CredRiskModBuild1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the Data and Selecting the Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_data_inputs_train = pd.read_csv('loan_data_inputs_train.csv', index_col = 0)\n",
    "loan_data_targets_train = pd.read_csv('loan_data_targets_train.csv', index_col = 0)\n",
    "loan_data_inputs_test = pd.read_csv('loan_data_inputs_test.csv', index_col = 0)\n",
    "loan_data_targets_test = pd.read_csv('loan_data_targets_test.csv', index_col = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>...</th>\n",
       "      <th>dti:21.7-22.4</th>\n",
       "      <th>dti:22.4-35</th>\n",
       "      <th>dti:&gt;35</th>\n",
       "      <th>mths_since_last_record:Missing</th>\n",
       "      <th>mths_since_last_record:0-2</th>\n",
       "      <th>mths_since_last_record:3-20</th>\n",
       "      <th>mths_since_last_record:21-31</th>\n",
       "      <th>mths_since_last_record:32-80</th>\n",
       "      <th>mths_since_last_record:81-86</th>\n",
       "      <th>mths_since_last_record:&gt;86</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>36 months</td>\n",
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       "      <td>762.08</td>\n",
       "      <td>A</td>\n",
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       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>14.33</td>\n",
       "      <td>343.39</td>\n",
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       "      <td>6556909</td>\n",
       "      <td>20425</td>\n",
       "      <td>20425</td>\n",
       "      <td>20425.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>8.90</td>\n",
       "      <td>648.56</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>412305</th>\n",
       "      <td>412305</td>\n",
       "      <td>13827698</td>\n",
       "      <td>15890016</td>\n",
       "      <td>17200</td>\n",
       "      <td>17200</td>\n",
       "      <td>17200.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>16.59</td>\n",
       "      <td>609.73</td>\n",
       "      <td>D</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>36159</th>\n",
       "      <td>36159</td>\n",
       "      <td>422455</td>\n",
       "      <td>496525</td>\n",
       "      <td>8400</td>\n",
       "      <td>8400</td>\n",
       "      <td>7450.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>12.84</td>\n",
       "      <td>282.40</td>\n",
       "      <td>C</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 327 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0.1        id  member_id  loan_amnt  funded_amnt  \\\n",
       "427211        427211  12796369   14818505      24000        24000   \n",
       "206088        206088   1439740    1691948      10000        10000   \n",
       "136020        136020   5214749    6556909      20425        20425   \n",
       "412305        412305  13827698   15890016      17200        17200   \n",
       "36159          36159    422455     496525       8400         8400   \n",
       "\n",
       "        funded_amnt_inv        term  int_rate  installment grade  ...  \\\n",
       "427211          24000.0   36 months      8.90       762.08     A  ...   \n",
       "206088          10000.0   36 months     14.33       343.39     C  ...   \n",
       "136020          20425.0   36 months      8.90       648.56     A  ...   \n",
       "412305          17200.0   36 months     16.59       609.73     D  ...   \n",
       "36159            7450.0   36 months     12.84       282.40     C  ...   \n",
       "\n",
       "       dti:21.7-22.4 dti:22.4-35 dti:>35 mths_since_last_record:Missing  \\\n",
       "427211             1           0       0                              1   \n",
       "206088             0           0       0                              1   \n",
       "136020             0           0       0                              1   \n",
       "412305             0           0       0                              1   \n",
       "36159              0           1       0                              1   \n",
       "\n",
       "        mths_since_last_record:0-2 mths_since_last_record:3-20  \\\n",
       "427211                           0                           0   \n",
       "206088                           0                           0   \n",
       "136020                           0                           0   \n",
       "412305                           0                           0   \n",
       "36159                            0                           0   \n",
       "\n",
       "       mths_since_last_record:21-31 mths_since_last_record:32-80  \\\n",
       "427211                            0                            0   \n",
       "206088                            0                            0   \n",
       "136020                            0                            0   \n",
       "412305                            0                            0   \n",
       "36159                             0                            0   \n",
       "\n",
       "       mths_since_last_record:81-86 mths_since_last_record:>86  \n",
       "427211                            0                          0  \n",
       "206088                            0                          0  \n",
       "136020                            0                          0  \n",
       "412305                            0                          0  \n",
       "36159                             0                          0  \n",
       "\n",
       "[5 rows x 327 columns]"
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     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_data_inputs_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "loan_data_targets_train.head()"
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  {
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    "loan_data_targets_train.shape"
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       "(93257, 327)"
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     "execution_count": 22,
     "metadata": {},
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   "source": [
    "loan_data_inputs_test.shape"
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  {
   "cell_type": "code",
   "execution_count": 23,
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     "execution_count": 23,
     "metadata": {},
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   "source": [
    "loan_data_targets_test.shape"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting the Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we select a limited set of input variables in a new dataframe.\n",
    "inputs_train_with_ref_cat = loan_data_inputs_train.loc[: , ['grade:A',\n",
    "'grade:B',\n",
    "'grade:C',\n",
    "'grade:D',\n",
    "'grade:E',\n",
    "'grade:F',\n",
    "'grade:G',\n",
    "'home_ownership:RENT_OTHER_NONE_ANY',\n",
    "'home_ownership:OWN',\n",
    "'home_ownership:MORTGAGE',\n",
    "'addr_state:ND_NE_IA_NV_FL_HI_AL',\n",
    "'addr_state:NM_VA',\n",
    "'addr_state:NY',\n",
    "'addr_state:OK_TN_MO_LA_MD_NC',\n",
    "'addr_state:CA',\n",
    "'addr_state:UT_KY_AZ_NJ',\n",
    "'addr_state:AR_MI_PA_OH_MN',\n",
    "'addr_state:RI_MA_DE_SD_IN',\n",
    "'addr_state:GA_WA_OR',\n",
    "'addr_state:WI_MT',\n",
    "'addr_state:TX',\n",
    "'addr_state:IL_CT',\n",
    "'addr_state:KS_SC_CO_VT_AK_MS',\n",
    "'addr_state:WV_NH_WY_DC_ME_ID',\n",
    "'verification_status:Not Verified',\n",
    "'verification_status:Source Verified',\n",
    "'verification_status:Verified',\n",
    "'purpose:educ__sm_b__wedd__ren_en__mov__house',\n",
    "'purpose:credit_card',\n",
    "'purpose:debt_consolidation',\n",
    "'purpose:oth__med__vacation',\n",
    "'purpose:major_purch__car__home_impr',\n",
    "'initial_list_status:f',\n",
    "'initial_list_status:w',\n",
    "'term:36',\n",
    "'term:60',\n",
    "'emp_length:0',\n",
    "'emp_length:1',\n",
    "'emp_length:2-4',\n",
    "'emp_length:5-6',\n",
    "'emp_length:7-9',\n",
    "'emp_length:10',\n",
    "'mths_since_issue_d:<38',\n",
    "'mths_since_issue_d:38-39',\n",
    "'mths_since_issue_d:40-41',\n",
    "'mths_since_issue_d:42-48',\n",
    "'mths_since_issue_d:49-52',\n",
    "'mths_since_issue_d:53-64',\n",
    "'mths_since_issue_d:65-84',\n",
    "'mths_since_issue_d:>84',\n",
    "'int_rate:<9.548',\n",
    "'int_rate:9.548-12.025',\n",
    "'int_rate:12.025-15.74',\n",
    "'int_rate:15.74-20.281',\n",
    "'int_rate:>20.281',\n",
    "'mths_since_earliest_cr_line:<140',\n",
    "'mths_since_earliest_cr_line:141-164',\n",
    "'mths_since_earliest_cr_line:165-247',\n",
    "'mths_since_earliest_cr_line:248-270',\n",
    "'mths_since_earliest_cr_line:271-352',\n",
    "'mths_since_earliest_cr_line:>352',\n",
    "'delinq_2yrs:0',\n",
    "'delinq_2yrs:1-3',\n",
    "'delinq_2yrs:>=4',\n",
    "'inq_last_6mths:0',\n",
    "'inq_last_6mths:1-2',\n",
    "'inq_last_6mths:3-6',\n",
    "'inq_last_6mths:>6',\n",
    "'open_acc:0',\n",
    "'open_acc:1-3',\n",
    "'open_acc:4-12',\n",
    "'open_acc:13-17',\n",
    "'open_acc:18-22',\n",
    "'open_acc:23-25',\n",
    "'open_acc:26-30',\n",
    "'open_acc:>=31',\n",
    "'pub_rec:0-2',\n",
    "'pub_rec:3-4',\n",
    "'pub_rec:>=5',\n",
    "'total_acc:<=27',\n",
    "'total_acc:28-51',\n",
    "'total_acc:>=52',\n",
    "'acc_now_delinq:0',\n",
    "'acc_now_delinq:>=1',\n",
    "'total_rev_hi_lim:<=5K',\n",
    "'total_rev_hi_lim:5K-10K',\n",
    "'total_rev_hi_lim:10K-20K',\n",
    "'total_rev_hi_lim:20K-30K',\n",
    "'total_rev_hi_lim:30K-40K',\n",
    "'total_rev_hi_lim:40K-55K',\n",
    "'total_rev_hi_lim:55K-95K',\n",
    "'total_rev_hi_lim:>95K',\n",
    "'annual_inc:<20K',\n",
    "'annual_inc:20K-30K',\n",
    "'annual_inc:30K-40K',\n",
    "'annual_inc:40K-50K',\n",
    "'annual_inc:50K-60K',\n",
    "'annual_inc:60K-70K',\n",
    "'annual_inc:70K-80K',\n",
    "'annual_inc:80K-90K',\n",
    "'annual_inc:90K-100K',\n",
    "'annual_inc:100K-120K',\n",
    "'annual_inc:120K-140K',\n",
    "'annual_inc:>140K',\n",
    "'dti:<=1.4',\n",
    "'dti:1.4-3.5',\n",
    "'dti:3.5-7.7',\n",
    "'dti:7.7-10.5',\n",
    "'dti:10.5-16.1',\n",
    "'dti:16.1-20.3',\n",
    "'dti:20.3-21.7',\n",
    "'dti:21.7-22.4',\n",
    "'dti:22.4-35',\n",
    "'dti:>35',\n",
    "'mths_since_last_delinq:Missing',\n",
    "'mths_since_last_delinq:0-3',\n",
    "'mths_since_last_delinq:4-30',\n",
    "'mths_since_last_delinq:31-56',\n",
    "'mths_since_last_delinq:>=57',\n",
    "'mths_since_last_record:Missing',\n",
    "'mths_since_last_record:0-2',\n",
    "'mths_since_last_record:3-20',\n",
    "'mths_since_last_record:21-31',\n",
    "'mths_since_last_record:32-80',\n",
    "'mths_since_last_record:81-86',\n",
    "'mths_since_last_record:>86',\n",
    "]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we store the names of the reference category dummy variables in a list.\n",
    "ref_categories = ['grade:G',\n",
    "'home_ownership:RENT_OTHER_NONE_ANY',\n",
    "'addr_state:ND_NE_IA_NV_FL_HI_AL',\n",
    "'verification_status:Verified',\n",
    "'purpose:educ__sm_b__wedd__ren_en__mov__house',\n",
    "'initial_list_status:f',\n",
    "'term:60',\n",
    "'emp_length:0',\n",
    "'mths_since_issue_d:>84',\n",
    "'int_rate:>20.281',\n",
    "'mths_since_earliest_cr_line:<140',\n",
    "'delinq_2yrs:>=4',\n",
    "'inq_last_6mths:>6',\n",
    "'open_acc:0',\n",
    "'pub_rec:0-2',\n",
    "'total_acc:<=27',\n",
    "'acc_now_delinq:0',\n",
    "'total_rev_hi_lim:<=5K',\n",
    "'annual_inc:<20K',\n",
    "'dti:>35',\n",
    "'mths_since_last_delinq:0-3',\n",
    "'mths_since_last_record:0-2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th>grade:A</th>\n",
       "      <th>grade:B</th>\n",
       "      <th>grade:C</th>\n",
       "      <th>grade:D</th>\n",
       "      <th>grade:E</th>\n",
       "      <th>grade:F</th>\n",
       "      <th>home_ownership:OWN</th>\n",
       "      <th>home_ownership:MORTGAGE</th>\n",
       "      <th>addr_state:NM_VA</th>\n",
       "      <th>addr_state:NY</th>\n",
       "      <th>...</th>\n",
       "      <th>mths_since_last_delinq:Missing</th>\n",
       "      <th>mths_since_last_delinq:4-30</th>\n",
       "      <th>mths_since_last_delinq:31-56</th>\n",
       "      <th>mths_since_last_delinq:&gt;=57</th>\n",
       "      <th>mths_since_last_record:Missing</th>\n",
       "      <th>mths_since_last_record:3-20</th>\n",
       "      <th>mths_since_last_record:21-31</th>\n",
       "      <th>mths_since_last_record:32-80</th>\n",
       "      <th>mths_since_last_record:81-86</th>\n",
       "      <th>mths_since_last_record:&gt;86</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>427211</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206088</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136020</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412305</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36159</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 104 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        grade:A  grade:B  grade:C  grade:D  grade:E  grade:F  \\\n",
       "427211        1        0        0        0        0        0   \n",
       "206088        0        0        1        0        0        0   \n",
       "136020        1        0        0        0        0        0   \n",
       "412305        0        0        0        1        0        0   \n",
       "36159         0        0        1        0        0        0   \n",
       "\n",
       "        home_ownership:OWN  home_ownership:MORTGAGE  addr_state:NM_VA  \\\n",
       "427211                   0                        1                 0   \n",
       "206088                   0                        1                 0   \n",
       "136020                   0                        1                 0   \n",
       "412305                   0                        0                 0   \n",
       "36159                    0                        1                 0   \n",
       "\n",
       "        addr_state:NY  ...  mths_since_last_delinq:Missing  \\\n",
       "427211              0  ...                               1   \n",
       "206088              0  ...                               0   \n",
       "136020              0  ...                               0   \n",
       "412305              0  ...                               0   \n",
       "36159               0  ...                               1   \n",
       "\n",
       "        mths_since_last_delinq:4-30  mths_since_last_delinq:31-56  \\\n",
       "427211                            0                             0   \n",
       "206088                            1                             0   \n",
       "136020                            0                             1   \n",
       "412305                            1                             0   \n",
       "36159                             0                             0   \n",
       "\n",
       "        mths_since_last_delinq:>=57  mths_since_last_record:Missing  \\\n",
       "427211                            0                               1   \n",
       "206088                            0                               1   \n",
       "136020                            0                               1   \n",
       "412305                            0                               1   \n",
       "36159                             0                               1   \n",
       "\n",
       "        mths_since_last_record:3-20  mths_since_last_record:21-31  \\\n",
       "427211                            0                             0   \n",
       "206088                            0                             0   \n",
       "136020                            0                             0   \n",
       "412305                            0                             0   \n",
       "36159                             0                             0   \n",
       "\n",
       "        mths_since_last_record:32-80  mths_since_last_record:81-86  \\\n",
       "427211                             0                             0   \n",
       "206088                             0                             0   \n",
       "136020                             0                             0   \n",
       "412305                             0                             0   \n",
       "36159                              0                             0   \n",
       "\n",
       "        mths_since_last_record:>86  \n",
       "427211                           0  \n",
       "206088                           0  \n",
       "136020                           0  \n",
       "412305                           0  \n",
       "36159                            0  \n",
       "\n",
       "[5 rows x 104 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs_train = inputs_train_with_ref_cat.drop(ref_categories, axis = 1)\n",
    "# From the dataframe with input variables, we drop the variables with variable names in the list with reference categories. \n",
    "inputs_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PD Model Estimation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = LogisticRegression()\n",
    "# We create an instance of an object from the 'LogisticRegression' class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = None\n",
    "# Sets the pandas dataframe options to display all columns/ rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\Anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:73: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "C:\\Users\\ASUS\\Anaconda3\\Lib\\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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(inputs_train, loan_data_targets_train)\n",
    "# Estimates the coefficients of the object from the 'LogisticRegression' class\n",
    "# with inputs (independent variables) contained in the first dataframe\n",
    "# and targets (dependent variables) contained in the second dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.33108355])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_\n",
    "# Displays the intercept contain in the estimated (\"fitted\") object from the 'LogisticRegression' class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.16006962,  0.90604324,  0.70885487,  0.51905842,  0.3368381 ,\n",
       "         0.14489718,  0.0907978 ,  0.10678393,  0.02967001,  0.05525702,\n",
       "         0.05661573,  0.05941036,  0.07498052,  0.13245334,  0.09937436,\n",
       "         0.18113223,  0.22498461,  0.22035568,  0.25916387,  0.31663314,\n",
       "         0.51750558,  0.08806973, -0.01075162,  0.30676693,  0.20250961,\n",
       "         0.21415768,  0.26666979,  0.05443165,  0.07808204,  0.10018082,\n",
       "         0.12530492,  0.08960366,  0.06084578,  0.12334684,  1.07505578,\n",
       "         0.87435193,  0.77403337,  0.57294119,  0.41066583,  0.16587738,\n",
       "        -0.06980612,  0.8505805 ,  0.53835412,  0.28958907,  0.10065393,\n",
       "         0.05614334,  0.03784728,  0.07957666,  0.11957131,  0.1262671 ,\n",
       "         0.07428759,  0.03130795,  0.63475158,  0.49135844,  0.2819949 ,\n",
       "        -0.01100386, -0.1086544 , -0.13433526, -0.1497422 , -0.15435821,\n",
       "        -0.11931539, -0.20970492,  0.12264663,  0.17842857, -0.0209367 ,\n",
       "         0.02532037,  0.21757843,  0.03470888,  0.00274204,  0.00124651,\n",
       "         0.01450133,  0.03491242,  0.06104213,  0.20175257, -0.06016226,\n",
       "         0.0097342 ,  0.10682351,  0.17591061,  0.25041909,  0.32487643,\n",
       "         0.40129491,  0.41911122,  0.49751686,  0.58506412,  0.51956168,\n",
       "         0.20192232,  0.33250685,  0.35764194,  0.29866257,  0.22515867,\n",
       "         0.12726927,  0.11595939,  0.08167594,  0.04260109,  0.08300911,\n",
       "         0.15152311,  0.16195939,  0.11386651,  0.34036088,  0.43559862,\n",
       "         0.36447166,  0.52705774,  0.20183656,  0.25467864]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_\n",
