PD Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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\n",
    "\n",
    "We reload the inputs and targets preprocessed data work in first step :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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, header = None)\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, header = None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>427211</td>\n",
       "      <td>427211</td>\n",
       "      <td>12796369</td>\n",
       "      <td>14818505</td>\n",
       "      <td>24000</td>\n",
       "      <td>24000</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>8.90</td>\n",
       "      <td>762.08</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>206088</td>\n",
       "      <td>206088</td>\n",
       "      <td>1439740</td>\n",
       "      <td>1691948</td>\n",
       "      <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",
       "      <td>C</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>136020</td>\n",
       "      <td>136020</td>\n",
       "      <td>5214749</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>412305</td>\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",
       "      <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",
       "    <tr>\n",
       "      <td>36159</td>\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",
       "      <td>0</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 × 324 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 324 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_data_inputs_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 5,
     "metadata": {},
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   "source": [
    "loan_data_targets_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(373028, 324)"
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "loan_data_inputs_train.shape"
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(373028, 1)"
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "loan_data_targets_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(93257, 324)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_data_inputs_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(93257, 1)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_data_targets_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting the Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We decide to only select the features we choose in the preprocessing step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<center> Recall that for each feature, we kept  the category with the lower WoE as the reference categorie. </center> \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>addr_state:NM_VA</th>\n",
       "      <th>addr_state:NY</th>\n",
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       "      <th>mths_since_last_delinq:4-30</th>\n",
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       "      <th>mths_since_last_delinq:&gt;=57</th>\n",
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       "      <th>mths_since_last_record:&gt;86</th>\n",
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       "      <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",
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       "    <tr>\n",
       "      <td>412305</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>36159</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "<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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We drop the columns with variable names in the list with reference categories. \n",
    "inputs_train = inputs_train_with_ref_cat.drop(ref_categories, axis = 1)\n",
    "inputs_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PD Model Estimation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 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.\n",
    "\n",
    "reg.fit(inputs_train, loan_data_targets_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.70379977])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displays the intercept \n",
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.11623674,  0.87859502,  0.68445935,  0.49652362,  0.32114499,\n",
       "         0.13296388,  0.09124677,  0.1067525 ,  0.03739756,  0.05940361,\n",
       "         0.06019763,  0.06274922,  0.07880915,  0.13639194,  0.1007273 ,\n",
       "         0.18524321,  0.2407948 ,  0.22455863,  0.26317771,  0.32134522,\n",
       "         0.52296159,  0.08813789, -0.01071227,  0.3044634 ,  0.20094547,\n",
       "         0.21359492,  0.26665554,  0.05397901,  0.07922776,  0.10042894,\n",
       "         0.12548329,  0.0906677 ,  0.06057889,  0.12324541,  1.07167877,\n",
       "         0.871346  ,  0.77105195,  0.57014273,  0.4086418 ,  0.16344655,\n",
       "        -0.07238807,  0.88183231,  0.55469732,  0.30333608,  0.11130771,\n",
       "         0.05590375,  0.03756645,  0.07859487,  0.1192599 ,  0.12487054,\n",
       "         0.08992534,  0.04742607,  0.66575458,  0.52203567,  0.31268827,\n",
       "         0.32550232,  0.22914117,  0.20309041,  0.1867138 ,  0.18514828,\n",
       "         0.21386006,  0.12739662,  0.12333193,  0.15699215, -0.02025808,\n",
       "         0.02660103,  0.20105142,  0.04252795,  0.01147025,  0.01083441,\n",
       "         0.02517095,  0.04672494,  0.07254524,  0.22375762, -0.07089857,\n",
       "        -0.00273631,  0.0949291 ,  0.16368789,  0.23957989,  0.31331041,\n",
       "         0.38883207,  0.40762799,  0.48481792,  0.57289299,  0.49935498,\n",
       "         0.20381748,  0.33012855,  0.35924067,  0.2996664 ,  0.22370586,\n",
       "         0.12586679,  0.114808  ,  0.08082785,  0.04108984,  0.07315155,\n",
       "         0.14145255,  0.15235299,  0.10449487,  0.35397296,  0.44402052,\n",
       "         0.37775224,  0.53971227,  0.2058407 ,  0.270218  ]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displays the coefficients for each columns (category - features)\n",
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's store the names of the columns of the inputs dataframe use in the Regression.\n",
    "feature_name = inputs_train.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature name</th>\n",
       "      <th>Coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.703800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.116237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.878595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.684459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.496524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>grade:E</td>\n",
       "      <td>0.321145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>grade:F</td>\n",
       "      <td>0.132964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>home_ownership:OWN</td>\n",
       "      <td>0.091247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>home_ownership:MORTGAGE</td>\n",
       "      <td>0.106753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>addr_state:NM_VA</td>\n",
       "      <td>0.037398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>addr_state:NY</td>\n",
       "      <td>0.059404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>addr_state:OK_TN_MO_LA_MD_NC</td>\n",
       "      <td>0.060198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>addr_state:CA</td>\n",
       "      <td>0.062749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>addr_state:UT_KY_AZ_NJ</td>\n",
       "      <td>0.078809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>addr_state:AR_MI_PA_OH_MN</td>\n",
       "      <td>0.136392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>addr_state:RI_MA_DE_SD_IN</td>\n",
       "      <td>0.100727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>addr_state:GA_WA_OR</td>\n",
       "      <td>0.185243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>addr_state:WI_MT</td>\n",
       "      <td>0.240795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>addr_state:TX</td>\n",
       "      <td>0.224559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>addr_state:IL_CT</td>\n",
       "      <td>0.263178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>addr_state:KS_SC_CO_VT_AK_MS</td>\n",
       "      <td>0.321345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>addr_state:WV_NH_WY_DC_ME_ID</td>\n",
       "      <td>0.522962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>verification_status:Not Verified</td>\n",
       "      <td>0.088138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>verification_status:Source Verified</td>\n",
       "      <td>-0.010712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>purpose:credit_card</td>\n",
       "      <td>0.304463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>purpose:debt_consolidation</td>\n",
       "      <td>0.200945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>purpose:oth__med__vacation</td>\n",
       "      <td>0.213595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>purpose:major_purch__car__home_impr</td>\n",
       "      <td>0.266656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>initial_list_status:w</td>\n",
       "      <td>0.053979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>term:36</td>\n",
       "      <td>0.079228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>emp_length:1</td>\n",
       "      <td>0.100429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>emp_length:2-4</td>\n",
       "      <td>0.125483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>emp_length:5-6</td>\n",
       "      <td>0.090668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>emp_length:7-9</td>\n",
       "      <td>0.060579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>emp_length:10</td>\n",
       "      <td>0.123245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>mths_since_issue_d:&lt;38</td>\n",
       "      <td>1.071679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>mths_since_issue_d:38-39</td>\n",
       "      <td>0.871346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>mths_since_issue_d:40-41</td>\n",
       "      <td>0.771052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>mths_since_issue_d:42-48</td>\n",
       "      <td>0.570143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>mths_since_issue_d:49-52</td>\n",
       "      <td>0.408642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>mths_since_issue_d:53-64</td>\n",
       "      <td>0.163447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>mths_since_issue_d:65-84</td>\n",
       "      <td>-0.072388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>int_rate:&lt;9.548</td>\n",
       "      <td>0.881832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>int_rate:9.548-12.025</td>\n",
       "      <td>0.554697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>int_rate:12.025-15.74</td>\n",
       "      <td>0.303336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>int_rate:15.74-20.281</td>\n",
       "      <td>0.111308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>mths_since_earliest_cr_line:141-164</td>\n",
       "      <td>0.055904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>mths_since_earliest_cr_line:165-247</td>\n",
       "      <td>0.037566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>mths_since_earliest_cr_line:248-270</td>\n",
       "      <td>0.078595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>mths_since_earliest_cr_line:271-352</td>\n",
       "      <td>0.119260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>mths_since_earliest_cr_line:&gt;352</td>\n",
       "      <td>0.124871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>delinq_2yrs:0</td>\n",
       "      <td>0.089925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>delinq_2yrs:1-3</td>\n",
       "      <td>0.047426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>inq_last_6mths:0</td>\n",
       "      <td>0.665755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>inq_last_6mths:1-2</td>\n",
       "      <td>0.522036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>inq_last_6mths:3-6</td>\n",
       "      <td>0.312688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>open_acc:1-3</td>\n",
       "      <td>0.325502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>open_acc:4-12</td>\n",
       "      <td>0.229141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>open_acc:13-17</td>\n",
       "      <td>0.203090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>open_acc:18-22</td>\n",
       "      <td>0.186714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>open_acc:23-25</td>\n",
       "      <td>0.185148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>open_acc:26-30</td>\n",
       "      <td>0.213860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>open_acc:&gt;=31</td>\n",
       "      <td>0.127397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>pub_rec:3-4</td>\n",
       "      <td>0.123332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>pub_rec:&gt;=5</td>\n",
       "      <td>0.156992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>total_acc:28-51</td>\n",
       "      <td>-0.020258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>total_acc:&gt;=52</td>\n",
       "      <td>0.026601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>acc_now_delinq:&gt;=1</td>\n",
       "      <td>0.201051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>total_rev_hi_lim:5K-10K</td>\n",
       "      <td>0.042528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>total_rev_hi_lim:10K-20K</td>\n",
       "      <td>0.011470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>total_rev_hi_lim:20K-30K</td>\n",
       "      <td>0.010834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>total_rev_hi_lim:30K-40K</td>\n",
       "      <td>0.025171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>total_rev_hi_lim:40K-55K</td>\n",
       "      <td>0.046725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>total_rev_hi_lim:55K-95K</td>\n",
       "      <td>0.072545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>total_rev_hi_lim:&gt;95K</td>\n",
       "      <td>0.223758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>annual_inc:20K-30K</td>\n",
       "      <td>-0.070899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>annual_inc:30K-40K</td>\n",
       "      <td>-0.002736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>annual_inc:40K-50K</td>\n",
       "      <td>0.094929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>annual_inc:50K-60K</td>\n",
       "      <td>0.163688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>annual_inc:60K-70K</td>\n",
       "      <td>0.239580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>annual_inc:70K-80K</td>\n",
       "      <td>0.313310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>annual_inc:80K-90K</td>\n",
       "      <td>0.388832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>annual_inc:90K-100K</td>\n",
       "      <td>0.407628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>annual_inc:100K-120K</td>\n",
       "      <td>0.484818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>annual_inc:120K-140K</td>\n",
       "      <td>0.572893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>annual_inc:&gt;140K</td>\n",
       "      <td>0.499355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>dti:&lt;=1.4</td>\n",
       "      <td>0.203817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>dti:1.4-3.5</td>\n",
       "      <td>0.330129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>dti:3.5-7.7</td>\n",
       "      <td>0.359241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>dti:7.7-10.5</td>\n",
       "      <td>0.299666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>dti:10.5-16.1</td>\n",
       "      <td>0.223706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>dti:16.1-20.3</td>\n",
       "      <td>0.125867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>dti:20.3-21.7</td>\n",
       "      <td>0.114808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>dti:21.7-22.4</td>\n",
       "      <td>0.080828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>dti:22.4-35</td>\n",
       "      <td>0.041090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>mths_since_last_delinq:Missing</td>\n",
       "      <td>0.073152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>mths_since_last_delinq:4-30</td>\n",
       "      <td>0.141453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>mths_since_last_delinq:31-56</td>\n",
       "      <td>0.152353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>mths_since_last_delinq:&gt;=57</td>\n",
       "      <td>0.104495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>mths_since_last_record:Missing</td>\n",
       "      <td>0.353973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>mths_since_last_record:3-20</td>\n",
       "      <td>0.444021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>mths_since_last_record:21-31</td>\n",
       "      <td>0.377752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>mths_since_last_record:32-80</td>\n",
       "      <td>0.539712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>mths_since_last_record:81-86</td>\n",
       "      <td>0.205841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>mths_since_last_record:&gt;86</td>\n",
       "      <td>0.270218</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Feature name  Coefficients\n",
       "0                              Intercept     -1.703800\n",
       "1                                grade:A      1.116237\n",
       "2                                grade:B      0.878595\n",
       "3                                grade:C      0.684459\n",
       "4                                grade:D      0.496524\n",
       "5                                grade:E      0.321145\n",
       "6                                grade:F      0.132964\n",
       "7                     home_ownership:OWN      0.091247\n",
       "8                home_ownership:MORTGAGE      0.106753\n",
       "9                       addr_state:NM_VA      0.037398\n",
       "10                         addr_state:NY      0.059404\n",
       "11          addr_state:OK_TN_MO_LA_MD_NC      0.060198\n",
       "12                         addr_state:CA      0.062749\n",
       "13                addr_state:UT_KY_AZ_NJ      0.078809\n",
       "14             addr_state:AR_MI_PA_OH_MN      0.136392\n",
       "15             addr_state:RI_MA_DE_SD_IN      0.100727\n",
       "16                   addr_state:GA_WA_OR      0.185243\n",
       "17                      addr_state:WI_MT      0.240795\n",
       "18                         addr_state:TX      0.224559\n",
       "19                      addr_state:IL_CT      0.263178\n",
       "20          addr_state:KS_SC_CO_VT_AK_MS      0.321345\n",
       "21          addr_state:WV_NH_WY_DC_ME_ID      0.522962\n",
       "22      verification_status:Not Verified      0.088138\n",
       "23   verification_status:Source Verified     -0.010712\n",
       "24                   purpose:credit_card      0.304463\n",
       "25            purpose:debt_consolidation      0.200945\n",
       "26            purpose:oth__med__vacation      0.213595\n",
       "27   purpose:major_purch__car__home_impr      0.266656\n",
       "28                 initial_list_status:w      0.053979\n",
       "29                               term:36      0.079228\n",
       "30                          emp_length:1      0.100429\n",
       "31                        emp_length:2-4      0.125483\n",
       "32                        emp_length:5-6      0.090668\n",
       "33                        emp_length:7-9      0.060579\n",
       "34                         emp_length:10      0.123245\n",
       "35                mths_since_issue_d:<38      1.071679\n",
       "36              mths_since_issue_d:38-39      0.871346\n",
       "37              mths_since_issue_d:40-41      0.771052\n",
       "38              mths_since_issue_d:42-48      0.570143\n",
       "39              mths_since_issue_d:49-52      0.408642\n",
       "40              mths_since_issue_d:53-64      0.163447\n",
       "41              mths_since_issue_d:65-84     -0.072388\n",
       "42                       int_rate:<9.548      0.881832\n",
       "43                 int_rate:9.548-12.025      0.554697\n",
       "44                 int_rate:12.025-15.74      0.303336\n",
       "45                 int_rate:15.74-20.281      0.111308\n",
       "46   mths_since_earliest_cr_line:141-164      0.055904\n",
       "47   mths_since_earliest_cr_line:165-247      0.037566\n",
       "48   mths_since_earliest_cr_line:248-270      0.078595\n",
       "49   mths_since_earliest_cr_line:271-352      0.119260\n",
       "50      mths_since_earliest_cr_line:>352      0.124871\n",
       "51                         delinq_2yrs:0      0.089925\n",
       "52                       delinq_2yrs:1-3      0.047426\n",
       "53                      inq_last_6mths:0      0.665755\n",
       "54                    inq_last_6mths:1-2      0.522036\n",
       "55                    inq_last_6mths:3-6      0.312688\n",
       "56                          open_acc:1-3      0.325502\n",
       "57                         open_acc:4-12      0.229141\n",
       "58                        open_acc:13-17      0.203090\n",
       "59                        open_acc:18-22      0.186714\n",
       "60                        open_acc:23-25      0.185148\n",
       "61                        open_acc:26-30      0.213860\n",
       "62                         open_acc:>=31      0.127397\n",
       "63                           pub_rec:3-4      0.123332\n",
       "64                           pub_rec:>=5      0.156992\n",
       "65                       total_acc:28-51     -0.020258\n",
       "66                        total_acc:>=52      0.026601\n",
       "67                    acc_now_delinq:>=1      0.201051\n",
       "68               total_rev_hi_lim:5K-10K      0.042528\n",
       "69              total_rev_hi_lim:10K-20K      0.011470\n",
       "70              total_rev_hi_lim:20K-30K      0.010834\n",
       "71              total_rev_hi_lim:30K-40K      0.025171\n",
       "72              total_rev_hi_lim:40K-55K      0.046725\n",
       "73              total_rev_hi_lim:55K-95K      0.072545\n",
       "74                 total_rev_hi_lim:>95K      0.223758\n",
       "75                    annual_inc:20K-30K     -0.070899\n",
       "76                    annual_inc:30K-40K     -0.002736\n",
       "77                    annual_inc:40K-50K      0.094929\n",
       "78                    annual_inc:50K-60K      0.163688\n",
       "79                    annual_inc:60K-70K      0.239580\n",
       "80                    annual_inc:70K-80K      0.313310\n",
       "81                    annual_inc:80K-90K      0.388832\n",
       "82                   annual_inc:90K-100K      0.407628\n",
       "83                  annual_inc:100K-120K      0.484818\n",
       "84                  annual_inc:120K-140K      0.572893\n",
       "85                      annual_inc:>140K      0.499355\n",
       "86                             dti:<=1.4      0.203817\n",
       "87                           dti:1.4-3.5      0.330129\n",
       "88                           dti:3.5-7.7      0.359241\n",
       "89                          dti:7.7-10.5      0.299666\n",
       "90                         dti:10.5-16.1      0.223706\n",
       "91                         dti:16.1-20.3      0.125867\n",
       "92                         dti:20.3-21.7      0.114808\n",
       "93                         dti:21.7-22.4      0.080828\n",
       "94                           dti:22.4-35      0.041090\n",
       "95        mths_since_last_delinq:Missing      0.073152\n",
       "96           mths_since_last_delinq:4-30      0.141453\n",
       "97          mths_since_last_delinq:31-56      0.152353\n",
       "98           mths_since_last_delinq:>=57      0.104495\n",
       "99        mths_since_last_record:Missing      0.353973\n",
       "100          mths_since_last_record:3-20      0.444021\n",
       "101         mths_since_last_record:21-31      0.377752\n",
       "102         mths_since_last_record:32-80      0.539712\n",
       "103         mths_since_last_record:81-86      0.205841\n",
       "104           mths_since_last_record:>86      0.270218"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store in a summary table the coefficients for each feature and \n",
    "# save the intercept in the first row\n",
    "\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": "markdown",
   "metadata": {},
   "source": [
    "## Build a Logistic Regression Model with P-Values\n",
    "\n",
    "Need a model to asses , how much each feature  contributed to predicting borrower default or not. So the aim is to keep those who predict the </b>good/bad</b> outcome. \n",
    "\n",
    "A statistical well-know method to do that is to estimate p-values on coefficients using a likelihood ratio test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = LogisticRegression_with_p_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Estimates the coefficients of the object from the 'LogisticRegression' class\n",
    "reg.fit(inputs_train, loan_data_targets_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
       "      <td>0</td>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.703800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.116237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.878595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.684459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.496524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>grade:E</td>\n",
       "      <td>0.321145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>grade:F</td>\n",
       "      <td>0.132964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>home_ownership:OWN</td>\n",
       "      <td>0.091247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>home_ownership:MORTGAGE</td>\n",
       "      <td>0.106753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>addr_state:NM_VA</td>\n",
       "      <td>0.037398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>addr_state:NY</td>\n",
       "      <td>0.059404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>addr_state:OK_TN_MO_LA_MD_NC</td>\n",
       "      <td>0.060198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>addr_state:CA</td>\n",
       "      <td>0.062749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>addr_state:UT_KY_AZ_NJ</td>\n",
       "      <td>0.078809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>addr_state:AR_MI_PA_OH_MN</td>\n",
       "      <td>0.136392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>addr_state:RI_MA_DE_SD_IN</td>\n",
       "      <td>0.100727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>addr_state:GA_WA_OR</td>\n",
       "      <td>0.185243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>addr_state:WI_MT</td>\n",
       "      <td>0.240795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>addr_state:TX</td>\n",
       "      <td>0.224559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>addr_state:IL_CT</td>\n",
