Credit Risk Evaluator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "train_df = pd.read_csv(Path('Resources/2019loans.csv'))\n",
    "test_df = pd.read_csv(Path('Resources/2020Q1loans.csv'))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### checking columns for the 2 dfs"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "train_cols=train_df.columns\n",
    "test_cols=test_df.columns\n",
    "train_types = train_df.dtypes\n",
    "test_types= test_df.dtypes"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "df_compare = list(zip(train_cols, train_types, test_cols, test_types))\n",
    "df_compare"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[('loan_amnt', dtype('float64'), 'loan_amnt', dtype('float64')),\n",
       " ('int_rate', dtype('float64'), 'int_rate', dtype('float64')),\n",
       " ('installment', dtype('float64'), 'installment', dtype('float64')),\n",
       " ('home_ownership', dtype('O'), 'home_ownership', dtype('O')),\n",
       " ('annual_inc', dtype('float64'), 'annual_inc', dtype('float64')),\n",
       " ('verification_status', dtype('O'), 'verification_status', dtype('O')),\n",
       " ('pymnt_plan', dtype('O'), 'pymnt_plan', dtype('O')),\n",
       " ('dti', dtype('float64'), 'dti', dtype('float64')),\n",
       " ('delinq_2yrs', dtype('float64'), 'delinq_2yrs', dtype('float64')),\n",
       " ('inq_last_6mths', dtype('float64'), 'inq_last_6mths', dtype('float64')),\n",
       " ('open_acc', dtype('float64'), 'open_acc', dtype('float64')),\n",
       " ('pub_rec', dtype('float64'), 'pub_rec', dtype('float64')),\n",
       " ('revol_bal', dtype('float64'), 'revol_bal', dtype('float64')),\n",
       " ('total_acc', dtype('float64'), 'total_acc', dtype('float64')),\n",
       " ('initial_list_status', dtype('O'), 'initial_list_status', dtype('O')),\n",
       " ('out_prncp', dtype('float64'), 'out_prncp', dtype('float64')),\n",
       " ('out_prncp_inv', dtype('float64'), 'out_prncp_inv', dtype('float64')),\n",
       " ('total_pymnt', dtype('float64'), 'total_pymnt', dtype('float64')),\n",
       " ('total_pymnt_inv', dtype('float64'), 'total_pymnt_inv', dtype('float64')),\n",
       " ('total_rec_prncp', dtype('float64'), 'total_rec_prncp', dtype('float64')),\n",
       " ('total_rec_int', dtype('float64'), 'total_rec_int', dtype('float64')),\n",
       " ('total_rec_late_fee',\n",
       "  dtype('float64'),\n",
       "  'total_rec_late_fee',\n",
       "  dtype('float64')),\n",
       " ('recoveries', dtype('float64'), 'recoveries', dtype('float64')),\n",
       " ('collection_recovery_fee',\n",
       "  dtype('float64'),\n",
       "  'collection_recovery_fee',\n",
       "  dtype('float64')),\n",
       " ('last_pymnt_amnt', dtype('float64'), 'last_pymnt_amnt', dtype('float64')),\n",
       " ('collections_12_mths_ex_med',\n",
       "  dtype('float64'),\n",
       "  'collections_12_mths_ex_med',\n",
       "  dtype('float64')),\n",
       " ('policy_code', dtype('float64'), 'policy_code', dtype('float64')),\n",
       " ('application_type', dtype('O'), 'application_type', dtype('O')),\n",
       " ('acc_now_delinq', dtype('float64'), 'acc_now_delinq', dtype('float64')),\n",
       " ('tot_coll_amt', dtype('float64'), 'tot_coll_amt', dtype('float64')),\n",
       " ('tot_cur_bal', dtype('float64'), 'tot_cur_bal', dtype('float64')),\n",
       " ('open_acc_6m', dtype('float64'), 'open_acc_6m', dtype('float64')),\n",
       " ('open_act_il', dtype('float64'), 'open_act_il', dtype('float64')),\n",
       " ('open_il_12m', dtype('float64'), 'open_il_12m', dtype('float64')),\n",
       " ('open_il_24m', dtype('float64'), 'open_il_24m', dtype('float64')),\n",
       " ('mths_since_rcnt_il',\n",
       "  dtype('float64'),\n",
       "  'mths_since_rcnt_il',\n",
       "  dtype('float64')),\n",
       " ('total_bal_il', dtype('float64'), 'total_bal_il', dtype('float64')),\n",
       " ('il_util', dtype('float64'), 'il_util', dtype('float64')),\n",
       " ('open_rv_12m', dtype('float64'), 'open_rv_12m', dtype('float64')),\n",
       " ('open_rv_24m', dtype('float64'), 'open_rv_24m', dtype('float64')),\n",
       " ('max_bal_bc', dtype('float64'), 'max_bal_bc', dtype('float64')),\n",
       " ('all_util', dtype('float64'), 'all_util', dtype('float64')),\n",
       " ('total_rev_hi_lim', dtype('float64'), 'total_rev_hi_lim', dtype('float64')),\n",
       " ('inq_fi', dtype('float64'), 'inq_fi', dtype('float64')),\n",
       " ('total_cu_tl', dtype('float64'), 'total_cu_tl', dtype('float64')),\n",
       " ('inq_last_12m', dtype('float64'), 'inq_last_12m', dtype('float64')),\n",
       " ('acc_open_past_24mths',\n",
       "  dtype('float64'),\n",
       "  'acc_open_past_24mths',\n",
       "  dtype('float64')),\n",
       " ('avg_cur_bal', dtype('float64'), 'avg_cur_bal', dtype('float64')),\n",
       " ('bc_open_to_buy', dtype('float64'), 'bc_open_to_buy', dtype('float64')),\n",
       " ('bc_util', dtype('float64'), 'bc_util', dtype('float64')),\n",
       " ('chargeoff_within_12_mths',\n",
       "  dtype('float64'),\n",
