",
       "      <td>-3.93</td>\n",
       "      <td>-9.51</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-8.29</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-9.55</td>\n",
       "      <td>-7.76</td>\n",
       "      <td>...</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-5.79</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-9.65</td>\n",
       "      <td>-9.30</td>\n",
       "      <td>-9.71</td>\n",
       "      <td>-9.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-5.38</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>-6.08</td>\n",
       "      <td>-9.37</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-7.71</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-9.39</td>\n",
       "      <td>-7.52</td>\n",
       "      <td>...</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-8.95</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-9.42</td>\n",
       "      <td>-8.94</td>\n",
       "      <td>-9.23</td>\n",
       "      <td>-8.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.77</td>\n",
       "      <td>-2.96</td>\n",
       "      <td>-4.05</td>\n",
       "      <td>-9.62</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-6.57</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-9.69</td>\n",
       "      <td>-6.89</td>\n",
       "      <td>...</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-8.95</td>\n",
       "      <td>10.00</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-8.69</td>\n",
       "      <td>-9.60</td>\n",
       "      <td>-8.92</td>\n",
       "      <td>-9.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 69 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  int_rate  installment   dti  delinq_2yrs  inq_last_6mths  \\\n",
       "0      -5.13     -0.99        -5.81 -9.45        -10.0           -10.0   \n",
       "1       2.31      1.27         0.92 -9.59        -10.0           -10.0   \n",
       "2      -0.26      1.27        -3.93 -9.51        -10.0           -10.0   \n",
       "3      -5.38     -1.63        -6.08 -9.37        -10.0            -6.0   \n",
       "4       0.77     -2.96        -4.05 -9.62        -10.0            -6.0   \n",
       "\n",
       "   open_acc  pub_rec  revol_bal  total_acc  ...  num_tl_90g_dpd_24m  \\\n",
       "0     -8.29    -10.0      -9.95      -8.63  ...               -10.0   \n",
       "1     -5.71     -5.0      -9.37      -6.15  ...               -10.0   \n",
       "2     -8.29    -10.0      -9.55      -7.76  ...               -10.0   \n",
       "3     -7.71     -5.0      -9.39      -7.52  ...               -10.0   \n",
       "4     -6.57    -10.0      -9.69      -6.89  ...               -10.0   \n",
       "\n",
       "   num_tl_op_past_12m  pct_tl_nvr_dlq  percent_bc_gt_75  pub_rec_bankruptcies  \\\n",
       "0               -6.84            6.43              10.0                 -10.0   \n",
       "1               -3.68            7.80               0.0                  -5.0   \n",
       "2               -5.79            1.68               0.0                 -10.0   \n",
       "3               -8.95           10.00               0.0                  -5.0   \n",
       "4               -8.95           10.00             -10.0                 -10.0   \n",
       "\n",
       "   tax_liens  tot_hi_cred_lim  total_bal_ex_mort  total_bc_limit  \\\n",
       "0      -10.0            -9.62              -9.41           -9.93   \n",
       "1      -10.0            -8.37              -9.07           -8.39   \n",
       "2      -10.0            -9.65              -9.30           -9.71   \n",
       "3      -10.0            -9.42              -8.94           -9.23   \n",
       "4      -10.0            -8.69              -9.60           -8.92   \n",
       "\n",
       "   total_il_high_credit_limit  \n",
       "0                       -9.13  \n",
       "1                       -9.31  \n",
       "2                       -9.40  \n",
       "3                       -8.93  \n",
       "4                       -9.72  \n",
       "\n",
       "[5 rows x 69 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scale all the X data \n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "data_scaler = MinMaxScaler(feature_range=(-10, 10))\n",
    "X_scaled = data_scaler.fit_transform(X)\n",
    "\n",
    "#converting back to dataframe\n",
    "X = pd.DataFrame(X_scaled, index=X.index, columns=X.columns)\n",
    "#rounding to 2 decimal places \n",
    "X = X.round(2)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>...</th>\n",
       "      <th>num_tl_90g_dpd_24m</th>\n",
       "      <th>num_tl_op_past_12m</th>\n",
       "      <th>pct_tl_nvr_dlq</th>\n",
       "      <th>percent_bc_gt_75</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "      <th>tot_hi_cred_lim</th>\n",
       "      <th>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "      <th>total_il_high_credit_limit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.00000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>...</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>68817.000000</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>-1.959989</td>\n",
       "      <td>-4.54778</td>\n",
       "      <td>-4.532837</td>\n",
       "      <td>-9.563994</td>\n",
       "      <td>-9.758233</td>\n",
       "      <td>-8.009213</td>\n",
       "      <td>-6.975087</td>\n",
       "      <td>-9.369850</td>\n",
       "      <td>-9.400402</td>\n",
       "      <td>-7.308626</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.942113</td>\n",
       "      <td>-7.663124</td>\n",
       "      <td>8.764626</td>\n",
       "      <td>-3.874757</td>\n",
       "      <td>-9.370141</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-8.744753</td>\n",
       "      <td>-9.056484</td>\n",
       "      <td>-8.836311</td>\n",
       "      <td>-9.220679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.270321</td>\n",
       "      <td>3.87541</td>\n",
       "      <td>3.501522</td>\n",
       "      <td>0.404396</td>\n",
       "      <td>0.797837</td>\n",
       "      <td>3.032486</td>\n",
       "      <td>1.720878</td>\n",
       "      <td>1.683984</td>\n",
       "      <td>0.743748</td>\n",
       "      <td>1.530366</td>\n",
       "      <td>...</td>\n",
       "      <td>0.433903</td>\n",