       "      <td>0.263178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>addr_state:KS_SC_CO_VT_AK_MS</td>\n",
       "      <td>0.321345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>addr_state:WV_NH_WY_DC_ME_ID</td>\n",
       "      <td>0.522962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>verification_status:Not Verified</td>\n",
       "      <td>0.088138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>verification_status:Source Verified</td>\n",
       "      <td>-0.010712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>purpose:credit_card</td>\n",
       "      <td>0.304463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>purpose:debt_consolidation</td>\n",
       "      <td>0.200945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>purpose:oth__med__vacation</td>\n",
       "      <td>0.213595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>purpose:major_purch__car__home_impr</td>\n",
       "      <td>0.266656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>initial_list_status:w</td>\n",
       "      <td>0.053979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>term:36</td>\n",
       "      <td>0.079228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>emp_length:1</td>\n",
       "      <td>0.100429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>emp_length:2-4</td>\n",
       "      <td>0.125483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>emp_length:5-6</td>\n",
       "      <td>0.090668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>emp_length:7-9</td>\n",
       "      <td>0.060579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>emp_length:10</td>\n",
       "      <td>0.123245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>mths_since_issue_d:&lt;38</td>\n",
       "      <td>1.071679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>mths_since_issue_d:38-39</td>\n",
       "      <td>0.871346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>mths_since_issue_d:40-41</td>\n",
       "      <td>0.771052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>mths_since_issue_d:42-48</td>\n",
       "      <td>0.570143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>mths_since_issue_d:49-52</td>\n",
       "      <td>0.408642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>mths_since_issue_d:53-64</td>\n",
       "      <td>0.163447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>mths_since_issue_d:65-84</td>\n",
       "      <td>-0.072388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>int_rate:&lt;9.548</td>\n",
       "      <td>0.881832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>int_rate:9.548-12.025</td>\n",
       "      <td>0.554697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>int_rate:12.025-15.74</td>\n",
       "      <td>0.303336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>int_rate:15.74-20.281</td>\n",
       "      <td>0.111308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>mths_since_earliest_cr_line:141-164</td>\n",
       "      <td>0.055904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>mths_since_earliest_cr_line:165-247</td>\n",
       "      <td>0.037566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>mths_since_earliest_cr_line:248-270</td>\n",
       "      <td>0.078595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>mths_since_earliest_cr_line:271-352</td>\n",
       "      <td>0.119260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>mths_since_earliest_cr_line:&gt;352</td>\n",
       "      <td>0.124871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>delinq_2yrs:0</td>\n",
       "      <td>0.089925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>delinq_2yrs:1-3</td>\n",
       "      <td>0.047426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>inq_last_6mths:0</td>\n",
       "      <td>0.665755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>inq_last_6mths:1-2</td>\n",
       "      <td>0.522036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>inq_last_6mths:3-6</td>\n",
       "      <td>0.312688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>open_acc:1-3</td>\n",
       "      <td>0.325502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>open_acc:4-12</td>\n",
       "      <td>0.229141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>open_acc:13-17</td>\n",
       "      <td>0.203090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>open_acc:18-22</td>\n",
       "      <td>0.186714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>open_acc:23-25</td>\n",
       "      <td>0.185148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>open_acc:26-30</td>\n",
       "      <td>0.213860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>open_acc:&gt;=31</td>\n",
       "      <td>0.127397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>pub_rec:3-4</td>\n",
       "      <td>0.123332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>pub_rec:&gt;=5</td>\n",
       "      <td>0.156992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>total_acc:28-51</td>\n",
       "      <td>-0.020258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>total_acc:&gt;=52</td>\n",
       "      <td>0.026601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>acc_now_delinq:&gt;=1</td>\n",
       "      <td>0.201051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>total_rev_hi_lim:5K-10K</td>\n",
       "      <td>0.042528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>total_rev_hi_lim:10K-20K</td>\n",
       "      <td>0.011470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>total_rev_hi_lim:20K-30K</td>\n",
       "      <td>0.010834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>total_rev_hi_lim:30K-40K</td>\n",
       "      <td>0.025171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>total_rev_hi_lim:40K-55K</td>\n",
       "      <td>0.046725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>total_rev_hi_lim:55K-95K</td>\n",
       "      <td>0.072545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>total_rev_hi_lim:&gt;95K</td>\n",
       "      <td>0.223758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>annual_inc:20K-30K</td>\n",
       "      <td>-0.070899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>annual_inc:30K-40K</td>\n",
       "      <td>-0.002736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>annual_inc:40K-50K</td>\n",
       "      <td>0.094929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>annual_inc:50K-60K</td>\n",
       "      <td>0.163688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>annual_inc:60K-70K</td>\n",
       "      <td>0.239580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>annual_inc:70K-80K</td>\n",
       "      <td>0.313310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>annual_inc:80K-90K</td>\n",
       "      <td>0.388832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>annual_inc:90K-100K</td>\n",
       "      <td>0.407628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>annual_inc:100K-120K</td>\n",
       "      <td>0.484818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>annual_inc:120K-140K</td>\n",
       "      <td>0.572893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>annual_inc:&gt;140K</td>\n",
       "      <td>0.499355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>dti:&lt;=1.4</td>\n",
       "      <td>0.203817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>dti:1.4-3.5</td>\n",
       "      <td>0.330129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>dti:3.5-7.7</td>\n",
       "      <td>0.359241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>dti:7.7-10.5</td>\n",
       "      <td>0.299666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>dti:10.5-16.1</td>\n",
       "      <td>0.223706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>dti:16.1-20.3</td>\n",
       "      <td>0.125867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>dti:20.3-21.7</td>\n",
       "      <td>0.114808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>dti:21.7-22.4</td>\n",
       "      <td>0.080828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>dti:22.4-35</td>\n",
       "      <td>0.041090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>mths_since_last_delinq:Missing</td>\n",
       "      <td>0.073152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>mths_since_last_delinq:4-30</td>\n",
       "      <td>0.141453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>mths_since_last_delinq:31-56</td>\n",
       "      <td>0.152353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>mths_since_last_delinq:&gt;=57</td>\n",
       "      <td>0.104495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>mths_since_last_record:Missing</td>\n",
       "      <td>0.353973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>mths_since_last_record:3-20</td>\n",
       "      <td>0.444021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>mths_since_last_record:21-31</td>\n",
       "      <td>0.377752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>mths_since_last_record:32-80</td>\n",
       "      <td>0.539712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>mths_since_last_record:81-86</td>\n",
       "      <td>0.205841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>mths_since_last_record:&gt;86</td>\n",
       "      <td>0.270218</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Feature name  Coefficients\n",
       "0                              Intercept     -1.703800\n",
       "1                                grade:A      1.116237\n",
       "2                                grade:B      0.878595\n",
       "3                                grade:C      0.684459\n",
       "4                                grade:D      0.496524\n",
       "5                                grade:E      0.321145\n",
       "6                                grade:F      0.132964\n",
       "7                     home_ownership:OWN      0.091247\n",
       "8                home_ownership:MORTGAGE      0.106753\n",
       "9                       addr_state:NM_VA      0.037398\n",
       "10                         addr_state:NY      0.059404\n",
       "11          addr_state:OK_TN_MO_LA_MD_NC      0.060198\n",
       "12                         addr_state:CA      0.062749\n",
       "13                addr_state:UT_KY_AZ_NJ      0.078809\n",
       "14             addr_state:AR_MI_PA_OH_MN      0.136392\n",
       "15             addr_state:RI_MA_DE_SD_IN      0.100727\n",
       "16                   addr_state:GA_WA_OR      0.185243\n",
       "17                      addr_state:WI_MT      0.240795\n",
       "18                         addr_state:TX      0.224559\n",
       "19                      addr_state:IL_CT      0.263178\n",
       "20          addr_state:KS_SC_CO_VT_AK_MS      0.321345\n",
       "21          addr_state:WV_NH_WY_DC_ME_ID      0.522962\n",
       "22      verification_status:Not Verified      0.088138\n",
       "23   verification_status:Source Verified     -0.010712\n",
       "24                   purpose:credit_card      0.304463\n",
       "25            purpose:debt_consolidation      0.200945\n",
       "26            purpose:oth__med__vacation      0.213595\n",
       "27   purpose:major_purch__car__home_impr      0.266656\n",
       "28                 initial_list_status:w      0.053979\n",
       "29                               term:36      0.079228\n",
       "30                          emp_length:1      0.100429\n",
       "31                        emp_length:2-4      0.125483\n",
       "32                        emp_length:5-6      0.090668\n",
       "33                        emp_length:7-9      0.060579\n",
       "34                         emp_length:10      0.123245\n",
       "35                mths_since_issue_d:<38      1.071679\n",
       "36              mths_since_issue_d:38-39      0.871346\n",
       "37              mths_since_issue_d:40-41      0.771052\n",
       "38              mths_since_issue_d:42-48      0.570143\n",
       "39              mths_since_issue_d:49-52      0.408642\n",
       "40              mths_since_issue_d:53-64      0.163447\n",
       "41              mths_since_issue_d:65-84     -0.072388\n",
       "42                       int_rate:<9.548      0.881832\n",
       "43                 int_rate:9.548-12.025      0.554697\n",
       "44                 int_rate:12.025-15.74      0.303336\n",
       "45                 int_rate:15.74-20.281      0.111308\n",
       "46   mths_since_earliest_cr_line:141-164      0.055904\n",
       "47   mths_since_earliest_cr_line:165-247      0.037566\n",
       "48   mths_since_earliest_cr_line:248-270      0.078595\n",
       "49   mths_since_earliest_cr_line:271-352      0.119260\n",
       "50      mths_since_earliest_cr_line:>352      0.124871\n",
       "51                         delinq_2yrs:0      0.089925\n",
       "52                       delinq_2yrs:1-3      0.047426\n",
       "53                      inq_last_6mths:0      0.665755\n",
       "54                    inq_last_6mths:1-2      0.522036\n",
       "55                    inq_last_6mths:3-6      0.312688\n",
       "56                          open_acc:1-3      0.325502\n",
       "57                         open_acc:4-12      0.229141\n",
       "58                        open_acc:13-17      0.203090\n",
       "59                        open_acc:18-22      0.186714\n",
       "60                        open_acc:23-25      0.185148\n",
       "61                        open_acc:26-30      0.213860\n",
       "62                         open_acc:>=31      0.127397\n",
       "63                           pub_rec:3-4      0.123332\n",
       "64                           pub_rec:>=5      0.156992\n",
       "65                       total_acc:28-51     -0.020258\n",
       "66                        total_acc:>=52      0.026601\n",
       "67                    acc_now_delinq:>=1      0.201051\n",
       "68               total_rev_hi_lim:5K-10K      0.042528\n",
       "69              total_rev_hi_lim:10K-20K      0.011470\n",
       "70              total_rev_hi_lim:20K-30K      0.010834\n",
       "71              total_rev_hi_lim:30K-40K      0.025171\n",
       "72              total_rev_hi_lim:40K-55K      0.046725\n",
       "73              total_rev_hi_lim:55K-95K      0.072545\n",
       "74                 total_rev_hi_lim:>95K      0.223758\n",
       "75                    annual_inc:20K-30K     -0.070899\n",
       "76                    annual_inc:30K-40K     -0.002736\n",
       "77                    annual_inc:40K-50K      0.094929\n",
       "78                    annual_inc:50K-60K      0.163688\n",
       "79                    annual_inc:60K-70K      0.239580\n",
       "80                    annual_inc:70K-80K      0.313310\n",
       "81                    annual_inc:80K-90K      0.388832\n",
       "82                   annual_inc:90K-100K      0.407628\n",
       "83                  annual_inc:100K-120K      0.484818\n",
       "84                  annual_inc:120K-140K      0.572893\n",
       "85                      annual_inc:>140K      0.499355\n",
       "86                             dti:<=1.4      0.203817\n",
       "87                           dti:1.4-3.5      0.330129\n",
       "88                           dti:3.5-7.7      0.359241\n",
       "89                          dti:7.7-10.5      0.299666\n",
       "90                         dti:10.5-16.1      0.223706\n",
       "91                         dti:16.1-20.3      0.125867\n",
       "92                         dti:20.3-21.7      0.114808\n",
       "93                         dti:21.7-22.4      0.080828\n",
       "94                           dti:22.4-35      0.041090\n",
       "95        mths_since_last_delinq:Missing      0.073152\n",
       "96           mths_since_last_delinq:4-30      0.141453\n",
       "97          mths_since_last_delinq:31-56      0.152353\n",
       "98           mths_since_last_delinq:>=57      0.104495\n",
       "99        mths_since_last_record:Missing      0.353973\n",
       "100          mths_since_last_record:3-20      0.444021\n",
       "101         mths_since_last_record:21-31      0.377752\n",
       "102         mths_since_last_record:32-80      0.539712\n",
       "103         mths_since_last_record:81-86      0.205841\n",
       "104           mths_since_last_record:>86      0.270218"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Same as above, we summarize in a table the results\n",
    "\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": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is the list of p_values\n",
    "p_values = reg.p_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the p_value for the intercept to NaN\n",
    "p_values = np.append(np.nan, np.array(p_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "summary_table['p_values'] = p_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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",
       "      <td>0</td>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.703800</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.116237</td>\n",
       "      <td>1.276871e-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.878595</td>\n",
       "      <td>9.572292e-47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.684459</td>\n",
       "      <td>1.644180e-33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.496524</td>\n",
       "      <td>3.086977e-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>grade:E</td>\n",
       "      <td>0.321145</td>\n",
       "      <td>2.619122e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>grade:F</td>\n",
       "      <td>0.132964</td>\n",
       "      <td>8.103058e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>home_ownership:OWN</td>\n",
       "      <td>0.091247</td>\n",
       "      <td>6.146562e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>home_ownership:MORTGAGE</td>\n",
       "      <td>0.106753</td>\n",
       "      <td>4.297800e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>addr_state:NM_VA</td>\n",
       "      <td>0.037398</td>\n",
       "      <td>2.435558e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>addr_state:NY</td>\n",
       "      <td>0.059404</td>\n",
       "      <td>1.446841e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>addr_state:OK_TN_MO_LA_MD_NC</td>\n",
       "      <td>0.060198</td>\n",
       "      <td>9.500870e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>addr_state:CA</td>\n",
       "      <td>0.062749</td>\n",
       "      <td>3.141745e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>addr_state:UT_KY_AZ_NJ</td>\n",
       "      <td>0.078809</td>\n",
       "      <td>1.591138e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>addr_state:AR_MI_PA_OH_MN</td>\n",
       "      <td>0.136392</td>\n",
       "      <td>1.547476e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>addr_state:RI_MA_DE_SD_IN</td>\n",
       "      <td>0.100727</td>\n",
       "      <td>7.494978e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>addr_state:GA_WA_OR</td>\n",
       "      <td>0.185243</td>\n",
       "      <td>4.112072e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>addr_state:WI_MT</td>\n",
       "      <td>0.240795</td>\n",
       "      <td>4.213440e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>addr_state:TX</td>\n",
       "      <td>0.224559</td>\n",
       "      <td>6.208137e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>addr_state:IL_CT</td>\n",
       "      <td>0.263178</td>\n",
       "      <td>2.401193e-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>addr_state:KS_SC_CO_VT_AK_MS</td>\n",
       "      <td>0.321345</td>\n",
       "      <td>2.310642e-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>addr_state:WV_NH_WY_DC_ME_ID</td>\n",
       "      <td>0.522962</td>\n",
       "      <td>9.159706e-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>verification_status:Not Verified</td>\n",
       "      <td>0.088138</td>\n",
       "      <td>2.079029e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>verification_status:Source Verified</td>\n",
       "      <td>-0.010712</td>\n",
       "      <td>4.266354e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>purpose:credit_card</td>\n",
       "      <td>0.304463</td>\n",
       "      <td>9.943889e-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>purpose:debt_consolidation</td>\n",
       "      <td>0.200945</td>\n",
       "      <td>1.608000e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>purpose:oth__med__vacation</td>\n",
       "      <td>0.213595</td>\n",
       "      <td>8.102714e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>purpose:major_purch__car__home_impr</td>\n",
       "      <td>0.266656</td>\n",
       "      <td>1.908977e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>initial_list_status:w</td>\n",
       "      <td>0.053979</td>\n",
       "      <td>3.565662e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>term:36</td>\n",
       "      <td>0.079228</td>\n",
       "      <td>2.315451e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>emp_length:1</td>\n",
       "      <td>0.100429</td>\n",
       "      <td>1.098253e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>emp_length:2-4</td>\n",
       "      <td>0.125483</td>\n",
       "      <td>3.107827e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>emp_length:5-6</td>\n",
       "      <td>0.090668</td>\n",
       "      <td>2.304023e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>emp_length:7-9</td>\n",
       "      <td>0.060579</td>\n",
       "      <td>3.603726e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>emp_length:10</td>\n",
       "      <td>0.123245</td>\n",
       "      <td>2.384051e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>mths_since_issue_d:&lt;38</td>\n",
       "      <td>1.071679</td>\n",
       "      <td>6.768430e-170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>mths_since_issue_d:38-39</td>\n",
       "      <td>0.871346</td>\n",
       "      <td>7.515429e-140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>mths_since_issue_d:40-41</td>\n",
       "      <td>0.771052</td>\n",
       "      <td>3.338606e-115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>mths_since_issue_d:42-48</td>\n",
       "      <td>0.570143</td>\n",
       "      <td>2.826729e-80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>mths_since_issue_d:49-52</td>\n",
       "      <td>0.408642</td>\n",
       "      <td>2.540487e-37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>mths_since_issue_d:53-64</td>\n",
       "      <td>0.163447</td>\n",
       "      <td>5.923934e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>mths_since_issue_d:65-84</td>\n",
       "      <td>-0.072388</td>\n",
       "      <td>1.363425e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>int_rate:&lt;9.548</td>\n",
       "      <td>0.881832</td>\n",
       "      <td>7.445577e-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>int_rate:9.548-12.025</td>\n",
       "      <td>0.554697</td>\n",
       "      <td>5.222734e-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>int_rate:12.025-15.74</td>\n",
       "      <td>0.303336</td>\n",
       "      <td>2.266071e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>int_rate:15.74-20.281</td>\n",
       "      <td>0.111308</td>\n",
       "      <td>1.337810e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>mths_since_earliest_cr_line:141-164</td>\n",
       "      <td>0.055904</td>\n",
       "      <td>2.524852e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>mths_since_earliest_cr_line:165-247</td>\n",
       "      <td>0.037566</td>\n",
       "      <td>6.525491e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>mths_since_earliest_cr_line:248-270</td>\n",
       "      <td>0.078595</td>\n",
       "      <td>3.146096e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>mths_since_earliest_cr_line:271-352</td>\n",
       "      <td>0.119260</td>\n",
       "      <td>6.161506e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>mths_since_earliest_cr_line:&gt;352</td>\n",
       "      <td>0.124871</td>\n",
       "      <td>3.926199e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>delinq_2yrs:0</td>\n",
       "      <td>0.089925</td>\n",
       "      <td>1.134498e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>delinq_2yrs:1-3</td>\n",
       "      <td>0.047426</td>\n",
       "      <td>3.745414e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>inq_last_6mths:0</td>\n",
       "      <td>0.665755</td>\n",
       "      <td>5.944854e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>inq_last_6mths:1-2</td>\n",
       "      <td>0.522036</td>\n",
       "      <td>4.908732e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>inq_last_6mths:3-6</td>\n",
       "      <td>0.312688</td>\n",
       "      <td>6.434677e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>open_acc:1-3</td>\n",
       "      <td>0.325502</td>\n",
       "      <td>4.752339e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>open_acc:4-12</td>\n",
       "      <td>0.229141</td>\n",
       "      <td>1.533465e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>open_acc:13-17</td>\n",
       "      <td>0.203090</td>\n",
       "      <td>2.066232e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>open_acc:18-22</td>\n",
       "      <td>0.186714</td>\n",
       "      <td>2.485511e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>open_acc:23-25</td>\n",
       "      <td>0.185148</td>\n",
       "      <td>2.661122e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>open_acc:26-30</td>\n",
       "      <td>0.213860</td>\n",
       "      <td>2.131303e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>open_acc:&gt;=31</td>\n",
       "      <td>0.127397</td>\n",
       "      <td>4.928898e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>pub_rec:3-4</td>\n",
       "      <td>0.123332</td>\n",
       "      <td>1.933225e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>pub_rec:&gt;=5</td>\n",
       "      <td>0.156992</td>\n",
       "      <td>4.113936e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>total_acc:28-51</td>\n",
       "      <td>-0.020258</td>\n",
       "      <td>1.538882e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>total_acc:&gt;=52</td>\n",
       "      <td>0.026601</td>\n",
       "      <td>5.034181e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>acc_now_delinq:&gt;=1</td>\n",
       "      <td>0.201051</td>\n",
       "      <td>5.876168e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>total_rev_hi_lim:5K-10K</td>\n",
       "      <td>0.042528</td>\n",
       "      <td>8.420361e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>total_rev_hi_lim:10K-20K</td>\n",
       "      <td>0.011470</td>\n",
       "      <td>6.309052e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>total_rev_hi_lim:20K-30K</td>\n",
       "      <td>0.010834</td>\n",
       "      <td>6.796732e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>total_rev_hi_lim:30K-40K</td>\n",
       "      <td>0.025171</td>\n",
       "      <td>3.927829e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>total_rev_hi_lim:40K-55K</td>\n",