       "  'chargeoff_within_12_mths',\n",
       "  dtype('float64')),\n",
       " ('delinq_amnt', dtype('float64'), 'delinq_amnt', dtype('float64')),\n",
       " ('mo_sin_old_il_acct',\n",
       "  dtype('float64'),\n",
       "  'mo_sin_old_il_acct',\n",
       "  dtype('float64')),\n",
       " ('mo_sin_old_rev_tl_op',\n",
       "  dtype('float64'),\n",
       "  'mo_sin_old_rev_tl_op',\n",
       "  dtype('float64')),\n",
       " ('mo_sin_rcnt_rev_tl_op',\n",
       "  dtype('float64'),\n",
       "  'mo_sin_rcnt_rev_tl_op',\n",
       "  dtype('float64')),\n",
       " ('mo_sin_rcnt_tl', dtype('float64'), 'mo_sin_rcnt_tl', dtype('float64')),\n",
       " ('mort_acc', dtype('float64'), 'mort_acc', dtype('float64')),\n",
       " ('mths_since_recent_bc',\n",
       "  dtype('float64'),\n",
       "  'mths_since_recent_bc',\n",
       "  dtype('float64')),\n",
       " ('mths_since_recent_inq',\n",
       "  dtype('float64'),\n",
       "  'mths_since_recent_inq',\n",
       "  dtype('float64')),\n",
       " ('num_accts_ever_120_pd',\n",
       "  dtype('float64'),\n",
       "  'num_accts_ever_120_pd',\n",
       "  dtype('float64')),\n",
       " ('num_actv_bc_tl', dtype('float64'), 'num_actv_bc_tl', dtype('float64')),\n",
       " ('num_actv_rev_tl', dtype('float64'), 'num_actv_rev_tl', dtype('float64')),\n",
       " ('num_bc_sats', dtype('float64'), 'num_bc_sats', dtype('float64')),\n",
       " ('num_bc_tl', dtype('float64'), 'num_bc_tl', dtype('float64')),\n",
       " ('num_il_tl', dtype('float64'), 'num_il_tl', dtype('float64')),\n",
       " ('num_op_rev_tl', dtype('float64'), 'num_op_rev_tl', dtype('float64')),\n",
       " ('num_rev_accts', dtype('float64'), 'num_rev_accts', dtype('float64')),\n",
       " ('num_rev_tl_bal_gt_0',\n",
       "  dtype('float64'),\n",
       "  'num_rev_tl_bal_gt_0',\n",
       "  dtype('float64')),\n",
       " ('num_sats', dtype('float64'), 'num_sats', dtype('float64')),\n",
       " ('num_tl_120dpd_2m', dtype('float64'), 'num_tl_120dpd_2m', dtype('float64')),\n",
       " ('num_tl_30dpd', dtype('float64'), 'num_tl_30dpd', dtype('float64')),\n",
       " ('num_tl_90g_dpd_24m',\n",
       "  dtype('float64'),\n",
       "  'num_tl_90g_dpd_24m',\n",
       "  dtype('float64')),\n",
       " ('num_tl_op_past_12m',\n",
       "  dtype('float64'),\n",
       "  'num_tl_op_past_12m',\n",
       "  dtype('float64')),\n",
       " ('pct_tl_nvr_dlq', dtype('float64'), 'pct_tl_nvr_dlq', dtype('float64')),\n",
       " ('percent_bc_gt_75', dtype('float64'), 'percent_bc_gt_75', dtype('float64')),\n",
       " ('pub_rec_bankruptcies',\n",
       "  dtype('float64'),\n",
       "  'pub_rec_bankruptcies',\n",
       "  dtype('float64')),\n",
       " ('tax_liens', dtype('float64'), 'tax_liens', dtype('float64')),\n",
       " ('tot_hi_cred_lim', dtype('float64'), 'tot_hi_cred_lim', dtype('float64')),\n",
       " ('total_bal_ex_mort',\n",
       "  dtype('float64'),\n",
       "  'total_bal_ex_mort',\n",
       "  dtype('float64')),\n",
       " ('total_bc_limit', dtype('float64'), 'total_bc_limit', dtype('float64')),\n",
       " ('total_il_high_credit_limit',\n",
       "  dtype('float64'),\n",
       "  'total_il_high_credit_limit',\n",
       "  dtype('float64')),\n",
       " ('hardship_flag', dtype('O'), 'hardship_flag', dtype('O')),\n",
       " ('debt_settlement_flag', dtype('O'), 'debt_settlement_flag', dtype('O')),\n",
       " ('target', dtype('O'), 'target', dtype('O'))]"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Convert categorical data to numeric and separate target feature for training data"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "train_convert_df = pd.get_dummies(train_df)\n",
    "train_convert_df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>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>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",
       "      <th>target_high_risk</th>\n",
       "      <th>target_low_risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7000.0</td>\n",
       "      <td>0.1894</td>\n",
       "      <td>256.38</td>\n",
       "      <td>75000.0</td>\n",
       "      <td>28.62</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40414.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.1614</td>\n",
       "      <td>975.71</td>\n",
       "      <td>102000.0</td>\n",
       "      <td>11.72</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43531.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11000.0</td>\n",
       "      <td>0.2055</td>\n",
       "      <td>294.81</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>37.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8242.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4000.0</td>\n",
       "      <td>0.1612</td>\n",
       "      <td>140.87</td>\n",
       "      <td>38000.0</td>\n",
       "      <td>42.89</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12767.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14000.0</td>\n",