       "      <td>1.997693</td>\n",
       "      <td>2.081479</td>\n",
       "      <td>6.726293</td>\n",
       "      <td>1.683661</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.172376</td>\n",
       "      <td>0.886151</td>\n",
       "      <td>1.052289</td>\n",
       "      <td>0.714275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.00000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-5.900000</td>\n",
       "      <td>-7.74000</td>\n",
       "      <td>-7.150000</td>\n",
       "      <td>-9.720000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-8.290000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-9.790000</td>\n",
       "      <td>-8.390000</td>\n",
       "      <td>...</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-8.950000</td>\n",
       "      <td>8.250000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-9.610000</td>\n",
       "      <td>-9.590000</td>\n",
       "      <td>-9.550000</td>\n",
       "      <td>-9.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-2.820000</td>\n",
       "      <td>-5.33000</td>\n",
       "      <td>-5.460000</td>\n",
       "      <td>-9.600000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-7.430000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-9.590000</td>\n",
       "      <td>-7.520000</td>\n",
       "      <td>...</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-7.890000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>-6.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-9.130000</td>\n",
       "      <td>-9.300000</td>\n",
       "      <td>-9.140000</td>\n",
       "      <td>-9.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.790000</td>\n",
       "      <td>-2.29000</td>\n",
       "      <td>-2.500000</td>\n",
       "      <td>-9.470000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-6.000000</td>\n",
       "      <td>-6.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-9.260000</td>\n",
       "      <td>-6.520000</td>\n",
       "      <td>...</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-6.840000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-8.180000</td>\n",
       "      <td>-8.820000</td>\n",
       "      <td>-8.460000</td>\n",
       "      <td>-8.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 69 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          loan_amnt     int_rate   installment           dti   delinq_2yrs  \\\n",
       "count  68817.000000  68817.00000  68817.000000  68817.000000  68817.000000   \n",
       "mean      -1.959989     -4.54778     -4.532837     -9.563994     -9.758233   \n",
       "std        5.270321      3.87541      3.501522      0.404396      0.797837   \n",
       "min      -10.000000    -10.00000    -10.000000    -10.000000    -10.000000   \n",
       "25%       -5.900000     -7.74000     -7.150000     -9.720000    -10.000000   \n",
       "50%       -2.820000     -5.33000     -5.460000     -9.600000    -10.000000   \n",
       "75%        1.790000     -2.29000     -2.500000     -9.470000    -10.000000   \n",
       "max       10.000000     10.00000     10.000000     10.000000     10.000000   \n",
       "\n",
       "       inq_last_6mths      open_acc       pub_rec     revol_bal     total_acc  \\\n",
       "count    68817.000000  68817.000000  68817.000000  68817.000000  68817.000000   \n",
       "mean        -8.009213     -6.975087     -9.369850     -9.400402     -7.308626   \n",
       "std          3.032486      1.720878      1.683984      0.743748      1.530366   \n",
       "min        -10.000000    -10.000000    -10.000000    -10.000000    -10.000000   \n",
       "25%        -10.000000     -8.290000    -10.000000     -9.790000     -8.390000   \n",
       "50%        -10.000000     -7.430000    -10.000000     -9.590000     -7.520000   \n",
       "75%         -6.000000     -6.000000    -10.000000     -9.260000     -6.520000   \n",
       "max         10.000000     10.000000     10.000000     10.000000     10.000000   \n",
       "\n",
       "       ...  num_tl_90g_dpd_24m  num_tl_op_past_12m  pct_tl_nvr_dlq  \\\n",
       "count  ...        68817.000000        68817.000000    68817.000000   \n",
       "mean   ...           -9.942113           -7.663124        8.764626   \n",
       "std    ...            0.433903            1.997693        2.081479   \n",
       "min    ...          -10.000000          -10.000000      -10.000000   \n",
       "25%    ...          -10.000000           -8.950000        8.250000   \n",
       "50%    ...          -10.000000           -7.890000       10.000000   \n",
       "75%    ...          -10.000000           -6.840000       10.000000   \n",
       "max    ...           10.000000           10.000000       10.000000   \n",
       "\n",
       "       percent_bc_gt_75  pub_rec_bankruptcies  tax_liens  tot_hi_cred_lim  \\\n",
       "count      68817.000000          68817.000000    68817.0     68817.000000   \n",
       "mean          -3.874757             -9.370141      -10.0        -8.744753   \n",
       "std            6.726293              1.683661        0.0         1.172376   \n",
       "min          -10.000000            -10.000000      -10.0       -10.000000   \n",
       "25%          -10.000000            -10.000000      -10.0        -9.610000   \n",
       "50%           -6.000000            -10.000000      -10.0        -9.130000   \n",
       "75%            0.000000            -10.000000      -10.0        -8.180000   \n",
       "max           10.000000             10.000000      -10.0        10.000000   \n",
       "\n",
       "       total_bal_ex_mort  total_bc_limit  total_il_high_credit_limit  \n",
       "count       68817.000000    68817.000000                68817.000000  \n",
       "mean           -9.056484       -8.836311                   -9.220679  \n",
       "std             0.886151        1.052289                    0.714275  \n",
       "min           -10.000000      -10.000000                  -10.000000  \n",
       "25%            -9.590000       -9.550000                   -9.680000  \n",
       "50%            -9.300000       -9.140000                   -9.410000  \n",
       "75%            -8.820000       -8.460000                   -8.990000  \n",
       "max            10.000000       10.000000                   10.000000  \n",
       "\n",