       "      <td>0.046725</td>\n",
       "      <td>1.463091e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>total_rev_hi_lim:55K-95K</td>\n",
       "      <td>0.072545</td>\n",
       "      <td>4.414662e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>total_rev_hi_lim:&gt;95K</td>\n",
       "      <td>0.223758</td>\n",
       "      <td>7.849305e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>annual_inc:20K-30K</td>\n",
       "      <td>-0.070899</td>\n",
       "      <td>9.925215e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>annual_inc:30K-40K</td>\n",
       "      <td>-0.002736</td>\n",
       "      <td>9.474445e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>annual_inc:40K-50K</td>\n",
       "      <td>0.094929</td>\n",
       "      <td>2.234929e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>annual_inc:50K-60K</td>\n",
       "      <td>0.163688</td>\n",
       "      <td>1.027044e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>annual_inc:60K-70K</td>\n",
       "      <td>0.239580</td>\n",
       "      <td>2.825619e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>annual_inc:70K-80K</td>\n",
       "      <td>0.313310</td>\n",
       "      <td>1.156093e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>annual_inc:80K-90K</td>\n",
       "      <td>0.388832</td>\n",
       "      <td>2.341447e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>annual_inc:90K-100K</td>\n",
       "      <td>0.407628</td>\n",
       "      <td>9.113329e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>annual_inc:100K-120K</td>\n",
       "      <td>0.484818</td>\n",
       "      <td>9.445080e-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>annual_inc:120K-140K</td>\n",
       "      <td>0.572893</td>\n",
       "      <td>7.804942e-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>annual_inc:&gt;140K</td>\n",
       "      <td>0.499355</td>\n",
       "      <td>7.877327e-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>dti:&lt;=1.4</td>\n",
       "      <td>0.203817</td>\n",
       "      <td>2.114412e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>dti:1.4-3.5</td>\n",
       "      <td>0.330129</td>\n",
       "      <td>1.030194e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>dti:3.5-7.7</td>\n",
       "      <td>0.359241</td>\n",
       "      <td>3.760445e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>dti:7.7-10.5</td>\n",
       "      <td>0.299666</td>\n",
       "      <td>3.649688e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>dti:10.5-16.1</td>\n",
       "      <td>0.223706</td>\n",
       "      <td>3.626690e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>dti:16.1-20.3</td>\n",
       "      <td>0.125867</td>\n",
       "      <td>4.470991e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>dti:20.3-21.7</td>\n",
       "      <td>0.114808</td>\n",
       "      <td>7.806200e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>dti:21.7-22.4</td>\n",
       "      <td>0.080828</td>\n",
       "      <td>2.411998e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>dti:22.4-35</td>\n",
       "      <td>0.041090</td>\n",
       "      <td>5.071627e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>mths_since_last_delinq:Missing</td>\n",
       "      <td>0.073152</td>\n",
       "      <td>2.033823e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>mths_since_last_delinq:4-30</td>\n",
       "      <td>0.141453</td>\n",
       "      <td>7.772523e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>mths_since_last_delinq:31-56</td>\n",
       "      <td>0.152353</td>\n",
       "      <td>9.312964e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>mths_since_last_delinq:&gt;=57</td>\n",
       "      <td>0.104495</td>\n",
       "      <td>8.023597e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>mths_since_last_record:Missing</td>\n",
       "      <td>0.353973</td>\n",
       "      <td>3.775867e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>mths_since_last_record:3-20</td>\n",
       "      <td>0.444021</td>\n",
       "      <td>4.624012e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>mths_since_last_record:21-31</td>\n",
       "      <td>0.377752</td>\n",
       "      <td>1.187424e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>mths_since_last_record:32-80</td>\n",
       "      <td>0.539712</td>\n",
       "      <td>2.048370e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>mths_since_last_record:81-86</td>\n",
       "      <td>0.205841</td>\n",
       "      <td>5.228975e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>mths_since_last_record:&gt;86</td>\n",
       "      <td>0.270218</td>\n",
       "      <td>2.292111e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Feature name  Coefficients       p_values\n",
       "0                              Intercept     -1.703800            NaN\n",
       "1                                grade:A      1.116237   1.276871e-34\n",
       "2                                grade:B      0.878595   9.572292e-47\n",
       "3                                grade:C      0.684459   1.644180e-33\n",
       "4                                grade:D      0.496524   3.086977e-20\n",
       "5                                grade:E      0.321145   2.619122e-11\n",
       "6                                grade:F      0.132964   8.103058e-03\n",
       "7                     home_ownership:OWN      0.091247   6.146562e-06\n",
       "8                home_ownership:MORTGAGE      0.106753   4.297800e-17\n",
       "9                       addr_state:NM_VA      0.037398   2.435558e-01\n",
       "10                         addr_state:NY      0.059404   1.446841e-02\n",
       "11          addr_state:OK_TN_MO_LA_MD_NC      0.060198   9.500870e-03\n",
       "12                         addr_state:CA      0.062749   3.141745e-03\n",
       "13                addr_state:UT_KY_AZ_NJ      0.078809   1.591138e-03\n",
       "14             addr_state:AR_MI_PA_OH_MN      0.136392   1.547476e-09\n",
       "15             addr_state:RI_MA_DE_SD_IN      0.100727   7.494978e-04\n",
       "16                   addr_state:GA_WA_OR      0.185243   4.112072e-12\n",
       "17                      addr_state:WI_MT      0.240795   4.213440e-07\n",
       "18                         addr_state:TX      0.224559   6.208137e-18\n",
       "19                      addr_state:IL_CT      0.263178   2.401193e-19\n",
       "20          addr_state:KS_SC_CO_VT_AK_MS      0.321345   2.310642e-25\n",
       "21          addr_state:WV_NH_WY_DC_ME_ID      0.522962   9.159706e-23\n",
       "22      verification_status:Not Verified      0.088138   2.079029e-09\n",
       "23   verification_status:Source Verified     -0.010712   4.266354e-01\n",
       "24                   purpose:credit_card      0.304463   9.943889e-27\n",
       "25            purpose:debt_consolidation      0.200945   1.608000e-14\n",
       "26            purpose:oth__med__vacation      0.213595   8.102714e-12\n",
       "27   purpose:major_purch__car__home_impr      0.266656   1.908977e-17\n",
       "28                 initial_list_status:w      0.053979   3.565662e-05\n",
       "29                               term:36      0.079228   2.315451e-08\n",
       "30                          emp_length:1      0.100429   1.098253e-04\n",
       "31                        emp_length:2-4      0.125483   3.107827e-11\n",
       "32                        emp_length:5-6      0.090668   2.304023e-05\n",
       "33                        emp_length:7-9      0.060579   3.603726e-03\n",
       "34                         emp_length:10      0.123245   2.384051e-11\n",
       "35                mths_since_issue_d:<38      1.071679  6.768430e-170\n",
       "36              mths_since_issue_d:38-39      0.871346  7.515429e-140\n",
       "37              mths_since_issue_d:40-41      0.771052  3.338606e-115\n",
       "38              mths_since_issue_d:42-48      0.570143   2.826729e-80\n",
       "39              mths_since_issue_d:49-52      0.408642   2.540487e-37\n",
       "40              mths_since_issue_d:53-64      0.163447   5.923934e-08\n",
       "41              mths_since_issue_d:65-84     -0.072388   1.363425e-02\n",
       "42                       int_rate:<9.548      0.881832   7.445577e-31\n",
       "43                 int_rate:9.548-12.025      0.554697   5.222734e-35\n",
       "44                 int_rate:12.025-15.74      0.303336   2.266071e-16\n",
       "45                 int_rate:15.74-20.281      0.111308   1.337810e-04\n",
       "46   mths_since_earliest_cr_line:141-164      0.055904   2.524852e-02\n",
       "47   mths_since_earliest_cr_line:165-247      0.037566   6.525491e-02\n",
       "48   mths_since_earliest_cr_line:248-270      0.078595   3.146096e-03\n",
       "49   mths_since_earliest_cr_line:271-352      0.119260   6.161506e-07\n",
       "50      mths_since_earliest_cr_line:>352      0.124871   3.926199e-06\n",
       "51                         delinq_2yrs:0      0.089925   1.134498e-01\n",
       "52                       delinq_2yrs:1-3      0.047426   3.745414e-01\n",
       "53                      inq_last_6mths:0      0.665755   5.944854e-09\n",
       "54                    inq_last_6mths:1-2      0.522036   4.908732e-06\n",
       "55                    inq_last_6mths:3-6      0.312688   6.434677e-03\n",
       "56                          open_acc:1-3      0.325502   4.752339e-02\n",
       "57                         open_acc:4-12      0.229141   1.533465e-01\n",
       "58                        open_acc:13-17      0.203090   2.066232e-01\n",
       "59                        open_acc:18-22      0.186714   2.485511e-01\n",
       "60                        open_acc:23-25      0.185148   2.661122e-01\n",
       "61                        open_acc:26-30      0.213860   2.131303e-01\n",
       "62                         open_acc:>=31      0.127397   4.928898e-01\n",
       "63                           pub_rec:3-4      0.123332   1.933225e-01\n",
       "64                           pub_rec:>=5      0.156992   4.113936e-01\n",
       "65                       total_acc:28-51     -0.020258   1.538882e-01\n",
       "66                        total_acc:>=52      0.026601   5.034181e-01\n",
       "67                    acc_now_delinq:>=1      0.201051   5.876168e-02\n",
       "68               total_rev_hi_lim:5K-10K      0.042528   8.420361e-02\n",
       "69              total_rev_hi_lim:10K-20K      0.011470   6.309052e-01\n",
       "70              total_rev_hi_lim:20K-30K      0.010834   6.796732e-01\n",
       "71              total_rev_hi_lim:30K-40K      0.025171   3.927829e-01\n",
       "72              total_rev_hi_lim:40K-55K      0.046725   1.463091e-01\n",
       "73              total_rev_hi_lim:55K-95K      0.072545   4.414662e-02\n",
       "74                 total_rev_hi_lim:>95K      0.223758   7.849305e-05\n",
       "75                    annual_inc:20K-30K     -0.070899   9.925215e-02\n",
       "76                    annual_inc:30K-40K     -0.002736   9.474445e-01\n",
       "77                    annual_inc:40K-50K      0.094929   2.234929e-02\n",
       "78                    annual_inc:50K-60K      0.163688   1.027044e-04\n",
       "79                    annual_inc:60K-70K      0.239580   2.825619e-08\n",
       "80                    annual_inc:70K-80K      0.313310   1.156093e-12\n",
       "81                    annual_inc:80K-90K      0.388832   2.341447e-17\n",
       "82                   annual_inc:90K-100K      0.407628   9.113329e-18\n",
       "83                  annual_inc:100K-120K      0.484818   9.445080e-25\n",
       "84                  annual_inc:120K-140K      0.572893   7.804942e-27\n",
       "85                      annual_inc:>140K      0.499355   7.877327e-24\n",
       "86                             dti:<=1.4      0.203817   2.114412e-02\n",
       "87                           dti:1.4-3.5      0.330129   1.030194e-05\n",
       "88                           dti:3.5-7.7      0.359241   3.760445e-08\n",
       "89                          dti:7.7-10.5      0.299666   3.649688e-06\n",
       "90                         dti:10.5-16.1      0.223706   3.626690e-04\n",
       "91                         dti:16.1-20.3      0.125867   4.470991e-02\n",
       "92                         dti:20.3-21.7      0.114808   7.806200e-02\n",
       "93                         dti:21.7-22.4      0.080828   2.411998e-01\n",
       "94                           dti:22.4-35      0.041090   5.071627e-01\n",
       "95        mths_since_last_delinq:Missing      0.073152   2.033823e-01\n",
       "96           mths_since_last_delinq:4-30      0.141453   7.772523e-03\n",
       "97          mths_since_last_delinq:31-56      0.152353   9.312964e-03\n",
       "98           mths_since_last_delinq:>=57      0.104495   8.023597e-02\n",
       "99        mths_since_last_record:Missing      0.353973   3.775867e-05\n",
       "100          mths_since_last_record:3-20      0.444021   4.624012e-04\n",
       "101         mths_since_last_record:21-31      0.377752   1.187424e-03\n",
       "102         mths_since_last_record:32-80      0.539712   2.048370e-09\n",
       "103         mths_since_last_record:81-86      0.205841   5.228975e-02\n",
       "104           mths_since_last_record:>86      0.270218   2.292111e-03"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "##### We are going to remove some features, the coefficients for all or almost all of the dummy variables for which are not statistically significant.\n",
    "\n",
    "\n",
    "Looking at each p-value individually, it would be to drastic to simply remove unsignificants dummy variables.\n",
    "\n",
    "So we will keep  all the  dummy variables representing one feature  for which one of few dummy variables are statistically significant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at their corresponding <b>p values</b> summary table above, we will remove  all\n",
    "<center> <b> delinq_2yrs , open_acc , pub_rec, total_acc and total_rev_hi_lim </b> </center>\n",
    "from the dummy variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We do that by specifying another list of dummy variables as reference categories, \n",
    "# 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",
    "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",
    "# The new reference categories without the remove features is \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": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<center>Need to retrain the model without those features </center>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "        grade:A  grade:B  grade:C  grade:D  grade:E  grade:F  \\\n",
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       "\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": 30,
     "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": 31,
   "metadata": {},
   "outputs": [],
   "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": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_name = inputs_train.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.385395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.121520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.881507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.687851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.500477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>grade:E</td>\n",
       "      <td>0.325091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>grade:F</td>\n",
       "      <td>0.137039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>home_ownership:OWN</td>\n",
       "      <td>0.093505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>home_ownership:MORTGAGE</td>\n",
       "      <td>0.105977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>addr_state:NM_VA</td>\n",
       "      <td>0.037053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>addr_state:NY</td>\n",
       "      <td>0.057066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>addr_state:OK_TN_MO_LA_MD_NC</td>\n",
       "      <td>0.059627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>addr_state:CA</td>\n",
       "      <td>0.063601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>addr_state:UT_KY_AZ_NJ</td>\n",
       "      <td>0.078480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>addr_state:AR_MI_PA_OH_MN</td>\n",
       "      <td>0.135479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>addr_state:RI_MA_DE_SD_IN</td>\n",
       "      <td>0.100868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>addr_state:GA_WA_OR</td>\n",
       "      <td>0.185784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>addr_state:WI_MT</td>\n",
       "      <td>0.240481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>addr_state:TX</td>\n",
       "      <td>0.222372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>addr_state:IL_CT</td>\n",
       "      <td>0.261843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>addr_state:KS_SC_CO_VT_AK_MS</td>\n",
       "      <td>0.320744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>addr_state:WV_NH_WY_DC_ME_ID</td>\n",
       "      <td>0.522261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>verification_status:Not Verified</td>\n",
       "      <td>0.085864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>verification_status:Source Verified</td>\n",
       "      <td>-0.010907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>purpose:credit_card</td>\n",
       "      <td>0.301411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>purpose:debt_consolidation</td>\n",
       "      <td>0.197790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>purpose:oth__med__vacation</td>\n",
       "      <td>0.213355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>purpose:major_purch__car__home_impr</td>\n",
       "      <td>0.265439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>initial_list_status:w</td>\n",
       "      <td>0.054413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>term:36</td>\n",
       "      <td>0.078530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>emp_length:1</td>\n",
       "      <td>0.099320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>emp_length:2-4</td>\n",
       "      <td>0.125188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>emp_length:5-6</td>\n",
       "      <td>0.090508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>emp_length:7-9</td>\n",
       "      <td>0.060713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>emp_length:10</td>\n",
       "      <td>0.124822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>mths_since_issue_d:&lt;38</td>\n",
       "      <td>1.076656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>mths_since_issue_d:38-39</td>\n",
       "      <td>0.874363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>mths_since_issue_d:40-41</td>\n",
       "      <td>0.773143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>mths_since_issue_d:42-48</td>\n",
       "      <td>0.572739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>mths_since_issue_d:49-52</td>\n",
       "      <td>0.411661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>mths_since_issue_d:53-64</td>\n",
       "      <td>0.166235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>mths_since_issue_d:65-84</td>\n",
       "      <td>-0.074976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>int_rate:&lt;9.548</td>\n",
       "      <td>0.889336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>int_rate:9.548-12.025</td>\n",
       "      <td>0.559782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>int_rate:12.025-15.74</td>\n",
       "      <td>0.306816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>int_rate:15.74-20.281</td>\n",
       "      <td>0.113399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>mths_since_earliest_cr_line:141-164</td>\n",
       "      <td>0.054006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>mths_since_earliest_cr_line:165-247</td>\n",
       "      <td>0.034950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>mths_since_earliest_cr_line:248-270</td>\n",
       "      <td>0.076711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>mths_since_earliest_cr_line:271-352</td>\n",
       "      <td>0.119321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>mths_since_earliest_cr_line:&gt;352</td>\n",
       "      <td>0.127038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>inq_last_6mths:0</td>\n",
       "      <td>0.667286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>inq_last_6mths:1-2</td>\n",
       "      <td>0.520869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>inq_last_6mths:3-6</td>\n",
       "      <td>0.310262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>acc_now_delinq:&gt;=1</td>\n",
       "      <td>0.193324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>annual_inc:20K-30K</td>\n",
       "      <td>-0.076546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>annual_inc:30K-40K</td>\n",
       "      <td>-0.013045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>annual_inc:40K-50K</td>\n",
       "      <td>0.081668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>annual_inc:50K-60K</td>\n",
       "      <td>0.148741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>annual_inc:60K-70K</td>\n",
       "      <td>0.224316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>annual_inc:70K-80K</td>\n",
       "      <td>0.298252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>annual_inc:80K-90K</td>\n",
       "      <td>0.374168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>annual_inc:90K-100K</td>\n",
       "      <td>0.393907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>annual_inc:100K-120K</td>\n",
       "      <td>0.472350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>annual_inc:120K-140K</td>\n",
       "      <td>0.563210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>annual_inc:&gt;140K</td>\n",
       "      <td>0.499159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>dti:&lt;=1.4</td>\n",
       "      <td>0.245709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>dti:1.4-3.5</td>\n",
       "      <td>0.361029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>dti:3.5-7.7</td>\n",
       "      <td>0.379480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>dti:7.7-10.5</td>\n",
       "      <td>0.314826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>dti:10.5-16.1</td>\n",
       "      <td>0.235991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>dti:16.1-20.3</td>\n",
       "      <td>0.134808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>dti:20.3-21.7</td>\n",
       "      <td>0.122554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>dti:21.7-22.4</td>\n",
       "      <td>0.087639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>dti:22.4-35</td>\n",
       "      <td>0.045907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>mths_since_last_delinq:Missing</td>\n",
       "      <td>0.116762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>mths_since_last_delinq:4-30</td>\n",
       "      <td>0.143818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>mths_since_last_delinq:31-56</td>\n",
       "      <td>0.189952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>mths_since_last_delinq:&gt;=57</td>\n",
       "      <td>0.142870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>mths_since_last_record:Missing</td>\n",
       "      <td>0.324814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>mths_since_last_record:3-20</td>\n",
       "      <td>0.435920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>mths_since_last_record:21-31</td>\n",
       "      <td>0.357063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>mths_since_last_record:32-80</td>\n",
       "      <td>0.509914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>mths_since_last_record:81-86</td>\n",
       "      <td>0.171742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>mths_since_last_record:&gt;86</td>\n",
       "      <td>0.237603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Feature name  Coefficients\n",
       "0                             Intercept     -1.385395\n",
       "1                               grade:A      1.121520\n",
       "2                               grade:B      0.881507\n",
       "3                               grade:C      0.687851\n",
       "4                               grade:D      0.500477\n",
       "5                               grade:E      0.325091\n",
       "6                               grade:F      0.137039\n",
       "7                    home_ownership:OWN      0.093505\n",
       "8               home_ownership:MORTGAGE      0.105977\n",
       "9                      addr_state:NM_VA      0.037053\n",
       "10                        addr_state:NY      0.057066\n",
       "11         addr_state:OK_TN_MO_LA_MD_NC      0.059627\n",
       "12                        addr_state:CA      0.063601\n",
       "13               addr_state:UT_KY_AZ_NJ      0.078480\n",
       "14            addr_state:AR_MI_PA_OH_MN      0.135479\n",
       "15            addr_state:RI_MA_DE_SD_IN      0.100868\n",
       "16                  addr_state:GA_WA_OR      0.185784\n",
       "17                     addr_state:WI_MT      0.240481\n",
       "18                        addr_state:TX      0.222372\n",
       "19                     addr_state:IL_CT      0.261843\n",
       "20         addr_state:KS_SC_CO_VT_AK_MS      0.320744\n",
       "21         addr_state:WV_NH_WY_DC_ME_ID      0.522261\n",
       "22     verification_status:Not Verified      0.085864\n",
       "23  verification_status:Source Verified     -0.010907\n",
       "24                  purpose:credit_card      0.301411\n",
       "25           purpose:debt_consolidation      0.197790\n",
       "26           purpose:oth__med__vacation      0.213355\n",
       "27  purpose:major_purch__car__home_impr      0.265439\n",
       "28                initial_list_status:w      0.054413\n",
       "29                              term:36      0.078530\n",
       "30                         emp_length:1      0.099320\n",
       "31                       emp_length:2-4      0.125188\n",
       "32                       emp_length:5-6      0.090508\n",
       "33                       emp_length:7-9      0.060713\n",
       "34                        emp_length:10      0.124822\n",
       "35               mths_since_issue_d:<38      1.076656\n",
       "36             mths_since_issue_d:38-39      0.874363\n",
       "37             mths_since_issue_d:40-41      0.773143\n",
       "38             mths_since_issue_d:42-48      0.572739\n",
       "39             mths_since_issue_d:49-52      0.411661\n",
       "40             mths_since_issue_d:53-64      0.166235\n",
       "41             mths_since_issue_d:65-84     -0.074976\n",
       "42                      int_rate:<9.548      0.889336\n",
       "43                int_rate:9.548-12.025      0.559782\n",
       "44                int_rate:12.025-15.74      0.306816\n",
       "45                int_rate:15.74-20.281      0.113399\n",
       "46  mths_since_earliest_cr_line:141-164      0.054006\n",