    "# Displays the coefficients contained in the estimated (\"fitted\") object from the 'LogisticRegression' class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_name = inputs_train.columns.values\n",
    "# Stores the names of the columns of a dataframe in a variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature name</th>\n",
       "      <th>Coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.331084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.160070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.906043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.708855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.519058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>grade:E</td>\n",
       "      <td>0.336838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>grade:F</td>\n",
       "      <td>0.144897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>home_ownership:OWN</td>\n",
       "      <td>0.090798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>home_ownership:MORTGAGE</td>\n",
       "      <td>0.106784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>addr_state:NM_VA</td>\n",
       "      <td>0.029670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>addr_state:NY</td>\n",
       "      <td>0.055257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>addr_state:OK_TN_MO_LA_MD_NC</td>\n",
       "      <td>0.056616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>addr_state:CA</td>\n",
       "      <td>0.059410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>addr_state:UT_KY_AZ_NJ</td>\n",
       "      <td>0.074981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>addr_state:AR_MI_PA_OH_MN</td>\n",
       "      <td>0.132453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>addr_state:RI_MA_DE_SD_IN</td>\n",
       "      <td>0.099374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>addr_state:GA_WA_OR</td>\n",
       "      <td>0.181132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>addr_state:WI_MT</td>\n",
       "      <td>0.224985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>addr_state:TX</td>\n",
       "      <td>0.220356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>addr_state:IL_CT</td>\n",
       "      <td>0.259164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>addr_state:KS_SC_CO_VT_AK_MS</td>\n",
       "      <td>0.316633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>addr_state:WV_NH_WY_DC_ME_ID</td>\n",
       "      <td>0.517506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>verification_status:Not Verified</td>\n",
       "      <td>0.088070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>verification_status:Source Verified</td>\n",
       "      <td>-0.010752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>purpose:credit_card</td>\n",
       "      <td>0.306767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>purpose:debt_consolidation</td>\n",
       "      <td>0.202510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>purpose:oth__med__vacation</td>\n",
       "      <td>0.214158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>purpose:major_purch__car__home_impr</td>\n",
       "      <td>0.266670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>initial_list_status:w</td>\n",
       "      <td>0.054432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>term:36</td>\n",
       "      <td>0.078082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>emp_length:1</td>\n",
       "      <td>0.100181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>emp_length:2-4</td>\n",
       "      <td>0.125305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>emp_length:5-6</td>\n",
       "      <td>0.089604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>emp_length:7-9</td>\n",
       "      <td>0.060846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>emp_length:10</td>\n",
       "      <td>0.123347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>mths_since_issue_d:&lt;38</td>\n",
       "      <td>1.075056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>mths_since_issue_d:38-39</td>\n",
       "      <td>0.874352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>mths_since_issue_d:40-41</td>\n",
       "      <td>0.774033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>mths_since_issue_d:42-48</td>\n",
       "      <td>0.572941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>mths_since_issue_d:49-52</td>\n",
       "      <td>0.410666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>mths_since_issue_d:53-64</td>\n",
       "      <td>0.165877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>mths_since_issue_d:65-84</td>\n",
       "      <td>-0.069806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>int_rate:&lt;9.548</td>\n",
       "      <td>0.850580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>int_rate:9.548-12.025</td>\n",
       "      <td>0.538354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>int_rate:12.025-15.74</td>\n",
       "      <td>0.289589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>int_rate:15.74-20.281</td>\n",
       "      <td>0.100654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>mths_since_earliest_cr_line:141-164</td>\n",
       "      <td>0.056143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>mths_since_earliest_cr_line:165-247</td>\n",
       "      <td>0.037847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>mths_since_earliest_cr_line:248-270</td>\n",
       "      <td>0.079577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>mths_since_earliest_cr_line:271-352</td>\n",
       "      <td>0.119571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>mths_since_earliest_cr_line:&gt;352</td>\n",
       "      <td>0.126267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>delinq_2yrs:0</td>\n",
       "      <td>0.074288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>delinq_2yrs:1-3</td>\n",
       "      <td>0.031308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>inq_last_6mths:0</td>\n",
       "      <td>0.634752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>inq_last_6mths:1-2</td>\n",
       "      <td>0.491358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>inq_last_6mths:3-6</td>\n",
       "      <td>0.281995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>open_acc:1-3</td>\n",
       "      <td>-0.011004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>open_acc:4-12</td>\n",
       "      <td>-0.108654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>open_acc:13-17</td>\n",
       "      <td>-0.134335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>open_acc:18-22</td>\n",
       "      <td>-0.149742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>open_acc:23-25</td>\n",
       "      <td>-0.154358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>open_acc:26-30</td>\n",
       "      <td>-0.119315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>open_acc:&gt;=31</td>\n",
       "      <td>-0.209705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>pub_rec:3-4</td>\n",
       "      <td>0.122647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>pub_rec:&gt;=5</td>\n",
       "      <td>0.178429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>total_acc:28-51</td>\n",
       "      <td>-0.020937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>total_acc:&gt;=52</td>\n",
       "      <td>0.025320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>acc_now_delinq:&gt;=1</td>\n",
       "      <td>0.217578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>total_rev_hi_lim:5K-10K</td>\n",
       "      <td>0.034709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>total_rev_hi_lim:10K-20K</td>\n",
       "      <td>0.002742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>total_rev_hi_lim:20K-30K</td>\n",
       "      <td>0.001247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>total_rev_hi_lim:30K-40K</td>\n",
       "      <td>0.014501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>total_rev_hi_lim:40K-55K</td>\n",
       "      <td>0.034912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>total_rev_hi_lim:55K-95K</td>\n",
       "      <td>0.061042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>total_rev_hi_lim:&gt;95K</td>\n",
       "      <td>0.201753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>annual_inc:20K-30K</td>\n",
       "      <td>-0.060162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>annual_inc:30K-40K</td>\n",
       "      <td>0.009734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>annual_inc:40K-50K</td>\n",
       "      <td>0.106824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>annual_inc:50K-60K</td>\n",
       "      <td>0.175911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>annual_inc:60K-70K</td>\n",
       "      <td>0.250419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>annual_inc:70K-80K</td>\n",
       "      <td>0.324876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>annual_inc:80K-90K</td>\n",
       "      <td>0.401295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>annual_inc:90K-100K</td>\n",
       "      <td>0.419111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>annual_inc:100K-120K</td>\n",
       "      <td>0.497517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>annual_inc:120K-140K</td>\n",
       "      <td>0.585064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>annual_inc:&gt;140K</td>\n",
       "      <td>0.519562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>dti:&lt;=1.4</td>\n",
       "      <td>0.201922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>dti:1.4-3.5</td>\n",
       "      <td>0.332507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>dti:3.5-7.7</td>\n",
       "      <td>0.357642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>dti:7.7-10.5</td>\n",
       "      <td>0.298663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>dti:10.5-16.1</td>\n",
       "      <td>0.225159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>dti:16.1-20.3</td>\n",
       "      <td>0.127269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>dti:20.3-21.7</td>\n",
       "      <td>0.115959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>dti:21.7-22.4</td>\n",
       "      <td>0.081676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>dti:22.4-35</td>\n",
       "      <td>0.042601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>mths_since_last_delinq:Missing</td>\n",
       "      <td>0.083009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>mths_since_last_delinq:4-30</td>\n",
       "      <td>0.151523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>mths_since_last_delinq:31-56</td>\n",
       "      <td>0.161959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>mths_since_last_delinq:&gt;=57</td>\n",
       "      <td>0.113867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>mths_since_last_record:Missing</td>\n",
       "      <td>0.340361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>mths_since_last_record:3-20</td>\n",
       "      <td>0.435599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>mths_since_last_record:21-31</td>\n",
       "      <td>0.364472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>mths_since_last_record:32-80</td>\n",
       "      <td>0.527058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>mths_since_last_record:81-86</td>\n",
       "      <td>0.201837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>mths_since_last_record:&gt;86</td>\n",
       "      <td>0.254679</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Feature name  Coefficients\n",
       "0                              Intercept     -1.331084\n",
       "1                                grade:A      1.160070\n",
       "2                                grade:B      0.906043\n",
       "3                                grade:C      0.708855\n",
       "4                                grade:D      0.519058\n",
       "5                                grade:E      0.336838\n",
       "6                                grade:F      0.144897\n",
       "7                     home_ownership:OWN      0.090798\n",
       "8                home_ownership:MORTGAGE      0.106784\n",
       "9                       addr_state:NM_VA      0.029670\n",
       "10                         addr_state:NY      0.055257\n",
       "11          addr_state:OK_TN_MO_LA_MD_NC      0.056616\n",
       "12                         addr_state:CA      0.059410\n",
       "13                addr_state:UT_KY_AZ_NJ      0.074981\n",
       "14             addr_state:AR_MI_PA_OH_MN      0.132453\n",
       "15             addr_state:RI_MA_DE_SD_IN      0.099374\n",
       "16                   addr_state:GA_WA_OR      0.181132\n",
       "17                      addr_state:WI_MT      0.224985\n",
       "18                         addr_state:TX      0.220356\n",
       "19                      addr_state:IL_CT      0.259164\n",
       "20          addr_state:KS_SC_CO_VT_AK_MS      0.316633\n",
       "21          addr_state:WV_NH_WY_DC_ME_ID      0.517506\n",
       "22      verification_status:Not Verified      0.088070\n",
       "23   verification_status:Source Verified     -0.010752\n",
       "24                   purpose:credit_card      0.306767\n",
       "25            purpose:debt_consolidation      0.202510\n",
       "26            purpose:oth__med__vacation      0.214158\n",
       "27   purpose:major_purch__car__home_impr      0.266670\n",
       "28                 initial_list_status:w      0.054432\n",
       "29                               term:36      0.078082\n",
       "30                          emp_length:1      0.100181\n",
       "31                        emp_length:2-4      0.125305\n",
       "32                        emp_length:5-6      0.089604\n",
       "33                        emp_length:7-9      0.060846\n",
       "34                         emp_length:10      0.123347\n",
       "35                mths_since_issue_d:<38      1.075056\n",
       "36              mths_since_issue_d:38-39      0.874352\n",
       "37              mths_since_issue_d:40-41      0.774033\n",
       "38              mths_since_issue_d:42-48      0.572941\n",
       "39              mths_since_issue_d:49-52      0.410666\n",
       "40              mths_since_issue_d:53-64      0.165877\n",
       "41              mths_since_issue_d:65-84     -0.069806\n",
       "42                       int_rate:<9.548      0.850580\n",
       "43                 int_rate:9.548-12.025      0.538354\n",
       "44                 int_rate:12.025-15.74      0.289589\n",
       "45                 int_rate:15.74-20.281      0.100654\n",
       "46   mths_since_earliest_cr_line:141-164      0.056143\n",
       "47   mths_since_earliest_cr_line:165-247      0.037847\n",
       "48   mths_since_earliest_cr_line:248-270      0.079577\n",
       "49   mths_since_earliest_cr_line:271-352      0.119571\n",
       "50      mths_since_earliest_cr_line:>352      0.126267\n",
       "51                         delinq_2yrs:0      0.074288\n",
       "52                       delinq_2yrs:1-3      0.031308\n",
       "53                      inq_last_6mths:0      0.634752\n",
       "54                    inq_last_6mths:1-2      0.491358\n",
       "55                    inq_last_6mths:3-6      0.281995\n",
       "56                          open_acc:1-3     -0.011004\n",
       "57                         open_acc:4-12     -0.108654\n",
       "58                        open_acc:13-17     -0.134335\n",
       "59                        open_acc:18-22     -0.149742\n",
       "60                        open_acc:23-25     -0.154358\n",
       "61                        open_acc:26-30     -0.119315\n",
       "62                         open_acc:>=31     -0.209705\n",
       "63                           pub_rec:3-4      0.122647\n",
       "64                           pub_rec:>=5      0.178429\n",
       "65                       total_acc:28-51     -0.020937\n",
       "66                        total_acc:>=52      0.025320\n",
       "67                    acc_now_delinq:>=1      0.217578\n",
       "68               total_rev_hi_lim:5K-10K      0.034709\n",
       "69              total_rev_hi_lim:10K-20K      0.002742\n",
       "70              total_rev_hi_lim:20K-30K      0.001247\n",
       "71              total_rev_hi_lim:30K-40K      0.014501\n",
       "72              total_rev_hi_lim:40K-55K      0.034912\n",
       "73              total_rev_hi_lim:55K-95K      0.061042\n",
       "74                 total_rev_hi_lim:>95K      0.201753\n",
       "75                    annual_inc:20K-30K     -0.060162\n",
       "76                    annual_inc:30K-40K      0.009734\n",
       "77                    annual_inc:40K-50K      0.106824\n",
       "78                    annual_inc:50K-60K      0.175911\n",
       "79                    annual_inc:60K-70K      0.250419\n",
       "80                    annual_inc:70K-80K      0.324876\n",
       "81                    annual_inc:80K-90K      0.401295\n",
       "82                   annual_inc:90K-100K      0.419111\n",
       "83                  annual_inc:100K-120K      0.497517\n",
       "84                  annual_inc:120K-140K      0.585064\n",
       "85                      annual_inc:>140K      0.519562\n",
       "86                             dti:<=1.4      0.201922\n",
       "87                           dti:1.4-3.5      0.332507\n",
       "88                           dti:3.5-7.7      0.357642\n",
       "89                          dti:7.7-10.5      0.298663\n",
       "90                         dti:10.5-16.1      0.225159\n",
       "91                         dti:16.1-20.3      0.127269\n",
       "92                         dti:20.3-21.7      0.115959\n",
       "93                         dti:21.7-22.4      0.081676\n",
       "94                           dti:22.4-35      0.042601\n",
       "95        mths_since_last_delinq:Missing      0.083009\n",
       "96           mths_since_last_delinq:4-30      0.151523\n",
       "97          mths_since_last_delinq:31-56      0.161959\n",
       "98           mths_since_last_delinq:>=57      0.113867\n",
       "99        mths_since_last_record:Missing      0.340361\n",
       "100          mths_since_last_record:3-20      0.435599\n",
       "101         mths_since_last_record:21-31      0.364472\n",
       "102         mths_since_last_record:32-80      0.527058\n",
       "103         mths_since_last_record:81-86      0.201837\n",
       "104           mths_since_last_record:>86      0.254679"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_table = pd.DataFrame(columns = ['Feature name'], data = feature_name)\n",
    "# Creates a dataframe with a column titled 'Feature name' and row values contained in the 'feature_name' variable.\n",
    "summary_table['Coefficients'] = np.transpose(reg.coef_)\n",
    "# Creates a new column in the dataframe, called 'Coefficients',\n",
    "# with row values the transposed coefficients from the 'LogisticRegression' object.\n",
    "summary_table.index = summary_table.index + 1\n",
    "# Increases the index of every row of the dataframe with 1.\n",
    "summary_table.loc[0] = ['Intercept', reg.intercept_[0]]\n",
    "# Assigns values of the row with index 0 of the dataframe.\n",
    "summary_table = summary_table.sort_index()\n",
    "# Sorts the dataframe by index.\n",
    "summary_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build a Logistic Regression Model with P-Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# P values for sklearn logistic regression.\n",
    "\n",
    "# Class to display p-values for logistic regression in sklearn.\n",
    "\n",
    "from sklearn import linear_model\n",
    "import scipy.stats as stat\n",
    "\n",
    "class LogisticRegression_with_p_values:\n",
    "    \n",
    "    def __init__(self,*args,**kwargs):#,**kwargs):\n",
    "        self.model = linear_model.LogisticRegression(*args,**kwargs)#,**args)\n",
    "\n",
    "    def fit(self,X,y):\n",
    "        self.model.fit(X,y)\n",
