Credit Risk Evaluator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uploading files\n",
    "train_df = pd.read_csv(Path('Resources/2019loans.csv'))\n",
    "test_df = pd.read_csv(Path('Resources/2020Q1loans.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Unnamed: 0</th>\n",
       "      <th>index</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>loan_status</th>\n",
       "      <th>pymnt_plan</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>57107</td>\n",
       "      <td>57107</td>\n",
       "      <td>13375.0</td>\n",
       "      <td>0.1797</td>\n",
       "      <td>483.34</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>223000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>577150.0</td>\n",
       "      <td>122018.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>170200.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>141451</td>\n",
       "      <td>141451</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>0.1308</td>\n",
       "      <td>478.68</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>123000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>85.0</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132750.0</td>\n",
       "      <td>27896.0</td>\n",
       "      <td>15900.0</td>\n",
       "      <td>35398.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>321143</td>\n",
       "      <td>321143</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.1240</td>\n",
       "      <td>448.95</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>197000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>85.7</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>628160.0</td>\n",
       "      <td>114043.0</td>\n",
       "      <td>22600.0</td>\n",
       "      <td>90340.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11778</td>\n",
       "      <td>11778</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.1240</td>\n",
       "      <td>100.22</td>\n",
       "      <td>RENT</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>16.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42006.0</td>\n",
       "      <td>20761.0</td>\n",
       "      <td>19900.0</td>\n",
       "      <td>15406.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>169382</td>\n",
       "      <td>169382</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>0.1612</td>\n",
       "      <td>1056.49</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>133000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>283248.0</td>\n",
       "      <td>109056.0</td>\n",
       "      <td>79500.0</td>\n",
       "      <td>58778.0</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": [
       "   Unnamed: 0   index  loan_amnt  int_rate  installment home_ownership  \\\n",
       "0       57107   57107    13375.0    0.1797       483.34       MORTGAGE   \n",
       "1      141451  141451    21000.0    0.1308       478.68       MORTGAGE   \n",
       "2      321143  321143    20000.0    0.1240       448.95       MORTGAGE   \n",
       "3       11778   11778     3000.0    0.1240       100.22           RENT   \n",
       "4      169382  169382    30000.0    0.1612      1056.49       MORTGAGE   \n",
       "\n",
       "   annual_inc verification_status loan_status pymnt_plan  ...  pct_tl_nvr_dlq  \\\n",
       "0    223000.0        Not Verified    low_risk          n  ...           100.0   \n",
       "1    123000.0     Source Verified    low_risk          n  ...            85.0   \n",
       "2    197000.0     Source Verified    low_risk          n  ...            85.7   \n",
       "3     45000.0        Not Verified    low_risk          n  ...           100.0   \n",
       "4    133000.0     Source Verified    low_risk          n  ...           100.0   \n",
       "\n",
       "   percent_bc_gt_75  pub_rec_bankruptcies  tax_liens  tot_hi_cred_lim  \\\n",
       "0              50.0                   0.0        0.0         577150.0   \n",
       "1              33.3                   0.0        0.0         132750.0   \n",
       "2              33.3                   0.0        0.0         628160.0   \n",
       "3              16.7                   1.0        0.0          42006.0   \n",
       "4              66.7                   0.0        0.0         283248.0   \n",
       "\n",
       "   total_bal_ex_mort  total_bc_limit total_il_high_credit_limit  \\\n",
       "0           122018.0         32000.0                   170200.0   \n",
       "1            27896.0         15900.0                    35398.0   \n",
       "2           114043.0         22600.0                    90340.0   \n",
       "3            20761.0         19900.0                    15406.0   \n",
       "4           109056.0         79500.0                    58778.0   \n",
       "\n",
       "   hardship_flag  debt_settlement_flag  \n",
       "0              N                     N  \n",
       "1              N                     N  \n",
       "2              N                     N  \n",
       "3              N                     N  \n",
       "4              N                     N  \n",
       "\n",
       "[5 rows x 86 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12180, 86)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Unnamed: 0</th>\n",
       "      <th>index</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>loan_status</th>\n",
       "      <th>pymnt_plan</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",