       "47  mths_since_earliest_cr_line:165-247      0.034950\n",
       "48  mths_since_earliest_cr_line:248-270      0.076711\n",
       "49  mths_since_earliest_cr_line:271-352      0.119321\n",
       "50     mths_since_earliest_cr_line:>352      0.127038\n",
       "51                     inq_last_6mths:0      0.667286\n",
       "52                   inq_last_6mths:1-2      0.520869\n",
       "53                   inq_last_6mths:3-6      0.310262\n",
       "54                   acc_now_delinq:>=1      0.193324\n",
       "55                   annual_inc:20K-30K     -0.076546\n",
       "56                   annual_inc:30K-40K     -0.013045\n",
       "57                   annual_inc:40K-50K      0.081668\n",
       "58                   annual_inc:50K-60K      0.148741\n",
       "59                   annual_inc:60K-70K      0.224316\n",
       "60                   annual_inc:70K-80K      0.298252\n",
       "61                   annual_inc:80K-90K      0.374168\n",
       "62                  annual_inc:90K-100K      0.393907\n",
       "63                 annual_inc:100K-120K      0.472350\n",
       "64                 annual_inc:120K-140K      0.563210\n",
       "65                     annual_inc:>140K      0.499159\n",
       "66                            dti:<=1.4      0.245709\n",
       "67                          dti:1.4-3.5      0.361029\n",
       "68                          dti:3.5-7.7      0.379480\n",
       "69                         dti:7.7-10.5      0.314826\n",
       "70                        dti:10.5-16.1      0.235991\n",
       "71                        dti:16.1-20.3      0.134808\n",
       "72                        dti:20.3-21.7      0.122554\n",
       "73                        dti:21.7-22.4      0.087639\n",
       "74                          dti:22.4-35      0.045907\n",
       "75       mths_since_last_delinq:Missing      0.116762\n",
       "76          mths_since_last_delinq:4-30      0.143818\n",
       "77         mths_since_last_delinq:31-56      0.189952\n",
       "78          mths_since_last_delinq:>=57      0.142870\n",
       "79       mths_since_last_record:Missing      0.324814\n",
       "80          mths_since_last_record:3-20      0.435920\n",
       "81         mths_since_last_record:21-31      0.357063\n",
       "82         mths_since_last_record:32-80      0.509914\n",
       "83         mths_since_last_record:81-86      0.171742\n",
       "84           mths_since_last_record:>86      0.237603"
      ]
     },
     "execution_count": 33,
     "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": 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",
       "      <th>p_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.385395</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.121520</td>\n",
       "      <td>4.753387e-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.881507</td>\n",
       "      <td>2.158571e-47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.687851</td>\n",
       "      <td>3.271838e-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.500477</td>\n",
       "      <td>6.929364e-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>grade:E</td>\n",
       "      <td>0.325091</td>\n",
       "      <td>5.871894e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>grade:F</td>\n",
       "      <td>0.137039</td>\n",
       "      <td>5.231950e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>home_ownership:OWN</td>\n",
       "      <td>0.093505</td>\n",
       "      <td>3.462640e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>home_ownership:MORTGAGE</td>\n",
       "      <td>0.105977</td>\n",
       "      <td>5.303227e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>addr_state:NM_VA</td>\n",
       "      <td>0.037053</td>\n",
       "      <td>2.472817e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>addr_state:NY</td>\n",
       "      <td>0.057066</td>\n",
       "      <td>1.845927e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>addr_state:OK_TN_MO_LA_MD_NC</td>\n",
       "      <td>0.059627</td>\n",
       "      <td>1.000094e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>addr_state:CA</td>\n",
       "      <td>0.063601</td>\n",
       "      <td>2.693792e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>addr_state:UT_KY_AZ_NJ</td>\n",
       "      <td>0.078480</td>\n",
       "      <td>1.630257e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>addr_state:AR_MI_PA_OH_MN</td>\n",
       "      <td>0.135479</td>\n",
       "      <td>1.793406e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>addr_state:RI_MA_DE_SD_IN</td>\n",
       "      <td>0.100868</td>\n",
       "      <td>7.237427e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>addr_state:GA_WA_OR</td>\n",
       "      <td>0.185784</td>\n",
       "      <td>3.242749e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>addr_state:WI_MT</td>\n",
       "      <td>0.240481</td>\n",
       "      <td>4.262594e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>addr_state:TX</td>\n",
       "      <td>0.222372</td>\n",
       "      <td>1.127032e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>addr_state:IL_CT</td>\n",
       "      <td>0.261843</td>\n",
       "      <td>3.088720e-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>addr_state:KS_SC_CO_VT_AK_MS</td>\n",
       "      <td>0.320744</td>\n",
       "      <td>2.434110e-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>addr_state:WV_NH_WY_DC_ME_ID</td>\n",
       "      <td>0.522261</td>\n",
       "      <td>9.635088e-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>verification_status:Not Verified</td>\n",
       "      <td>0.085864</td>\n",
       "      <td>4.116527e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>verification_status:Source Verified</td>\n",
       "      <td>-0.010907</td>\n",
       "      <td>4.167432e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>purpose:credit_card</td>\n",
       "      <td>0.301411</td>\n",
       "      <td>1.495937e-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>purpose:debt_consolidation</td>\n",
       "      <td>0.197790</td>\n",
       "      <td>2.746032e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>purpose:oth__med__vacation</td>\n",
       "      <td>0.213355</td>\n",
       "      <td>6.900118e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>purpose:major_purch__car__home_impr</td>\n",
       "      <td>0.265439</td>\n",
       "      <td>1.846669e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>initial_list_status:w</td>\n",
       "      <td>0.054413</td>\n",
       "      <td>3.070754e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>term:36</td>\n",
       "      <td>0.078530</td>\n",
       "      <td>1.936743e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>emp_length:1</td>\n",
       "      <td>0.099320</td>\n",
       "      <td>1.283870e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>emp_length:2-4</td>\n",
       "      <td>0.125188</td>\n",
       "      <td>3.292712e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>emp_length:5-6</td>\n",
       "      <td>0.090508</td>\n",
       "      <td>2.349932e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>emp_length:7-9</td>\n",
       "      <td>0.060713</td>\n",
       "      <td>3.512433e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>emp_length:10</td>\n",
       "      <td>0.124822</td>\n",
       "      <td>1.281702e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>mths_since_issue_d:&lt;38</td>\n",
       "      <td>1.076656</td>\n",
       "      <td>1.298182e-178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>mths_since_issue_d:38-39</td>\n",
       "      <td>0.874363</td>\n",
       "      <td>5.872595e-148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>mths_since_issue_d:40-41</td>\n",
       "      <td>0.773143</td>\n",
       "      <td>2.876952e-122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>mths_since_issue_d:42-48</td>\n",
       "      <td>0.572739</td>\n",
       "      <td>2.871610e-86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>mths_since_issue_d:49-52</td>\n",
       "      <td>0.411661</td>\n",
       "      <td>5.203951e-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>mths_since_issue_d:53-64</td>\n",
       "      <td>0.166235</td>\n",
       "      <td>1.289234e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>mths_since_issue_d:65-84</td>\n",
       "      <td>-0.074976</td>\n",
       "      <td>1.038774e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>int_rate:&lt;9.548</td>\n",
       "      <td>0.889336</td>\n",
       "      <td>1.600893e-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>int_rate:9.548-12.025</td>\n",
       "      <td>0.559782</td>\n",
       "      <td>4.322923e-36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>int_rate:12.025-15.74</td>\n",
       "      <td>0.306816</td>\n",
       "      <td>6.205030e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>int_rate:15.74-20.281</td>\n",
       "      <td>0.113399</td>\n",
       "      <td>9.067425e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>mths_since_earliest_cr_line:141-164</td>\n",
       "      <td>0.054006</td>\n",
       "      <td>2.996284e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>mths_since_earliest_cr_line:165-247</td>\n",
       "      <td>0.034950</td>\n",
       "      <td>8.185699e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>mths_since_earliest_cr_line:248-270</td>\n",
       "      <td>0.076711</td>\n",
       "      <td>3.462911e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>mths_since_earliest_cr_line:271-352</td>\n",
       "      <td>0.119321</td>\n",
       "      <td>3.525173e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>mths_since_earliest_cr_line:&gt;352</td>\n",
       "      <td>0.127038</td>\n",
       "      <td>1.665873e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>inq_last_6mths:0</td>\n",
       "      <td>0.667286</td>\n",
       "      <td>3.180519e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>inq_last_6mths:1-2</td>\n",
       "      <td>0.520869</td>\n",
       "      <td>2.981658e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>inq_last_6mths:3-6</td>\n",
       "      <td>0.310262</td>\n",
       "      <td>4.454631e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>acc_now_delinq:&gt;=1</td>\n",
       "      <td>0.193324</td>\n",
       "      <td>6.837618e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>annual_inc:20K-30K</td>\n",
       "      <td>-0.076546</td>\n",
       "      <td>6.885605e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>annual_inc:30K-40K</td>\n",
       "      <td>-0.013045</td>\n",
       "      <td>7.469306e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>annual_inc:40K-50K</td>\n",
       "      <td>0.081668</td>\n",
       "      <td>4.307606e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>annual_inc:50K-60K</td>\n",
       "      <td>0.148741</td>\n",
       "      <td>2.754270e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>annual_inc:60K-70K</td>\n",
       "      <td>0.224316</td>\n",
       "      <td>8.151162e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>annual_inc:70K-80K</td>\n",
       "      <td>0.298252</td>\n",
       "      <td>2.716401e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>annual_inc:80K-90K</td>\n",
       "      <td>0.374168</td>\n",
       "      <td>3.701330e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>annual_inc:90K-100K</td>\n",
       "      <td>0.393907</td>\n",
       "      <td>1.108341e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>annual_inc:100K-120K</td>\n",
       "      <td>0.472350</td>\n",
       "      <td>3.990653e-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>annual_inc:120K-140K</td>\n",
       "      <td>0.563210</td>\n",
       "      <td>1.886763e-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>annual_inc:&gt;140K</td>\n",
       "      <td>0.499159</td>\n",
       "      <td>8.322286e-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>dti:&lt;=1.4</td>\n",
       "      <td>0.245709</td>\n",
       "      <td>3.209346e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>dti:1.4-3.5</td>\n",
       "      <td>0.361029</td>\n",
       "      <td>2.475348e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>dti:3.5-7.7</td>\n",
       "      <td>0.379480</td>\n",
       "      <td>3.095375e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>dti:7.7-10.5</td>\n",
       "      <td>0.314826</td>\n",
       "      <td>1.489433e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>dti:10.5-16.1</td>\n",
       "      <td>0.235991</td>\n",
       "      <td>4.597990e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>dti:16.1-20.3</td>\n",
       "      <td>0.134808</td>\n",
       "      <td>2.009874e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>dti:20.3-21.7</td>\n",
       "      <td>0.122554</td>\n",
       "      <td>4.348893e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>dti:21.7-22.4</td>\n",
       "      <td>0.087639</td>\n",
       "      <td>1.760704e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>dti:22.4-35</td>\n",
       "      <td>0.045907</td>\n",
       "      <td>4.231849e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>mths_since_last_delinq:Missing</td>\n",
       "      <td>0.116762</td>\n",
       "      <td>2.515592e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>mths_since_last_delinq:4-30</td>\n",
       "      <td>0.143818</td>\n",
       "      <td>6.337521e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>mths_since_last_delinq:31-56</td>\n",
       "      <td>0.189952</td>\n",
       "      <td>3.712932e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>mths_since_last_delinq:&gt;=57</td>\n",
       "      <td>0.142870</td>\n",
       "      <td>8.926129e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>mths_since_last_record:Missing</td>\n",
       "      <td>0.324814</td>\n",
       "      <td>6.397001e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>mths_since_last_record:3-20</td>\n",
       "      <td>0.435920</td>\n",
       "      <td>3.756425e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>mths_since_last_record:21-31</td>\n",
       "      <td>0.357063</td>\n",
       "      <td>1.509320e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>mths_since_last_record:32-80</td>\n",
       "      <td>0.509914</td>\n",
       "      <td>2.355095e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>mths_since_last_record:81-86</td>\n",
       "      <td>0.171742</td>\n",
       "      <td>9.307974e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>mths_since_last_record:&gt;86</td>\n",
       "      <td>0.237603</td>\n",
       "      <td>4.738771e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Feature name  Coefficients       p_values\n",
       "0                             Intercept     -1.385395            NaN\n",
       "1                               grade:A      1.121520   4.753387e-35\n",
       "2                               grade:B      0.881507   2.158571e-47\n",
       "3                               grade:C      0.687851   3.271838e-34\n",
       "4                               grade:D      0.500477   6.929364e-21\n",
       "5                               grade:E      0.325091   5.871894e-12\n",
       "6                               grade:F      0.137039   5.231950e-03\n",
       "7                    home_ownership:OWN      0.093505   3.462640e-06\n",
       "8               home_ownership:MORTGAGE      0.105977   5.303227e-17\n",
       "9                      addr_state:NM_VA      0.037053   2.472817e-01\n",
       "10                        addr_state:NY      0.057066   1.845927e-02\n",
       "11         addr_state:OK_TN_MO_LA_MD_NC      0.059627   1.000094e-02\n",
       "12                        addr_state:CA      0.063601   2.693792e-03\n",
       "13               addr_state:UT_KY_AZ_NJ      0.078480   1.630257e-03\n",
       "14            addr_state:AR_MI_PA_OH_MN      0.135479   1.793406e-09\n",
       "15            addr_state:RI_MA_DE_SD_IN      0.100868   7.237427e-04\n",
       "16                  addr_state:GA_WA_OR      0.185784   3.242749e-12\n",
       "17                     addr_state:WI_MT      0.240481   4.262594e-07\n",
       "18                        addr_state:TX      0.222372   1.127032e-17\n",
       "19                     addr_state:IL_CT      0.261843   3.088720e-19\n",
       "20         addr_state:KS_SC_CO_VT_AK_MS      0.320744   2.434110e-25\n",
       "21         addr_state:WV_NH_WY_DC_ME_ID      0.522261   9.635088e-23\n",
       "22     verification_status:Not Verified      0.085864   4.116527e-09\n",
       "23  verification_status:Source Verified     -0.010907   4.167432e-01\n",
       "24                  purpose:credit_card      0.301411   1.495937e-26\n",
       "25           purpose:debt_consolidation      0.197790   2.746032e-14\n",
       "26           purpose:oth__med__vacation      0.213355   6.900118e-12\n",
       "27  purpose:major_purch__car__home_impr      0.265439   1.846669e-17\n",
       "28                initial_list_status:w      0.054413   3.070754e-05\n",
       "29                              term:36      0.078530   1.936743e-08\n",
       "30                         emp_length:1      0.099320   1.283870e-04\n",
       "31                       emp_length:2-4      0.125188   3.292712e-11\n",
       "32                       emp_length:5-6      0.090508   2.349932e-05\n",
       "33                       emp_length:7-9      0.060713   3.512433e-03\n",
       "34                        emp_length:10      0.124822   1.281702e-11\n",
       "35               mths_since_issue_d:<38      1.076656  1.298182e-178\n",
       "36             mths_since_issue_d:38-39      0.874363  5.872595e-148\n",
       "37             mths_since_issue_d:40-41      0.773143  2.876952e-122\n",
       "38             mths_since_issue_d:42-48      0.572739   2.871610e-86\n",
       "39             mths_since_issue_d:49-52      0.411661   5.203951e-40\n",
       "40             mths_since_issue_d:53-64      0.166235   1.289234e-08\n",
       "41             mths_since_issue_d:65-84     -0.074976   1.038774e-02\n",
       "42                      int_rate:<9.548      0.889336   1.600893e-31\n",
       "43                int_rate:9.548-12.025      0.559782   4.322923e-36\n",
       "44                int_rate:12.025-15.74      0.306816   6.205030e-17\n",
       "45                int_rate:15.74-20.281      0.113399   9.067425e-05\n",
       "46  mths_since_earliest_cr_line:141-164      0.054006   2.996284e-02\n",
       "47  mths_since_earliest_cr_line:165-247      0.034950   8.185699e-02\n",
       "48  mths_since_earliest_cr_line:248-270      0.076711   3.462911e-03\n",
       "49  mths_since_earliest_cr_line:271-352      0.119321   3.525173e-07\n",
       "50     mths_since_earliest_cr_line:>352      0.127038   1.665873e-06\n",
       "51                     inq_last_6mths:0      0.667286   3.180519e-14\n",
       "52                   inq_last_6mths:1-2      0.520869   2.981658e-09\n",
       "53                   inq_last_6mths:3-6      0.310262   4.454631e-04\n",
       "54                   acc_now_delinq:>=1      0.193324   6.837618e-02\n",
       "55                   annual_inc:20K-30K     -0.076546   6.885605e-02\n",
       "56                   annual_inc:30K-40K     -0.013045   7.469306e-01\n",
       "57                   annual_inc:40K-50K      0.081668   4.307606e-02\n",
       "58                   annual_inc:50K-60K      0.148741   2.754270e-04\n",
       "59                   annual_inc:60K-70K      0.224316   8.151162e-08\n",
       "60                   annual_inc:70K-80K      0.298252   2.716401e-12\n",
       "61                   annual_inc:80K-90K      0.374168   3.701330e-17\n",
       "62                  annual_inc:90K-100K      0.393907   1.108341e-17\n",
       "63                 annual_inc:100K-120K      0.472350   3.990653e-25\n",
       "64                 annual_inc:120K-140K      0.563210   1.886763e-27\n",
       "65                     annual_inc:>140K      0.499159   8.322286e-26\n",
       "66                            dti:<=1.4      0.245709   3.209346e-03\n",
       "67                          dti:1.4-3.5      0.361029   2.475348e-07\n",
       "68                          dti:3.5-7.7      0.379480   3.095375e-10\n",
       "69                         dti:7.7-10.5      0.314826   1.489433e-07\n",
       "70                        dti:10.5-16.1      0.235991   4.597990e-05\n",
       "71                        dti:16.1-20.3      0.134808   2.009874e-02\n",
       "72                        dti:20.3-21.7      0.122554   4.348893e-02\n",
       "73                        dti:21.7-22.4      0.087639   1.760704e-01\n",
       "74                          dti:22.4-35      0.045907   4.231849e-01\n",
       "75       mths_since_last_delinq:Missing      0.116762   2.515592e-02\n",
       "76          mths_since_last_delinq:4-30      0.143818   6.337521e-03\n",
       "77         mths_since_last_delinq:31-56      0.189952   3.712932e-04\n",
       "78          mths_since_last_delinq:>=57      0.142870   8.926129e-03\n",
       "79       mths_since_last_record:Missing      0.324814   6.397001e-05\n",
       "80          mths_since_last_record:3-20      0.435920   3.756425e-04\n",
       "81         mths_since_last_record:21-31      0.357063   1.509320e-03\n",
       "82         mths_since_last_record:32-80      0.509914   2.355095e-09\n",
       "83         mths_since_last_record:81-86      0.171742   9.307974e-02\n",
       "84           mths_since_last_record:>86      0.237603   4.738771e-03"
      ]
     },
     "execution_count": 34,
     "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",
    "\n",
    "summary_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving the train LogisticRegression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here, from the dataframe with inputs for testing, \n",
    "# we keep the same variables that we used in our final PD model.\n",
    "\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": 38,
   "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",
    "\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": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
       "      <td>165001</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>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": 39,
     "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": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculates the predicted values for the dependent variable (targets)\n",
    "# based on the values of the independent variables (inputs) supplied as an argument.\n",
    "\n",
    "y_hat_test = reg2.model.predict(inputs_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculates the predicted probability values for the dependent variable (targets)\n",
    "# based on the values of the independent variables (inputs) supplied as an argument.\n",
    "\n",
    "y_hat_test_proba = reg2.model.predict_proba(inputs_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.07583108, 0.92416892],\n",
       "       [0.15145322, 0.84854678],\n",
       "       [0.11440192, 0.88559808],\n",
       "       ...,\n",
       "       [0.02678179, 0.97321821],\n",
       "       [0.04025139, 0.95974861],\n",
       "       [0.0476315 , 0.9523685 ]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat_test_proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.92416892, 0.84854678, 0.88559808, ..., 0.97321821, 0.95974861,\n",
       "       0.9523685 ])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we take only the probabilities for being 1. \n",
    "# The probabilities for being a good borrower \n",
    "\n",
    "y_hat_test_proba[:][:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We store these probabilities in a variable.\n",
    "y_hat_test_proba = y_hat_test_proba[: ][: , 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.92416892, 0.84854678, 0.88559808, ..., 0.97321821, 0.95974861,\n",
       "       0.9523685 ])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat_test_proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_data_targets_test_temp = loan_data_targets_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We reset the index of a dataframe.\n",
    "loan_data_targets_test_temp.reset_index(drop = True, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs = pd.concat([loan_data_targets_test_temp, pd.DataFrame(y_hat_test_proba)], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(93257, 2)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actual_predicted_probs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs.columns = ['loan_data_targets_test', 'y_hat_test_proba']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to keep the same index in this dataframe as the ones in the  inputs data test set. It would be important when we will merge the data Frame after some accuracy evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs.index = loan_data_inputs_test.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_data_targets_test</th>\n",
       "      <th>y_hat_test_proba</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>362514</td>\n",
       "      <td>1</td>\n",
       "      <td>0.924169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>288564</td>\n",
       "      <td>1</td>\n",
       "      <td>0.848547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>213591</td>\n",
       "      <td>1</td>\n",
       "      <td>0.885598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>263083</td>\n",
       "      <td>1</td>\n",
       "      <td>0.940579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>165001</td>\n",
       "      <td>1</td>\n",
       "      <td>0.968701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        loan_data_targets_test  y_hat_test_proba\n",
       "362514                       1          0.924169\n",
       "288564                       1          0.848547\n",
       "213591                       1          0.885598\n",
       "263083                       1          0.940579\n",
       "165001                       1          0.968701"
      ]
     },
     "execution_count": 53,
     "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": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>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",
       "      <td>362514</td>\n",
       "      <td>1</td>\n",
       "      <td>0.924169</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>288564</td>\n",
       "      <td>1</td>\n",
       "      <td>0.848547</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>213591</td>\n",
       "      <td>1</td>\n",
       "      <td>0.885598</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>263083</td>\n",
       "      <td>1</td>\n",
       "      <td>0.940579</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>165001</td>\n",
       "      <td>1</td>\n",
       "      <td>0.968701</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        loan_data_targets_test  y_hat_test_proba  y_hat_test\n",
       "362514                       1          0.924169           1\n",
       "288564                       1          0.848547           1\n",