    "        \n",
    "        #### Get p-values for the fitted model ####\n",
    "        denom = (2.0 * (1.0 + np.cosh(self.model.decision_function(X))))\n",
    "        denom = np.tile(denom,(X.shape[1],1)).T\n",
    "        F_ij = np.dot((X / denom).T,X) ## Fisher Information Matrix\n",
    "        Cramer_Rao = np.linalg.inv(F_ij) ## Inverse Information Matrix\n",
    "        sigma_estimates = np.sqrt(np.diagonal(Cramer_Rao))\n",
    "        z_scores = self.model.coef_[0] / sigma_estimates # z-score for eaach model coefficient\n",
    "        p_values = [stat.norm.sf(abs(x)) * 2 for x in z_scores] ### two tailed test for p-values\n",
    "        \n",
    "        self.coef_ = self.model.coef_\n",
    "        self.intercept_ = self.model.intercept_\n",
    "        #self.z_scores = z_scores\n",
    "        self.p_values = p_values\n",
    "        #self.sigma_estimates = sigma_estimates\n",
    "        #self.F_ij = F_ij"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "import scipy.stats as stat\n",
    "\n",
    "class LogisticRegression_with_p_values:\n",
    "    \n",
    "    def __init__(self,*args,**kwargs):\n",
    "        self.model = linear_model.LogisticRegression(*args,**kwargs)\n",
    "\n",
    "    def fit(self,X,y):\n",
    "        self.model.fit(X,y)\n",
    "        denom = (2.0 * (1.0 + np.cosh(self.model.decision_function(X))))\n",
    "        denom = np.tile(denom,(X.shape[1],1)).T\n",
    "        F_ij = np.dot((X / denom).T,X)\n",
    "        Cramer_Rao = np.linalg.inv(F_ij)\n",
    "        sigma_estimates = np.sqrt(np.diagonal(Cramer_Rao))\n",
    "        z_scores = self.model.coef_[0] / sigma_estimates\n",
    "        p_values = [stat.norm.sf(abs(x)) * 2 for x in z_scores]\n",
    "        self.coef_ = self.model.coef_\n",
    "        self.intercept_ = self.model.intercept_\n",
    "        self.p_values = p_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = LogisticRegression_with_p_values()\n",
    "# We create an instance of an object from the newly created 'LogisticRegression_with_p_values()' class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\Anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:73: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "C:\\Users\\ASUS\\Anaconda3\\Lib\\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": [
    "reg.fit(inputs_train, loan_data_targets_train)\n",
    "# Estimates the coefficients of the object from the 'LogisticRegression' class\n",
    "# with inputs (independent variables) contained in the first dataframe\n",
    "# and targets (dependent variables) contained in the second dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature name</th>\n",
       "      <th>Coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.331084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.160070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.906043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.708855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.519058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>grade:E</td>\n",
       "      <td>0.336838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>grade:F</td>\n",
       "      <td>0.144897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>home_ownership:OWN</td>\n",
       "      <td>0.090798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>home_ownership:MORTGAGE</td>\n",
       "      <td>0.106784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>addr_state:NM_VA</td>\n",
       "      <td>0.029670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>addr_state:NY</td>\n",
       "      <td>0.055257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>addr_state:OK_TN_MO_LA_MD_NC</td>\n",
       "      <td>0.056616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>addr_state:CA</td>\n",
       "      <td>0.059410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>addr_state:UT_KY_AZ_NJ</td>\n",
       "      <td>0.074981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>addr_state:AR_MI_PA_OH_MN</td>\n",
       "      <td>0.132453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>addr_state:RI_MA_DE_SD_IN</td>\n",
       "      <td>0.099374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>addr_state:GA_WA_OR</td>\n",
       "      <td>0.181132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>addr_state:WI_MT</td>\n",
       "      <td>0.224985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>addr_state:TX</td>\n",
       "      <td>0.220356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>addr_state:IL_CT</td>\n",
       "      <td>0.259164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>addr_state:KS_SC_CO_VT_AK_MS</td>\n",
       "      <td>0.316633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>addr_state:WV_NH_WY_DC_ME_ID</td>\n",
       "      <td>0.517506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>verification_status:Not Verified</td>\n",
       "      <td>0.088070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>verification_status:Source Verified</td>\n",
       "      <td>-0.010752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>purpose:credit_card</td>\n",
       "      <td>0.306767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>purpose:debt_consolidation</td>\n",
       "      <td>0.202510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>purpose:oth__med__vacation</td>\n",
       "      <td>0.214158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>purpose:major_purch__car__home_impr</td>\n",
       "      <td>0.266670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>initial_list_status:w</td>\n",
       "      <td>0.054432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>term:36</td>\n",
       "      <td>0.078082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>emp_length:1</td>\n",
       "      <td>0.100181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>emp_length:2-4</td>\n",
       "      <td>0.125305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>emp_length:5-6</td>\n",
       "      <td>0.089604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>emp_length:7-9</td>\n",
       "      <td>0.060846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>emp_length:10</td>\n",
       "      <td>0.123347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>mths_since_issue_d:&lt;38</td>\n",
       "      <td>1.075056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>mths_since_issue_d:38-39</td>\n",
       "      <td>0.874352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>mths_since_issue_d:40-41</td>\n",
       "      <td>0.774033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>mths_since_issue_d:42-48</td>\n",
       "      <td>0.572941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>mths_since_issue_d:49-52</td>\n",
       "      <td>0.410666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>mths_since_issue_d:53-64</td>\n",
       "      <td>0.165877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>mths_since_issue_d:65-84</td>\n",
       "      <td>-0.069806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>int_rate:&lt;9.548</td>\n",
       "      <td>0.850580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>int_rate:9.548-12.025</td>\n",
       "      <td>0.538354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>int_rate:12.025-15.74</td>\n",
       "      <td>0.289589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>int_rate:15.74-20.281</td>\n",
       "      <td>0.100654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>mths_since_earliest_cr_line:141-164</td>\n",
       "      <td>0.056143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>mths_since_earliest_cr_line:165-247</td>\n",
       "      <td>0.037847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>mths_since_earliest_cr_line:248-270</td>\n",
       "      <td>0.079577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>mths_since_earliest_cr_line:271-352</td>\n",
       "      <td>0.119571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>mths_since_earliest_cr_line:&gt;352</td>\n",
       "      <td>0.126267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>delinq_2yrs:0</td>\n",
       "      <td>0.074288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>delinq_2yrs:1-3</td>\n",
       "      <td>0.031308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>inq_last_6mths:0</td>\n",
       "      <td>0.634752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>inq_last_6mths:1-2</td>\n",
       "      <td>0.491358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>inq_last_6mths:3-6</td>\n",
       "      <td>0.281995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>open_acc:1-3</td>\n",
       "      <td>-0.011004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>open_acc:4-12</td>\n",
       "      <td>-0.108654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>open_acc:13-17</td>\n",
       "      <td>-0.134335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>open_acc:18-22</td>\n",
       "      <td>-0.149742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>open_acc:23-25</td>\n",
       "      <td>-0.154358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>open_acc:26-30</td>\n",
       "      <td>-0.119315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>open_acc:&gt;=31</td>\n",
       "      <td>-0.209705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>pub_rec:3-4</td>\n",
       "      <td>0.122647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>pub_rec:&gt;=5</td>\n",
       "      <td>0.178429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>total_acc:28-51</td>\n",
       "      <td>-0.020937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>total_acc:&gt;=52</td>\n",
       "      <td>0.025320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>acc_now_delinq:&gt;=1</td>\n",
       "      <td>0.217578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>total_rev_hi_lim:5K-10K</td>\n",
       "      <td>0.034709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>total_rev_hi_lim:10K-20K</td>\n",
       "      <td>0.002742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>total_rev_hi_lim:20K-30K</td>\n",
       "      <td>0.001247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>total_rev_hi_lim:30K-40K</td>\n",
       "      <td>0.014501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>total_rev_hi_lim:40K-55K</td>\n",
       "      <td>0.034912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>total_rev_hi_lim:55K-95K</td>\n",
       "      <td>0.061042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>total_rev_hi_lim:&gt;95K</td>\n",
       "      <td>0.201753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>annual_inc:20K-30K</td>\n",
       "      <td>-0.060162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>annual_inc:30K-40K</td>\n",
       "      <td>0.009734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>annual_inc:40K-50K</td>\n",
       "      <td>0.106824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>annual_inc:50K-60K</td>\n",
       "      <td>0.175911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>annual_inc:60K-70K</td>\n",
       "      <td>0.250419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>annual_inc:70K-80K</td>\n",
       "      <td>0.324876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>annual_inc:80K-90K</td>\n",
       "      <td>0.401295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>annual_inc:90K-100K</td>\n",
       "      <td>0.419111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>annual_inc:100K-120K</td>\n",
       "      <td>0.497517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>annual_inc:120K-140K</td>\n",
       "      <td>0.585064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>annual_inc:&gt;140K</td>\n",
       "      <td>0.519562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>dti:&lt;=1.4</td>\n",
       "      <td>0.201922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>dti:1.4-3.5</td>\n",
       "      <td>0.332507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>dti:3.5-7.7</td>\n",
       "      <td>0.357642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>dti:7.7-10.5</td>\n",
       "      <td>0.298663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>dti:10.5-16.1</td>\n",
       "      <td>0.225159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>dti:16.1-20.3</td>\n",
       "      <td>0.127269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>dti:20.3-21.7</td>\n",
       "      <td>0.115959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>dti:21.7-22.4</td>\n",
       "      <td>0.081676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>dti:22.4-35</td>\n",
       "      <td>0.042601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>mths_since_last_delinq:Missing</td>\n",
       "      <td>0.083009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>mths_since_last_delinq:4-30</td>\n",
       "      <td>0.151523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>mths_since_last_delinq:31-56</td>\n",
       "      <td>0.161959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>mths_since_last_delinq:&gt;=57</td>\n",
       "      <td>0.113867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>mths_since_last_record:Missing</td>\n",
       "      <td>0.340361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>mths_since_last_record:3-20</td>\n",
       "      <td>0.435599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>mths_since_last_record:21-31</td>\n",
       "      <td>0.364472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>mths_since_last_record:32-80</td>\n",
       "      <td>0.527058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>mths_since_last_record:81-86</td>\n",
       "      <td>0.201837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>mths_since_last_record:&gt;86</td>\n",
       "      <td>0.254679</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Feature name  Coefficients\n",
       "0                              Intercept     -1.331084\n",
       "1                                grade:A      1.160070\n",
       "2                                grade:B      0.906043\n",
       "3                                grade:C      0.708855\n",
       "4                                grade:D      0.519058\n",
       "5                                grade:E      0.336838\n",
       "6                                grade:F      0.144897\n",
       "7                     home_ownership:OWN      0.090798\n",
       "8                home_ownership:MORTGAGE      0.106784\n",
       "9                       addr_state:NM_VA      0.029670\n",
       "10                         addr_state:NY      0.055257\n",
       "11          addr_state:OK_TN_MO_LA_MD_NC      0.056616\n",
       "12                         addr_state:CA      0.059410\n",
       "13                addr_state:UT_KY_AZ_NJ      0.074981\n",
       "14             addr_state:AR_MI_PA_OH_MN      0.132453\n",
       "15             addr_state:RI_MA_DE_SD_IN      0.099374\n",
       "16                   addr_state:GA_WA_OR      0.181132\n",
       "17                      addr_state:WI_MT      0.224985\n",
       "18                         addr_state:TX      0.220356\n",
       "19                      addr_state:IL_CT      0.259164\n",
       "20          addr_state:KS_SC_CO_VT_AK_MS      0.316633\n",
       "21          addr_state:WV_NH_WY_DC_ME_ID      0.517506\n",
       "22      verification_status:Not Verified      0.088070\n",
       "23   verification_status:Source Verified     -0.010752\n",
       "24                   purpose:credit_card      0.306767\n",
       "25            purpose:debt_consolidation      0.202510\n",
       "26            purpose:oth__med__vacation      0.214158\n",
       "27   purpose:major_purch__car__home_impr      0.266670\n",
       "28                 initial_list_status:w      0.054432\n",
       "29                               term:36      0.078082\n",
       "30                          emp_length:1      0.100181\n",
       "31                        emp_length:2-4      0.125305\n",
       "32                        emp_length:5-6      0.089604\n",
       "33                        emp_length:7-9      0.060846\n",
       "34                         emp_length:10      0.123347\n",
       "35                mths_since_issue_d:<38      1.075056\n",
       "36              mths_since_issue_d:38-39      0.874352\n",
       "37              mths_since_issue_d:40-41      0.774033\n",
       "38              mths_since_issue_d:42-48      0.572941\n",
       "39              mths_since_issue_d:49-52      0.410666\n",
       "40              mths_since_issue_d:53-64      0.165877\n",
       "41              mths_since_issue_d:65-84     -0.069806\n",
       "42                       int_rate:<9.548      0.850580\n",
       "43                 int_rate:9.548-12.025      0.538354\n",
       "44                 int_rate:12.025-15.74      0.289589\n",
       "45                 int_rate:15.74-20.281      0.100654\n",
       "46   mths_since_earliest_cr_line:141-164      0.056143\n",
       "47   mths_since_earliest_cr_line:165-247      0.037847\n",
       "48   mths_since_earliest_cr_line:248-270      0.079577\n",
       "49   mths_since_earliest_cr_line:271-352      0.119571\n",
       "50      mths_since_earliest_cr_line:>352      0.126267\n",
       "51                         delinq_2yrs:0      0.074288\n",
       "52                       delinq_2yrs:1-3      0.031308\n",
       "53                      inq_last_6mths:0      0.634752\n",
       "54                    inq_last_6mths:1-2      0.491358\n",
       "55                    inq_last_6mths:3-6      0.281995\n",
       "56                          open_acc:1-3     -0.011004\n",
       "57                         open_acc:4-12     -0.108654\n",
       "58                        open_acc:13-17     -0.134335\n",
       "59                        open_acc:18-22     -0.149742\n",
       "60                        open_acc:23-25     -0.154358\n",
       "61                        open_acc:26-30     -0.119315\n",
       "62                         open_acc:>=31     -0.209705\n",
       "63                           pub_rec:3-4      0.122647\n",
       "64                           pub_rec:>=5      0.178429\n",
       "65                       total_acc:28-51     -0.020937\n",
       "66                        total_acc:>=52      0.025320\n",
       "67                    acc_now_delinq:>=1      0.217578\n",
       "68               total_rev_hi_lim:5K-10K      0.034709\n",
       "69              total_rev_hi_lim:10K-20K      0.002742\n",
       "70              total_rev_hi_lim:20K-30K      0.001247\n",
       "71              total_rev_hi_lim:30K-40K      0.014501\n",
       "72              total_rev_hi_lim:40K-55K      0.034912\n",
       "73              total_rev_hi_lim:55K-95K      0.061042\n",
       "74                 total_rev_hi_lim:>95K      0.201753\n",
       "75                    annual_inc:20K-30K     -0.060162\n",
       "76                    annual_inc:30K-40K      0.009734\n",
       "77                    annual_inc:40K-50K      0.106824\n",
       "78                    annual_inc:50K-60K      0.175911\n",
       "79                    annual_inc:60K-70K      0.250419\n",
       "80                    annual_inc:70K-80K      0.324876\n",
       "81                    annual_inc:80K-90K      0.401295\n",
       "82                   annual_inc:90K-100K      0.419111\n",
       "83                  annual_inc:100K-120K      0.497517\n",
       "84                  annual_inc:120K-140K      0.585064\n",
       "85                      annual_inc:>140K      0.519562\n",
       "86                             dti:<=1.4      0.201922\n",
       "87                           dti:1.4-3.5      0.332507\n",
       "88                           dti:3.5-7.7      0.357642\n",
       "89                          dti:7.7-10.5      0.298663\n",
       "90                         dti:10.5-16.1      0.225159\n",
       "91                         dti:16.1-20.3      0.127269\n",