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       "      <th>0</th>\n",
       "      <td>67991</td>\n",
       "      <td>67991</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0819</td>\n",
       "      <td>814.70</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>140000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>97.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>527975.0</td>\n",
       "      <td>70914.0</td>\n",
       "      <td>74600.0</td>\n",
       "      <td>99475.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25429</td>\n",
       "      <td>25429</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>0.1524</td>\n",
       "      <td>208.70</td>\n",
       "      <td>RENT</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>66.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34628.0</td>\n",
       "      <td>23460.0</td>\n",
       "      <td>5900.0</td>\n",
       "      <td>23628.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38496</td>\n",
       "      <td>38496</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>0.1695</td>\n",
       "      <td>128.27</td>\n",
       "      <td>RENT</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23100.0</td>\n",
       "      <td>19183.0</td>\n",
       "      <td>7300.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19667</td>\n",
       "      <td>19667</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.1524</td>\n",
       "      <td>478.33</td>\n",
       "      <td>RENT</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56481.0</td>\n",
       "      <td>43817.0</td>\n",
       "      <td>13800.0</td>\n",
       "      <td>35981.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37505</td>\n",
       "      <td>37505</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>0.1240</td>\n",
       "      <td>120.27</td>\n",
       "      <td>RENT</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45977.0</td>\n",
       "      <td>32448.0</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>24977.0</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": [
       "   Unnamed: 0  index  loan_amnt  int_rate  installment home_ownership  \\\n",
       "0       67991  67991    40000.0    0.0819       814.70       MORTGAGE   \n",
       "1       25429  25429     6000.0    0.1524       208.70           RENT   \n",
       "2       38496  38496     3600.0    0.1695       128.27           RENT   \n",
       "3       19667  19667    20000.0    0.1524       478.33           RENT   \n",
       "4       37505  37505     3600.0    0.1240       120.27           RENT   \n",
       "\n",
       "   annual_inc verification_status loan_status pymnt_plan  ...  pct_tl_nvr_dlq  \\\n",
       "0    140000.0        Not Verified    low_risk          n  ...            97.7   \n",
       "1     55000.0        Not Verified    low_risk          n  ...            66.7   \n",
       "2     42000.0        Not Verified    low_risk          n  ...           100.0   \n",
       "3    100000.0        Not Verified    low_risk          n  ...           100.0   \n",
       "4     50000.0        Not Verified    low_risk          n  ...           100.0   \n",
       "\n",
       "   percent_bc_gt_75  pub_rec_bankruptcies  tax_liens  tot_hi_cred_lim  \\\n",
       "0               0.0                   0.0        0.0         527975.0   \n",
       "1               0.0                   0.0        0.0          34628.0   \n",
       "2               0.0                   0.0        0.0          23100.0   \n",
       "3              50.0                   0.0        0.0          56481.0   \n",
       "4              25.0                   0.0        0.0          45977.0   \n",
       "\n",
       "   total_bal_ex_mort  total_bc_limit total_il_high_credit_limit  \\\n",
       "0            70914.0         74600.0                    99475.0   \n",
       "1            23460.0          5900.0                    23628.0   \n",
       "2            19183.0          7300.0                    15000.0   \n",
       "3            43817.0         13800.0                    35981.0   \n",
       "4            32448.0         21000.0                    24977.0   \n",
       "\n",
       "   hardship_flag  debt_settlement_flag  \n",
       "0              N                     N  \n",
       "1              N                     N  \n",
       "2              N                     N  \n",
       "3              N                     N  \n",
       "4              N                     N  \n",
       "\n",
       "[5 rows x 86 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4702, 86)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>...</th>\n",
       "      <th>verification_status_Verified</th>\n",
       "      <th>pymnt_plan_n</th>\n",
       "      <th>initial_list_status_f</th>\n",
       "      <th>initial_list_status_w</th>\n",
       "      <th>application_type_Individual</th>\n",
       "      <th>application_type_Joint App</th>\n",
       "      <th>hardship_flag_N</th>\n",
       "      <th>hardship_flag_Y</th>\n",
       "      <th>debt_settlement_flag_N</th>\n",
       "      <th>debt_settlement_flag_Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13375.0</td>\n",
       "      <td>0.1797</td>\n",
       "      <td>483.34</td>\n",
       "      <td>223000.0</td>\n",
       "      <td>29.99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39728.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21000.0</td>\n",