       "213591                       1          0.885598           1\n",
       "263083                       1          0.940579           1\n",
       "165001                       1          0.968701           1"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set the thresholds parameter\n",
    "tr = 0.5\n",
    "\n",
    "# set all the predicted probas to be 0 when they are lower or equal than the threshold\n",
    "df_actual_predicted_probs['y_hat_test'] = np.where((df_actual_predicted_probs['y_hat_test_proba']>tr), 1, 0)\n",
    "df_actual_predicted_probs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>10184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>83061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted  0      1\n",
       "Actual             \n",
       "0          6  10184\n",
       "1          6  83061"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confusion Matrix \n",
    "pd.crosstab(df_actual_predicted_probs['loan_data_targets_test'], df_actual_predicted_probs['y_hat_test'],\n",
    "           rownames = ['Actual'], colnames = ['Predicted'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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",
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       "\n",
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       "    }\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",
       "      <td>0</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.109204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.890668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted         0         1\n",
       "Actual                       \n",
       "0          0.000064  0.109204\n",
       "1          0.000064  0.890668"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confusion matrix \n",
    "\n",
    "pd.crosstab(df_actual_predicted_probs['loan_data_targets_test'], df_actual_predicted_probs['y_hat_test'],\n",
    "           rownames = ['Actual'], colnames = ['Predicted'])/df_actual_predicted_probs.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.890732063008675"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accuracy score : number of true prediction (bads as bads and goods as goods)\n",
    "\n",
    "(pd.crosstab(df_actual_predicted_probs['loan_data_targets_test'], df_actual_predicted_probs['y_hat_test'],\n",
    "           rownames = ['Actual'], colnames = ['Predicted'])/df_actual_predicted_probs.shape[0]).iloc[0,0]\\\n",
    "+\\\n",
    "(pd.crosstab(df_actual_predicted_probs['loan_data_targets_test'], df_actual_predicted_probs['y_hat_test'],\n",
    "           rownames = ['Actual'], colnames = ['Predicted'])/df_actual_predicted_probs.shape[0]).iloc[1,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "we can see that under the threshold <b>tr = 0.5</b> , within the 10190 bads,  <b>10184</b> are predicted to be good.\n",
    "\n",
    "This is actually a real problem for <b> the business purpose </b>\n",
    "\n",
    "So we will adopt a more conservative cut-off <b>tr = 0.9 </b>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We set the threshold parameter\n",
    "tr = 0.9\n",
    "# and set every predicted probabilities lower than the threshold at 0 (default)\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": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<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",
       "      <td>0</td>\n",
       "      <td>7375</td>\n",
       "      <td>2815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>35811</td>\n",
       "      <td>47256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted      0      1\n",
       "Actual                 \n",
       "0           7375   2815\n",
       "1          35811  47256"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confusion Matrix.\n",
    "pd.crosstab(df_actual_predicted_probs['loan_data_targets_test'], df_actual_predicted_probs['y_hat_test'], rownames = ['Actual'], colnames = ['Predicted'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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",
       "      <td>0</td>\n",
       "      <td>0.079083</td>\n",
       "      <td>0.030185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.384003</td>\n",
       "      <td>0.506729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted         0         1\n",
       "Actual                       \n",
       "0          0.079083  0.030185\n",
       "1          0.384003  0.506729"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# thus getting percentages, or, rates.\n",
    "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]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5858112527745907"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here we calculate Accuracy of the model, which is the sum of the diagonal rates.\n",
    "(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]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "With a high threshold  <b>tr = 0.9</b>, the accuracy decrease but it does not really matter. The import fact is that, instead of almost <b>11%</b> in the previous threshold, we have now only <b>3%</b> of Defaulted predicted as non-Defaulted.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.        , 0.        , 0.        , ..., 0.99950932, 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([1.99266112, 0.99266112, 0.99073202, ..., 0.48797074, 0.39312673,\n",
       "        0.37262574]))"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 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.\n",
    "\n",
    "roc_curve(df_actual_predicted_probs['loan_data_targets_test'], df_actual_predicted_probs['y_hat_test_proba'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = roc_curve(df_actual_predicted_probs['loan_data_targets_test'], df_actual_predicted_probs['y_hat_test_proba'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ROC curve')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fpr, tpr)\n",
    "plt.plot(fpr, fpr, linestyle = '--', color = 'k')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7022177629458412"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculates the Area Under the Receiver Operating Characteristic Curve (AUROC)\n",
    "AUROC = roc_auc_score(df_actual_predicted_probs['loan_data_targets_test'], df_actual_predicted_probs['y_hat_test_proba'])\n",
    "AUROC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does the AUC  is efficient ?  Difficult to say.\n",
    "\n",
    "Commonly\n",
    "\n",
    "Interpretation | AUC\n",
    "---------------------\n",
    "Bad | 50% to 60%\n",
    "-\n",
    "Poor | 60% to 70%\n",
    "-\n",
    "Fair | 70% to 80%\n",
    "-\n",
    "Good | 80% to 90%\n",
    "-\n",
    "Excellent | 90% to 100%\n",
    "\n",
    "\n",
    "\n",
    "So for  our model , it is <b>Fair</b>\n",
    "\n",
    "<center> Not so Bad , yeah ? </center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gini and Kolmogorov-Smirnov\n",
    "\n",
    "In credit risk, these are two well-known measures to see how good the model perfom.\n",
    "\n",
    "<b>Gini coefficient</b> is used to measure <b>inequality</b> between non-defaulted (<b>good</b>) and defaulted (<b>Bad</b>) borrowers.\n",
    "\n",
    "<b>Kolmogorov coefficient</b> is used to show how the model seperate the non_defaulted (<b>good</b>) from the defaulted (<b>Bad</b>) borrowers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs = df_actual_predicted_probs.sort_values('y_hat_test_proba')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>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",
       "      <td>42341</td>\n",
       "      <td>1</td>\n",
       "      <td>0.372626</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42344</td>\n",
       "      <td>1</td>\n",
       "      <td>0.391437</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39810</td>\n",
       "      <td>0</td>\n",
       "      <td>0.393127</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40518</td>\n",
       "      <td>0</td>\n",
       "      <td>0.448663</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42396</td>\n",
       "      <td>0</td>\n",
       "      <td>0.455010</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       loan_data_targets_test  y_hat_test_proba  y_hat_test\n",
       "42341                       1          0.372626           0\n",
       "42344                       1          0.391437           0\n",
       "39810                       0          0.393127           0\n",
       "40518                       0          0.448663           0\n",
       "42396                       0          0.455010           0"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actual_predicted_probs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_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",
       "      <td>262480</td>\n",
       "      <td>1</td>\n",
       "      <td>0.991249</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>231463</td>\n",
       "      <td>1</td>\n",
       "      <td>0.991305</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>239228</td>\n",
       "      <td>1</td>\n",
       "      <td>0.991660</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>261086</td>\n",
       "      <td>1</td>\n",
       "      <td>0.992065</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>242624</td>\n",
       "      <td>1</td>\n",
       "      <td>0.992661</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        loan_data_targets_test  y_hat_test_proba  y_hat_test\n",
       "262480                       1          0.991249           1\n",
       "231463                       1          0.991305           1\n",
       "239228                       1          0.991660           1\n",
       "261086                       1          0.992065           1\n",
       "242624                       1          0.992661           1"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actual_predicted_probs.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We reset the index of a dataframe and overwrite it.\n",
    "# keep the previous one as a column\n",
    "\n",
    "df_actual_predicted_probs = df_actual_predicted_probs.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</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",
       "      <td>0</td>\n",
       "      <td>42341</td>\n",
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       "      <td>0.372626</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>42344</td>\n",
       "      <td>1</td>\n",
       "      <td>0.391437</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>39810</td>\n",
       "      <td>0</td>\n",
       "      <td>0.393127</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>40518</td>\n",
       "      <td>0</td>\n",
       "      <td>0.448663</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>42396</td>\n",
       "      <td>0</td>\n",
       "      <td>0.455010</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  loan_data_targets_test  y_hat_test_proba  y_hat_test\n",
       "0  42341                       1          0.372626           0\n",
       "1  42344                       1          0.391437           0\n",
       "2  39810                       0          0.393127           0\n",
       "3  40518                       0          0.448663           0\n",
       "4  42396                       0          0.455010           0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actual_predicted_probs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We calculate the cumulative number of all observations using the reset index\n",
    "df_actual_predicted_probs['Cumulative N Population'] = df_actual_predicted_probs.index + 1\n",
    "\n",
    "# We calculate cumulative number of 'good'\n",
    "df_actual_predicted_probs['Cumulative N Good'] = df_actual_predicted_probs['loan_data_targets_test'].cumsum()\n",
    "\n",
    "# We calculate cumulative number of 'bad', which is\n",
    "df_actual_predicted_probs['Cumulative N Bad'] = df_actual_predicted_probs['Cumulative N Population'] - df_actual_predicted_probs['loan_data_targets_test'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>loan_data_targets_test</th>\n",
       "      <th>y_hat_test_proba</th>\n",
       "      <th>y_hat_test</th>\n",
       "      <th>Cumulative N Population</th>\n",
       "      <th>Cumulative N Good</th>\n",
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       "  </thead>\n",
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       "      <td>0</td>\n",
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       "      <td>0.393127</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>40518</td>\n",
       "      <td>0</td>\n",
       "      <td>0.448663</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>42396</td>\n",
       "      <td>0</td>\n",
       "      <td>0.455010</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  loan_data_targets_test  y_hat_test_proba  y_hat_test  \\\n",
       "0  42341                       1          0.372626           0   \n",
       "1  42344                       1          0.391437           0   \n",
       "2  39810                       0          0.393127           0   \n",
       "3  40518                       0          0.448663           0   \n",
       "4  42396                       0          0.455010           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": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actual_predicted_probs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We calculate the cumulative percentage of all observations.\n",
    "df_actual_predicted_probs['Cumulative Perc Population'] = df_actual_predicted_probs['Cumulative N Population'] / (df_actual_predicted_probs.shape[0])\n",
    "\n",
    "# We calculate cumulative percentage of 'good'.\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",
    "\n",
    "# We calculate the cumulative percentage of 'bad'.\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())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>index</th>\n",
       "      <th>loan_data_targets_test</th>\n",
       "      <th>y_hat_test_proba</th>\n",
       "      <th>y_hat_test</th>\n",
       "      <th>Cumulative N Population</th>\n",
       "      <th>Cumulative N Good</th>\n",
       "      <th>Cumulative N Bad</th>\n",
       "      <th>Cumulative Perc Population</th>\n",
       "      <th>Cumulative Perc Good</th>\n",
       "      <th>Cumulative Perc Bad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>42341</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.000011</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.000021</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>2</td>\n",
       "      <td>39810</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>40518</td>\n",
       "      <td>0</td>\n",
       "      <td>0.448663</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000196</td>\n",
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       "      <td>4</td>\n",
       "      <td>42396</td>\n",
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       "      <td>0.455010</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  loan_data_targets_test  y_hat_test_proba  y_hat_test  \\\n",
       "0  42341                       1          0.372626           0   \n",
       "1  42344                       1          0.391437           0   \n",
       "2  39810                       0          0.393127           0   \n",
       "3  40518                       0          0.448663           0   \n",
       "4  42396                       0          0.455010           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": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actual_predicted_probs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>loan_data_targets_test</th>\n",
       "      <th>y_hat_test_proba</th>\n",
       "      <th>y_hat_test</th>\n",
       "      <th>Cumulative N Population</th>\n",
       "      <th>Cumulative N Good</th>\n",
       "      <th>Cumulative N Bad</th>\n",
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       "      <th>Cumulative Perc Good</th>\n",
       "      <th>Cumulative Perc Bad</th>\n",
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       "      <td>93252</td>\n",
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       "      <td>0.999964</td>\n",
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       "      <td>0.999976</td>\n",
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       "      <td>93256</td>\n",
       "      <td>83066</td>\n",
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       "      <td>0.999989</td>\n",
       "      <td>0.999988</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93256</td>\n",
       "      <td>242624</td>\n",
       "      <td>1</td>\n",
       "      <td>0.992661</td>\n",
       "      <td>1</td>\n",
       "      <td>93257</td>\n",
       "      <td>83067</td>\n",
       "      <td>10190</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        index  loan_data_targets_test  y_hat_test_proba  y_hat_test  \\\n",
       "93252  262480                       1          0.991249           1   \n",
       "93253  231463                       1          0.991305           1   \n",
       "93254  239228                       1          0.991660           1   \n",
       "93255  261086                       1          0.992065           1   \n",
       "93256  242624                       1          0.992661           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": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actual_predicted_probs.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Gini')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Gini\n",
    "# We plot the cumulative percentage of all along the x-axis and \n",
    "# the cumulative percentage 'good' along the y-axis\n",
    "\n",
    "plt.plot(df_actual_predicted_probs['Cumulative Perc Population'], df_actual_predicted_probs['Cumulative Perc Bad'])\n",
    "plt.plot(df_actual_predicted_probs['Cumulative Perc Population'], df_actual_predicted_probs['Cumulative Perc Population'], linestyle = '--', color = 'k')\n",
    "plt.xlabel('Cumulative % Population')\n",
    "plt.ylabel('Cumulative % Bad')\n",
    "plt.title('Gini')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4044355258916823"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gini's formula using the AUROC.\n",
    "Gini = AUROC * 2 - 1\n",
    "Gini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Kolmogorov-Smirnov')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot KS\n",
    "# We plot the predicted (estimated) probabilities along the x-axis and \n",
    "# the cumulative percentage 'bad' along the y-axis\n",
    "\n",
    "plt.plot(df_actual_predicted_probs['y_hat_test_proba'], df_actual_predicted_probs['Cumulative Perc Bad'], color = 'r')\n",
    "plt.plot(df_actual_predicted_probs['y_hat_test_proba'], df_actual_predicted_probs['Cumulative Perc Good'], color = 'b')\n",
    "plt.xlabel('Estimated Probability for being Good')\n",
    "plt.ylabel('Cumulative %')\n",
    "plt.title('Kolmogorov-Smirnov')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2966177993188114"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We calculate KS from the data. It is the maximum of the difference between \n",
    "# the cumulative percentage of 'bad' and the cumulative percentage of 'good'.\n",
    "\n",
    "KS = max(df_actual_predicted_probs['Cumulative Perc Bad'] - df_actual_predicted_probs['Cumulative Perc Good'])\n",
    "KS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying the PD Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating PD of individual accounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_columns = None\n",
    "# Sets the pandas dataframe options to display all columns/ rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>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>grade:G</th>\n",
       "      <th>home_ownership:RENT_OTHER_NONE_ANY</th>\n",
       "      <th>home_ownership:OWN</th>\n",
       "      <th>home_ownership:MORTGAGE</th>\n",
       "      <th>addr_state:ND_NE_IA_NV_FL_HI_AL</th>\n",
       "      <th>addr_state:NM_VA</th>\n",
       "      <th>addr_state:NY</th>\n",
       "      <th>addr_state:OK_TN_MO_LA_MD_NC</th>\n",
       "      <th>addr_state:CA</th>\n",
       "      <th>addr_state:UT_KY_AZ_NJ</th>\n",
       "      <th>addr_state:AR_MI_PA_OH_MN</th>\n",
       "      <th>addr_state:RI_MA_DE_SD_IN</th>\n",
       "      <th>addr_state:GA_WA_OR</th>\n",
       "      <th>addr_state:WI_MT</th>\n",
       "      <th>addr_state:TX</th>\n",
       "      <th>addr_state:IL_CT</th>\n",
       "      <th>addr_state:KS_SC_CO_VT_AK_MS</th>\n",
       "      <th>addr_state:WV_NH_WY_DC_ME_ID</th>\n",
       "      <th>verification_status:Not Verified</th>\n",
       "      <th>verification_status:Source Verified</th>\n",
       "      <th>verification_status:Verified</th>\n",
       "      <th>purpose:educ__sm_b__wedd__ren_en__mov__house</th>\n",
       "      <th>purpose:credit_card</th>\n",
       "      <th>purpose:debt_consolidation</th>\n",
       "      <th>purpose:oth__med__vacation</th>\n",
       "      <th>purpose:major_purch__car__home_impr</th>\n",
       "      <th>initial_list_status:f</th>\n",
       "      <th>initial_list_status:w</th>\n",
       "      <th>term:36</th>\n",
       "      <th>term:60</th>\n",
       "      <th>emp_length:0</th>\n",
       "      <th>emp_length:1</th>\n",
       "      <th>emp_length:2-4</th>\n",
       "      <th>emp_length:5-6</th>\n",
       "      <th>emp_length:7-9</th>\n",
       "      <th>emp_length:10</th>\n",
       "      <th>mths_since_issue_d:&lt;38</th>\n",
       "      <th>mths_since_issue_d:38-39</th>\n",
       "      <th>mths_since_issue_d:40-41</th>\n",
       "      <th>mths_since_issue_d:42-48</th>\n",
       "      <th>mths_since_issue_d:49-52</th>\n",
       "      <th>mths_since_issue_d:53-64</th>\n",
       "      <th>mths_since_issue_d:65-84</th>\n",
       "      <th>mths_since_issue_d:&gt;84</th>\n",
       "      <th>int_rate:&lt;9.548</th>\n",
       "      <th>int_rate:9.548-12.025</th>\n",
       "      <th>int_rate:12.025-15.74</th>\n",
       "      <th>int_rate:15.74-20.281</th>\n",
       "      <th>int_rate:&gt;20.281</th>\n",
       "      <th>mths_since_earliest_cr_line:&lt;140</th>\n",
       "      <th>mths_since_earliest_cr_line:141-164</th>\n",
       "      <th>mths_since_earliest_cr_line:165-247</th>\n",
       "      <th>mths_since_earliest_cr_line:248-270</th>\n",
       "      <th>mths_since_earliest_cr_line:271-352</th>\n",
       "      <th>mths_since_earliest_cr_line:&gt;352</th>\n",
       "      <th>inq_last_6mths:0</th>\n",
       "      <th>inq_last_6mths:1-2</th>\n",
       "      <th>inq_last_6mths:3-6</th>\n",
       "      <th>inq_last_6mths:&gt;6</th>\n",
       "      <th>acc_now_delinq:0</th>\n",
       "      <th>acc_now_delinq:&gt;=1</th>\n",
       "      <th>annual_inc:&lt;20K</th>\n",
       "      <th>annual_inc:20K-30K</th>\n",
       "      <th>annual_inc:30K-40K</th>\n",
       "      <th>annual_inc:40K-50K</th>\n",
       "      <th>annual_inc:50K-60K</th>\n",
       "      <th>annual_inc:60K-70K</th>\n",
       "      <th>annual_inc:70K-80K</th>\n",
       "      <th>annual_inc:80K-90K</th>\n",
       "      <th>annual_inc:90K-100K</th>\n",
       "      <th>annual_inc:100K-120K</th>\n",
       "      <th>annual_inc:120K-140K</th>\n",
       "      <th>annual_inc:&gt;140K</th>\n",
       "      <th>dti:&lt;=1.4</th>\n",
       "      <th>dti:1.4-3.5</th>\n",
       "      <th>dti:3.5-7.7</th>\n",
       "      <th>dti:7.7-10.5</th>\n",
       "      <th>dti:10.5-16.1</th>\n",
       "      <th>dti:16.1-20.3</th>\n",
       "      <th>dti:20.3-21.7</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_delinq:Missing</th>\n",
       "      <th>mths_since_last_delinq:0-3</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: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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>362514</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>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>0</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>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>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>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>0</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",
       "      <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>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>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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>288564</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>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>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>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>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",
       "      <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",
       "      <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>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>1</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>0</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>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>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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>213591</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>1</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>0</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>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>1</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",
       "      <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>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>0</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",
       "      <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>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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>263083</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>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>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>0</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>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>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>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>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>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>0</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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>165001</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>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>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>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>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",
       "      <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>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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <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>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>0</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        grade:A  grade:B  grade:C  grade:D  grade:E  grade:F  grade:G  \\\n",