       "92                         dti:20.3-21.7      0.115959\n",
       "93                         dti:21.7-22.4      0.081676\n",
       "94                           dti:22.4-35      0.042601\n",
       "95        mths_since_last_delinq:Missing      0.083009\n",
       "96           mths_since_last_delinq:4-30      0.151523\n",
       "97          mths_since_last_delinq:31-56      0.161959\n",
       "98           mths_since_last_delinq:>=57      0.113867\n",
       "99        mths_since_last_record:Missing      0.340361\n",
       "100          mths_since_last_record:3-20      0.435599\n",
       "101         mths_since_last_record:21-31      0.364472\n",
       "102         mths_since_last_record:32-80      0.527058\n",
       "103         mths_since_last_record:81-86      0.201837\n",
       "104           mths_since_last_record:>86      0.254679"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Same as above.\n",
    "summary_table = pd.DataFrame(columns = ['Feature name'], data = feature_name)\n",
    "summary_table['Coefficients'] = np.transpose(reg.coef_)\n",
    "summary_table.index = summary_table.index + 1\n",
    "summary_table.loc[0] = ['Intercept', reg.intercept_[0]]\n",
    "summary_table = summary_table.sort_index()\n",
    "summary_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is a list.\n",
    "p_values = reg.p_values\n",
    "# We take the result of the newly added method 'p_values' and store it in a variable 'p_values'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the intercept for completeness.\n",
    "p_values = np.append(np.nan, np.array(p_values))\n",
    "# We add the value 'NaN' in the beginning of the variable with p-values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "summary_table['p_values'] = p_values\n",
    "# In the 'summary_table' dataframe, we add a new column, called 'p_values', containing the values from the 'p_values' variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature name</th>\n",
       "      <th>Coefficients</th>\n",
       "      <th>p_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.331084</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.160070</td>\n",
       "      <td>1.712553e-37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.906043</td>\n",
       "      <td>1.042545e-49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.708855</td>\n",
       "      <td>6.560598e-36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.519058</td>\n",
       "      <td>4.999924e-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>grade:E</td>\n",
       "      <td>0.336838</td>\n",
       "      <td>2.510340e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>grade:F</td>\n",
       "      <td>0.144897</td>\n",
       "      <td>3.858731e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>home_ownership:OWN</td>\n",
       "      <td>0.090798</td>\n",
       "      <td>6.807239e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>home_ownership:MORTGAGE</td>\n",
       "      <td>0.106784</td>\n",
       "      <td>4.240771e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>addr_state:NM_VA</td>\n",
       "      <td>0.029670</td>\n",
       "      <td>3.545534e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>addr_state:NY</td>\n",
       "      <td>0.055257</td>\n",
       "      <td>2.299423e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>addr_state:OK_TN_MO_LA_MD_NC</td>\n",
       "      <td>0.056616</td>\n",
       "      <td>1.479332e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>addr_state:CA</td>\n",
       "      <td>0.059410</td>\n",
       "      <td>5.205282e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>addr_state:UT_KY_AZ_NJ</td>\n",
       "      <td>0.074981</td>\n",
       "      <td>2.678740e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>addr_state:AR_MI_PA_OH_MN</td>\n",
       "      <td>0.132453</td>\n",
       "      <td>4.601712e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>addr_state:RI_MA_DE_SD_IN</td>\n",
       "      <td>0.099374</td>\n",
       "      <td>8.933810e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>addr_state:GA_WA_OR</td>\n",
       "      <td>0.181132</td>\n",
       "      <td>1.230505e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>addr_state:WI_MT</td>\n",
       "      <td>0.224985</td>\n",
       "      <td>2.091247e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>addr_state:TX</td>\n",
       "      <td>0.220356</td>\n",
       "      <td>2.597072e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>addr_state:IL_CT</td>\n",
       "      <td>0.259164</td>\n",
       "      <td>8.533362e-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>addr_state:KS_SC_CO_VT_AK_MS</td>\n",
       "      <td>0.316633</td>\n",
       "      <td>1.153160e-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>addr_state:WV_NH_WY_DC_ME_ID</td>\n",
       "      <td>0.517506</td>\n",
       "      <td>2.421959e-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>verification_status:Not Verified</td>\n",
       "      <td>0.088070</td>\n",
       "      <td>2.140220e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>verification_status:Source Verified</td>\n",
       "      <td>-0.010752</td>\n",
       "      <td>4.249717e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>purpose:credit_card</td>\n",
       "      <td>0.306767</td>\n",
       "      <td>4.125542e-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>purpose:debt_consolidation</td>\n",
       "      <td>0.202510</td>\n",
       "      <td>1.002915e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>purpose:oth__med__vacation</td>\n",
       "      <td>0.214158</td>\n",
       "      <td>7.166641e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>purpose:major_purch__car__home_impr</td>\n",
       "      <td>0.266670</td>\n",
       "      <td>1.884815e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>initial_list_status:w</td>\n",
       "      <td>0.054432</td>\n",
       "      <td>3.066622e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>term:36</td>\n",
       "      <td>0.078082</td>\n",
       "      <td>3.686936e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>emp_length:1</td>\n",
       "      <td>0.100181</td>\n",
       "      <td>1.142987e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>emp_length:2-4</td>\n",
       "      <td>0.125305</td>\n",
       "      <td>3.320346e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>emp_length:5-6</td>\n",
       "      <td>0.089604</td>\n",
       "      <td>2.866536e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>emp_length:7-9</td>\n",
       "      <td>0.060846</td>\n",
       "      <td>3.462720e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>emp_length:10</td>\n",
       "      <td>0.123347</td>\n",
       "      <td>2.302711e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>mths_since_issue_d:&lt;38</td>\n",
       "      <td>1.075056</td>\n",
       "      <td>4.460775e-171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>mths_since_issue_d:38-39</td>\n",
       "      <td>0.874352</td>\n",
       "      <td>6.696484e-141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>mths_since_issue_d:40-41</td>\n",
       "      <td>0.774033</td>\n",
       "      <td>3.728415e-116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>mths_since_issue_d:42-48</td>\n",
       "      <td>0.572941</td>\n",
       "      <td>4.314297e-81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>mths_since_issue_d:49-52</td>\n",
       "      <td>0.410666</td>\n",
       "      <td>1.072467e-37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>mths_since_issue_d:53-64</td>\n",
       "      <td>0.165877</td>\n",
       "      <td>3.728054e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>mths_since_issue_d:65-84</td>\n",
       "      <td>-0.069806</td>\n",
       "      <td>1.734539e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>int_rate:&lt;9.548</td>\n",
       "      <td>0.850580</td>\n",
       "      <td>4.845795e-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>int_rate:9.548-12.025</td>\n",
       "      <td>0.538354</td>\n",
       "      <td>4.519483e-33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>int_rate:12.025-15.74</td>\n",
       "      <td>0.289589</td>\n",
       "      <td>4.663951e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>int_rate:15.74-20.281</td>\n",
       "      <td>0.100654</td>\n",
       "      <td>5.507381e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>mths_since_earliest_cr_line:141-164</td>\n",
       "      <td>0.056143</td>\n",
       "      <td>2.463225e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>mths_since_earliest_cr_line:165-247</td>\n",
       "      <td>0.037847</td>\n",
       "      <td>6.327341e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>mths_since_earliest_cr_line:248-270</td>\n",
       "      <td>0.079577</td>\n",
       "      <td>2.792633e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>mths_since_earliest_cr_line:271-352</td>\n",
       "      <td>0.119571</td>\n",
       "      <td>5.759272e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>mths_since_earliest_cr_line:&gt;352</td>\n",
       "      <td>0.126267</td>\n",
       "      <td>3.068472e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>delinq_2yrs:0</td>\n",
       "      <td>0.074288</td>\n",
       "      <td>1.935258e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>delinq_2yrs:1-3</td>\n",
       "      <td>0.031308</td>\n",
       "      <td>5.601688e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>inq_last_6mths:0</td>\n",
       "      <td>0.634752</td>\n",
       "      <td>3.630564e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>inq_last_6mths:1-2</td>\n",
       "      <td>0.491358</td>\n",
       "      <td>1.957161e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>inq_last_6mths:3-6</td>\n",
       "      <td>0.281995</td>\n",
       "      <td>1.468458e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>open_acc:1-3</td>\n",
       "      <td>-0.011004</td>\n",
       "      <td>9.471232e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>open_acc:4-12</td>\n",
       "      <td>-0.108654</td>\n",
       "      <td>5.028629e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>open_acc:13-17</td>\n",
       "      <td>-0.134335</td>\n",
       "      <td>4.083967e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>open_acc:18-22</td>\n",
       "      <td>-0.149742</td>\n",
       "      <td>3.596940e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>open_acc:23-25</td>\n",
       "      <td>-0.154358</td>\n",
       "      <td>3.584834e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>open_acc:26-30</td>\n",
       "      <td>-0.119315</td>\n",
       "      <td>4.913019e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>open_acc:&gt;=31</td>\n",
       "      <td>-0.209705</td>\n",
       "      <td>2.626138e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>pub_rec:3-4</td>\n",
       "      <td>0.122647</td>\n",
       "      <td>1.960205e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>pub_rec:&gt;=5</td>\n",
       "      <td>0.178429</td>\n",
       "      <td>3.552115e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>total_acc:28-51</td>\n",
       "      <td>-0.020937</td>\n",
       "      <td>1.405816e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>total_acc:&gt;=52</td>\n",
       "      <td>0.025320</td>\n",
       "      <td>5.241688e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>acc_now_delinq:&gt;=1</td>\n",
       "      <td>0.217578</td>\n",
       "      <td>4.114158e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>total_rev_hi_lim:5K-10K</td>\n",
       "      <td>0.034709</td>\n",
       "      <td>1.594074e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>total_rev_hi_lim:10K-20K</td>\n",
       "      <td>0.002742</td>\n",
       "      <td>9.087228e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>total_rev_hi_lim:20K-30K</td>\n",
       "      <td>0.001247</td>\n",
       "      <td>9.621681e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>total_rev_hi_lim:30K-40K</td>\n",
       "      <td>0.014501</td>\n",
       "      <td>6.228916e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>total_rev_hi_lim:40K-55K</td>\n",
       "      <td>0.034912</td>\n",
       "      <td>2.781391e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>total_rev_hi_lim:55K-95K</td>\n",
       "      <td>0.061042</td>\n",
       "      <td>9.058508e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>total_rev_hi_lim:&gt;95K</td>\n",
       "      <td>0.201753</td>\n",
       "      <td>3.572037e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>annual_inc:20K-30K</td>\n",
       "      <td>-0.060162</td>\n",
       "      <td>1.609760e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>annual_inc:30K-40K</td>\n",
       "      <td>0.009734</td>\n",
       "      <td>8.142031e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>annual_inc:40K-50K</td>\n",
       "      <td>0.106824</td>\n",
       "      <td>9.985637e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>annual_inc:50K-60K</td>\n",
       "      <td>0.175911</td>\n",
       "      <td>2.874592e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>annual_inc:60K-70K</td>\n",
       "      <td>0.250419</td>\n",
       "      <td>6.047570e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>annual_inc:70K-80K</td>\n",
       "      <td>0.324876</td>\n",
       "      <td>1.492523e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>annual_inc:80K-90K</td>\n",
       "      <td>0.401295</td>\n",
       "      <td>1.898728e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>annual_inc:90K-100K</td>\n",
       "      <td>0.419111</td>\n",
       "      <td>9.375020e-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>annual_inc:100K-120K</td>\n",
       "      <td>0.497517</td>\n",
       "      <td>4.607675e-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>annual_inc:120K-140K</td>\n",
       "      <td>0.585064</td>\n",
       "      <td>5.365669e-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>annual_inc:&gt;140K</td>\n",
       "      <td>0.519562</td>\n",
       "      <td>1.052129e-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>dti:&lt;=1.4</td>\n",
       "      <td>0.201922</td>\n",
       "      <td>2.244566e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>dti:1.4-3.5</td>\n",
       "      <td>0.332507</td>\n",
       "      <td>8.992837e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>dti:3.5-7.7</td>\n",
       "      <td>0.357642</td>\n",
       "      <td>4.343547e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>dti:7.7-10.5</td>\n",
       "      <td>0.298663</td>\n",
       "      <td>3.951963e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>dti:10.5-16.1</td>\n",
       "      <td>0.225159</td>\n",
       "      <td>3.327838e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>dti:16.1-20.3</td>\n",
       "      <td>0.127269</td>\n",
       "      <td>4.242100e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>dti:20.3-21.7</td>\n",
       "      <td>0.115959</td>\n",
       "      <td>7.516687e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>dti:21.7-22.4</td>\n",
       "      <td>0.081676</td>\n",
       "      <td>2.363527e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>dti:22.4-35</td>\n",
       "      <td>0.042601</td>\n",
       "      <td>4.917441e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>mths_since_last_delinq:Missing</td>\n",
       "      <td>0.083009</td>\n",
       "      <td>1.481640e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>mths_since_last_delinq:4-30</td>\n",
       "      <td>0.151523</td>\n",
       "      <td>4.264450e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>mths_since_last_delinq:31-56</td>\n",
       "      <td>0.161959</td>\n",
       "      <td>5.619103e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>mths_since_last_delinq:&gt;=57</td>\n",
       "      <td>0.113867</td>\n",
       "      <td>5.620180e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>mths_since_last_record:Missing</td>\n",
       "      <td>0.340361</td>\n",
       "      <td>7.603546e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>mths_since_last_record:3-20</td>\n",
       "      <td>0.435599</td>\n",
       "      <td>6.077742e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>mths_since_last_record:21-31</td>\n",
       "      <td>0.364472</td>\n",
       "      <td>1.781249e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>mths_since_last_record:32-80</td>\n",
       "      <td>0.527058</td>\n",
       "      <td>5.050375e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>mths_since_last_record:81-86</td>\n",
       "      <td>0.201837</td>\n",
       "      <td>5.756503e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>mths_since_last_record:&gt;86</td>\n",
       "      <td>0.254679</td>\n",
       "      <td>4.099968e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Feature name  Coefficients       p_values\n",
       "0                              Intercept     -1.331084            NaN\n",
       "1                                grade:A      1.160070   1.712553e-37\n",
       "2                                grade:B      0.906043   1.042545e-49\n",
       "3                                grade:C      0.708855   6.560598e-36\n",
       "4                                grade:D      0.519058   4.999924e-22\n",
       "5                                grade:E      0.336838   2.510340e-12\n",
       "6                                grade:F      0.144897   3.858731e-03\n",
       "7                     home_ownership:OWN      0.090798   6.807239e-06\n",
       "8                home_ownership:MORTGAGE      0.106784   4.240771e-17\n",
       "9                       addr_state:NM_VA      0.029670   3.545534e-01\n",
       "10                         addr_state:NY      0.055257   2.299423e-02\n",
       "11          addr_state:OK_TN_MO_LA_MD_NC      0.056616   1.479332e-02\n",
       "12                         addr_state:CA      0.059410   5.205282e-03\n",
       "13                addr_state:UT_KY_AZ_NJ      0.074981   2.678740e-03\n",
       "14             addr_state:AR_MI_PA_OH_MN      0.132453   4.601712e-09\n",
       "15             addr_state:RI_MA_DE_SD_IN      0.099374   8.933810e-04\n",
       "16                   addr_state:GA_WA_OR      0.181132   1.230505e-11\n",
       "17                      addr_state:WI_MT      0.224985   2.091247e-06\n",
       "18                         addr_state:TX      0.220356   2.597072e-17\n",
       "19                      addr_state:IL_CT      0.259164   8.533362e-19\n",
       "20          addr_state:KS_SC_CO_VT_AK_MS      0.316633   1.153160e-24\n",
       "21          addr_state:WV_NH_WY_DC_ME_ID      0.517506   2.421959e-22\n",
       "22      verification_status:Not Verified      0.088070   2.140220e-09\n",
       "23   verification_status:Source Verified     -0.010752   4.249717e-01\n",
       "24                   purpose:credit_card      0.306767   4.125542e-27\n",
       "25            purpose:debt_consolidation      0.202510   1.002915e-14\n",
       "26            purpose:oth__med__vacation      0.214158   7.166641e-12\n",
       "27   purpose:major_purch__car__home_impr      0.266670   1.884815e-17\n",
       "28                 initial_list_status:w      0.054432   3.066622e-05\n",
       "29                               term:36      0.078082   3.686936e-08\n",
       "30                          emp_length:1      0.100181   1.142987e-04\n",
       "31                        emp_length:2-4      0.125305   3.320346e-11\n",
       "32                        emp_length:5-6      0.089604   2.866536e-05\n",
       "33                        emp_length:7-9      0.060846   3.462720e-03\n",
       "34                         emp_length:10      0.123347   2.302711e-11\n",