       "      <td>0.1308</td>\n",
       "      <td>478.68</td>\n",
       "      <td>123000.0</td>\n",
       "      <td>11.26</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9585.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.1240</td>\n",
       "      <td>448.95</td>\n",
       "      <td>197000.0</td>\n",
       "      <td>11.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16708.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.1240</td>\n",
       "      <td>100.22</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>18.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8809.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>0.1612</td>\n",
       "      <td>1056.49</td>\n",
       "      <td>133000.0</td>\n",
       "      <td>27.77</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65420.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  int_rate  installment  annual_inc    dti  delinq_2yrs  \\\n",
       "0    13375.0    0.1797       483.34    223000.0  29.99          0.0   \n",
       "1    21000.0    0.1308       478.68    123000.0  11.26          2.0   \n",
       "2    20000.0    0.1240       448.95    197000.0  11.28          0.0   \n",
       "3     3000.0    0.1240       100.22     45000.0  18.08          0.0   \n",
       "4    30000.0    0.1612      1056.49    133000.0  27.77          0.0   \n",
       "\n",
       "   inq_last_6mths  open_acc  pub_rec  revol_bal  ...  \\\n",
       "0             0.0      15.0      0.0    39728.0  ...   \n",
       "1             0.0      16.0      0.0     9585.0  ...   \n",
       "2             0.0      12.0      0.0    16708.0  ...   \n",
       "3             0.0      12.0      1.0     8809.0  ...   \n",
       "4             2.0      13.0      0.0    65420.0  ...   \n",
       "\n",
       "   verification_status_Verified  pymnt_plan_n  initial_list_status_f  \\\n",
       "0                           0.0           1.0                    0.0   \n",
       "1                           0.0           1.0                    0.0   \n",
       "2                           0.0           1.0                    0.0   \n",
       "3                           0.0           1.0                    0.0   \n",
       "4                           0.0           1.0                    0.0   \n",
       "\n",
       "   initial_list_status_w  application_type_Individual  \\\n",
       "0                    1.0                          1.0   \n",
       "1                    1.0                          1.0   \n",
       "2                    1.0                          1.0   \n",
       "3                    1.0                          1.0   \n",
       "4                    1.0                          1.0   \n",
       "\n",
       "   application_type_Joint App  hardship_flag_N  hardship_flag_Y  \\\n",
       "0                         0.0              1.0              0.0   \n",
       "1                         0.0              1.0              0.0   \n",
       "2                         0.0              1.0              0.0   \n",
       "3                         0.0              1.0              0.0   \n",
       "4                         0.0              1.0              0.0   \n",
       "\n",
       "   debt_settlement_flag_N  debt_settlement_flag_Y  \n",
       "0                     1.0                     0.0  \n",
       "1                     1.0                     0.0  \n",
       "2                     1.0                     0.0  \n",
       "3                     1.0                     0.0  \n",
       "4                     1.0                     0.0  \n",
       "\n",
       "[5 rows x 92 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert categorical data to numeric and separate target feature for training data\n",
    "train= train_df.drop(columns = ['Unnamed: 0' ,'index' ,'loan_status'])\n",
    "X_train = pd.get_dummies(train, dtype=float)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12180, 92)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "low_risk     6090\n",
       "high_risk    6090\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train = train_df['loan_status']\n",
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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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>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>verification_status_Source Verified</th>\n",
       "      <th>verification_status_Verified</th>\n",
       "      <th>pymnt_plan_n</th>\n",
       "      <th>initial_list_status_f</th>\n",
       "      <th>initial_list_status_w</th>\n",
       "      <th>application_type_Individual</th>\n",
       "      <th>application_type_Joint App</th>\n",
       "      <th>hardship_flag_N</th>\n",
       "      <th>hardship_flag_Y</th>\n",
       "      <th>debt_settlement_flag_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0819</td>\n",
       "      <td>814.70</td>\n",
       "      <td>140000.0</td>\n",
       "      <td>19.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9471.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6000.0</td>\n",
       "      <td>0.1524</td>\n",
       "      <td>208.70</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>11.52</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3600.0</td>\n",
       "      <td>0.1695</td>\n",
       "      <td>128.27</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>6.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4757.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.1524</td>\n",