       "362514        0        0        1        0        0        0        0   \n",
       "288564        0        0        0        0        1        0        0   \n",
       "213591        0        0        1        0        0        0        0   \n",
       "263083        0        0        1        0        0        0        0   \n",
       "165001        1        0        0        0        0        0        0   \n",
       "\n",
       "        home_ownership:RENT_OTHER_NONE_ANY  home_ownership:OWN  \\\n",
       "362514                                   0                   0   \n",
       "288564                                   0                   0   \n",
       "213591                                   0                   0   \n",
       "263083                                   0                   0   \n",
       "165001                                   0                   0   \n",
       "\n",
       "        home_ownership:MORTGAGE  addr_state:ND_NE_IA_NV_FL_HI_AL  \\\n",
       "362514                        1                                0   \n",
       "288564                        1                                0   \n",
       "213591                        1                                0   \n",
       "263083                        1                                0   \n",
       "165001                        1                                0   \n",
       "\n",
       "        addr_state:NM_VA  addr_state:NY  addr_state:OK_TN_MO_LA_MD_NC  \\\n",
       "362514                 0              0                             0   \n",
       "288564                 0              0                             0   \n",
       "213591                 0              0                             0   \n",
       "263083                 0              0                             0   \n",
       "165001                 0              0                             0   \n",
       "\n",
       "        addr_state:CA  addr_state:UT_KY_AZ_NJ  addr_state:AR_MI_PA_OH_MN  \\\n",
       "362514              1                       0                          0   \n",
       "288564              0                       0                          0   \n",
       "213591              0                       1                          0   \n",
       "263083              0                       0                          1   \n",
       "165001              0                       0                          1   \n",
       "\n",
       "        addr_state:RI_MA_DE_SD_IN  addr_state:GA_WA_OR  addr_state:WI_MT  \\\n",
       "362514                          0                    0                 0   \n",
       "288564                          1                    0                 0   \n",
       "213591                          0                    0                 0   \n",
       "263083                          0                    0                 0   \n",
       "165001                          0                    0                 0   \n",
       "\n",
       "        addr_state:TX  addr_state:IL_CT  addr_state:KS_SC_CO_VT_AK_MS  \\\n",
       "362514              0                 0                             0   \n",
       "288564              0                 0                             0   \n",
       "213591              0                 0                             0   \n",
       "263083              0                 0                             0   \n",
       "165001              0                 0                             0   \n",
       "\n",
       "        addr_state:WV_NH_WY_DC_ME_ID  verification_status:Not Verified  \\\n",
       "362514                             0                                 0   \n",
       "288564                             0                                 0   \n",
       "213591                             0                                 0   \n",
       "263083                             0                                 0   \n",
       "165001                             0                                 0   \n",
       "\n",
       "        verification_status:Source Verified  verification_status:Verified  \\\n",
       "362514                                    0                             1   \n",
       "288564                                    0                             1   \n",
       "213591                                    0                             1   \n",
       "263083                                    1                             0   \n",
       "165001                                    0                             1   \n",
       "\n",
       "        purpose:educ__sm_b__wedd__ren_en__mov__house  purpose:credit_card  \\\n",
       "362514                                             0                    0   \n",
       "288564                                             0                    1   \n",
       "213591                                             0                    0   \n",
       "263083                                             0                    0   \n",
       "165001                                             0                    0   \n",
       "\n",
       "        purpose:debt_consolidation  purpose:oth__med__vacation  \\\n",
       "362514                           0                           0   \n",
       "288564                           0                           0   \n",
       "213591                           1                           0   \n",
       "263083                           1                           0   \n",
       "165001                           1                           0   \n",
       "\n",
       "        purpose:major_purch__car__home_impr  initial_list_status:f  \\\n",
       "362514                                    1                      1   \n",
       "288564                                    0                      0   \n",
       "213591                                    0                      1   \n",
       "263083                                    0                      0   \n",
       "165001                                    0                      1   \n",
       "\n",
       "        initial_list_status:w  term:36  term:60  emp_length:0  emp_length:1  \\\n",
       "362514                      0        0        1             1             0   \n",
       "288564                      1        0        1             0             0   \n",
       "213591                      0        1        0             0             1   \n",
       "263083                      1        0        1             0             0   \n",
       "165001                      0        1        0             0             0   \n",
       "\n",
       "        emp_length:2-4  emp_length:5-6  emp_length:7-9  emp_length:10  \\\n",
       "362514               0               0               0              0   \n",
       "288564               0               0               0              1   \n",
       "213591               0               0               0              0   \n",
       "263083               1               0               0              0   \n",
       "165001               0               0               0              1   \n",
       "\n",
       "        mths_since_issue_d:<38  mths_since_issue_d:38-39  \\\n",
       "362514                       0                         0   \n",
       "288564                       0                         1   \n",
       "213591                       0                         0   \n",
       "263083                       1                         0   \n",
       "165001                       0                         0   \n",
       "\n",
       "        mths_since_issue_d:40-41  mths_since_issue_d:42-48  \\\n",
       "362514                         1                         0   \n",
       "288564                         0                         0   \n",
       "213591                         0                         0   \n",
       "263083                         0                         0   \n",
       "165001                         0                         0   \n",
       "\n",
       "        mths_since_issue_d:49-52  mths_since_issue_d:53-64  \\\n",
       "362514                         0                         0   \n",
       "288564                         0                         0   \n",
       "213591                         0                         0   \n",
       "263083                         0                         0   \n",
       "165001                         0                         1   \n",
       "\n",
       "        mths_since_issue_d:65-84  mths_since_issue_d:>84  int_rate:<9.548  \\\n",
       "362514                         0                       0                0   \n",
       "288564                         0                       0                0   \n",
       "213591                         1                       0                0   \n",
       "263083                         0                       0                0   \n",
       "165001                         0                       0                1   \n",
       "\n",
       "        int_rate:9.548-12.025  int_rate:12.025-15.74  int_rate:15.74-20.281  \\\n",
       "362514                      0                      1                      0   \n",
       "288564                      0                      0                      0   \n",
       "213591                      0                      1                      0   \n",
       "263083                      0                      1                      0   \n",
       "165001                      0                      0                      0   \n",
       "\n",
       "        int_rate:>20.281  mths_since_earliest_cr_line:<140  \\\n",
       "362514                 0                                 0   \n",
       "288564                 1                                 0   \n",
       "213591                 0                                 0   \n",
       "263083                 0                                 1   \n",
       "165001                 0                                 0   \n",
       "\n",
       "        mths_since_earliest_cr_line:141-164  \\\n",
       "362514                                    0   \n",
       "288564                                    0   \n",
       "213591                                    0   \n",
       "263083                                    0   \n",
       "165001                                    0   \n",
       "\n",
       "        mths_since_earliest_cr_line:165-247  \\\n",
       "362514                                    1   \n",
       "288564                                    1   \n",
       "213591                                    1   \n",
       "263083                                    0   \n",
       "165001                                    0   \n",
       "\n",
       "        mths_since_earliest_cr_line:248-270  \\\n",
       "362514                                    0   \n",
       "288564                                    0   \n",
       "213591                                    0   \n",
       "263083                                    0   \n",
       "165001                                    0   \n",
       "\n",
       "        mths_since_earliest_cr_line:271-352  mths_since_earliest_cr_line:>352  \\\n",
       "362514                                    0                                 0   \n",
       "288564                                    0                                 0   \n",
       "213591                                    0                                 0   \n",
       "263083                                    0                                 0   \n",
       "165001                                    0                                 1   \n",
       "\n",
       "        inq_last_6mths:0  inq_last_6mths:1-2  inq_last_6mths:3-6  \\\n",
       "362514                 1                   0                   0   \n",
       "288564                 1                   0                   0   \n",
       "213591                 1                   0                   0   \n",
       "263083                 1                   0                   0   \n",
       "165001                 1                   0                   0   \n",
       "\n",
       "        inq_last_6mths:>6  acc_now_delinq:0  acc_now_delinq:>=1  \\\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",
       "        annual_inc:<20K  annual_inc:20K-30K  annual_inc:30K-40K  \\\n",
       "362514                0                   0                   0   \n",
       "288564                0                   0                   0   \n",
       "213591                0                   0                   0   \n",
       "263083                0                   0                   0   \n",
       "165001                0                   0                   0   \n",
       "\n",
       "        annual_inc:40K-50K  annual_inc:50K-60K  annual_inc:60K-70K  \\\n",
       "362514                   0                   0                   1   \n",
       "288564                   0                   1                   0   \n",
       "213591                   0                   0                   0   \n",
       "263083                   0                   0                   1   \n",
       "165001                   0                   0                   0   \n",
       "\n",
       "        annual_inc:70K-80K  annual_inc:80K-90K  annual_inc:90K-100K  \\\n",
       "362514                   0                   0                    0   \n",
       "288564                   0                   0                    0   \n",
       "213591                   0                   0                    0   \n",
       "263083                   0                   0                    0   \n",
       "165001                   0                   1                    0   \n",
       "\n",
       "        annual_inc:100K-120K  annual_inc:120K-140K  annual_inc:>140K  \\\n",
       "362514                     0                     0                 0   \n",
       "288564                     0                     0                 0   \n",
       "213591                     0                     0                 1   \n",
       "263083                     0                     0                 0   \n",
       "165001                     0                     0                 0   \n",
       "\n",
       "        dti:<=1.4  dti:1.4-3.5  dti:3.5-7.7  dti:7.7-10.5  dti:10.5-16.1  \\\n",
       "362514          0            0            0             1              0   \n",
       "288564          0            0            0             0              0   \n",
       "213591          0            0            0             0              1   \n",
       "263083          0            0            0             0              0   \n",
       "165001          0            0            0             1              0   \n",
       "\n",
       "        dti:16.1-20.3  dti:20.3-21.7  dti:21.7-22.4  dti:22.4-35  dti:>35  \\\n",
       "362514              0              0              0            0        0   \n",
       "288564              0              0              0            1        0   \n",
       "213591              0              0              0            0        0   \n",
       "263083              1              0              0            0        0   \n",
       "165001              0              0              0            0        0   \n",
       "\n",
       "        mths_since_last_delinq:Missing  mths_since_last_delinq:0-3  \\\n",
       "362514                               1                           0   \n",
       "288564                               0                           1   \n",
       "213591                               0                           0   \n",
       "263083                               1                           0   \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:0-2  mths_since_last_record:3-20  \\\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:21-31  mths_since_last_record:32-80  \\\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:81-86  mths_since_last_record:>86  \n",
       "362514                             0                           0  \n",
       "288564                             0                           0  \n",
       "213591                             0                           0  \n",
       "263083                             0                           0  \n",
       "165001                             0                           0  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs_test_with_ref_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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",
       "      <td>0</td>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.385395</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.121520</td>\n",
       "      <td>4.753387e-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.881507</td>\n",
       "      <td>2.158571e-47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.687851</td>\n",
       "      <td>3.271838e-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.500477</td>\n",
       "      <td>6.929364e-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>grade:E</td>\n",
       "      <td>0.325091</td>\n",
       "      <td>5.871894e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>grade:F</td>\n",
       "      <td>0.137039</td>\n",
       "      <td>5.231950e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>home_ownership:OWN</td>\n",
       "      <td>0.093505</td>\n",
       "      <td>3.462640e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>home_ownership:MORTGAGE</td>\n",
       "      <td>0.105977</td>\n",
       "      <td>5.303227e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>addr_state:NM_VA</td>\n",
       "      <td>0.037053</td>\n",
       "      <td>2.472817e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>addr_state:NY</td>\n",
       "      <td>0.057066</td>\n",
       "      <td>1.845927e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>addr_state:OK_TN_MO_LA_MD_NC</td>\n",
       "      <td>0.059627</td>\n",
       "      <td>1.000094e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>addr_state:CA</td>\n",
       "      <td>0.063601</td>\n",
       "      <td>2.693792e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>addr_state:UT_KY_AZ_NJ</td>\n",
       "      <td>0.078480</td>\n",
       "      <td>1.630257e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>addr_state:AR_MI_PA_OH_MN</td>\n",
       "      <td>0.135479</td>\n",
       "      <td>1.793406e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>addr_state:RI_MA_DE_SD_IN</td>\n",
       "      <td>0.100868</td>\n",
       "      <td>7.237427e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>addr_state:GA_WA_OR</td>\n",
       "      <td>0.185784</td>\n",
       "      <td>3.242749e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>addr_state:WI_MT</td>\n",
       "      <td>0.240481</td>\n",
       "      <td>4.262594e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>addr_state:TX</td>\n",
       "      <td>0.222372</td>\n",
       "      <td>1.127032e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>addr_state:IL_CT</td>\n",
       "      <td>0.261843</td>\n",
       "      <td>3.088720e-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>addr_state:KS_SC_CO_VT_AK_MS</td>\n",
       "      <td>0.320744</td>\n",
       "      <td>2.434110e-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>addr_state:WV_NH_WY_DC_ME_ID</td>\n",
       "      <td>0.522261</td>\n",
       "      <td>9.635088e-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>verification_status:Not Verified</td>\n",
       "      <td>0.085864</td>\n",
       "      <td>4.116527e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>verification_status:Source Verified</td>\n",
       "      <td>-0.010907</td>\n",
       "      <td>4.167432e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>purpose:credit_card</td>\n",
       "      <td>0.301411</td>\n",
       "      <td>1.495937e-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>purpose:debt_consolidation</td>\n",
       "      <td>0.197790</td>\n",
       "      <td>2.746032e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>purpose:oth__med__vacation</td>\n",
       "      <td>0.213355</td>\n",
       "      <td>6.900118e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>purpose:major_purch__car__home_impr</td>\n",
       "      <td>0.265439</td>\n",
       "      <td>1.846669e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>initial_list_status:w</td>\n",
       "      <td>0.054413</td>\n",
       "      <td>3.070754e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>term:36</td>\n",
       "      <td>0.078530</td>\n",
       "      <td>1.936743e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>emp_length:1</td>\n",
       "      <td>0.099320</td>\n",
       "      <td>1.283870e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>emp_length:2-4</td>\n",
       "      <td>0.125188</td>\n",
       "      <td>3.292712e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>emp_length:5-6</td>\n",
       "      <td>0.090508</td>\n",
       "      <td>2.349932e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>emp_length:7-9</td>\n",
       "      <td>0.060713</td>\n",
       "      <td>3.512433e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>emp_length:10</td>\n",
       "      <td>0.124822</td>\n",
       "      <td>1.281702e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>mths_since_issue_d:&lt;38</td>\n",
       "      <td>1.076656</td>\n",
       "      <td>1.298182e-178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>mths_since_issue_d:38-39</td>\n",
       "      <td>0.874363</td>\n",
       "      <td>5.872595e-148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>mths_since_issue_d:40-41</td>\n",
       "      <td>0.773143</td>\n",
       "      <td>2.876952e-122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>mths_since_issue_d:42-48</td>\n",
       "      <td>0.572739</td>\n",
       "      <td>2.871610e-86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>mths_since_issue_d:49-52</td>\n",
       "      <td>0.411661</td>\n",
       "      <td>5.203951e-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>mths_since_issue_d:53-64</td>\n",
       "      <td>0.166235</td>\n",
       "      <td>1.289234e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>mths_since_issue_d:65-84</td>\n",
       "      <td>-0.074976</td>\n",
       "      <td>1.038774e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>int_rate:&lt;9.548</td>\n",
       "      <td>0.889336</td>\n",
       "      <td>1.600893e-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>int_rate:9.548-12.025</td>\n",
       "      <td>0.559782</td>\n",
       "      <td>4.322923e-36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>int_rate:12.025-15.74</td>\n",
       "      <td>0.306816</td>\n",
       "      <td>6.205030e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>int_rate:15.74-20.281</td>\n",
       "      <td>0.113399</td>\n",
       "      <td>9.067425e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>mths_since_earliest_cr_line:141-164</td>\n",
       "      <td>0.054006</td>\n",
       "      <td>2.996284e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>mths_since_earliest_cr_line:165-247</td>\n",
       "      <td>0.034950</td>\n",
       "      <td>8.185699e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>mths_since_earliest_cr_line:248-270</td>\n",
       "      <td>0.076711</td>\n",
       "      <td>3.462911e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>mths_since_earliest_cr_line:271-352</td>\n",
       "      <td>0.119321</td>\n",
       "      <td>3.525173e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>mths_since_earliest_cr_line:&gt;352</td>\n",
       "      <td>0.127038</td>\n",
       "      <td>1.665873e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>inq_last_6mths:0</td>\n",
       "      <td>0.667286</td>\n",
       "      <td>3.180519e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>inq_last_6mths:1-2</td>\n",
       "      <td>0.520869</td>\n",
       "      <td>2.981658e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>inq_last_6mths:3-6</td>\n",
       "      <td>0.310262</td>\n",
       "      <td>4.454631e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>acc_now_delinq:&gt;=1</td>\n",
       "      <td>0.193324</td>\n",
       "      <td>6.837618e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>annual_inc:20K-30K</td>\n",
       "      <td>-0.076546</td>\n",
       "      <td>6.885605e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>annual_inc:30K-40K</td>\n",
       "      <td>-0.013045</td>\n",
       "      <td>7.469306e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>annual_inc:40K-50K</td>\n",
       "      <td>0.081668</td>\n",
       "      <td>4.307606e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>annual_inc:50K-60K</td>\n",
       "      <td>0.148741</td>\n",
       "      <td>2.754270e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>annual_inc:60K-70K</td>\n",
       "      <td>0.224316</td>\n",
       "      <td>8.151162e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>annual_inc:70K-80K</td>\n",
       "      <td>0.298252</td>\n",
       "      <td>2.716401e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>annual_inc:80K-90K</td>\n",
       "      <td>0.374168</td>\n",
       "      <td>3.701330e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>annual_inc:90K-100K</td>\n",
       "      <td>0.393907</td>\n",
       "      <td>1.108341e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>annual_inc:100K-120K</td>\n",
       "      <td>0.472350</td>\n",
       "      <td>3.990653e-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>annual_inc:120K-140K</td>\n",
       "      <td>0.563210</td>\n",
       "      <td>1.886763e-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>annual_inc:&gt;140K</td>\n",
       "      <td>0.499159</td>\n",
       "      <td>8.322286e-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>dti:&lt;=1.4</td>\n",
       "      <td>0.245709</td>\n",
       "      <td>3.209346e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>dti:1.4-3.5</td>\n",
       "      <td>0.361029</td>\n",
       "      <td>2.475348e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>dti:3.5-7.7</td>\n",
       "      <td>0.379480</td>\n",
       "      <td>3.095375e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>dti:7.7-10.5</td>\n",
       "      <td>0.314826</td>\n",
       "      <td>1.489433e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>dti:10.5-16.1</td>\n",
       "      <td>0.235991</td>\n",
       "      <td>4.597990e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>dti:16.1-20.3</td>\n",
       "      <td>0.134808</td>\n",
       "      <td>2.009874e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>dti:20.3-21.7</td>\n",
       "      <td>0.122554</td>\n",
       "      <td>4.348893e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>dti:21.7-22.4</td>\n",
       "      <td>0.087639</td>\n",
       "      <td>1.760704e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>dti:22.4-35</td>\n",