       "35                mths_since_issue_d:<38      1.075056  4.460775e-171\n",
       "36              mths_since_issue_d:38-39      0.874352  6.696484e-141\n",
       "37              mths_since_issue_d:40-41      0.774033  3.728415e-116\n",
       "38              mths_since_issue_d:42-48      0.572941   4.314297e-81\n",
       "39              mths_since_issue_d:49-52      0.410666   1.072467e-37\n",
       "40              mths_since_issue_d:53-64      0.165877   3.728054e-08\n",
       "41              mths_since_issue_d:65-84     -0.069806   1.734539e-02\n",
       "42                       int_rate:<9.548      0.850580   4.845795e-29\n",
       "43                 int_rate:9.548-12.025      0.538354   4.519483e-33\n",
       "44                 int_rate:12.025-15.74      0.289589   4.663951e-15\n",
       "45                 int_rate:15.74-20.281      0.100654   5.507381e-04\n",
       "46   mths_since_earliest_cr_line:141-164      0.056143   2.463225e-02\n",
       "47   mths_since_earliest_cr_line:165-247      0.037847   6.327341e-02\n",
       "48   mths_since_earliest_cr_line:248-270      0.079577   2.792633e-03\n",
       "49   mths_since_earliest_cr_line:271-352      0.119571   5.759272e-07\n",
       "50      mths_since_earliest_cr_line:>352      0.126267   3.068472e-06\n",
       "51                         delinq_2yrs:0      0.074288   1.935258e-01\n",
       "52                       delinq_2yrs:1-3      0.031308   5.601688e-01\n",
       "53                      inq_last_6mths:0      0.634752   3.630564e-08\n",
       "54                    inq_last_6mths:1-2      0.491358   1.957161e-05\n",
       "55                    inq_last_6mths:3-6      0.281995   1.468458e-02\n",
       "56                          open_acc:1-3     -0.011004   9.471232e-01\n",
       "57                         open_acc:4-12     -0.108654   5.028629e-01\n",
       "58                        open_acc:13-17     -0.134335   4.083967e-01\n",
       "59                        open_acc:18-22     -0.149742   3.596940e-01\n",
       "60                        open_acc:23-25     -0.154358   3.584834e-01\n",
       "61                        open_acc:26-30     -0.119315   4.913019e-01\n",
       "62                         open_acc:>=31     -0.209705   2.626138e-01\n",
       "63                           pub_rec:3-4      0.122647   1.960205e-01\n",
       "64                           pub_rec:>=5      0.178429   3.552115e-01\n",
       "65                       total_acc:28-51     -0.020937   1.405816e-01\n",
       "66                        total_acc:>=52      0.025320   5.241688e-01\n",
       "67                    acc_now_delinq:>=1      0.217578   4.114158e-02\n",
       "68               total_rev_hi_lim:5K-10K      0.034709   1.594074e-01\n",
       "69              total_rev_hi_lim:10K-20K      0.002742   9.087228e-01\n",
       "70              total_rev_hi_lim:20K-30K      0.001247   9.621681e-01\n",
       "71              total_rev_hi_lim:30K-40K      0.014501   6.228916e-01\n",
       "72              total_rev_hi_lim:40K-55K      0.034912   2.781391e-01\n",
       "73              total_rev_hi_lim:55K-95K      0.061042   9.058508e-02\n",
       "74                 total_rev_hi_lim:>95K      0.201753   3.572037e-04\n",
       "75                    annual_inc:20K-30K     -0.060162   1.609760e-01\n",
       "76                    annual_inc:30K-40K      0.009734   8.142031e-01\n",
       "77                    annual_inc:40K-50K      0.106824   9.985637e-03\n",
       "78                    annual_inc:50K-60K      0.175911   2.874592e-05\n",
       "79                    annual_inc:60K-70K      0.250419   6.047570e-09\n",
       "80                    annual_inc:70K-80K      0.324876   1.492523e-13\n",
       "81                    annual_inc:80K-90K      0.401295   1.898728e-18\n",
       "82                   annual_inc:90K-100K      0.419111   9.375020e-19\n",
       "83                  annual_inc:100K-120K      0.497517   4.607675e-26\n",
       "84                  annual_inc:120K-140K      0.585064   5.365669e-28\n",
       "85                      annual_inc:>140K      0.519562   1.052129e-25\n",
       "86                             dti:<=1.4      0.201922   2.244566e-02\n",
       "87                           dti:1.4-3.5      0.332507   8.992837e-06\n",
       "88                           dti:3.5-7.7      0.357642   4.343547e-08\n",
       "89                          dti:7.7-10.5      0.298663   3.951963e-06\n",
       "90                         dti:10.5-16.1      0.225159   3.327838e-04\n",
       "91                         dti:16.1-20.3      0.127269   4.242100e-02\n",
       "92                         dti:20.3-21.7      0.115959   7.516687e-02\n",
       "93                         dti:21.7-22.4      0.081676   2.363527e-01\n",
       "94                           dti:22.4-35      0.042601   4.917441e-01\n",
       "95        mths_since_last_delinq:Missing      0.083009   1.481640e-01\n",
       "96           mths_since_last_delinq:4-30      0.151523   4.264450e-03\n",
       "97          mths_since_last_delinq:31-56      0.161959   5.619103e-03\n",
       "98           mths_since_last_delinq:>=57      0.113867   5.620180e-02\n",
       "99        mths_since_last_record:Missing      0.340361   7.603546e-05\n",
       "100          mths_since_last_record:3-20      0.435599   6.077742e-04\n",
       "101         mths_since_last_record:21-31      0.364472   1.781249e-03\n",
       "102         mths_since_last_record:32-80      0.527058   5.050375e-09\n",
       "103         mths_since_last_record:81-86      0.201837   5.756503e-02\n",
       "104           mths_since_last_record:>86      0.254679   4.099968e-03"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We are going to remove some features, the coefficients for all or almost all of the dummy variables for which,\n",
    "# are not tatistically significant.\n",
    "\n",
    "# We do that by specifying another list of dummy variables as reference categories, and a list of variables to remove.\n",
    "# Then, we are going to drop the two datasets from the original list of dummy variables.\n",
    "\n",
    "# Variables\n",
    "inputs_train_with_ref_cat = loan_data_inputs_train.loc[: , ['grade:A',\n",
    "'grade:B',\n",
    "'grade:C',\n",
    "'grade:D',\n",
    "'grade:E',\n",
    "'grade:F',\n",
    "'grade:G',\n",
    "'home_ownership:RENT_OTHER_NONE_ANY',\n",
    "'home_ownership:OWN',\n",
    "'home_ownership:MORTGAGE',\n",
    "'addr_state:ND_NE_IA_NV_FL_HI_AL',\n",
    "'addr_state:NM_VA',\n",
    "'addr_state:NY',\n",
    "'addr_state:OK_TN_MO_LA_MD_NC',\n",
    "'addr_state:CA',\n",
    "'addr_state:UT_KY_AZ_NJ',\n",
    "'addr_state:AR_MI_PA_OH_MN',\n",
    "'addr_state:RI_MA_DE_SD_IN',\n",
    "'addr_state:GA_WA_OR',\n",
    "'addr_state:WI_MT',\n",
    "'addr_state:TX',\n",
    "'addr_state:IL_CT',\n",
    "'addr_state:KS_SC_CO_VT_AK_MS',\n",
    "'addr_state:WV_NH_WY_DC_ME_ID',\n",
    "'verification_status:Not Verified',\n",
    "'verification_status:Source Verified',\n",
    "'verification_status:Verified',\n",
    "'purpose:educ__sm_b__wedd__ren_en__mov__house',\n",
    "'purpose:credit_card',\n",
    "'purpose:debt_consolidation',\n",
    "'purpose:oth__med__vacation',\n",
    "'purpose:major_purch__car__home_impr',\n",
    "'initial_list_status:f',\n",
    "'initial_list_status:w',\n",
    "'term:36',\n",
    "'term:60',\n",
    "'emp_length:0',\n",
    "'emp_length:1',\n",
    "'emp_length:2-4',\n",
    "'emp_length:5-6',\n",
    "'emp_length:7-9',\n",
    "'emp_length:10',\n",
    "'mths_since_issue_d:<38',\n",
    "'mths_since_issue_d:38-39',\n",
    "'mths_since_issue_d:40-41',\n",
    "'mths_since_issue_d:42-48',\n",
    "'mths_since_issue_d:49-52',\n",
    "'mths_since_issue_d:53-64',\n",
    "'mths_since_issue_d:65-84',\n",
    "'mths_since_issue_d:>84',\n",
    "'int_rate:<9.548',\n",
    "'int_rate:9.548-12.025',\n",
    "'int_rate:12.025-15.74',\n",
    "'int_rate:15.74-20.281',\n",
    "'int_rate:>20.281',\n",
    "'mths_since_earliest_cr_line:<140',\n",
    "'mths_since_earliest_cr_line:141-164',\n",
    "'mths_since_earliest_cr_line:165-247',\n",
    "'mths_since_earliest_cr_line:248-270',\n",
    "'mths_since_earliest_cr_line:271-352',\n",
    "'mths_since_earliest_cr_line:>352',\n",
    "'inq_last_6mths:0',\n",
    "'inq_last_6mths:1-2',\n",
    "'inq_last_6mths:3-6',\n",
    "'inq_last_6mths:>6',\n",
    "'acc_now_delinq:0',\n",
    "'acc_now_delinq:>=1',\n",
    "'annual_inc:<20K',\n",
    "'annual_inc:20K-30K',\n",
    "'annual_inc:30K-40K',\n",
    "'annual_inc:40K-50K',\n",
    "'annual_inc:50K-60K',\n",
    "'annual_inc:60K-70K',\n",
    "'annual_inc:70K-80K',\n",
    "'annual_inc:80K-90K',\n",
    "'annual_inc:90K-100K',\n",
    "'annual_inc:100K-120K',\n",
    "'annual_inc:120K-140K',\n",
    "'annual_inc:>140K',\n",
    "'dti:<=1.4',\n",
    "'dti:1.4-3.5',\n",
    "'dti:3.5-7.7',\n",
    "'dti:7.7-10.5',\n",
    "'dti:10.5-16.1',\n",
    "'dti:16.1-20.3',\n",
    "'dti:20.3-21.7',\n",
    "'dti:21.7-22.4',\n",
    "'dti:22.4-35',\n",
    "'dti:>35',\n",
    "'mths_since_last_delinq:Missing',\n",
    "'mths_since_last_delinq:0-3',\n",
    "'mths_since_last_delinq:4-30',\n",
    "'mths_since_last_delinq:31-56',\n",
    "'mths_since_last_delinq:>=57',\n",
    "'mths_since_last_record:Missing',\n",
    "'mths_since_last_record:0-2',\n",
    "'mths_since_last_record:3-20',\n",
    "'mths_since_last_record:21-31',\n",
    "'mths_since_last_record:32-80',\n",
    "'mths_since_last_record:81-86',\n",
    "'mths_since_last_record:>86',\n",
    "]]\n",
    "\n",
    "ref_categories = ['grade:G',\n",
    "'home_ownership:RENT_OTHER_NONE_ANY',\n",
    "'addr_state:ND_NE_IA_NV_FL_HI_AL',\n",
    "'verification_status:Verified',\n",
    "'purpose:educ__sm_b__wedd__ren_en__mov__house',\n",
    "'initial_list_status:f',\n",
    "'term:60',\n",
    "'emp_length:0',\n",
    "'mths_since_issue_d:>84',\n",
    "'int_rate:>20.281',\n",
    "'mths_since_earliest_cr_line:<140',\n",
    "'inq_last_6mths:>6',\n",
    "'acc_now_delinq:0',\n",
    "'annual_inc:<20K',\n",
    "'dti:>35',\n",
    "'mths_since_last_delinq:0-3',\n",
    "'mths_since_last_record:0-2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<p>5 rows × 84 columns</p>\n",
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      ],
      "text/plain": [
       "        grade:A  grade:B  grade:C  grade:D  grade:E  grade:F  \\\n",
       "427211        1        0        0        0        0        0   \n",
       "206088        0        0        1        0        0        0   \n",
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       "412305        0        0        0        1        0        0   \n",
       "36159         0        0        1        0        0        0   \n",
       "\n",
       "        home_ownership:OWN  home_ownership:MORTGAGE  addr_state:NM_VA  \\\n",
       "427211                   0                        1                 0   \n",
       "206088                   0                        1                 0   \n",
       "136020                   0                        1                 0   \n",
       "412305                   0                        0                 0   \n",
       "36159                    0                        1                 0   \n",
       "\n",
       "        addr_state:NY  ...  mths_since_last_delinq:Missing  \\\n",
       "427211              0  ...                               1   \n",
       "206088              0  ...                               0   \n",
       "136020              0  ...                               0   \n",
       "412305              0  ...                               0   \n",
       "36159               0  ...                               1   \n",
       "\n",
       "        mths_since_last_delinq:4-30  mths_since_last_delinq:31-56  \\\n",
       "427211                            0                             0   \n",
       "206088                            1                             0   \n",
       "136020                            0                             1   \n",
       "412305                            1                             0   \n",
       "36159                             0                             0   \n",
       "\n",
       "        mths_since_last_delinq:>=57  mths_since_last_record:Missing  \\\n",
       "427211                            0                               1   \n",
       "206088                            0                               1   \n",
       "136020                            0                               1   \n",
       "412305                            0                               1   \n",
       "36159                             0                               1   \n",
       "\n",
       "        mths_since_last_record:3-20  mths_since_last_record:21-31  \\\n",
       "427211                            0                             0   \n",
       "206088                            0                             0   \n",
       "136020                            0                             0   \n",
       "412305                            0                             0   \n",
       "36159                             0                             0   \n",
       "\n",
       "        mths_since_last_record:32-80  mths_since_last_record:81-86  \\\n",
       "427211                             0                             0   \n",
       "206088                             0                             0   \n",
       "136020                             0                             0   \n",
       "412305                             0                             0   \n",
       "36159                              0                             0   \n",
       "\n",
       "        mths_since_last_record:>86  \n",
       "427211                           0  \n",
       "206088                           0  \n",
       "136020                           0  \n",
       "412305                           0  \n",
       "36159                            0  \n",
       "\n",
       "[5 rows x 84 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs_train = inputs_train_with_ref_cat.drop(ref_categories, axis = 1)\n",
    "inputs_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\Anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:73: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "C:\\Users\\ASUS\\Anaconda3\\Lib\\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": [
    "# Here we run a new model.\n",
    "reg2 = LogisticRegression_with_p_values()\n",
    "reg2.fit(inputs_train, loan_data_targets_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_name = inputs_train.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature name</th>\n",
       "      <th>Coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.374094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.123640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.878930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.684806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.496923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>grade:E</td>\n",
       "      <td>0.321495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>grade:F</td>\n",
       "      <td>0.134280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>home_ownership:OWN</td>\n",
       "      <td>0.093507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>home_ownership:MORTGAGE</td>\n",
       "      <td>0.106254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>addr_state:NM_VA</td>\n",
       "      <td>0.036022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>addr_state:NY</td>\n",
       "      <td>0.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>addr_state:OK_TN_MO_LA_MD_NC</td>\n",
       "      <td>0.057998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>addr_state:CA</td>\n",
       "      <td>0.062154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>addr_state:UT_KY_AZ_NJ</td>\n",
       "      <td>0.076463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>addr_state:AR_MI_PA_OH_MN</td>\n",
       "      <td>0.133442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>addr_state:RI_MA_DE_SD_IN</td>\n",
       "      <td>0.097895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>addr_state:GA_WA_OR</td>\n",
       "      <td>0.183762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>addr_state:WI_MT</td>\n",
       "      <td>0.237943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>addr_state:TX</td>\n",
       "      <td>0.220963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>addr_state:IL_CT</td>\n",
       "      <td>0.259902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>addr_state:KS_SC_CO_VT_AK_MS</td>\n",
       "      <td>0.318570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>addr_state:WV_NH_WY_DC_ME_ID</td>\n",
       "      <td>0.521962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>verification_status:Not Verified</td>\n",
       "      <td>0.085725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>verification_status:Source Verified</td>\n",
       "      <td>-0.011180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>purpose:credit_card</td>\n",
       "      <td>0.301845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>purpose:debt_consolidation</td>\n",
       "      <td>0.198079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>purpose:oth__med__vacation</td>\n",
       "      <td>0.212831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>purpose:major_purch__car__home_impr</td>\n",
       "      <td>0.266262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>initial_list_status:w</td>\n",
       "      <td>0.053831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>term:36</td>\n",
       "      <td>0.078944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>emp_length:1</td>\n",
       "      <td>0.099156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>emp_length:2-4</td>\n",
       "      <td>0.125849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>emp_length:5-6</td>\n",
       "      <td>0.091089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>emp_length:7-9</td>\n",
       "      <td>0.061141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>emp_length:10</td>\n",
       "      <td>0.125045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>mths_since_issue_d:&lt;38</td>\n",
       "      <td>1.084186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>mths_since_issue_d:38-39</td>\n",
       "      <td>0.879175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>mths_since_issue_d:40-41</td>\n",
       "      <td>0.778231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>mths_since_issue_d:42-48</td>\n",
       "      <td>0.577132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>mths_since_issue_d:49-52</td>\n",
       "      <td>0.415661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>mths_since_issue_d:53-64</td>\n",
       "      <td>0.169915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>mths_since_issue_d:65-84</td>\n",
       "      <td>-0.071812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>int_rate:&lt;9.548</td>\n",
       "      <td>0.883187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>int_rate:9.548-12.025</td>\n",
       "      <td>0.558657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>int_rate:12.025-15.74</td>\n",
       "      <td>0.306131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>int_rate:15.74-20.281</td>\n",
       "      <td>0.113157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>mths_since_earliest_cr_line:141-164</td>\n",
       "      <td>0.055770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>mths_since_earliest_cr_line:165-247</td>\n",
       "      <td>0.037157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>mths_since_earliest_cr_line:248-270</td>\n",
       "      <td>0.079472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>mths_since_earliest_cr_line:271-352</td>\n",
       "      <td>0.122037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>mths_since_earliest_cr_line:&gt;352</td>\n",