       "      <td>478.33</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>12.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12731.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3600.0</td>\n",
       "      <td>0.1240</td>\n",
       "      <td>120.27</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>16.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10413.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 91 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  int_rate  installment  annual_inc    dti  delinq_2yrs  \\\n",
       "0    40000.0    0.0819       814.70    140000.0  19.75          0.0   \n",
       "1     6000.0    0.1524       208.70     55000.0  11.52          2.0   \n",
       "2     3600.0    0.1695       128.27     42000.0   6.74          0.0   \n",
       "3    20000.0    0.1524       478.33    100000.0  12.13          0.0   \n",
       "4     3600.0    0.1240       120.27     50000.0  16.08          0.0   \n",
       "\n",
       "   inq_last_6mths  open_acc  pub_rec  revol_bal  ...  \\\n",
       "0             1.0      18.0      0.0     9471.0  ...   \n",
       "1             0.0       8.0      0.0     1280.0  ...   \n",
       "2             0.0       6.0      0.0     4757.0  ...   \n",
       "3             2.0       7.0      0.0    12731.0  ...   \n",
       "4             3.0       6.0      0.0    10413.0  ...   \n",
       "\n",
       "   verification_status_Source Verified  verification_status_Verified  \\\n",
       "0                                  0.0                           0.0   \n",
       "1                                  0.0                           0.0   \n",
       "2                                  0.0                           0.0   \n",
       "3                                  0.0                           0.0   \n",
       "4                                  0.0                           0.0   \n",
       "\n",
       "   pymnt_plan_n  initial_list_status_f  initial_list_status_w  \\\n",
       "0           1.0                    0.0                    1.0   \n",
       "1           1.0                    0.0                    1.0   \n",
       "2           1.0                    0.0                    1.0   \n",
       "3           1.0                    0.0                    1.0   \n",
       "4           1.0                    0.0                    1.0   \n",
       "\n",
       "   application_type_Individual  application_type_Joint App  hardship_flag_N  \\\n",
       "0                          1.0                         0.0              1.0   \n",
       "1                          1.0                         0.0              1.0   \n",
       "2                          1.0                         0.0              1.0   \n",
       "3                          1.0                         0.0              1.0   \n",
       "4                          1.0                         0.0              1.0   \n",
       "\n",
       "   hardship_flag_Y  debt_settlement_flag_N  \n",
       "0              0.0                     1.0  \n",
       "1              0.0                     1.0  \n",
       "2              0.0                     1.0  \n",
       "3              0.0                     1.0  \n",
       "4              0.0                     1.0  \n",
       "\n",
       "[5 rows x 91 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert categorical data to numeric and separate target feature for testing data\n",
    "test= test_df.drop(columns = ['Unnamed: 0' ,'index' ,'loan_status'])\n",
    "X_test = pd.get_dummies(test, dtype=float)\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4702, 91)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "high_risk    2351\n",
       "low_risk     2351\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test = test_df['loan_status']\n",
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    low_risk\n",
       "1    low_risk\n",
       "2    low_risk\n",
       "3    low_risk\n",
       "4    low_risk\n",
       "Name: loan_status, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'debt_settlement_flag_Y'}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add missing dummy variables to testing set\n",
    "# Finding difference between X_train and X_test columns\n",
    "set(list(X_train.columns))- set(list(X_test.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'debt_settlement_flag_Y'},\n",
       " 0        0.0\n",
       " 1        0.0\n",
       " 2        0.0\n",
       " 3        0.0\n",
       " 4        0.0\n",
       "         ... \n",
       " 12175    0.0\n",
       " 12176    0.0\n",
       " 12177    0.0\n",
       " 12178    0.0\n",
       " 12179    0.0\n",
       " Name: debt_settlement_flag_Y, Length: 12180, dtype: float64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Debt_settlement_flag_Y values\n",
    "set(list(X_train.columns))- set(list(X_test.columns)), X_train['debt_settlement_flag_Y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>verification_status_Verified</th>\n",
       "      <th>pymnt_plan_n</th>\n",
       "      <th>initial_list_status_f</th>\n",
       "      <th>initial_list_status_w</th>\n",
       "      <th>application_type_Individual</th>\n",
       "      <th>application_type_Joint App</th>\n",
       "      <th>hardship_flag_N</th>\n",
       "      <th>hardship_flag_Y</th>\n",