       "      <td>0.1797</td>\n",
       "      <td>505.93</td>\n",
       "      <td>43000.0</td>\n",
       "      <td>22.16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11182.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  int_rate  installment  annual_inc    dti  delinq_2yrs  \\\n",
       "0     7000.0    0.1894       256.38     75000.0  28.62          0.0   \n",
       "1    40000.0    0.1614       975.71    102000.0  11.72          2.0   \n",
       "2    11000.0    0.2055       294.81     45000.0  37.25          1.0   \n",
       "3     4000.0    0.1612       140.87     38000.0  42.89          1.0   \n",
       "4    14000.0    0.1797       505.93     43000.0  22.16          1.0   \n",
       "\n",
       "   inq_last_6mths  open_acc  pub_rec  revol_bal  ...  initial_list_status_f  \\\n",
       "0             2.0      20.0      0.0    40414.0  ...                      1   \n",
       "1             0.0      10.0      0.0    43531.0  ...                      0   \n",
       "2             3.0      23.0      0.0     8242.0  ...                      0   \n",
       "3             0.0       7.0      0.0    12767.0  ...                      0   \n",
       "4             0.0      22.0      0.0    11182.0  ...                      0   \n",
       "\n",
       "   initial_list_status_w  application_type_Individual  \\\n",
       "0                      0                            1   \n",
       "1                      1                            1   \n",
       "2                      1                            1   \n",
       "3                      1                            0   \n",
       "4                      1                            1   \n",
       "\n",
       "   application_type_Joint App  hardship_flag_N  hardship_flag_Y  \\\n",
       "0                           0                1                0   \n",
       "1                           0                1                0   \n",
       "2                           0                1                0   \n",
       "3                           1                1                0   \n",
       "4                           0                1                0   \n",
       "\n",
       "   debt_settlement_flag_N  debt_settlement_flag_Y  target_high_risk  \\\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",
       "   target_low_risk  \n",
       "0                1  \n",
       "1                1  \n",
       "2                1  \n",
       "3                1  \n",
       "4                1  \n",
       "\n",
       "[5 rows x 94 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "train_con_cols = train_convert_df.columns"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Convert categorical data to numeric and separate target feature for testing data"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "test_convert_df = pd.get_dummies(test_df)\n",
    "test_convert_df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
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       "</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>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>target_high_risk</th>\n",
       "      <th>target_low_risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.1033</td>\n",
       "      <td>856.40</td>\n",
       "      <td>128700.0</td>\n",
       "      <td>12.47</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38113.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24450.0</td>\n",
       "      <td>0.1430</td>\n",
       "      <td>572.72</td>\n",
       "      <td>44574.0</td>\n",
       "      <td>15.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1665.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13500.0</td>\n",
       "      <td>0.1430</td>\n",
       "      <td>316.23</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>28.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13857.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10625.0</td>\n",
       "      <td>0.1774</td>\n",
       "      <td>268.31</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>15.70</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6216.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6375.0</td>\n",
       "      <td>0.1862</td>\n",
       "      <td>232.46</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>35.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12681.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 93 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  int_rate  installment  annual_inc    dti  delinq_2yrs  \\\n",
       "0    40000.0    0.1033       856.40    128700.0  12.47          0.0   \n",
       "1    24450.0    0.1430       572.72     44574.0  15.05          0.0   \n",
       "2    13500.0    0.1430       316.23     60000.0  28.72          0.0   \n",
       "3    10625.0    0.1774       268.31     60000.0  15.70          0.0   \n",
       "4     6375.0    0.1862       232.46     60000.0  35.50          0.0   \n",
       "\n",
       "   inq_last_6mths  open_acc  pub_rec  revol_bal  ...  pymnt_plan_n  \\\n",