       "[8 rows x 69 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    68470\n",
       "0      347\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the balance of our target values\n",
    "# 1 = low risk, 0 = high risk\n",
    "y['loan_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting y back to a series\n",
    "y = y['loan_status']\n",
    "type(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create 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, y, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Oversampling\n",
    "\n",
    "In this section, you will compare two oversampling algorithms to determine which algorithm results in the best performance. You will oversample the data using the naive random oversampling algorithm and the SMOTE algorithm. For each algorithm, be sure to complete the folliowing steps:\n",
    "\n",
    "1. View the count of the target classes using `Counter` from the collections library. \n",
    "3. Use the resampled data to train a logistic regression model.\n",
    "3. Calculate the balanced accuracy score from sklearn.metrics.\n",
    "4. Print the confusion matrix from sklearn.metrics.\n",
    "5. Generate a classication report using the `imbalanced_classification_report` from imbalanced-learn.\n",
    "\n",
    "Note: Use a random state of 1 for each sampling algorithm to ensure consistency between tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Random Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1: 51366, 0: 51366})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the training data with the RandomOversampler\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "ros = RandomOverSampler(random_state=1)\n",
    "X_resampled, y_resampled = ros.fit_resample(X_train, y_train)\n",
    "\n",
    "Counter(y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.642533678648501"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "y_pred = model.predict(X_test)\n",
    "balanced_accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   60,    41],\n",
       "       [ 5285, 11819]], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "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",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Random Oversampling\n",
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.01      0.59      0.69      0.02      0.64      0.41       101\n",
      "          1       1.00      0.69      0.59      0.82      0.64      0.41     17104\n",
      "\n",
      "avg / total       0.99      0.69      0.59      0.81      0.64      0.41     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "print('Naive Random Oversampling')\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SMOTE Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1: 51366, 0: 51366})"
      ]
     },
     "execution_count": 17,
     "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=1.0).fit_resample(X_train, y_train)\n",
    "Counter(y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Logistic Regression model using the resampled data\n",
    "model = LogisticRegression(solver='lbfgs', random_state=1)\n",
    "model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6328957849012216"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "y_pred = model.predict(X_test)\n",
    "balanced_accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   58,    43],\n",
       "       [ 5276, 11828]], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SMOTE Oversampling\n",
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.01      0.57      0.69      0.02      0.63      0.39       101\n",
      "          1       1.00      0.69      0.57      0.82      0.63      0.40     17104\n",
      "\n",
      "avg / total       0.99      0.69      0.57      0.81      0.63      0.40     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "print('SMOTE Oversampling')\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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 246, 1: 246})"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the data using the ClusterCentroids resampler\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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 25,
     "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6328957849012216"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "balanced_accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   64,    37],\n",
       "       [ 6240, 10864]], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "y_pred = model.predict(X_test)\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ClusterCentroids UnderSampling\n",
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.01      0.63      0.64      0.02      0.63      0.40       101\n",
      "          1       1.00      0.64      0.63      0.78      0.63      0.40     17104\n",
      "\n",
      "avg / total       0.99      0.64      0.63      0.77      0.63      0.40     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "print('ClusterCentroids UnderSampling')\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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 68470, 1: 62148})"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the training data with SMOTEENN\n",
    "from imblearn.combine import SMOTEENN\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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 30,
     "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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6344182126351082"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "balanced_accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   73,    28],\n",
       "       [ 6305, 10799]], dtype=int64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "y_pred = model.predict(X_test)\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combination SMOTEENN Sampling\n",
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.01      0.72      0.63      0.02      0.68      0.46       101\n",
      "          1       1.00      0.63      0.72      0.77      0.68      0.45     17104\n",
      "\n",
      "avg / total       0.99      0.63      0.72      0.77      0.68      0.45     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "print('Combination SMOTEENN Sampling')\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  }
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