       "      <th>debt_settlement_flag_N</th>\n",
       "      <th>debt_settlement_flag_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0819</td>\n",
       "      <td>814.70</td>\n",
       "      <td>140000.0</td>\n",
       "      <td>19.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9471.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6000.0</td>\n",
       "      <td>0.1524</td>\n",
       "      <td>208.70</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>11.52</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3600.0</td>\n",
       "      <td>0.1695</td>\n",
       "      <td>128.27</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>6.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4757.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.1524</td>\n",
       "      <td>478.33</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>12.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12731.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3600.0</td>\n",
       "      <td>0.1240</td>\n",
       "      <td>120.27</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>16.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10413.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  int_rate  installment  annual_inc    dti  delinq_2yrs  \\\n",
       "0    40000.0    0.0819       814.70    140000.0  19.75          0.0   \n",
       "1     6000.0    0.1524       208.70     55000.0  11.52          2.0   \n",
       "2     3600.0    0.1695       128.27     42000.0   6.74          0.0   \n",
       "3    20000.0    0.1524       478.33    100000.0  12.13          0.0   \n",
       "4     3600.0    0.1240       120.27     50000.0  16.08          0.0   \n",
       "\n",
       "   inq_last_6mths  open_acc  pub_rec  revol_bal  ...  \\\n",
       "0             1.0      18.0      0.0     9471.0  ...   \n",
       "1             0.0       8.0      0.0     1280.0  ...   \n",
       "2             0.0       6.0      0.0     4757.0  ...   \n",
       "3             2.0       7.0      0.0    12731.0  ...   \n",
       "4             3.0       6.0      0.0    10413.0  ...   \n",
       "\n",
       "   verification_status_Verified  pymnt_plan_n  initial_list_status_f  \\\n",
       "0                           0.0           1.0                    0.0   \n",
       "1                           0.0           1.0                    0.0   \n",
       "2                           0.0           1.0                    0.0   \n",
       "3                           0.0           1.0                    0.0   \n",
       "4                           0.0           1.0                    0.0   \n",
       "\n",
       "   initial_list_status_w  application_type_Individual  \\\n",
       "0                    1.0                          1.0   \n",
       "1                    1.0                          1.0   \n",
       "2                    1.0                          1.0   \n",
       "3                    1.0                          1.0   \n",
       "4                    1.0                          1.0   \n",
       "\n",
       "   application_type_Joint App  hardship_flag_N  hardship_flag_Y  \\\n",
       "0                         0.0              1.0              0.0   \n",
       "1                         0.0              1.0              0.0   \n",
       "2                         0.0              1.0              0.0   \n",
       "3                         0.0              1.0              0.0   \n",
       "4                         0.0              1.0              0.0   \n",
       "\n",
       "   debt_settlement_flag_N  debt_settlement_flag_y  \n",
       "0                     1.0                       0  \n",
       "1                     1.0                       0  \n",
       "2                     1.0                       0  \n",
       "3                     1.0                       0  \n",
       "4                     1.0                       0  \n",
       "\n",
       "[5 rows x 92 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comprenhension list for debt_settlement_flag_y\n",
    "X_test['debt_settlement_flag_y'] = pd.Series ([0 for x in range (len(X_test.index))])\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Logistic Regression model on the unscaled data and print the model score\n",
    "# Logistic Regression Model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression()\n",
    "classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/karlaflores/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/karlaflores/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "# Fit the model to the data\n",
    "classifier.fit(X_train, y_train)\n",
    "cl = classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.6509031198686371\n",
      "Testing Score: 0.5165886856656742\n"
     ]
    }
   ],
   "source": [
    "# Printing Training score and Testing score for Logistic Regression\n",
    "print(f'Training Score: {classifier.score(X_train, y_train)}')\n",
    "print(f'Testing Score: {classifier.score(X_test, y_test)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   high_risk       0.53      0.30      0.39      2351\n",
      "    low_risk       0.51      0.73      0.60      2351\n",
      "\n",
      "    accuracy                           0.52      4702\n",
      "   macro avg       0.52      0.52      0.49      4702\n",
      "weighted avg       0.52      0.52      0.49      4702\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification Report\n",