       "0             1.0       8.0      0.0    38113.0  ...             1   \n",
       "1             1.0       6.0      0.0     1665.0  ...             1   \n",
       "2             0.0       8.0      0.0    13857.0  ...             1   \n",
       "3             4.0      17.0      0.0     6216.0  ...             1   \n",
       "4             0.0      13.0      0.0    12681.0  ...             1   \n",
       "\n",
       "   initial_list_status_f  initial_list_status_w  application_type_Individual  \\\n",
       "0                      0                      1                            1   \n",
       "1                      0                      1                            1   \n",
       "2                      0                      1                            1   \n",
       "3                      0                      1                            1   \n",
       "4                      0                      1                            1   \n",
       "\n",
       "   application_type_Joint App  hardship_flag_N  hardship_flag_Y  \\\n",
       "0                           0                0                1   \n",
       "1                           0                1                0   \n",
       "2                           0                0                1   \n",
       "3                           0                1                0   \n",
       "4                           0                1                0   \n",
       "\n",
       "   debt_settlement_flag_N  target_high_risk  target_low_risk  \n",
       "0                       1                 0                1  \n",
       "1                       1                 0                1  \n",
       "2                       1                 0                1  \n",
       "3                       1                 0                1  \n",
       "4                       1                 0                1  \n",
       "\n",
       "[5 rows x 93 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "test_con_cols = test_convert_df.columns\n",
    "converted_compare = list(zip(train_con_cols, test_con_cols))\n",
    "converted_compare"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[('loan_amnt', 'loan_amnt'),\n",
       " ('int_rate', 'int_rate'),\n",
       " ('installment', 'installment'),\n",
       " ('annual_inc', 'annual_inc'),\n",
       " ('dti', 'dti'),\n",
       " ('delinq_2yrs', 'delinq_2yrs'),\n",
       " ('inq_last_6mths', 'inq_last_6mths'),\n",
       " ('open_acc', 'open_acc'),\n",
       " ('pub_rec', 'pub_rec'),\n",
       " ('revol_bal', 'revol_bal'),\n",
       " ('total_acc', 'total_acc'),\n",
       " ('out_prncp', 'out_prncp'),\n",
       " ('out_prncp_inv', 'out_prncp_inv'),\n",
       " ('total_pymnt', 'total_pymnt'),\n",
       " ('total_pymnt_inv', 'total_pymnt_inv'),\n",
       " ('total_rec_prncp', 'total_rec_prncp'),\n",
       " ('total_rec_int', 'total_rec_int'),\n",
       " ('total_rec_late_fee', 'total_rec_late_fee'),\n",
       " ('recoveries', 'recoveries'),\n",
       " ('collection_recovery_fee', 'collection_recovery_fee'),\n",
       " ('last_pymnt_amnt', 'last_pymnt_amnt'),\n",
       " ('collections_12_mths_ex_med', 'collections_12_mths_ex_med'),\n",
       " ('policy_code', 'policy_code'),\n",
       " ('acc_now_delinq', 'acc_now_delinq'),\n",
       " ('tot_coll_amt', 'tot_coll_amt'),\n",
       " ('tot_cur_bal', 'tot_cur_bal'),\n",
       " ('open_acc_6m', 'open_acc_6m'),\n",
       " ('open_act_il', 'open_act_il'),\n",
       " ('open_il_12m', 'open_il_12m'),\n",
       " ('open_il_24m', 'open_il_24m'),\n",
       " ('mths_since_rcnt_il', 'mths_since_rcnt_il'),\n",
       " ('total_bal_il', 'total_bal_il'),\n",
       " ('il_util', 'il_util'),\n",
       " ('open_rv_12m', 'open_rv_12m'),\n",
       " ('open_rv_24m', 'open_rv_24m'),\n",
       " ('max_bal_bc', 'max_bal_bc'),\n",
       " ('all_util', 'all_util'),\n",
       " ('total_rev_hi_lim', 'total_rev_hi_lim'),\n",
       " ('inq_fi', 'inq_fi'),\n",
       " ('total_cu_tl', 'total_cu_tl'),\n",
       " ('inq_last_12m', 'inq_last_12m'),\n",
       " ('acc_open_past_24mths', 'acc_open_past_24mths'),\n",
       " ('avg_cur_bal', 'avg_cur_bal'),\n",
       " ('bc_open_to_buy', 'bc_open_to_buy'),\n",
       " ('bc_util', 'bc_util'),\n",
       " ('chargeoff_within_12_mths', 'chargeoff_within_12_mths'),\n",
       " ('delinq_amnt', 'delinq_amnt'),\n",
       " ('mo_sin_old_il_acct', 'mo_sin_old_il_acct'),\n",
       " ('mo_sin_old_rev_tl_op', 'mo_sin_old_rev_tl_op'),\n",
       " ('mo_sin_rcnt_rev_tl_op', 'mo_sin_rcnt_rev_tl_op'),\n",
       " ('mo_sin_rcnt_tl', 'mo_sin_rcnt_tl'),\n",
       " ('mort_acc', 'mort_acc'),\n",
       " ('mths_since_recent_bc', 'mths_since_recent_bc'),\n",
       " ('mths_since_recent_inq', 'mths_since_recent_inq'),\n",
       " ('num_accts_ever_120_pd', 'num_accts_ever_120_pd'),\n",
       " ('num_actv_bc_tl', 'num_actv_bc_tl'),\n",
       " ('num_actv_rev_tl', 'num_actv_rev_tl'),\n",
       " ('num_bc_sats', 'num_bc_sats'),\n",