       "      <td>0.045907</td>\n",
       "      <td>4.231849e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>mths_since_last_delinq:Missing</td>\n",
       "      <td>0.116762</td>\n",
       "      <td>2.515592e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>mths_since_last_delinq:4-30</td>\n",
       "      <td>0.143818</td>\n",
       "      <td>6.337521e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>mths_since_last_delinq:31-56</td>\n",
       "      <td>0.189952</td>\n",
       "      <td>3.712932e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>mths_since_last_delinq:&gt;=57</td>\n",
       "      <td>0.142870</td>\n",
       "      <td>8.926129e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>mths_since_last_record:Missing</td>\n",
       "      <td>0.324814</td>\n",
       "      <td>6.397001e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>mths_since_last_record:3-20</td>\n",
       "      <td>0.435920</td>\n",
       "      <td>3.756425e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>mths_since_last_record:21-31</td>\n",
       "      <td>0.357063</td>\n",
       "      <td>1.509320e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>mths_since_last_record:32-80</td>\n",
       "      <td>0.509914</td>\n",
       "      <td>2.355095e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>mths_since_last_record:81-86</td>\n",
       "      <td>0.171742</td>\n",
       "      <td>9.307974e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>mths_since_last_record:&gt;86</td>\n",
       "      <td>0.237603</td>\n",
       "      <td>4.738771e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Feature name  Coefficients       p_values\n",
       "0                             Intercept     -1.385395            NaN\n",
       "1                               grade:A      1.121520   4.753387e-35\n",
       "2                               grade:B      0.881507   2.158571e-47\n",
       "3                               grade:C      0.687851   3.271838e-34\n",
       "4                               grade:D      0.500477   6.929364e-21\n",
       "5                               grade:E      0.325091   5.871894e-12\n",
       "6                               grade:F      0.137039   5.231950e-03\n",
       "7                    home_ownership:OWN      0.093505   3.462640e-06\n",
       "8               home_ownership:MORTGAGE      0.105977   5.303227e-17\n",
       "9                      addr_state:NM_VA      0.037053   2.472817e-01\n",
       "10                        addr_state:NY      0.057066   1.845927e-02\n",
       "11         addr_state:OK_TN_MO_LA_MD_NC      0.059627   1.000094e-02\n",
       "12                        addr_state:CA      0.063601   2.693792e-03\n",
       "13               addr_state:UT_KY_AZ_NJ      0.078480   1.630257e-03\n",
       "14            addr_state:AR_MI_PA_OH_MN      0.135479   1.793406e-09\n",
       "15            addr_state:RI_MA_DE_SD_IN      0.100868   7.237427e-04\n",
       "16                  addr_state:GA_WA_OR      0.185784   3.242749e-12\n",
       "17                     addr_state:WI_MT      0.240481   4.262594e-07\n",
       "18                        addr_state:TX      0.222372   1.127032e-17\n",
       "19                     addr_state:IL_CT      0.261843   3.088720e-19\n",
       "20         addr_state:KS_SC_CO_VT_AK_MS      0.320744   2.434110e-25\n",
       "21         addr_state:WV_NH_WY_DC_ME_ID      0.522261   9.635088e-23\n",
       "22     verification_status:Not Verified      0.085864   4.116527e-09\n",
       "23  verification_status:Source Verified     -0.010907   4.167432e-01\n",
       "24                  purpose:credit_card      0.301411   1.495937e-26\n",
       "25           purpose:debt_consolidation      0.197790   2.746032e-14\n",
       "26           purpose:oth__med__vacation      0.213355   6.900118e-12\n",
       "27  purpose:major_purch__car__home_impr      0.265439   1.846669e-17\n",
       "28                initial_list_status:w      0.054413   3.070754e-05\n",
       "29                              term:36      0.078530   1.936743e-08\n",
       "30                         emp_length:1      0.099320   1.283870e-04\n",
       "31                       emp_length:2-4      0.125188   3.292712e-11\n",
       "32                       emp_length:5-6      0.090508   2.349932e-05\n",
       "33                       emp_length:7-9      0.060713   3.512433e-03\n",
       "34                        emp_length:10      0.124822   1.281702e-11\n",
       "35               mths_since_issue_d:<38      1.076656  1.298182e-178\n",
       "36             mths_since_issue_d:38-39      0.874363  5.872595e-148\n",
       "37             mths_since_issue_d:40-41      0.773143  2.876952e-122\n",
       "38             mths_since_issue_d:42-48      0.572739   2.871610e-86\n",
       "39             mths_since_issue_d:49-52      0.411661   5.203951e-40\n",
       "40             mths_since_issue_d:53-64      0.166235   1.289234e-08\n",
       "41             mths_since_issue_d:65-84     -0.074976   1.038774e-02\n",
       "42                      int_rate:<9.548      0.889336   1.600893e-31\n",
       "43                int_rate:9.548-12.025      0.559782   4.322923e-36\n",
       "44                int_rate:12.025-15.74      0.306816   6.205030e-17\n",
       "45                int_rate:15.74-20.281      0.113399   9.067425e-05\n",
       "46  mths_since_earliest_cr_line:141-164      0.054006   2.996284e-02\n",
       "47  mths_since_earliest_cr_line:165-247      0.034950   8.185699e-02\n",
       "48  mths_since_earliest_cr_line:248-270      0.076711   3.462911e-03\n",
       "49  mths_since_earliest_cr_line:271-352      0.119321   3.525173e-07\n",
       "50     mths_since_earliest_cr_line:>352      0.127038   1.665873e-06\n",
       "51                     inq_last_6mths:0      0.667286   3.180519e-14\n",
       "52                   inq_last_6mths:1-2      0.520869   2.981658e-09\n",
       "53                   inq_last_6mths:3-6      0.310262   4.454631e-04\n",
       "54                   acc_now_delinq:>=1      0.193324   6.837618e-02\n",
       "55                   annual_inc:20K-30K     -0.076546   6.885605e-02\n",
       "56                   annual_inc:30K-40K     -0.013045   7.469306e-01\n",
       "57                   annual_inc:40K-50K      0.081668   4.307606e-02\n",
       "58                   annual_inc:50K-60K      0.148741   2.754270e-04\n",
       "59                   annual_inc:60K-70K      0.224316   8.151162e-08\n",
       "60                   annual_inc:70K-80K      0.298252   2.716401e-12\n",
       "61                   annual_inc:80K-90K      0.374168   3.701330e-17\n",
       "62                  annual_inc:90K-100K      0.393907   1.108341e-17\n",
       "63                 annual_inc:100K-120K      0.472350   3.990653e-25\n",
       "64                 annual_inc:120K-140K      0.563210   1.886763e-27\n",
       "65                     annual_inc:>140K      0.499159   8.322286e-26\n",
       "66                            dti:<=1.4      0.245709   3.209346e-03\n",
       "67                          dti:1.4-3.5      0.361029   2.475348e-07\n",
       "68                          dti:3.5-7.7      0.379480   3.095375e-10\n",
       "69                         dti:7.7-10.5      0.314826   1.489433e-07\n",
       "70                        dti:10.5-16.1      0.235991   4.597990e-05\n",
       "71                        dti:16.1-20.3      0.134808   2.009874e-02\n",
       "72                        dti:20.3-21.7      0.122554   4.348893e-02\n",
       "73                        dti:21.7-22.4      0.087639   1.760704e-01\n",
       "74                          dti:22.4-35      0.045907   4.231849e-01\n",
       "75       mths_since_last_delinq:Missing      0.116762   2.515592e-02\n",
       "76          mths_since_last_delinq:4-30      0.143818   6.337521e-03\n",
       "77         mths_since_last_delinq:31-56      0.189952   3.712932e-04\n",
       "78          mths_since_last_delinq:>=57      0.142870   8.926129e-03\n",
       "79       mths_since_last_record:Missing      0.324814   6.397001e-05\n",
       "80          mths_since_last_record:3-20      0.435920   3.756425e-04\n",
       "81         mths_since_last_record:21-31      0.357063   1.509320e-03\n",
       "82         mths_since_last_record:32-80      0.509914   2.355095e-09\n",
       "83         mths_since_last_record:81-86      0.171742   9.307974e-02\n",
       "84           mths_since_last_record:>86      0.237603   4.738771e-03"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.92416892, 0.84854678, 0.88559808, ..., 0.97321821, 0.95974861,\n",
       "       0.9523685 ])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat_test_proba"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Creating a Scorecard\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature name</th>\n",
       "      <th>Coefficients</th>\n",
       "      <th>p_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.385395</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.121520</td>\n",
       "      <td>4.753387e-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.881507</td>\n",
       "      <td>2.158571e-47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.687851</td>\n",
       "      <td>3.271838e-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.500477</td>\n",
       "      <td>6.929364e-21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Feature name  Coefficients      p_values\n",
       "0    Intercept     -1.385395           NaN\n",
       "1      grade:A      1.121520  4.753387e-35\n",
       "2      grade:B      0.881507  2.158571e-47\n",
       "3      grade:C      0.687851  3.271838e-34\n",
       "4      grade:D      0.500477  6.929364e-21"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['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']"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>p_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>grade:G</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>home_ownership:RENT_OTHER_NONE_ANY</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>addr_state:ND_NE_IA_NV_FL_HI_AL</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>verification_status:Verified</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>purpose:educ__sm_b__wedd__ren_en__mov__house</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>initial_list_status:f</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>term:60</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>emp_length:0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>mths_since_issue_d:&gt;84</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>int_rate:&gt;20.281</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>mths_since_earliest_cr_line:&lt;140</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>inq_last_6mths:&gt;6</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>acc_now_delinq:0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>annual_inc:&lt;20K</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>dti:&gt;35</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>mths_since_last_delinq:0-3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>mths_since_last_record:0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Feature name  Coefficients  p_values\n",
       "0                                        grade:G             0       NaN\n",
       "1             home_ownership:RENT_OTHER_NONE_ANY             0       NaN\n",
       "2                addr_state:ND_NE_IA_NV_FL_HI_AL             0       NaN\n",
       "3                   verification_status:Verified             0       NaN\n",
       "4   purpose:educ__sm_b__wedd__ren_en__mov__house             0       NaN\n",
       "5                          initial_list_status:f             0       NaN\n",
       "6                                        term:60             0       NaN\n",
       "7                                   emp_length:0             0       NaN\n",
       "8                         mths_since_issue_d:>84             0       NaN\n",
       "9                               int_rate:>20.281             0       NaN\n",
       "10              mths_since_earliest_cr_line:<140             0       NaN\n",
       "11                             inq_last_6mths:>6             0       NaN\n",
       "12                              acc_now_delinq:0             0       NaN\n",
       "13                               annual_inc:<20K             0       NaN\n",
       "14                                       dti:>35             0       NaN\n",
       "15                    mths_since_last_delinq:0-3             0       NaN\n",
       "16                    mths_since_last_record:0-2             0       NaN"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ref_categories = pd.DataFrame(ref_categories, columns = ['Feature name'])\n",
    "df_ref_categories['Coefficients'] = 0\n",
    "df_ref_categories['p_values'] = np.nan\n",
    "df_ref_categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "        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>index</th>\n",
       "      <th>Feature name</th>\n",
       "      <th>Coefficients</th>\n",
       "      <th>p_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.385395</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.121520</td>\n",
       "      <td>4.753387e-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.881507</td>\n",
       "      <td>2.158571e-47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.687851</td>\n",
       "      <td>3.271838e-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.500477</td>\n",
       "      <td>6.929364e-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>grade:E</td>\n",
       "      <td>0.325091</td>\n",
       "      <td>5.871894e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>grade:F</td>\n",
       "      <td>0.137039</td>\n",
       "      <td>5.231950e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>home_ownership:OWN</td>\n",
       "      <td>0.093505</td>\n",
       "      <td>3.462640e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>home_ownership:MORTGAGE</td>\n",
       "      <td>0.105977</td>\n",
       "      <td>5.303227e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>addr_state:NM_VA</td>\n",
       "      <td>0.037053</td>\n",
       "      <td>2.472817e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>addr_state:NY</td>\n",
       "      <td>0.057066</td>\n",
       "      <td>1.845927e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>addr_state:OK_TN_MO_LA_MD_NC</td>\n",
       "      <td>0.059627</td>\n",
       "      <td>1.000094e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>addr_state:CA</td>\n",
       "      <td>0.063601</td>\n",
       "      <td>2.693792e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>addr_state:UT_KY_AZ_NJ</td>\n",
       "      <td>0.078480</td>\n",
       "      <td>1.630257e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>addr_state:AR_MI_PA_OH_MN</td>\n",
       "      <td>0.135479</td>\n",
       "      <td>1.793406e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>addr_state:RI_MA_DE_SD_IN</td>\n",
       "      <td>0.100868</td>\n",
       "      <td>7.237427e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>addr_state:GA_WA_OR</td>\n",
       "      <td>0.185784</td>\n",
       "      <td>3.242749e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>addr_state:WI_MT</td>\n",
       "      <td>0.240481</td>\n",
       "      <td>4.262594e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>addr_state:TX</td>\n",
       "      <td>0.222372</td>\n",
       "      <td>1.127032e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>addr_state:IL_CT</td>\n",
       "      <td>0.261843</td>\n",
       "      <td>3.088720e-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>addr_state:KS_SC_CO_VT_AK_MS</td>\n",
       "      <td>0.320744</td>\n",
       "      <td>2.434110e-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>addr_state:WV_NH_WY_DC_ME_ID</td>\n",
       "      <td>0.522261</td>\n",
       "      <td>9.635088e-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>verification_status:Not Verified</td>\n",
       "      <td>0.085864</td>\n",
       "      <td>4.116527e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>verification_status:Source Verified</td>\n",
       "      <td>-0.010907</td>\n",
       "      <td>4.167432e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>purpose:credit_card</td>\n",
       "      <td>0.301411</td>\n",
       "      <td>1.495937e-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>purpose:debt_consolidation</td>\n",
       "      <td>0.197790</td>\n",
       "      <td>2.746032e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>purpose:oth__med__vacation</td>\n",
       "      <td>0.213355</td>\n",
       "      <td>6.900118e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>purpose:major_purch__car__home_impr</td>\n",
       "      <td>0.265439</td>\n",
       "      <td>1.846669e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>initial_list_status:w</td>\n",
       "      <td>0.054413</td>\n",
       "      <td>3.070754e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>term:36</td>\n",
       "      <td>0.078530</td>\n",
       "      <td>1.936743e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>emp_length:1</td>\n",
       "      <td>0.099320</td>\n",
       "      <td>1.283870e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>emp_length:2-4</td>\n",
       "      <td>0.125188</td>\n",
       "      <td>3.292712e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>emp_length:5-6</td>\n",
       "      <td>0.090508</td>\n",
       "      <td>2.349932e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>emp_length:7-9</td>\n",
       "      <td>0.060713</td>\n",
       "      <td>3.512433e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>emp_length:10</td>\n",
       "      <td>0.124822</td>\n",
       "      <td>1.281702e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>mths_since_issue_d:&lt;38</td>\n",
       "      <td>1.076656</td>\n",
       "      <td>1.298182e-178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>mths_since_issue_d:38-39</td>\n",
       "      <td>0.874363</td>\n",
       "      <td>5.872595e-148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>mths_since_issue_d:40-41</td>\n",
       "      <td>0.773143</td>\n",
       "      <td>2.876952e-122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>mths_since_issue_d:42-48</td>\n",
       "      <td>0.572739</td>\n",
       "      <td>2.871610e-86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>mths_since_issue_d:49-52</td>\n",
       "      <td>0.411661</td>\n",
       "      <td>5.203951e-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>mths_since_issue_d:53-64</td>\n",
       "      <td>0.166235</td>\n",
       "      <td>1.289234e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>mths_since_issue_d:65-84</td>\n",
       "      <td>-0.074976</td>\n",
       "      <td>1.038774e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>int_rate:&lt;9.548</td>\n",
       "      <td>0.889336</td>\n",
       "      <td>1.600893e-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>int_rate:9.548-12.025</td>\n",
       "      <td>0.559782</td>\n",
       "      <td>4.322923e-36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>int_rate:12.025-15.74</td>\n",
       "      <td>0.306816</td>\n",
       "      <td>6.205030e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>int_rate:15.74-20.281</td>\n",
       "      <td>0.113399</td>\n",
       "      <td>9.067425e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>mths_since_earliest_cr_line:141-164</td>\n",
       "      <td>0.054006</td>\n",
       "      <td>2.996284e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>mths_since_earliest_cr_line:165-247</td>\n",
       "      <td>0.034950</td>\n",
       "      <td>8.185699e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>mths_since_earliest_cr_line:248-270</td>\n",
       "      <td>0.076711</td>\n",
       "      <td>3.462911e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>mths_since_earliest_cr_line:271-352</td>\n",
       "      <td>0.119321</td>\n",
       "      <td>3.525173e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>mths_since_earliest_cr_line:&gt;352</td>\n",
       "      <td>0.127038</td>\n",
       "      <td>1.665873e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>inq_last_6mths:0</td>\n",
       "      <td>0.667286</td>\n",
       "      <td>3.180519e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>inq_last_6mths:1-2</td>\n",
       "      <td>0.520869</td>\n",
       "      <td>2.981658e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>inq_last_6mths:3-6</td>\n",
       "      <td>0.310262</td>\n",
       "      <td>4.454631e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>acc_now_delinq:&gt;=1</td>\n",
       "      <td>0.193324</td>\n",
       "      <td>6.837618e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>annual_inc:20K-30K</td>\n",
       "      <td>-0.076546</td>\n",
       "      <td>6.885605e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>annual_inc:30K-40K</td>\n",
       "      <td>-0.013045</td>\n",
       "      <td>7.469306e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>annual_inc:40K-50K</td>\n",
       "      <td>0.081668</td>\n",
       "      <td>4.307606e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>annual_inc:50K-60K</td>\n",
       "      <td>0.148741</td>\n",
       "      <td>2.754270e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>annual_inc:60K-70K</td>\n",
       "      <td>0.224316</td>\n",
       "      <td>8.151162e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>annual_inc:70K-80K</td>\n",
       "      <td>0.298252</td>\n",
       "      <td>2.716401e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>annual_inc:80K-90K</td>\n",
       "      <td>0.374168</td>\n",
       "      <td>3.701330e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "      <td>annual_inc:90K-100K</td>\n",
       "      <td>0.393907</td>\n",
       "      <td>1.108341e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>annual_inc:100K-120K</td>\n",
       "      <td>0.472350</td>\n",
       "      <td>3.990653e-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>annual_inc:120K-140K</td>\n",
       "      <td>0.563210</td>\n",
       "      <td>1.886763e-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>annual_inc:&gt;140K</td>\n",
       "      <td>0.499159</td>\n",
       "      <td>8.322286e-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>66</td>\n",
       "      <td>dti:&lt;=1.4</td>\n",
       "      <td>0.245709</td>\n",
       "      <td>3.209346e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>dti:1.4-3.5</td>\n",
       "      <td>0.361029</td>\n",
       "      <td>2.475348e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>dti:3.5-7.7</td>\n",
       "      <td>0.379480</td>\n",
       "      <td>3.095375e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>dti:7.7-10.5</td>\n",
       "      <td>0.314826</td>\n",
       "      <td>1.489433e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>dti:10.5-16.1</td>\n",
       "      <td>0.235991</td>\n",
       "      <td>4.597990e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>dti:16.1-20.3</td>\n",
       "      <td>0.134808</td>\n",
       "      <td>2.009874e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>dti:20.3-21.7</td>\n",
       "      <td>0.122554</td>\n",
       "      <td>4.348893e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>dti:21.7-22.4</td>\n",
       "      <td>0.087639</td>\n",
       "      <td>1.760704e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>dti:22.4-35</td>\n",
       "      <td>0.045907</td>\n",
       "      <td>4.231849e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>mths_since_last_delinq:Missing</td>\n",
       "      <td>0.116762</td>\n",
       "      <td>2.515592e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>mths_since_last_delinq:4-30</td>\n",
       "      <td>0.143818</td>\n",
       "      <td>6.337521e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>mths_since_last_delinq:31-56</td>\n",
       "      <td>0.189952</td>\n",
       "      <td>3.712932e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>mths_since_last_delinq:&gt;=57</td>\n",
       "      <td>0.142870</td>\n",
       "      <td>8.926129e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "      <td>mths_since_last_record:Missing</td>\n",
       "      <td>0.324814</td>\n",
       "      <td>6.397001e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>mths_since_last_record:3-20</td>\n",
       "      <td>0.435920</td>\n",
       "      <td>3.756425e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>mths_since_last_record:21-31</td>\n",
       "      <td>0.357063</td>\n",
       "      <td>1.509320e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>mths_since_last_record:32-80</td>\n",
       "      <td>0.509914</td>\n",
       "      <td>2.355095e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>mths_since_last_record:81-86</td>\n",
       "      <td>0.171742</td>\n",
       "      <td>9.307974e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>mths_since_last_record:&gt;86</td>\n",
       "      <td>0.237603</td>\n",
       "      <td>4.738771e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>grade:G</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>home_ownership:RENT_OTHER_NONE_ANY</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>2</td>\n",
       "      <td>addr_state:ND_NE_IA_NV_FL_HI_AL</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>3</td>\n",
       "      <td>verification_status:Verified</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>4</td>\n",
       "      <td>purpose:educ__sm_b__wedd__ren_en__mov__house</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>5</td>\n",
       "      <td>initial_list_status:f</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>6</td>\n",
       "      <td>term:60</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>7</td>\n",
       "      <td>emp_length:0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>8</td>\n",
       "      <td>mths_since_issue_d:&gt;84</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>9</td>\n",
       "      <td>int_rate:&gt;20.281</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>10</td>\n",
       "      <td>mths_since_earliest_cr_line:&lt;140</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>11</td>\n",
       "      <td>inq_last_6mths:&gt;6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>12</td>\n",
       "      <td>acc_now_delinq:0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>13</td>\n",
       "      <td>annual_inc:&lt;20K</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>14</td>\n",
       "      <td>dti:&gt;35</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>15</td>\n",
       "      <td>mths_since_last_delinq:0-3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>16</td>\n",
       "      <td>mths_since_last_record:0-2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     index                                  Feature name  Coefficients  \\\n",