       "      <td>0.129360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>inq_last_6mths:0</td>\n",
       "      <td>0.666308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>inq_last_6mths:1-2</td>\n",
       "      <td>0.519804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>inq_last_6mths:3-6</td>\n",
       "      <td>0.309583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>acc_now_delinq:&gt;=1</td>\n",
       "      <td>0.180351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>annual_inc:20K-30K</td>\n",
       "      <td>-0.081514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>annual_inc:30K-40K</td>\n",
       "      <td>-0.018913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>annual_inc:40K-50K</td>\n",
       "      <td>0.075682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>annual_inc:50K-60K</td>\n",
       "      <td>0.142551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>annual_inc:60K-70K</td>\n",
       "      <td>0.218095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>annual_inc:70K-80K</td>\n",
       "      <td>0.291551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>annual_inc:80K-90K</td>\n",
       "      <td>0.367383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>annual_inc:90K-100K</td>\n",
       "      <td>0.387204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>annual_inc:100K-120K</td>\n",
       "      <td>0.465107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>annual_inc:120K-140K</td>\n",
       "      <td>0.552372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>annual_inc:&gt;140K</td>\n",
       "      <td>0.491554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>dti:&lt;=1.4</td>\n",
       "      <td>0.252475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>dti:1.4-3.5</td>\n",
       "      <td>0.371548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>dti:3.5-7.7</td>\n",
       "      <td>0.384461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>dti:7.7-10.5</td>\n",
       "      <td>0.319729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>dti:10.5-16.1</td>\n",
       "      <td>0.241164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>dti:16.1-20.3</td>\n",
       "      <td>0.139861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>dti:20.3-21.7</td>\n",
       "      <td>0.127487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>dti:21.7-22.4</td>\n",
       "      <td>0.092051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>dti:22.4-35</td>\n",
       "      <td>0.050288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>mths_since_last_delinq:Missing</td>\n",
       "      <td>0.110417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>mths_since_last_delinq:4-30</td>\n",
       "      <td>0.136960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>mths_since_last_delinq:31-56</td>\n",
       "      <td>0.183105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>mths_since_last_delinq:&gt;=57</td>\n",
       "      <td>0.136117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>mths_since_last_record:Missing</td>\n",
       "      <td>0.320894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>mths_since_last_record:3-20</td>\n",
       "      <td>0.440628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>mths_since_last_record:21-31</td>\n",
       "      <td>0.350725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>mths_since_last_record:32-80</td>\n",
       "      <td>0.502984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>mths_since_last_record:81-86</td>\n",
       "      <td>0.175844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>mths_since_last_record:&gt;86</td>\n",
       "      <td>0.232736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Feature name  Coefficients\n",
       "0                             Intercept     -1.374094\n",
       "1                               grade:A      1.123640\n",
       "2                               grade:B      0.878930\n",
       "3                               grade:C      0.684806\n",
       "4                               grade:D      0.496923\n",
       "5                               grade:E      0.321495\n",
       "6                               grade:F      0.134280\n",
       "7                    home_ownership:OWN      0.093507\n",
       "8               home_ownership:MORTGAGE      0.106254\n",
       "9                      addr_state:NM_VA      0.036022\n",
       "10                        addr_state:NY      0.055556\n",
       "11         addr_state:OK_TN_MO_LA_MD_NC      0.057998\n",
       "12                        addr_state:CA      0.062154\n",
       "13               addr_state:UT_KY_AZ_NJ      0.076463\n",
       "14            addr_state:AR_MI_PA_OH_MN      0.133442\n",
       "15            addr_state:RI_MA_DE_SD_IN      0.097895\n",
       "16                  addr_state:GA_WA_OR      0.183762\n",
       "17                     addr_state:WI_MT      0.237943\n",
       "18                        addr_state:TX      0.220963\n",
       "19                     addr_state:IL_CT      0.259902\n",
       "20         addr_state:KS_SC_CO_VT_AK_MS      0.318570\n",
       "21         addr_state:WV_NH_WY_DC_ME_ID      0.521962\n",
       "22     verification_status:Not Verified      0.085725\n",
       "23  verification_status:Source Verified     -0.011180\n",
       "24                  purpose:credit_card      0.301845\n",
       "25           purpose:debt_consolidation      0.198079\n",
       "26           purpose:oth__med__vacation      0.212831\n",
       "27  purpose:major_purch__car__home_impr      0.266262\n",
       "28                initial_list_status:w      0.053831\n",
       "29                              term:36      0.078944\n",
       "30                         emp_length:1      0.099156\n",
       "31                       emp_length:2-4      0.125849\n",
       "32                       emp_length:5-6      0.091089\n",
       "33                       emp_length:7-9      0.061141\n",
       "34                        emp_length:10      0.125045\n",
       "35               mths_since_issue_d:<38      1.084186\n",
       "36             mths_since_issue_d:38-39      0.879175\n",
       "37             mths_since_issue_d:40-41      0.778231\n",
       "38             mths_since_issue_d:42-48      0.577132\n",
       "39             mths_since_issue_d:49-52      0.415661\n",
       "40             mths_since_issue_d:53-64      0.169915\n",
       "41             mths_since_issue_d:65-84     -0.071812\n",
       "42                      int_rate:<9.548      0.883187\n",
       "43                int_rate:9.548-12.025      0.558657\n",
       "44                int_rate:12.025-15.74      0.306131\n",
       "45                int_rate:15.74-20.281      0.113157\n",
       "46  mths_since_earliest_cr_line:141-164      0.055770\n",
       "47  mths_since_earliest_cr_line:165-247      0.037157\n",
       "48  mths_since_earliest_cr_line:248-270      0.079472\n",
       "49  mths_since_earliest_cr_line:271-352      0.122037\n",
       "50     mths_since_earliest_cr_line:>352      0.129360\n",
       "51                     inq_last_6mths:0      0.666308\n",
       "52                   inq_last_6mths:1-2      0.519804\n",
       "53                   inq_last_6mths:3-6      0.309583\n",
       "54                   acc_now_delinq:>=1      0.180351\n",
       "55                   annual_inc:20K-30K     -0.081514\n",
       "56                   annual_inc:30K-40K     -0.018913\n",
       "57                   annual_inc:40K-50K      0.075682\n",
       "58                   annual_inc:50K-60K      0.142551\n",
       "59                   annual_inc:60K-70K      0.218095\n",
       "60                   annual_inc:70K-80K      0.291551\n",
       "61                   annual_inc:80K-90K      0.367383\n",
       "62                  annual_inc:90K-100K      0.387204\n",
       "63                 annual_inc:100K-120K      0.465107\n",
       "64                 annual_inc:120K-140K      0.552372\n",
       "65                     annual_inc:>140K      0.491554\n",
       "66                            dti:<=1.4      0.252475\n",
       "67                          dti:1.4-3.5      0.371548\n",
       "68                          dti:3.5-7.7      0.384461\n",
       "69                         dti:7.7-10.5      0.319729\n",
       "70                        dti:10.5-16.1      0.241164\n",
       "71                        dti:16.1-20.3      0.139861\n",
       "72                        dti:20.3-21.7      0.127487\n",
       "73                        dti:21.7-22.4      0.092051\n",
       "74                          dti:22.4-35      0.050288\n",
       "75       mths_since_last_delinq:Missing      0.110417\n",
       "76          mths_since_last_delinq:4-30      0.136960\n",
       "77         mths_since_last_delinq:31-56      0.183105\n",
       "78          mths_since_last_delinq:>=57      0.136117\n",
       "79       mths_since_last_record:Missing      0.320894\n",
       "80          mths_since_last_record:3-20      0.440628\n",
       "81         mths_since_last_record:21-31      0.350725\n",
       "82         mths_since_last_record:32-80      0.502984\n",
       "83         mths_since_last_record:81-86      0.175844\n",
       "84           mths_since_last_record:>86      0.232736"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Same as above.\n",
    "summary_table = pd.DataFrame(columns = ['Feature name'], data = feature_name)\n",
    "summary_table['Coefficients'] = np.transpose(reg2.coef_)\n",
    "summary_table.index = summary_table.index + 1\n",
    "summary_table.loc[0] = ['Intercept', reg2.intercept_[0]]\n",
    "summary_table = summary_table.sort_index()\n",
    "summary_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature name</th>\n",
       "      <th>Coefficients</th>\n",
       "      <th>p_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.374094</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.123640</td>\n",
       "      <td>3.243722e-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.878930</td>\n",
       "      <td>4.267381e-47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.684806</td>\n",
       "      <td>6.698227e-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.496923</td>\n",
       "      <td>1.346872e-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>grade:E</td>\n",
       "      <td>0.321495</td>\n",
       "      <td>1.025037e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>grade:F</td>\n",
       "      <td>0.134280</td>\n",
       "      <td>6.243393e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>home_ownership:OWN</td>\n",
       "      <td>0.093507</td>\n",
       "      <td>3.468415e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>home_ownership:MORTGAGE</td>\n",
       "      <td>0.106254</td>\n",
       "      <td>4.406981e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>addr_state:NM_VA</td>\n",
       "      <td>0.036022</td>\n",
       "      <td>2.608121e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>addr_state:NY</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>2.183148e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>addr_state:OK_TN_MO_LA_MD_NC</td>\n",
       "      <td>0.057998</td>\n",
       "      <td>1.225565e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>addr_state:CA</td>\n",
       "      <td>0.062154</td>\n",
       "      <td>3.373685e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>addr_state:UT_KY_AZ_NJ</td>\n",
       "      <td>0.076463</td>\n",
       "      <td>2.147549e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>addr_state:AR_MI_PA_OH_MN</td>\n",
       "      <td>0.133442</td>\n",
       "      <td>3.149168e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>addr_state:RI_MA_DE_SD_IN</td>\n",
       "      <td>0.097895</td>\n",
       "      <td>1.033190e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>addr_state:GA_WA_OR</td>\n",
       "      <td>0.183762</td>\n",
       "      <td>5.574147e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>addr_state:WI_MT</td>\n",
       "      <td>0.237943</td>\n",
       "      <td>5.610616e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>addr_state:TX</td>\n",
       "      <td>0.220963</td>\n",
       "      <td>1.834854e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>addr_state:IL_CT</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>5.673202e-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>addr_state:KS_SC_CO_VT_AK_MS</td>\n",
       "      <td>0.318570</td>\n",
       "      <td>5.110290e-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>addr_state:WV_NH_WY_DC_ME_ID</td>\n",
       "      <td>0.521962</td>\n",
       "      <td>1.072257e-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>verification_status:Not Verified</td>\n",
       "      <td>0.085725</td>\n",
       "      <td>4.366381e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>verification_status:Source Verified</td>\n",
       "      <td>-0.011180</td>\n",
       "      <td>4.051825e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>purpose:credit_card</td>\n",
       "      <td>0.301845</td>\n",
       "      <td>1.260279e-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>purpose:debt_consolidation</td>\n",
       "      <td>0.198079</td>\n",
       "      <td>2.508572e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>purpose:oth__med__vacation</td>\n",
       "      <td>0.212831</td>\n",
       "      <td>7.719356e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>purpose:major_purch__car__home_impr</td>\n",
       "      <td>0.266262</td>\n",
       "      <td>1.473849e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>initial_list_status:w</td>\n",
       "      <td>0.053831</td>\n",
       "      <td>3.734629e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>term:36</td>\n",
       "      <td>0.078944</td>\n",
       "      <td>1.629742e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>emp_length:1</td>\n",
       "      <td>0.099156</td>\n",
       "      <td>1.314963e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>emp_length:2-4</td>\n",
       "      <td>0.125849</td>\n",
       "      <td>2.599726e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>emp_length:5-6</td>\n",
       "      <td>0.091089</td>\n",
       "      <td>2.082842e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>emp_length:7-9</td>\n",
       "      <td>0.061141</td>\n",
       "      <td>3.287832e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>emp_length:10</td>\n",
       "      <td>0.125045</td>\n",
       "      <td>1.178546e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>mths_since_issue_d:&lt;38</td>\n",
       "      <td>1.084186</td>\n",
       "      <td>4.848740e-181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>mths_since_issue_d:38-39</td>\n",
       "      <td>0.879175</td>\n",
       "      <td>1.049850e-149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>mths_since_issue_d:40-41</td>\n",
       "      <td>0.778231</td>\n",
       "      <td>5.764887e-124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>mths_since_issue_d:42-48</td>\n",
       "      <td>0.577132</td>\n",
       "      <td>1.109960e-87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>mths_since_issue_d:49-52</td>\n",
       "      <td>0.415661</td>\n",
       "      <td>8.366755e-41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>mths_since_issue_d:53-64</td>\n",
       "      <td>0.169915</td>\n",
       "      <td>5.978415e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>mths_since_issue_d:65-84</td>\n",
       "      <td>-0.071812</td>\n",
       "      <td>1.404148e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>int_rate:&lt;9.548</td>\n",
       "      <td>0.883187</td>\n",
       "      <td>3.569580e-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>int_rate:9.548-12.025</td>\n",
       "      <td>0.558657</td>\n",
       "      <td>5.942544e-36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>int_rate:12.025-15.74</td>\n",
       "      <td>0.306131</td>\n",
       "      <td>7.245438e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>int_rate:15.74-20.281</td>\n",
       "      <td>0.113157</td>\n",
       "      <td>9.376735e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>mths_since_earliest_cr_line:141-164</td>\n",
       "      <td>0.055770</td>\n",
       "      <td>2.498414e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>mths_since_earliest_cr_line:165-247</td>\n",
       "      <td>0.037157</td>\n",
       "      <td>6.428721e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>mths_since_earliest_cr_line:248-270</td>\n",
       "      <td>0.079472</td>\n",
       "      <td>2.456137e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>mths_since_earliest_cr_line:271-352</td>\n",
       "      <td>0.122037</td>\n",
       "      <td>1.894197e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>mths_since_earliest_cr_line:&gt;352</td>\n",
       "      <td>0.129360</td>\n",
       "      <td>1.069460e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>inq_last_6mths:0</td>\n",
       "      <td>0.666308</td>\n",
       "      <td>3.514184e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>inq_last_6mths:1-2</td>\n",
       "      <td>0.519804</td>\n",
       "      <td>3.237608e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>inq_last_6mths:3-6</td>\n",
       "      <td>0.309583</td>\n",
       "      <td>4.599372e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>acc_now_delinq:&gt;=1</td>\n",
       "      <td>0.180351</td>\n",
       "      <td>8.870239e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>annual_inc:20K-30K</td>\n",
       "      <td>-0.081514</td>\n",
       "      <td>5.301446e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>annual_inc:30K-40K</td>\n",
       "      <td>-0.018913</td>\n",
       "      <td>6.403734e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>annual_inc:40K-50K</td>\n",
       "      <td>0.075682</td>\n",
       "      <td>6.120503e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>annual_inc:50K-60K</td>\n",
       "      <td>0.142551</td>\n",
       "      <td>4.993065e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>annual_inc:60K-70K</td>\n",
       "      <td>0.218095</td>\n",
       "      <td>1.909134e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>annual_inc:70K-80K</td>\n",
       "      <td>0.291551</td>\n",
       "      <td>8.738820e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>annual_inc:80K-90K</td>\n",
       "      <td>0.367383</td>\n",
       "      <td>1.461177e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>annual_inc:90K-100K</td>\n",
       "      <td>0.387204</td>\n",
       "      <td>4.201866e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>annual_inc:100K-120K</td>\n",
       "      <td>0.465107</td>\n",
       "      <td>2.318356e-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>annual_inc:120K-140K</td>\n",
       "      <td>0.552372</td>\n",
       "      <td>1.860474e-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>annual_inc:&gt;140K</td>\n",
       "      <td>0.491554</td>\n",
       "      <td>4.976731e-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>dti:&lt;=1.4</td>\n",
       "      <td>0.252475</td>\n",
       "      <td>2.456138e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>dti:1.4-3.5</td>\n",
       "      <td>0.371548</td>\n",
       "      <td>1.099590e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>dti:3.5-7.7</td>\n",
       "      <td>0.384461</td>\n",
       "      <td>1.757122e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>dti:7.7-10.5</td>\n",
       "      <td>0.319729</td>\n",
       "      <td>9.312494e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>dti:10.5-16.1</td>\n",
       "      <td>0.241164</td>\n",
       "      <td>3.075646e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>dti:16.1-20.3</td>\n",
       "      <td>0.139861</td>\n",