       " ('num_bc_tl', 'num_bc_tl'),\n",
       " ('num_il_tl', 'num_il_tl'),\n",
       " ('num_op_rev_tl', 'num_op_rev_tl'),\n",
       " ('num_rev_accts', 'num_rev_accts'),\n",
       " ('num_rev_tl_bal_gt_0', 'num_rev_tl_bal_gt_0'),\n",
       " ('num_sats', 'num_sats'),\n",
       " ('num_tl_120dpd_2m', 'num_tl_120dpd_2m'),\n",
       " ('num_tl_30dpd', 'num_tl_30dpd'),\n",
       " ('num_tl_90g_dpd_24m', 'num_tl_90g_dpd_24m'),\n",
       " ('num_tl_op_past_12m', 'num_tl_op_past_12m'),\n",
       " ('pct_tl_nvr_dlq', 'pct_tl_nvr_dlq'),\n",
       " ('percent_bc_gt_75', 'percent_bc_gt_75'),\n",
       " ('pub_rec_bankruptcies', 'pub_rec_bankruptcies'),\n",
       " ('tax_liens', 'tax_liens'),\n",
       " ('tot_hi_cred_lim', 'tot_hi_cred_lim'),\n",
       " ('total_bal_ex_mort', 'total_bal_ex_mort'),\n",
       " ('total_bc_limit', 'total_bc_limit'),\n",
       " ('total_il_high_credit_limit', 'total_il_high_credit_limit'),\n",
       " ('home_ownership_ANY', 'home_ownership_ANY'),\n",
       " ('home_ownership_MORTGAGE', 'home_ownership_MORTGAGE'),\n",
       " ('home_ownership_OWN', 'home_ownership_OWN'),\n",
       " ('home_ownership_RENT', 'home_ownership_RENT'),\n",
       " ('verification_status_Not Verified', 'verification_status_Not Verified'),\n",
       " ('verification_status_Source Verified',\n",
       "  'verification_status_Source Verified'),\n",
       " ('verification_status_Verified', 'verification_status_Verified'),\n",
       " ('pymnt_plan_n', 'pymnt_plan_n'),\n",
       " ('initial_list_status_f', 'initial_list_status_f'),\n",
       " ('initial_list_status_w', 'initial_list_status_w'),\n",
       " ('application_type_Individual', 'application_type_Individual'),\n",
       " ('application_type_Joint App', 'application_type_Joint App'),\n",
       " ('hardship_flag_N', 'hardship_flag_N'),\n",
       " ('hardship_flag_Y', 'hardship_flag_Y'),\n",
       " ('debt_settlement_flag_N', 'debt_settlement_flag_N'),\n",
       " ('debt_settlement_flag_Y', 'target_high_risk'),\n",
       " ('target_high_risk', 'target_low_risk')]"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### add missing dummy variables to testing set"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "test_convert_df['debt_settlement_flag_Y'] = np.where(test_convert_df['debt_settlement_flag_N'] == 1, 0, 1)\n",
    "test_convert_df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>...</th>\n",
       "      <th>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>target_high_risk</th>\n",
       "      <th>target_low_risk</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>0.1033</td>\n",
       "      <td>856.40</td>\n",
       "      <td>128700.0</td>\n",
       "      <td>12.47</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38113.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24450.0</td>\n",
       "      <td>0.1430</td>\n",
       "      <td>572.72</td>\n",
       "      <td>44574.0</td>\n",
       "      <td>15.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1665.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13500.0</td>\n",
       "      <td>0.1430</td>\n",
       "      <td>316.23</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>28.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13857.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10625.0</td>\n",
       "      <td>0.1774</td>\n",
       "      <td>268.31</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>15.70</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6216.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6375.0</td>\n",
       "      <td>0.1862</td>\n",
       "      <td>232.46</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>35.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12681.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  int_rate  installment  annual_inc    dti  delinq_2yrs  \\\n",
       "0    40000.0    0.1033       856.40    128700.0  12.47          0.0   \n",
       "1    24450.0    0.1430       572.72     44574.0  15.05          0.0   \n",
       "2    13500.0    0.1430       316.23     60000.0  28.72          0.0   \n",
       "3    10625.0    0.1774       268.31     60000.0  15.70          0.0   \n",
       "4     6375.0    0.1862       232.46     60000.0  35.50          0.0   \n",
       "\n",
       "   inq_last_6mths  open_acc  pub_rec  revol_bal  ...  initial_list_status_f  \\\n",
       "0             1.0       8.0      0.0    38113.0  ...                      0   \n",
       "1             1.0       6.0      0.0     1665.0  ...                      0   \n",
       "2             0.0       8.0      0.0    13857.0  ...                      0   \n",
       "3             4.0      17.0      0.0     6216.0  ...                      0   \n",
       "4             0.0      13.0      0.0    12681.0  ...                      0   \n",
       "\n",
       "   initial_list_status_w  application_type_Individual  \\\n",
       "0                      1                            1   \n",