    "from sklearn.metrics import classification_report\n",
    "y_true = y_test\n",
    "y_pred = classifier.predict(X_test)\n",
    "print(classification_report(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 1.0\n",
      "Testing Score: 0.6433432581880051\n"
     ]
    }
   ],
   "source": [
    "# Train a Random Forest Classifier model and print the model score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "classifier = RandomForestClassifier(random_state=1, n_estimators=500).fit(X_train, y_train)\n",
    "print(f'Training Score: {classifier.score(X_train, y_train)}')\n",
    "print(f'Testing Score: {classifier.score(X_test, y_test)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   high_risk       0.61      0.80      0.69      2351\n",
      "    low_risk       0.71      0.48      0.58      2351\n",
      "\n",
      "    accuracy                           0.64      4702\n",
      "   macro avg       0.66      0.64      0.63      4702\n",
      "weighted avg       0.66      0.64      0.63      4702\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification Report\n",
    "from sklearn.metrics import classification_report\n",
    "y_true = y_test\n",
    "y_pred = classifier.predict(X_test)\n",
    "print(classification_report(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the data\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.20755943, -1.12001617,  1.0371484 ,  0.2974932 , -0.11879708,\n",
       "       -0.32002561,  0.53213327,  0.86437267, -0.35198632, -0.35939018,\n",
       "        1.6408751 ,  2.25599638,  2.25595752,  0.67642899,  0.67658556,\n",
       "        0.75678349,  0.27544777, -0.15098128,  0.        ,  0.        ,\n",
       "        0.45353021, -0.13312632,  0.        , -0.00906138, -0.13971415,\n",
       "        1.29387623, -0.04305549,  0.85646683,  0.12620873,  0.01389393,\n",
       "       -0.36898244,  0.33413335, -0.87759252, -0.22528046, -0.66929854,\n",
       "       -0.5361904 , -1.73531986,  1.1753957 , -0.90827924,  0.10235081,\n",
       "       -0.53462945, -0.61674152,  0.51199143,  2.65250191, -1.34596659,\n",
       "       -0.07108199, -0.00906138,  1.13730366,  0.16180251, -0.70933534,\n",
       "       -0.81409859,  1.01813477,  0.70776118, -0.87468909, -0.35942048,\n",
       "        0.7884271 ,  0.04318247,  0.22223451,  0.58239031,  1.32738286,\n",
       "        0.44378311,  1.0904904 ,  0.04140276,  0.86688248,  0.        ,\n",
       "       -0.00906138, -0.1488685 , -0.18256126,  0.35884239, -0.97538958,\n",
       "       -0.35198632,  0.        ,  1.71011902,  0.1442602 ,  1.83815204,\n",
       "        0.83062398, -0.07324786,  1.04880885, -0.35614428, -0.82658963,\n",
       "        1.13915744, -0.81775408, -0.44297702,  0.        , -0.30264414,\n",
       "        0.30264414,  0.41370744, -0.41370744,  0.17149859, -0.17149859,\n",
       "        0.02026518, -0.02026518])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_scaled[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression model on the scaled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.7078817733990148\n",
      "Testing Score: 0.767333049766057\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/karlaflores/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "# Train the Logistic Regression model on the scaled data and print the model score\n",
    "classifier=LogisticRegression().fit(X_train_scaled, y_train)\n",
    "print(f'Training Score: {classifier.score(X_train_scaled, y_train)}')\n",
    "print(f'Testing Score: {classifier.score(X_test_scaled, y_test)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   high_risk       0.55      0.72      0.62      2351\n",
      "    low_risk       0.59      0.40      0.48      2351\n",
      "\n",
      "    accuracy                           0.56      4702\n",
      "   macro avg       0.57      0.56      0.55      4702\n",
      "weighted avg       0.57      0.56      0.55      4702\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification Report\n",
    "from sklearn.metrics import classification_report\n",
    "y_true = y_test\n",
    "y_pred = classifier.predict(X_test)\n",
    "print(classification_report(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classifier model on the scaled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 1.0\n",
      "Testing Score: 0.6420672054444917\n"
     ]
    }
   ],
   "source": [
    "# Train a Random Forest Classifier model on the scaled data and print the model score\n",
    "classifier = RandomForestClassifier(random_state=1, n_estimators=500).fit(X_train_scaled, y_train)\n",
    "print(f'Training Score: {classifier.score(X_train_scaled, y_train)}')\n",
    "print(f'Testing Score: {classifier.score(X_test_scaled, y_test)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   high_risk       0.43      0.00      0.00      2351\n",
      "    low_risk       0.50      1.00      0.67      2351\n",
      "\n",
      "    accuracy                           0.50      4702\n",
      "   macro avg       0.46      0.50      0.33      4702\n",
      "weighted avg       0.46      0.50      0.33      4702\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification Report\n",
    "from sklearn.metrics import classification_report\n",
    "y_true = y_test\n",
    "y_pred = classifier.predict(X_test)\n",
    "print(classification_report(y_true, y_pred))"
   ]
  }
 ],
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