       "      <td>1.579938e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>dti:20.3-21.7</td>\n",
       "      <td>0.127487</td>\n",
       "      <td>3.557575e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>dti:21.7-22.4</td>\n",
       "      <td>0.092051</td>\n",
       "      <td>1.550249e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>dti:22.4-35</td>\n",
       "      <td>0.050288</td>\n",
       "      <td>3.799187e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>mths_since_last_delinq:Missing</td>\n",
       "      <td>0.110417</td>\n",
       "      <td>3.458843e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>mths_since_last_delinq:4-30</td>\n",
       "      <td>0.136960</td>\n",
       "      <td>9.470295e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>mths_since_last_delinq:31-56</td>\n",
       "      <td>0.183105</td>\n",
       "      <td>6.149598e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>mths_since_last_delinq:&gt;=57</td>\n",
       "      <td>0.136117</td>\n",
       "      <td>1.288942e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>mths_since_last_record:Missing</td>\n",
       "      <td>0.320894</td>\n",
       "      <td>7.942468e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>mths_since_last_record:3-20</td>\n",
       "      <td>0.440628</td>\n",
       "      <td>3.346663e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>mths_since_last_record:21-31</td>\n",
       "      <td>0.350725</td>\n",
       "      <td>1.831615e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>mths_since_last_record:32-80</td>\n",
       "      <td>0.502984</td>\n",
       "      <td>3.956627e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>mths_since_last_record:81-86</td>\n",
       "      <td>0.175844</td>\n",
       "      <td>8.603745e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>mths_since_last_record:&gt;86</td>\n",
       "      <td>0.232736</td>\n",
       "      <td>5.704389e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Feature name  Coefficients       p_values\n",
       "0                             Intercept     -1.374094            NaN\n",
       "1                               grade:A      1.123640   3.243722e-35\n",
       "2                               grade:B      0.878930   4.267381e-47\n",
       "3                               grade:C      0.684806   6.698227e-34\n",
       "4                               grade:D      0.496923   1.346872e-20\n",
       "5                               grade:E      0.321495   1.025037e-11\n",
       "6                               grade:F      0.134280   6.243393e-03\n",
       "7                    home_ownership:OWN      0.093507   3.468415e-06\n",
       "8               home_ownership:MORTGAGE      0.106254   4.406981e-17\n",
       "9                      addr_state:NM_VA      0.036022   2.608121e-01\n",
       "10                        addr_state:NY      0.055556   2.183148e-02\n",
       "11         addr_state:OK_TN_MO_LA_MD_NC      0.057998   1.225565e-02\n",
       "12                        addr_state:CA      0.062154   3.373685e-03\n",
       "13               addr_state:UT_KY_AZ_NJ      0.076463   2.147549e-03\n",
       "14            addr_state:AR_MI_PA_OH_MN      0.133442   3.149168e-09\n",
       "15            addr_state:RI_MA_DE_SD_IN      0.097895   1.033190e-03\n",
       "16                  addr_state:GA_WA_OR      0.183762   5.574147e-12\n",
       "17                     addr_state:WI_MT      0.237943   5.610616e-07\n",
       "18                        addr_state:TX      0.220963   1.834854e-17\n",
       "19                     addr_state:IL_CT      0.259902   5.673202e-19\n",
       "20         addr_state:KS_SC_CO_VT_AK_MS      0.318570   5.110290e-25\n",
       "21         addr_state:WV_NH_WY_DC_ME_ID      0.521962   1.072257e-22\n",
       "22     verification_status:Not Verified      0.085725   4.366381e-09\n",
       "23  verification_status:Source Verified     -0.011180   4.051825e-01\n",
       "24                  purpose:credit_card      0.301845   1.260279e-26\n",
       "25           purpose:debt_consolidation      0.198079   2.508572e-14\n",
       "26           purpose:oth__med__vacation      0.212831   7.719356e-12\n",
       "27  purpose:major_purch__car__home_impr      0.266262   1.473849e-17\n",
       "28                initial_list_status:w      0.053831   3.734629e-05\n",
       "29                              term:36      0.078944   1.629742e-08\n",
       "30                         emp_length:1      0.099156   1.314963e-04\n",
       "31                       emp_length:2-4      0.125849   2.599726e-11\n",
       "32                       emp_length:5-6      0.091089   2.082842e-05\n",
       "33                       emp_length:7-9      0.061141   3.287832e-03\n",
       "34                        emp_length:10      0.125045   1.178546e-11\n",
       "35               mths_since_issue_d:<38      1.084186  4.848740e-181\n",
       "36             mths_since_issue_d:38-39      0.879175  1.049850e-149\n",
       "37             mths_since_issue_d:40-41      0.778231  5.764887e-124\n",
       "38             mths_since_issue_d:42-48      0.577132   1.109960e-87\n",
       "39             mths_since_issue_d:49-52      0.415661   8.366755e-41\n",
       "40             mths_since_issue_d:53-64      0.169915   5.978415e-09\n",
       "41             mths_since_issue_d:65-84     -0.071812   1.404148e-02\n",
       "42                      int_rate:<9.548      0.883187   3.569580e-31\n",
       "43                int_rate:9.548-12.025      0.558657   5.942544e-36\n",
       "44                int_rate:12.025-15.74      0.306131   7.245438e-17\n",
       "45                int_rate:15.74-20.281      0.113157   9.376735e-05\n",
       "46  mths_since_earliest_cr_line:141-164      0.055770   2.498414e-02\n",
       "47  mths_since_earliest_cr_line:165-247      0.037157   6.428721e-02\n",
       "48  mths_since_earliest_cr_line:248-270      0.079472   2.456137e-03\n",
       "49  mths_since_earliest_cr_line:271-352      0.122037   1.894197e-07\n",
       "50     mths_since_earliest_cr_line:>352      0.129360   1.069460e-06\n",
       "51                     inq_last_6mths:0      0.666308   3.514184e-14\n",
       "52                   inq_last_6mths:1-2      0.519804   3.237608e-09\n",
       "53                   inq_last_6mths:3-6      0.309583   4.599372e-04\n",
       "54                   acc_now_delinq:>=1      0.180351   8.870239e-02\n",
       "55                   annual_inc:20K-30K     -0.081514   5.301446e-02\n",
       "56                   annual_inc:30K-40K     -0.018913   6.403734e-01\n",
       "57                   annual_inc:40K-50K      0.075682   6.120503e-02\n",
       "58                   annual_inc:50K-60K      0.142551   4.993065e-04\n",
       "59                   annual_inc:60K-70K      0.218095   1.909134e-07\n",
       "60                   annual_inc:70K-80K      0.291551   8.738820e-12\n",
       "61                   annual_inc:80K-90K      0.367383   1.461177e-16\n",
       "62                  annual_inc:90K-100K      0.387204   4.201866e-17\n",
       "63                 annual_inc:100K-120K      0.465107   2.318356e-24\n",
       "64                 annual_inc:120K-140K      0.552372   1.860474e-26\n",
       "65                     annual_inc:>140K      0.491554   4.976731e-25\n",
       "66                            dti:<=1.4      0.252475   2.456138e-03\n",
       "67                          dti:1.4-3.5      0.371548   1.099590e-07\n",
       "68                          dti:3.5-7.7      0.384461   1.757122e-10\n",
       "69                         dti:7.7-10.5      0.319729   9.312494e-08\n",
       "70                        dti:10.5-16.1      0.241164   3.075646e-05\n",
       "71                        dti:16.1-20.3      0.139861   1.579938e-02\n",
       "72                        dti:20.3-21.7      0.127487   3.557575e-02\n",
       "73                        dti:21.7-22.4      0.092051   1.550249e-01\n",
       "74                          dti:22.4-35      0.050288   3.799187e-01\n",
       "75       mths_since_last_delinq:Missing      0.110417   3.458843e-02\n",
       "76          mths_since_last_delinq:4-30      0.136960   9.470295e-03\n",
       "77         mths_since_last_delinq:31-56      0.183105   6.149598e-04\n",
       "78          mths_since_last_delinq:>=57      0.136117   1.288942e-02\n",
       "79       mths_since_last_record:Missing      0.320894   7.942468e-05\n",
       "80          mths_since_last_record:3-20      0.440628   3.346663e-04\n",
       "81         mths_since_last_record:21-31      0.350725   1.831615e-03\n",
       "82         mths_since_last_record:32-80      0.502984   3.956627e-09\n",
       "83         mths_since_last_record:81-86      0.175844   8.603745e-02\n",
       "84           mths_since_last_record:>86      0.232736   5.704389e-03"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We add the 'p_values' here, just as we did before.\n",
    "p_values = reg2.p_values\n",
    "p_values = np.append(np.nan,np.array(p_values))\n",
    "summary_table['p_values'] = p_values\n",
    "summary_table\n",
    "# Here we get the results for our final PD model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(reg2, open('pd_model.sav', 'wb'))\n",
    "# Here we export our model to a 'SAV' file with file name 'pd_model.sav'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PD Model Validation (Test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Out-of-sample validation (test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here, from the dataframe with inputs for testing, we keep the same variables that we used in our final PD model.\n",
    "inputs_test_with_ref_cat = loan_data_inputs_test.loc[: , ['grade:A',\n",
    "'grade:B',\n",
    "'grade:C',\n",
    "'grade:D',\n",
    "'grade:E',\n",
    "'grade:F',\n",
    "'grade:G',\n",
    "'home_ownership:RENT_OTHER_NONE_ANY',\n",
    "'home_ownership:OWN',\n",
    "'home_ownership:MORTGAGE',\n",
    "'addr_state:ND_NE_IA_NV_FL_HI_AL',\n",
    "'addr_state:NM_VA',\n",
    "'addr_state:NY',\n",
    "'addr_state:OK_TN_MO_LA_MD_NC',\n",
    "'addr_state:CA',\n",
    "'addr_state:UT_KY_AZ_NJ',\n",
    "'addr_state:AR_MI_PA_OH_MN',\n",
    "'addr_state:RI_MA_DE_SD_IN',\n",
    "'addr_state:GA_WA_OR',\n",
    "'addr_state:WI_MT',\n",
    "'addr_state:TX',\n",
    "'addr_state:IL_CT',\n",
    "'addr_state:KS_SC_CO_VT_AK_MS',\n",
    "'addr_state:WV_NH_WY_DC_ME_ID',\n",
    "'verification_status:Not Verified',\n",
    "'verification_status:Source Verified',\n",
    "'verification_status:Verified',\n",
    "'purpose:educ__sm_b__wedd__ren_en__mov__house',\n",
    "'purpose:credit_card',\n",
    "'purpose:debt_consolidation',\n",
    "'purpose:oth__med__vacation',\n",
    "'purpose:major_purch__car__home_impr',\n",
    "'initial_list_status:f',\n",
    "'initial_list_status:w',\n",
    "'term:36',\n",
    "'term:60',\n",
    "'emp_length:0',\n",
    "'emp_length:1',\n",
    "'emp_length:2-4',\n",
    "'emp_length:5-6',\n",
    "'emp_length:7-9',\n",
    "'emp_length:10',\n",
    "'mths_since_issue_d:<38',\n",
    "'mths_since_issue_d:38-39',\n",
    "'mths_since_issue_d:40-41',\n",
    "'mths_since_issue_d:42-48',\n",
    "'mths_since_issue_d:49-52',\n",
    "'mths_since_issue_d:53-64',\n",
    "'mths_since_issue_d:65-84',\n",
    "'mths_since_issue_d:>84',\n",
    "'int_rate:<9.548',\n",
    "'int_rate:9.548-12.025',\n",
    "'int_rate:12.025-15.74',\n",
    "'int_rate:15.74-20.281',\n",
    "'int_rate:>20.281',\n",
    "'mths_since_earliest_cr_line:<140',\n",
    "'mths_since_earliest_cr_line:141-164',\n",
    "'mths_since_earliest_cr_line:165-247',\n",
    "'mths_since_earliest_cr_line:248-270',\n",
    "'mths_since_earliest_cr_line:271-352',\n",
    "'mths_since_earliest_cr_line:>352',\n",
    "'inq_last_6mths:0',\n",
    "'inq_last_6mths:1-2',\n",
    "'inq_last_6mths:3-6',\n",
    "'inq_last_6mths:>6',\n",
    "'acc_now_delinq:0',\n",
    "'acc_now_delinq:>=1',\n",
    "'annual_inc:<20K',\n",
    "'annual_inc:20K-30K',\n",
    "'annual_inc:30K-40K',\n",
    "'annual_inc:40K-50K',\n",
    "'annual_inc:50K-60K',\n",
    "'annual_inc:60K-70K',\n",
    "'annual_inc:70K-80K',\n",
    "'annual_inc:80K-90K',\n",
    "'annual_inc:90K-100K',\n",
    "'annual_inc:100K-120K',\n",
    "'annual_inc:120K-140K',\n",
    "'annual_inc:>140K',\n",
    "'dti:<=1.4',\n",
    "'dti:1.4-3.5',\n",
    "'dti:3.5-7.7',\n",
    "'dti:7.7-10.5',\n",
    "'dti:10.5-16.1',\n",
    "'dti:16.1-20.3',\n",
    "'dti:20.3-21.7',\n",
    "'dti:21.7-22.4',\n",
    "'dti:22.4-35',\n",
    "'dti:>35',\n",
    "'mths_since_last_delinq:Missing',\n",
    "'mths_since_last_delinq:0-3',\n",
    "'mths_since_last_delinq:4-30',\n",
    "'mths_since_last_delinq:31-56',\n",
    "'mths_since_last_delinq:>=57',\n",
    "'mths_since_last_record:Missing',\n",
    "'mths_since_last_record:0-2',\n",
    "'mths_since_last_record:3-20',\n",
    "'mths_since_last_record:21-31',\n",
    "'mths_since_last_record:32-80',\n",
    "'mths_since_last_record:81-86',\n",
    "'mths_since_last_record:>86',\n",
    "]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# And here, in the list below, we keep the variable names for the reference categories,\n",
    "# only for the variables we used in our final PD model.\n",
    "ref_categories = ['grade:G',\n",
    "'home_ownership:RENT_OTHER_NONE_ANY',\n",
    "'addr_state:ND_NE_IA_NV_FL_HI_AL',\n",
    "'verification_status:Verified',\n",
    "'purpose:educ__sm_b__wedd__ren_en__mov__house',\n",
    "'initial_list_status:f',\n",
    "'term:60',\n",
    "'emp_length:0',\n",
    "'mths_since_issue_d:>84',\n",
    "'int_rate:>20.281',\n",
    "'mths_since_earliest_cr_line:<140',\n",
    "'inq_last_6mths:>6',\n",
    "'acc_now_delinq:0',\n",
    "'annual_inc:<20K',\n",
    "'dti:>35',\n",
    "'mths_since_last_delinq:0-3',\n",
    "'mths_since_last_record:0-2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grade:A</th>\n",
       "      <th>grade:B</th>\n",
       "      <th>grade:C</th>\n",
       "      <th>grade:D</th>\n",
       "      <th>grade:E</th>\n",
       "      <th>grade:F</th>\n",
       "      <th>home_ownership:OWN</th>\n",
       "      <th>home_ownership:MORTGAGE</th>\n",
       "      <th>addr_state:NM_VA</th>\n",
       "      <th>addr_state:NY</th>\n",
       "      <th>...</th>\n",
       "      <th>mths_since_last_delinq:Missing</th>\n",
       "      <th>mths_since_last_delinq:4-30</th>\n",
       "      <th>mths_since_last_delinq:31-56</th>\n",
       "      <th>mths_since_last_delinq:&gt;=57</th>\n",
       "      <th>mths_since_last_record:Missing</th>\n",
       "      <th>mths_since_last_record:3-20</th>\n",
       "      <th>mths_since_last_record:21-31</th>\n",
       "      <th>mths_since_last_record:32-80</th>\n",
       "      <th>mths_since_last_record:81-86</th>\n",
       "      <th>mths_since_last_record:&gt;86</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>362514</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288564</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213591</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263083</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165001</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        grade:A  grade:B  grade:C  grade:D  grade:E  grade:F  \\\n",
       "362514        0        0        1        0        0        0   \n",
       "288564        0        0        0        0        1        0   \n",
       "213591        0        0        1        0        0        0   \n",
       "263083        0        0        1        0        0        0   \n",
       "165001        1        0        0        0        0        0   \n",
       "\n",
       "        home_ownership:OWN  home_ownership:MORTGAGE  addr_state:NM_VA  \\\n",
       "362514                   0                        1                 0   \n",
       "288564                   0                        1                 0   \n",
       "213591                   0                        1                 0   \n",
       "263083                   0                        1                 0   \n",
       "165001                   0                        1                 0   \n",
       "\n",
       "        addr_state:NY  ...  mths_since_last_delinq:Missing  \\\n",
       "362514              0  ...                               1   \n",
       "288564              0  ...                               0   \n",
       "213591              0  ...                               0   \n",
       "263083              0  ...                               1   \n",
       "165001              0  ...                               0   \n",
       "\n",
       "        mths_since_last_delinq:4-30  mths_since_last_delinq:31-56  \\\n",
       "362514                            0                             0   \n",
       "288564                            0                             0   \n",
       "213591                            0                             1   \n",
       "263083                            0                             0   \n",
       "165001                            0                             1   \n",
       "\n",
       "        mths_since_last_delinq:>=57  mths_since_last_record:Missing  \\\n",
       "362514                            0                               1   \n",
       "288564                            0                               1   \n",
       "213591                            0                               1   \n",
       "263083                            0                               1   \n",
       "165001                            0                               1   \n",
       "\n",
       "        mths_since_last_record:3-20  mths_since_last_record:21-31  \\\n",
       "362514                            0                             0   \n",
       "288564                            0                             0   \n",
       "213591                            0                             0   \n",
       "263083                            0                             0   \n",
       "165001                            0                             0   \n",
       "\n",
       "        mths_since_last_record:32-80  mths_since_last_record:81-86  \\\n",
       "362514                             0                             0   \n",
       "288564                             0                             0   \n",
       "213591                             0                             0   \n",
       "263083                             0                             0   \n",
       "165001                             0                             0   \n",
       "\n",
       "        mths_since_last_record:>86  \n",
       "362514                           0  \n",
       "288564                           0  \n",
       "213591                           0  \n",
       "263083                           0  \n",
       "165001                           0  \n",
       "\n",
       "[5 rows x 84 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs_test = inputs_test_with_ref_cat.drop(ref_categories, axis = 1)\n",
    "inputs_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat_test = reg2.model.predict(inputs_test)\n",
    "# Calculates the predicted values for the dependent variable (targets)\n",
    "# based on the values of the independent variables (inputs) supplied as an argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat_test\n",
    "# This is an array of predicted discrete classess (in this case, 0s and 1s)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat_test_proba = reg2.model.predict_proba(inputs_test)\n",
    "# Calculates the predicted probability values for the dependent variable (targets)\n",
    "# based on the values of the independent variables (inputs) supplied as an argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.07569418, 0.92430582],\n",
       "       [0.15076157, 0.84923843],\n",
       "       [0.11464912, 0.88535088],\n",
       "       ...,\n",
       "       [0.02678639, 0.97321361],\n",
       "       [0.04020822, 0.95979178],\n",