       "1                      1                            1   \n",
       "2                      1                            1   \n",
       "3                      1                            1   \n",
       "4                      1                            1   \n",
       "\n",
       "   application_type_Joint App  hardship_flag_N  hardship_flag_Y  \\\n",
       "0                           0                0                1   \n",
       "1                           0                1                0   \n",
       "2                           0                0                1   \n",
       "3                           0                1                0   \n",
       "4                           0                1                0   \n",
       "\n",
       "   debt_settlement_flag_N  target_high_risk  target_low_risk  \\\n",
       "0                       1                 0                1   \n",
       "1                       1                 0                1   \n",
       "2                       1                 0                1   \n",
       "3                       1                 0                1   \n",
       "4                       1                 0                1   \n",
       "\n",
       "   debt_settlement_flag_Y  \n",
       "0                       0  \n",
       "1                       0  \n",
       "2                       0  \n",
       "3                       0  \n",
       "4                       0  \n",
       "\n",
       "[5 rows x 94 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Consider the models...\n",
    "\n",
    "### Which model will perform better: logistic regression or random forests classifier?\n",
    "\n",
    "* I predict the Random Forest will outperform the Logistic Regression. I'm grounding this in 2 scholarly articles I\n",
    "found online that said Random Forest models increasingly outperform Logistic Regression.\n",
    "\n",
    "* <https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2264-5>\n",
    "* <https://scholar.smu.edu/cgi/viewcontent.cgi?article=1041&context=datasciencereview>"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Train the Logistic Regression model on the unscaled data and print the model score"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "train_convert_df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>...</th>\n",
       "      <th>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",
       "      <th>target_high_risk</th>\n",
       "      <th>target_low_risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7000.0</td>\n",
       "      <td>0.1894</td>\n",
       "      <td>256.38</td>\n",
       "      <td>75000.0</td>\n",
       "      <td>28.62</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40414.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.1614</td>\n",
       "      <td>975.71</td>\n",
       "      <td>102000.0</td>\n",
       "      <td>11.72</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43531.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11000.0</td>\n",
       "      <td>0.2055</td>\n",
       "      <td>294.81</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>37.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8242.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4000.0</td>\n",
       "      <td>0.1612</td>\n",
       "      <td>140.87</td>\n",
       "      <td>38000.0</td>\n",
       "      <td>42.89</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12767.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14000.0</td>\n",
       "      <td>0.1797</td>\n",
       "      <td>505.93</td>\n",
       "      <td>43000.0</td>\n",
       "      <td>22.16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11182.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  int_rate  installment  annual_inc    dti  delinq_2yrs  \\\n",
       "0     7000.0    0.1894       256.38     75000.0  28.62          0.0   \n",
       "1    40000.0    0.1614       975.71    102000.0  11.72          2.0   \n",
       "2    11000.0    0.2055       294.81     45000.0  37.25          1.0   \n",
       "3     4000.0    0.1612       140.87     38000.0  42.89          1.0   \n",
       "4    14000.0    0.1797       505.93     43000.0  22.16          1.0   \n",
       "\n",
       "   inq_last_6mths  open_acc  pub_rec  revol_bal  ...  initial_list_status_f  \\\n",
       "0             2.0      20.0      0.0    40414.0  ...                      1   \n",
       "1             0.0      10.0      0.0    43531.0  ...                      0   \n",
       "2             3.0      23.0      0.0     8242.0  ...                      0   \n",
       "3             0.0       7.0      0.0    12767.0  ...                      0   \n",
       "4             0.0      22.0      0.0    11182.0  ...                      0   \n",
       "\n",
       "   initial_list_status_w  application_type_Individual  \\\n",
       "0                      0                            1   \n",
       "1                      1                            1   \n",