       "       [0.04763338, 0.95236662]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat_test_proba\n",
    "# This is an array of arrays of predicted class probabilities for all classes.\n",
    "# In this case, the first value of every sub-array is the probability for the observation to belong to the first class, i.e. 0,\n",
    "# and the second value is the probability for the observation to belong to the first class, i.e. 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.92430582, 0.84923843, 0.88535088, ..., 0.97321361, 0.95979178,\n",
       "       0.95236662])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat_test_proba[:][:,1]\n",
    "# Here we take all the arrays in the array, and from each array, we take all rows, and only the element with index 1,\n",
    "# that is, the second element.\n",
    "# In other words, we take only the probabilities for being 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat_test_proba = y_hat_test_proba[: ][: , 1]\n",
    "# We store these probabilities in a variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.92430582, 0.84923843, 0.88535088, ..., 0.97321361, 0.95979178,\n",
       "       0.95236662])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat_test_proba\n",
    "# This variable contains an array of probabilities of being 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_data_targets_test_temp = loan_data_targets_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_data_targets_test_temp.reset_index(drop = True, inplace = True)\n",
    "# We reset the index of a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs = pd.concat([loan_data_targets_test_temp, pd.DataFrame(y_hat_test_proba)], axis = 1)\n",
    "# Concatenates two dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(93257, 2)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actual_predicted_probs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs.columns = ['loan_data_targets_test', 'y_hat_test_proba']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs.index = loan_data_inputs_test.index\n",
    "# Makes the index of one dataframe equal to the index of another dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_data_targets_test</th>\n",
       "      <th>y_hat_test_proba</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>362514</th>\n",
       "      <td>1</td>\n",
       "      <td>0.924306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288564</th>\n",
       "      <td>1</td>\n",
       "      <td>0.849238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213591</th>\n",
       "      <td>1</td>\n",
       "      <td>0.885351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263083</th>\n",
       "      <td>1</td>\n",
       "      <td>0.940637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165001</th>\n",
       "      <td>1</td>\n",
       "      <td>0.968665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        loan_data_targets_test  y_hat_test_proba\n",
       "362514                       1          0.924306\n",
       "288564                       1          0.849238\n",
       "213591                       1          0.885351\n",
       "263083                       1          0.940637\n",
       "165001                       1          0.968665"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actual_predicted_probs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy and Area under the Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "tr = 0.9\n",
    "# We create a new column with an indicator,\n",
    "# where every observation that has predicted probability greater than the threshold has a value of 1,\n",
    "# and every observation that has predicted probability lower than the threshold has a value of 0.\n",
    "df_actual_predicted_probs['y_hat_test'] = np.where(df_actual_predicted_probs['y_hat_test_proba'] > tr, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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",
       "        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>Predicted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7374</td>\n",
       "      <td>2816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35810</td>\n",
       "      <td>47257</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted      0      1\n",
       "Actual                 \n",
       "0           7374   2816\n",
       "1          35810  47257"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_actual_predicted_probs['loan_data_targets_test'], df_actual_predicted_probs['y_hat_test'], rownames = ['Actual'], colnames = ['Predicted'])\n",
    "# Creates a cross-table where the actual values are displayed by rows and the predicted values by columns.\n",
    "# This table is known as a Confusion Matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.079072</td>\n",
       "      <td>0.030196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.383993</td>\n",
       "      <td>0.506739</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted         0         1\n",
       "Actual                       \n",
       "0          0.079072  0.030196\n",
       "1          0.383993  0.506739"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_actual_predicted_probs['loan_data_targets_test'], df_actual_predicted_probs['y_hat_test'], rownames = ['Actual'], colnames = ['Predicted']) / df_actual_predicted_probs.shape[0]\n",
    "# Here we divide each value of the table by the total number of observations,\n",
    "# thus getting percentages, or, rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5858112527745907"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pd.crosstab(df_actual_predicted_probs['loan_data_targets_test'], df_actual_predicted_probs['y_hat_test'], rownames = ['Actual'], colnames = ['Predicted']) / df_actual_predicted_probs.shape[0]).iloc[0, 0] + (pd.crosstab(df_actual_predicted_probs['loan_data_targets_test'], df_actual_predicted_probs['y_hat_test'], rownames = ['Actual'], colnames = ['Predicted']) / df_actual_predicted_probs.shape[0]).iloc[1, 1]\n",
    "# Here we calculate Accuracy of the model, which is the sum of the diagonal rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.        , 0.        , 0.        , ..., 0.99960746, 1.        ,\n",
       "        1.        ]),\n",
       " array([0.00000000e+00, 1.20384750e-05, 1.20384750e-04, ...,\n",
       "        9.99975923e-01, 9.99975923e-01, 1.00000000e+00]),\n",
       " array([1.99262885, 0.99262885, 0.99069812, ..., 0.48791257, 0.39372783,\n",
       "        0.3752709 ]))"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_curve(df_actual_predicted_probs['loan_data_targets_test'], df_actual_predicted_probs['y_hat_test_proba'])\n",
    "# Returns the Receiver Operating Characteristic (ROC) Curve from a set of actual values and their predicted probabilities.\n",
    "# As a result, we get three arrays: the false positive rates, the true positive rates, and the thresholds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = roc_curve(df_actual_predicted_probs['loan_data_targets_test'], df_actual_predicted_probs['y_hat_test_proba'])\n",
    "# Here we store each of the three arrays in a separate variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ROC curve')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fpr, tpr)\n",
    "# We plot the false positive rate along the x-axis and the true positive rate along the y-axis,\n",
    "# thus plotting the ROC curve.\n",
    "plt.plot(fpr, fpr, linestyle = '--', color = 'k')\n",
    "# We plot a seconary diagonal line, with dashed line style and black color.\n",
    "plt.xlabel('False positive rate')\n",
    "# We name the x-axis \"False positive rate\".\n",
    "plt.ylabel('True positive rate')\n",
    "# We name the x-axis \"True positive rate\".\n",
    "plt.title('ROC curve')\n",
    "# We name the graph \"ROC curve\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7022080352909961"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AUROC = roc_auc_score(df_actual_predicted_probs['loan_data_targets_test'], df_actual_predicted_probs['y_hat_test_proba'])\n",
    "# Calculates the Area Under the Receiver Operating Characteristic Curve (AUROC)\n",
    "# from a set of actual values and their predicted probabilities.\n",
    "AUROC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gini and Kolmogorov-Smirnov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs = df_actual_predicted_probs.sort_values('y_hat_test_proba')\n",
    "# Sorts a dataframe by the values of a specific column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_data_targets_test</th>\n",
       "      <th>y_hat_test_proba</th>\n",
       "      <th>y_hat_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42341</th>\n",
       "      <td>1</td>\n",
       "      <td>0.375271</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42344</th>\n",
       "      <td>1</td>\n",
       "      <td>0.392094</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39810</th>\n",
       "      <td>0</td>\n",
       "      <td>0.393728</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40518</th>\n",
       "      <td>0</td>\n",
       "      <td>0.448965</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>42396</th>\n",
       "      <td>0</td>\n",
       "      <td>0.457731</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       loan_data_targets_test  y_hat_test_proba  y_hat_test\n",
       "42341                       1          0.375271           0\n",
       "42344                       1          0.392094           0\n",
       "39810                       0          0.393728           0\n",
       "40518                       0          0.448965           0\n",
       "42396                       0          0.457731           0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actual_predicted_probs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        loan_data_targets_test  y_hat_test_proba  y_hat_test\n",
       "262480                       1          0.991292           1\n",
       "231463                       1          0.991304           1\n",
       "239228                       1          0.991652           1\n",
       "261086                       1          0.992058           1\n",
       "242624                       1          0.992629           1"
      ]
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     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df_actual_predicted_probs.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs = df_actual_predicted_probs.reset_index()\n",
    "# We reset the index of a dataframe and overwrite it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   index  loan_data_targets_test  y_hat_test_proba  y_hat_test\n",
       "0  42341                       1          0.375271           0\n",
       "1  42344                       1          0.392094           0\n",
       "2  39810                       0          0.393728           0\n",
       "3  40518                       0          0.448965           0\n",
       "4  42396                       0          0.457731           0"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actual_predicted_probs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs['Cumulative N Population'] = df_actual_predicted_probs.index + 1\n",
    "# We calculate the cumulative number of all observations.\n",
    "# We use the new index for that. Since indexing in ython starts from 0, we add 1 to each index.\n",
    "df_actual_predicted_probs['Cumulative N Good'] = df_actual_predicted_probs['loan_data_targets_test'].cumsum()\n",
    "# We calculate cumulative number of 'good', which is the cumulative sum of the column with actual observations.\n",
    "df_actual_predicted_probs['Cumulative N Bad'] = df_actual_predicted_probs['Cumulative N Population'] - df_actual_predicted_probs['loan_data_targets_test'].cumsum()\n",
    "# We calculate cumulative number of 'bad', which is\n",
    "# the difference between the cumulative number of all observations and cumulative number of 'good' for each row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   index  loan_data_targets_test  y_hat_test_proba  y_hat_test  \\\n",
       "0  42341                       1          0.375271           0   \n",
       "1  42344                       1          0.392094           0   \n",
       "2  39810                       0          0.393728           0   \n",
       "3  40518                       0          0.448965           0   \n",
       "4  42396                       0          0.457731           0   \n",
       "\n",
       "   Cumulative N Population  Cumulative N Good  Cumulative N Bad  \n",
       "0                        1                  1                 0  \n",
       "1                        2                  2                 0  \n",
       "2                        3                  2                 1  \n",
       "3                        4                  2                 2  \n",
       "4                        5                  2                 3  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actual_predicted_probs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs['Cumulative Perc Population'] = df_actual_predicted_probs['Cumulative N Population'] / (df_actual_predicted_probs.shape[0])\n",
    "# We calculate the cumulative percentage of all observations.\n",
    "df_actual_predicted_probs['Cumulative Perc Good'] = df_actual_predicted_probs['Cumulative N Good'] / df_actual_predicted_probs['loan_data_targets_test'].sum()\n",
    "# We calculate cumulative percentage of 'good'.\n",
    "df_actual_predicted_probs['Cumulative Perc Bad'] = df_actual_predicted_probs['Cumulative N Bad'] / (df_actual_predicted_probs.shape[0] - df_actual_predicted_probs['loan_data_targets_test'].sum())\n",
    "# We calculate the cumulative percentage of 'bad'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.000043</td>\n",
       "      <td>0.000024</td>\n",
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       "      <td>0.457731</td>\n",
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       "      <td>3</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.000024</td>\n",
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      "text/plain": [
       "   index  loan_data_targets_test  y_hat_test_proba  y_hat_test  \\\n",
       "0  42341                       1          0.375271           0   \n",
       "1  42344                       1          0.392094           0   \n",
       "2  39810                       0          0.393728           0   \n",
       "3  40518                       0          0.448965           0   \n",
       "4  42396                       0          0.457731           0   \n",
       "\n",
       "   Cumulative N Population  Cumulative N Good  Cumulative N Bad  \\\n",
       "0                        1                  1                 0   \n",
       "1                        2                  2                 0   \n",
       "2                        3                  2                 1   \n",
       "3                        4                  2                 2   \n",
       "4                        5                  2                 3   \n",
       "\n",
       "   Cumulative Perc Population  Cumulative Perc Good  Cumulative Perc Bad  \n",
       "0                    0.000011              0.000012             0.000000  \n",
       "1                    0.000021              0.000024             0.000000  \n",
       "2                    0.000032              0.000024             0.000098  \n",
       "3                    0.000043              0.000024             0.000196  \n",
       "4                    0.000054              0.000024             0.000294  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actual_predicted_probs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "        index  loan_data_targets_test  y_hat_test_proba  y_hat_test  \\\n",
       "93252  262480                       1          0.991292           1   \n",
       "93253  231463                       1          0.991304           1   \n",
       "93254  239228                       1          0.991652           1   \n",
       "93255  261086                       1          0.992058           1   \n",
       "93256  242624                       1          0.992629           1   \n",
       "\n",
       "       Cumulative N Population  Cumulative N Good  Cumulative N Bad  \\\n",
       "93252                    93253              83063             10190   \n",
       "93253                    93254              83064             10190   \n",
       "93254                    93255              83065             10190   \n",
       "93255                    93256              83066             10190   \n",
       "93256                    93257              83067             10190   \n",
       "\n",
       "       Cumulative Perc Population  Cumulative Perc Good  Cumulative Perc Bad  \n",
       "93252                    0.999957              0.999952                  1.0  \n",
       "93253                    0.999968              0.999964                  1.0  \n",
       "93254                    0.999979              0.999976                  1.0  \n",
       "93255                    0.999989              0.999988                  1.0  \n",
       "93256                    1.000000              1.000000                  1.0  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actual_predicted_probs.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Gini')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Gini\n",
    "plt.plot(df_actual_predicted_probs['Cumulative Perc Population'], df_actual_predicted_probs['Cumulative Perc Bad'])\n",
    "# We plot the cumulative percentage of all along the x-axis and the cumulative percentage 'good' along the y-axis,\n",
    "# thus plotting the Gini curve.\n",
    "plt.plot(df_actual_predicted_probs['Cumulative Perc Population'], df_actual_predicted_probs['Cumulative Perc Population'], linestyle = '--', color = 'k')\n",
    "# We plot a seconary diagonal line, with dashed line style and black color.\n",
    "plt.xlabel('Cumulative % Population')\n",
    "# We name the x-axis \"Cumulative % Population\".\n",
    "plt.ylabel('Cumulative % Bad')\n",
    "# We name the y-axis \"Cumulative % Bad\".\n",
    "plt.title('Gini')\n",
    "# We name the graph \"Gini\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.40441607058199214"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Gini = AUROC * 2 - 1\n",
    "# Here we calculate Gini from AUROC.\n",
    "Gini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Kolmogorov-Smirnov')"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot KS\n",
    "plt.plot(df_actual_predicted_probs['y_hat_test_proba'], df_actual_predicted_probs['Cumulative Perc Bad'], color = 'r')\n",
    "# We plot the predicted (estimated) probabilities along the x-axis and the cumulative percentage 'bad' along the y-axis,\n",
    "# colored in red.\n",
    "plt.plot(df_actual_predicted_probs['y_hat_test_proba'], df_actual_predicted_probs['Cumulative Perc Good'], color = 'b')\n",
    "# We plot the predicted (estimated) probabilities along the x-axis and the cumulative percentage 'good' along the y-axis,\n",
    "# colored in red.\n",
    "plt.xlabel('Estimated Probability for being Good')\n",
    "# We name the x-axis \"Estimated Probability for being Good\".\n",
    "plt.ylabel('Cumulative %')\n",
    "# We name the y-axis \"Cumulative %\".\n",
    "plt.title('Kolmogorov-Smirnov')\n",
    "# We name the graph \"Kolmogorov-Smirnov\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2966746932223847"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KS = max(df_actual_predicted_probs['Cumulative Perc Bad'] - df_actual_predicted_probs['Cumulative Perc Good'])\n",
    "# We calculate KS from the data. It is the maximum of the difference between the cumulative percentage of 'bad'\n",
    "# and the cumulative percentage of 'good'.\n",
    "KS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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