       "2                      1                            1   \n",
       "3                      1                            0   \n",
       "4                      1                            1   \n",
       "\n",
       "   application_type_Joint App  hardship_flag_N  hardship_flag_Y  \\\n",
       "0                           0                1                0   \n",
       "1                           0                1                0   \n",
       "2                           0                1                0   \n",
       "3                           1                1                0   \n",
       "4                           0                1                0   \n",
       "\n",
       "   debt_settlement_flag_N  debt_settlement_flag_Y  target_high_risk  \\\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",
       "   target_low_risk  \n",
       "0                1  \n",
       "1                1  \n",
       "2                1  \n",
       "3                1  \n",
       "4                1  \n",
       "\n",
       "[5 rows x 94 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "# Assign the data to X and y\n",
    "# Note: Sklearn requires a two-dimensional array of values\n",
    "# so we use reshape() to create this\n",
    "\n",
    "X = train_convert_df.drop(['target_high_risk', 'target_low_risk'], axis=1)\n",
    "y = train_convert_df['target_high_risk']   # outcome variable from training data\n",
    "\n",
    "print(\"Shape: \", X.shape, y.shape)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Shape:  (12180, 92) (12180,)\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "# 2020 Q1 data that we are trying to predict\n",
    "X_20 = test_convert_df.drop(['target_high_risk', 'target_low_risk'], axis=1)\n",
    "y_20 = test_convert_df['target_high_risk']   # outcome variable from training data\n",
    "\n",
    "print(\"Shape: \", X_20.shape, y_20.shape)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Shape:  (4702, 92) (4702,)\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=7)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "# kept getting error about increasing max iterations, so went up to 20k\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier= LogisticRegression(max_iter=20000)\n",
    "classifier"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=20000)"
      ]
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "classifier.fit(X_train, y_train)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=20000)"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "print(f\"Training Data Score: {classifier.score(X_train, y_train)}\")\n",
    "print(f\"Testing Data Score (from training set): {classifier.score(X_test, y_test)}\")\n",
    "print(f'First Quarter 2020 fit: {classifier.score(X_20, y_20)}')"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training Data Score: 0.7094690749863164\n",
      "Testing Data Score (from training set): 0.69688013136289\n",
      "First Quarter 2020 fit: 0.5546575925138238\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "source": [
    "# setting vars to create table later\n",
    "train_logreg = classifier.score(X_train, y_train)\n",
    "test_logreg = classifier.score(X_test, y_test)\n",
    "Q1_2020_logreg = classifier.score(X_20, y_20)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Train a Random Forest Classifier model and print the model score"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "* not entirely sure what hyperparameters I would change"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "source": [
    "clf = RandomForestClassifier(random_state=7, n_estimators=500).fit(X_train, y_train)\n",
    "print(f\"Training Data Score: {clf.score(X_train, y_train)}\")\n",
    "print(f\"Testing Data Score (from training set): {clf.score(X_test, y_test)}\")\n",
    "print(f'First Quarter 2020 fit: {clf.score(X_20, y_20)}')"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training Data Score: 1.0\n",
      "Testing Data Score (from training set): 0.7878489326765189\n",
      "First Quarter 2020 fit: 0.6461080391322841\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "source": [
    "# setting vars to create table later\n",
    "train_clf = clf.score(X_train, y_train)\n",
    "test_clf = clf.score(X_test, y_test)\n",
    "Q1_2020_clf = clf.score(X_20, y_20)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Results\n",
    "* As predicted, the random forest model outperformed the logistic regression model. \n",
    "* Logistic regression had Training Score: 0.71, Testing Score (from training set): 0.70, and more important First Quarter 2020 fit: 0.55\n",
    "* Random forest had Training Score: 1.0, Testing Score (from training set): 0.79, and First Quarter 2020 fit: 0.65"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Revisit Preprocessing: Scale the data\n",
    "* I predict scaling data will improve both models. With the standard scaling procedure, scores within each column are converted to z-scores. When each column is converted to z-scores, it puts each column on an even playing field compared to the others such that columns with larger raw values (like income) do not overpower the model compared to other columns that might have smaller raw values, but may still play am important role in predicting loan risk. "
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "source": [
    "scaler = StandardScaler().fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "X_20_scaled=scaler.transform(X_20)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Train the Logistic Regression model on the scaled data and print the model score"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "classifier.fit(X_train_scaled, y_train)\n",
    "print(f\"Training Data Score: {classifier.score(X_train_scaled, y_train)}\")\n",
    "print(f\"Testing Data Score (from training set): {classifier.score(X_test_scaled, y_test)}\")\n",
    "print(f'First Quarter 2020 fit: {classifier.score(X_20_scaled, y_20)}')"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training Data Score: 0.7139573070607553\n",
      "Testing Data Score (from training set): 0.7004926108374384\n",
      "First Quarter 2020 fit: 0.753721820501914\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "source": [
    "# setting vars to create table later\n",
    "train_logreg_scaled = classifier.score(X_train_scaled, y_train)\n",
    "test_logreg_scaled = classifier.score(X_test_scaled, y_test)\n",
    "Q1_2020_logreg_scaled = classifier.score(X_20_scaled, y_20)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Train a Random Forest Classifier model on the scaled data and print the model score"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "source": [
    "clf = RandomForestClassifier(random_state=7, n_estimators=500).fit(X_train_scaled, y_train)\n",
    "print(f\"Training Data Score: {clf.score(X_train_scaled, y_train)}\")\n",
    "print(f\"Testing Data Score (from training set): {clf.score(X_test_scaled, y_test)}\")\n",
    "print(f'First Quarter 2020 fit: {clf.score(X_20_scaled, y_20)}')"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training Data Score: 1.0\n",
      "Testing Data Score (from training set): 0.7878489326765189\n",
      "First Quarter 2020 fit: 0.6452573373032752\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "source": [
    "train_clf_scaled = clf.score(X_train_scaled, y_train)\n",
    "test_clf_scaled = clf.score(X_test_scaled, y_test)\n",
    "Q1_2020_clf_scaled = clf.score(X_20_scaled, y_20)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "source": [
    "# creating a df to hold the results\n",
    "results = {'data_set': ['2019_training', '2019_test', '2020_Q1'], 'LogReg_Unscaled': [train_logreg, test_logreg, Q1_2020_logreg],\n",
    "    'RandomForest_Unscaled': [train_clf, test_clf, Q1_2020_clf], 'LogReg_Scaled': [train_logreg_scaled, test_logreg_scaled, Q1_2020_logreg_scaled],\n",
    "    'RandomForest_Scaled': [train_clf_scaled, test_clf_scaled, Q1_2020_clf_scaled]}\n",
    "results_df = pd.DataFrame(data=results)\n",
    "results_df"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>data_set</th>\n",
       "      <th>LogReg_Unscaled</th>\n",
       "      <th>RandomForest_Unscaled</th>\n",
       "      <th>LogReg_Scaled</th>\n",
       "      <th>RandomForest_Scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019_training</td>\n",
       "      <td>0.709469</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.713957</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019_test</td>\n",
       "      <td>0.696880</td>\n",
       "      <td>0.787849</td>\n",
       "      <td>0.700493</td>\n",
       "      <td>0.787849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020_Q1</td>\n",
       "      <td>0.554658</td>\n",
       "      <td>0.646108</td>\n",
       "      <td>0.753722</td>\n",
       "      <td>0.645257</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        data_set  LogReg_Unscaled  RandomForest_Unscaled  LogReg_Scaled  \\\n",
       "0  2019_training         0.709469               1.000000       0.713957   \n",
       "1      2019_test         0.696880               0.787849       0.700493   \n",
       "2        2020_Q1         0.554658               0.646108       0.753722   \n",
       "\n",
       "   RandomForest_Scaled  \n",
       "0             1.000000  \n",
       "1             0.787849  \n",
       "2             0.645257  "
      ]
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Wrap-up\n",
    "* Scaling data did not improve model fit for the training or 2019 test data. It did, however significantly improve logistic regression model fit for the 2020_Q1 data which jumped from .55 to .75 (which was higher than the model performed on itself). The random forest models performed so similarly that I checked multiple times to make sure I had modified the code to show the correct values.\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {}
  }
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