       "0        0                                     Intercept     -1.385395   \n",
       "1        1                                       grade:A      1.121520   \n",
       "2        2                                       grade:B      0.881507   \n",
       "3        3                                       grade:C      0.687851   \n",
       "4        4                                       grade:D      0.500477   \n",
       "5        5                                       grade:E      0.325091   \n",
       "6        6                                       grade:F      0.137039   \n",
       "7        7                            home_ownership:OWN      0.093505   \n",
       "8        8                       home_ownership:MORTGAGE      0.105977   \n",
       "9        9                              addr_state:NM_VA      0.037053   \n",
       "10      10                                 addr_state:NY      0.057066   \n",
       "11      11                  addr_state:OK_TN_MO_LA_MD_NC      0.059627   \n",
       "12      12                                 addr_state:CA      0.063601   \n",
       "13      13                        addr_state:UT_KY_AZ_NJ      0.078480   \n",
       "14      14                     addr_state:AR_MI_PA_OH_MN      0.135479   \n",
       "15      15                     addr_state:RI_MA_DE_SD_IN      0.100868   \n",
       "16      16                           addr_state:GA_WA_OR      0.185784   \n",
       "17      17                              addr_state:WI_MT      0.240481   \n",
       "18      18                                 addr_state:TX      0.222372   \n",
       "19      19                              addr_state:IL_CT      0.261843   \n",
       "20      20                  addr_state:KS_SC_CO_VT_AK_MS      0.320744   \n",
       "21      21                  addr_state:WV_NH_WY_DC_ME_ID      0.522261   \n",
       "22      22              verification_status:Not Verified      0.085864   \n",
       "23      23           verification_status:Source Verified     -0.010907   \n",
       "24      24                           purpose:credit_card      0.301411   \n",
       "25      25                    purpose:debt_consolidation      0.197790   \n",
       "26      26                    purpose:oth__med__vacation      0.213355   \n",
       "27      27           purpose:major_purch__car__home_impr      0.265439   \n",
       "28      28                         initial_list_status:w      0.054413   \n",
       "29      29                                       term:36      0.078530   \n",
       "30      30                                  emp_length:1      0.099320   \n",
       "31      31                                emp_length:2-4      0.125188   \n",
       "32      32                                emp_length:5-6      0.090508   \n",
       "33      33                                emp_length:7-9      0.060713   \n",
       "34      34                                 emp_length:10      0.124822   \n",
       "35      35                        mths_since_issue_d:<38      1.076656   \n",
       "36      36                      mths_since_issue_d:38-39      0.874363   \n",
       "37      37                      mths_since_issue_d:40-41      0.773143   \n",
       "38      38                      mths_since_issue_d:42-48      0.572739   \n",
       "39      39                      mths_since_issue_d:49-52      0.411661   \n",
       "40      40                      mths_since_issue_d:53-64      0.166235   \n",
       "41      41                      mths_since_issue_d:65-84     -0.074976   \n",
       "42      42                               int_rate:<9.548      0.889336   \n",
       "43      43                         int_rate:9.548-12.025      0.559782   \n",
       "44      44                         int_rate:12.025-15.74      0.306816   \n",
       "45      45                         int_rate:15.74-20.281      0.113399   \n",
       "46      46           mths_since_earliest_cr_line:141-164      0.054006   \n",
       "47      47           mths_since_earliest_cr_line:165-247      0.034950   \n",
       "48      48           mths_since_earliest_cr_line:248-270      0.076711   \n",
       "49      49           mths_since_earliest_cr_line:271-352      0.119321   \n",
       "50      50              mths_since_earliest_cr_line:>352      0.127038   \n",
       "51      51                              inq_last_6mths:0      0.667286   \n",
       "52      52                            inq_last_6mths:1-2      0.520869   \n",
       "53      53                            inq_last_6mths:3-6      0.310262   \n",
       "54      54                            acc_now_delinq:>=1      0.193324   \n",
       "55      55                            annual_inc:20K-30K     -0.076546   \n",
       "56      56                            annual_inc:30K-40K     -0.013045   \n",
       "57      57                            annual_inc:40K-50K      0.081668   \n",
       "58      58                            annual_inc:50K-60K      0.148741   \n",
       "59      59                            annual_inc:60K-70K      0.224316   \n",
       "60      60                            annual_inc:70K-80K      0.298252   \n",
       "61      61                            annual_inc:80K-90K      0.374168   \n",
       "62      62                           annual_inc:90K-100K      0.393907   \n",
       "63      63                          annual_inc:100K-120K      0.472350   \n",
       "64      64                          annual_inc:120K-140K      0.563210   \n",
       "65      65                              annual_inc:>140K      0.499159   \n",
       "66      66                                     dti:<=1.4      0.245709   \n",
       "67      67                                   dti:1.4-3.5      0.361029   \n",
       "68      68                                   dti:3.5-7.7      0.379480   \n",
       "69      69                                  dti:7.7-10.5      0.314826   \n",
       "70      70                                 dti:10.5-16.1      0.235991   \n",
       "71      71                                 dti:16.1-20.3      0.134808   \n",
       "72      72                                 dti:20.3-21.7      0.122554   \n",
       "73      73                                 dti:21.7-22.4      0.087639   \n",
       "74      74                                   dti:22.4-35      0.045907   \n",
       "75      75                mths_since_last_delinq:Missing      0.116762   \n",
       "76      76                   mths_since_last_delinq:4-30      0.143818   \n",
       "77      77                  mths_since_last_delinq:31-56      0.189952   \n",
       "78      78                   mths_since_last_delinq:>=57      0.142870   \n",
       "79      79                mths_since_last_record:Missing      0.324814   \n",
       "80      80                   mths_since_last_record:3-20      0.435920   \n",
       "81      81                  mths_since_last_record:21-31      0.357063   \n",
       "82      82                  mths_since_last_record:32-80      0.509914   \n",
       "83      83                  mths_since_last_record:81-86      0.171742   \n",
       "84      84                    mths_since_last_record:>86      0.237603   \n",
       "85       0                                       grade:G      0.000000   \n",
       "86       1            home_ownership:RENT_OTHER_NONE_ANY      0.000000   \n",
       "87       2               addr_state:ND_NE_IA_NV_FL_HI_AL      0.000000   \n",
       "88       3                  verification_status:Verified      0.000000   \n",
       "89       4  purpose:educ__sm_b__wedd__ren_en__mov__house      0.000000   \n",
       "90       5                         initial_list_status:f      0.000000   \n",
       "91       6                                       term:60      0.000000   \n",
       "92       7                                  emp_length:0      0.000000   \n",
       "93       8                        mths_since_issue_d:>84      0.000000   \n",
       "94       9                              int_rate:>20.281      0.000000   \n",
       "95      10              mths_since_earliest_cr_line:<140      0.000000   \n",
       "96      11                             inq_last_6mths:>6      0.000000   \n",
       "97      12                              acc_now_delinq:0      0.000000   \n",
       "98      13                               annual_inc:<20K      0.000000   \n",
       "99      14                                       dti:>35      0.000000   \n",
       "100     15                    mths_since_last_delinq:0-3      0.000000   \n",
       "101     16                    mths_since_last_record:0-2      0.000000   \n",
       "\n",
       "          p_values  \n",
       "0              NaN  \n",
       "1     4.753387e-35  \n",
       "2     2.158571e-47  \n",
       "3     3.271838e-34  \n",
       "4     6.929364e-21  \n",
       "5     5.871894e-12  \n",
       "6     5.231950e-03  \n",
       "7     3.462640e-06  \n",
       "8     5.303227e-17  \n",
       "9     2.472817e-01  \n",
       "10    1.845927e-02  \n",
       "11    1.000094e-02  \n",
       "12    2.693792e-03  \n",
       "13    1.630257e-03  \n",
       "14    1.793406e-09  \n",
       "15    7.237427e-04  \n",
       "16    3.242749e-12  \n",
       "17    4.262594e-07  \n",
       "18    1.127032e-17  \n",
       "19    3.088720e-19  \n",
       "20    2.434110e-25  \n",
       "21    9.635088e-23  \n",
       "22    4.116527e-09  \n",
       "23    4.167432e-01  \n",
       "24    1.495937e-26  \n",
       "25    2.746032e-14  \n",
       "26    6.900118e-12  \n",
       "27    1.846669e-17  \n",
       "28    3.070754e-05  \n",
       "29    1.936743e-08  \n",
       "30    1.283870e-04  \n",
       "31    3.292712e-11  \n",
       "32    2.349932e-05  \n",
       "33    3.512433e-03  \n",
       "34    1.281702e-11  \n",
       "35   1.298182e-178  \n",
       "36   5.872595e-148  \n",
       "37   2.876952e-122  \n",
       "38    2.871610e-86  \n",
       "39    5.203951e-40  \n",
       "40    1.289234e-08  \n",
       "41    1.038774e-02  \n",
       "42    1.600893e-31  \n",
       "43    4.322923e-36  \n",
       "44    6.205030e-17  \n",
       "45    9.067425e-05  \n",
       "46    2.996284e-02  \n",
       "47    8.185699e-02  \n",
       "48    3.462911e-03  \n",
       "49    3.525173e-07  \n",
       "50    1.665873e-06  \n",
       "51    3.180519e-14  \n",
       "52    2.981658e-09  \n",
       "53    4.454631e-04  \n",
       "54    6.837618e-02  \n",
       "55    6.885605e-02  \n",
       "56    7.469306e-01  \n",
       "57    4.307606e-02  \n",
       "58    2.754270e-04  \n",
       "59    8.151162e-08  \n",
       "60    2.716401e-12  \n",
       "61    3.701330e-17  \n",
       "62    1.108341e-17  \n",
       "63    3.990653e-25  \n",
       "64    1.886763e-27  \n",
       "65    8.322286e-26  \n",
       "66    3.209346e-03  \n",
       "67    2.475348e-07  \n",
       "68    3.095375e-10  \n",
       "69    1.489433e-07  \n",
       "70    4.597990e-05  \n",
       "71    2.009874e-02  \n",
       "72    4.348893e-02  \n",
       "73    1.760704e-01  \n",
       "74    4.231849e-01  \n",
       "75    2.515592e-02  \n",
       "76    6.337521e-03  \n",
       "77    3.712932e-04  \n",
       "78    8.926129e-03  \n",
       "79    6.397001e-05  \n",
       "80    3.756425e-04  \n",
       "81    1.509320e-03  \n",
       "82    2.355095e-09  \n",
       "83    9.307974e-02  \n",
       "84    4.738771e-03  \n",
       "85             NaN  \n",
       "86             NaN  \n",
       "87             NaN  \n",
       "88             NaN  \n",
       "89             NaN  \n",
       "90             NaN  \n",
       "91             NaN  \n",
       "92             NaN  \n",
       "93             NaN  \n",
       "94             NaN  \n",
       "95             NaN  \n",
       "96             NaN  \n",
       "97             NaN  \n",
       "98             NaN  \n",
       "99             NaN  \n",
       "100            NaN  \n",
       "101            NaN  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scorecard = pd.concat([summary_table, df_ref_categories])\n",
    "df_scorecard = df_scorecard.reset_index()\n",
    "\n",
    "df_scorecard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Feature name</th>\n",
       "      <th>Coefficients</th>\n",
       "      <th>p_values</th>\n",
       "      <th>Original feature name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.385395</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Intercept</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.121520</td>\n",
       "      <td>4.753387e-35</td>\n",
       "      <td>grade</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.881507</td>\n",
       "      <td>2.158571e-47</td>\n",
       "      <td>grade</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.687851</td>\n",
       "      <td>3.271838e-34</td>\n",
       "      <td>grade</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.500477</td>\n",
       "      <td>6.929364e-21</td>\n",
       "      <td>grade</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>grade:E</td>\n",
       "      <td>0.325091</td>\n",
       "      <td>5.871894e-12</td>\n",
       "      <td>grade</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>grade:F</td>\n",
       "      <td>0.137039</td>\n",
       "      <td>5.231950e-03</td>\n",
       "      <td>grade</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>home_ownership:OWN</td>\n",
       "      <td>0.093505</td>\n",
       "      <td>3.462640e-06</td>\n",
       "      <td>home_ownership</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>home_ownership:MORTGAGE</td>\n",
       "      <td>0.105977</td>\n",
       "      <td>5.303227e-17</td>\n",
       "      <td>home_ownership</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>addr_state:NM_VA</td>\n",
       "      <td>0.037053</td>\n",
       "      <td>2.472817e-01</td>\n",
       "      <td>addr_state</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>addr_state:NY</td>\n",
       "      <td>0.057066</td>\n",
       "      <td>1.845927e-02</td>\n",
       "      <td>addr_state</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>addr_state:OK_TN_MO_LA_MD_NC</td>\n",
       "      <td>0.059627</td>\n",
       "      <td>1.000094e-02</td>\n",
       "      <td>addr_state</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>addr_state:CA</td>\n",
       "      <td>0.063601</td>\n",
       "      <td>2.693792e-03</td>\n",
       "      <td>addr_state</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>addr_state:UT_KY_AZ_NJ</td>\n",
       "      <td>0.078480</td>\n",
       "      <td>1.630257e-03</td>\n",
       "      <td>addr_state</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>addr_state:AR_MI_PA_OH_MN</td>\n",
       "      <td>0.135479</td>\n",
       "      <td>1.793406e-09</td>\n",
       "      <td>addr_state</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>addr_state:RI_MA_DE_SD_IN</td>\n",
       "      <td>0.100868</td>\n",
       "      <td>7.237427e-04</td>\n",
       "      <td>addr_state</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>addr_state:GA_WA_OR</td>\n",
       "      <td>0.185784</td>\n",
       "      <td>3.242749e-12</td>\n",
       "      <td>addr_state</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>addr_state:WI_MT</td>\n",
       "      <td>0.240481</td>\n",
       "      <td>4.262594e-07</td>\n",
       "      <td>addr_state</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>addr_state:TX</td>\n",
       "      <td>0.222372</td>\n",
       "      <td>1.127032e-17</td>\n",
       "      <td>addr_state</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>addr_state:IL_CT</td>\n",
       "      <td>0.261843</td>\n",
       "      <td>3.088720e-19</td>\n",
       "      <td>addr_state</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>addr_state:KS_SC_CO_VT_AK_MS</td>\n",
       "      <td>0.320744</td>\n",
       "      <td>2.434110e-25</td>\n",
       "      <td>addr_state</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>addr_state:WV_NH_WY_DC_ME_ID</td>\n",
       "      <td>0.522261</td>\n",
       "      <td>9.635088e-23</td>\n",
       "      <td>addr_state</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>verification_status:Not Verified</td>\n",
       "      <td>0.085864</td>\n",
       "      <td>4.116527e-09</td>\n",
       "      <td>verification_status</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>verification_status:Source Verified</td>\n",
       "      <td>-0.010907</td>\n",
       "      <td>4.167432e-01</td>\n",
       "      <td>verification_status</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>purpose:credit_card</td>\n",
       "      <td>0.301411</td>\n",
       "      <td>1.495937e-26</td>\n",
       "      <td>purpose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>purpose:debt_consolidation</td>\n",
       "      <td>0.197790</td>\n",
       "      <td>2.746032e-14</td>\n",
       "      <td>purpose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>purpose:oth__med__vacation</td>\n",
       "      <td>0.213355</td>\n",
       "      <td>6.900118e-12</td>\n",
       "      <td>purpose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>purpose:major_purch__car__home_impr</td>\n",
       "      <td>0.265439</td>\n",
       "      <td>1.846669e-17</td>\n",
       "      <td>purpose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>initial_list_status:w</td>\n",
       "      <td>0.054413</td>\n",
       "      <td>3.070754e-05</td>\n",
       "      <td>initial_list_status</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>term:36</td>\n",
       "      <td>0.078530</td>\n",
       "      <td>1.936743e-08</td>\n",
       "      <td>term</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>emp_length:1</td>\n",
       "      <td>0.099320</td>\n",
       "      <td>1.283870e-04</td>\n",
       "      <td>emp_length</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>emp_length:2-4</td>\n",
       "      <td>0.125188</td>\n",
       "      <td>3.292712e-11</td>\n",
       "      <td>emp_length</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>emp_length:5-6</td>\n",
       "      <td>0.090508</td>\n",
       "      <td>2.349932e-05</td>\n",
       "      <td>emp_length</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>emp_length:7-9</td>\n",
       "      <td>0.060713</td>\n",
       "      <td>3.512433e-03</td>\n",
       "      <td>emp_length</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>emp_length:10</td>\n",
       "      <td>0.124822</td>\n",
       "      <td>1.281702e-11</td>\n",
       "      <td>emp_length</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>mths_since_issue_d:&lt;38</td>\n",
       "      <td>1.076656</td>\n",
       "      <td>1.298182e-178</td>\n",
       "      <td>mths_since_issue_d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>mths_since_issue_d:38-39</td>\n",
       "      <td>0.874363</td>\n",
       "      <td>5.872595e-148</td>\n",
       "      <td>mths_since_issue_d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>mths_since_issue_d:40-41</td>\n",
       "      <td>0.773143</td>\n",
       "      <td>2.876952e-122</td>\n",
       "      <td>mths_since_issue_d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>mths_since_issue_d:42-48</td>\n",
       "      <td>0.572739</td>\n",
       "      <td>2.871610e-86</td>\n",
       "      <td>mths_since_issue_d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>mths_since_issue_d:49-52</td>\n",
       "      <td>0.411661</td>\n",
       "      <td>5.203951e-40</td>\n",
       "      <td>mths_since_issue_d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>mths_since_issue_d:53-64</td>\n",
       "      <td>0.166235</td>\n",
       "      <td>1.289234e-08</td>\n",
       "      <td>mths_since_issue_d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>mths_since_issue_d:65-84</td>\n",
       "      <td>-0.074976</td>\n",
       "      <td>1.038774e-02</td>\n",
       "      <td>mths_since_issue_d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>int_rate:&lt;9.548</td>\n",
       "      <td>0.889336</td>\n",
       "      <td>1.600893e-31</td>\n",
       "      <td>int_rate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>int_rate:9.548-12.025</td>\n",
       "      <td>0.559782</td>\n",
       "      <td>4.322923e-36</td>\n",
       "      <td>int_rate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>int_rate:12.025-15.74</td>\n",
       "      <td>0.306816</td>\n",
       "      <td>6.205030e-17</td>\n",
       "      <td>int_rate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>int_rate:15.74-20.281</td>\n",
       "      <td>0.113399</td>\n",
       "      <td>9.067425e-05</td>\n",
       "      <td>int_rate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>mths_since_earliest_cr_line:141-164</td>\n",
       "      <td>0.054006</td>\n",
       "      <td>2.996284e-02</td>\n",
       "      <td>mths_since_earliest_cr_line</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>mths_since_earliest_cr_line:165-247</td>\n",
       "      <td>0.034950</td>\n",
       "      <td>8.185699e-02</td>\n",
       "      <td>mths_since_earliest_cr_line</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>mths_since_earliest_cr_line:248-270</td>\n",
       "      <td>0.076711</td>\n",
       "      <td>3.462911e-03</td>\n",
       "      <td>mths_since_earliest_cr_line</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>mths_since_earliest_cr_line:271-352</td>\n",
       "      <td>0.119321</td>\n",
       "      <td>3.525173e-07</td>\n",
       "      <td>mths_since_earliest_cr_line</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>mths_since_earliest_cr_line:&gt;352</td>\n",
       "      <td>0.127038</td>\n",
       "      <td>1.665873e-06</td>\n",
       "      <td>mths_since_earliest_cr_line</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>inq_last_6mths:0</td>\n",
       "      <td>0.667286</td>\n",
       "      <td>3.180519e-14</td>\n",
       "      <td>inq_last_6mths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>inq_last_6mths:1-2</td>\n",
       "      <td>0.520869</td>\n",
       "      <td>2.981658e-09</td>\n",
       "      <td>inq_last_6mths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>inq_last_6mths:3-6</td>\n",
       "      <td>0.310262</td>\n",
       "      <td>4.454631e-04</td>\n",
       "      <td>inq_last_6mths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>acc_now_delinq:&gt;=1</td>\n",
       "      <td>0.193324</td>\n",
       "      <td>6.837618e-02</td>\n",
       "      <td>acc_now_delinq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>annual_inc:20K-30K</td>\n",
       "      <td>-0.076546</td>\n",
       "      <td>6.885605e-02</td>\n",
       "      <td>annual_inc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>annual_inc:30K-40K</td>\n",
       "      <td>-0.013045</td>\n",
       "      <td>7.469306e-01</td>\n",
       "      <td>annual_inc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>annual_inc:40K-50K</td>\n",
       "      <td>0.081668</td>\n",
       "      <td>4.307606e-02</td>\n",
       "      <td>annual_inc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>annual_inc:50K-60K</td>\n",
       "      <td>0.148741</td>\n",
       "      <td>2.754270e-04</td>\n",
       "      <td>annual_inc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>annual_inc:60K-70K</td>\n",
       "      <td>0.224316</td>\n",
       "      <td>8.151162e-08</td>\n",
       "      <td>annual_inc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>annual_inc:70K-80K</td>\n",
       "      <td>0.298252</td>\n",
       "      <td>2.716401e-12</td>\n",
       "      <td>annual_inc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>annual_inc:80K-90K</td>\n",
       "      <td>0.374168</td>\n",
       "      <td>3.701330e-17</td>\n",
       "      <td>annual_inc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "      <td>annual_inc:90K-100K</td>\n",
       "      <td>0.393907</td>\n",
       "      <td>1.108341e-17</td>\n",
       "      <td>annual_inc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>annual_inc:100K-120K</td>\n",
       "      <td>0.472350</td>\n",
       "      <td>3.990653e-25</td>\n",
       "      <td>annual_inc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>annual_inc:120K-140K</td>\n",
       "      <td>0.563210</td>\n",
       "      <td>1.886763e-27</td>\n",
       "      <td>annual_inc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>annual_inc:&gt;140K</td>\n",
       "      <td>0.499159</td>\n",
       "      <td>8.322286e-26</td>\n",
       "      <td>annual_inc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>66</td>\n",
       "      <td>dti:&lt;=1.4</td>\n",
       "      <td>0.245709</td>\n",
       "      <td>3.209346e-03</td>\n",
       "      <td>dti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>dti:1.4-3.5</td>\n",
       "      <td>0.361029</td>\n",
       "      <td>2.475348e-07</td>\n",
       "      <td>dti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>dti:3.5-7.7</td>\n",
       "      <td>0.379480</td>\n",
       "      <td>3.095375e-10</td>\n",
       "      <td>dti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>dti:7.7-10.5</td>\n",
       "      <td>0.314826</td>\n",
       "      <td>1.489433e-07</td>\n",
       "      <td>dti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>dti:10.5-16.1</td>\n",
       "      <td>0.235991</td>\n",
       "      <td>4.597990e-05</td>\n",
       "      <td>dti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>dti:16.1-20.3</td>\n",
       "      <td>0.134808</td>\n",
       "      <td>2.009874e-02</td>\n",
       "      <td>dti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>dti:20.3-21.7</td>\n",
       "      <td>0.122554</td>\n",
       "      <td>4.348893e-02</td>\n",
       "      <td>dti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>dti:21.7-22.4</td>\n",
       "      <td>0.087639</td>\n",
       "      <td>1.760704e-01</td>\n",
       "      <td>dti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>dti:22.4-35</td>\n",
       "      <td>0.045907</td>\n",
       "      <td>4.231849e-01</td>\n",
       "      <td>dti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>mths_since_last_delinq:Missing</td>\n",
       "      <td>0.116762</td>\n",
       "      <td>2.515592e-02</td>\n",
       "      <td>mths_since_last_delinq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>mths_since_last_delinq:4-30</td>\n",
       "      <td>0.143818</td>\n",
       "      <td>6.337521e-03</td>\n",
       "      <td>mths_since_last_delinq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>mths_since_last_delinq:31-56</td>\n",
       "      <td>0.189952</td>\n",
       "      <td>3.712932e-04</td>\n",
       "      <td>mths_since_last_delinq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>mths_since_last_delinq:&gt;=57</td>\n",
       "      <td>0.142870</td>\n",
       "      <td>8.926129e-03</td>\n",
       "      <td>mths_since_last_delinq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "      <td>mths_since_last_record:Missing</td>\n",
       "      <td>0.324814</td>\n",
       "      <td>6.397001e-05</td>\n",
       "      <td>mths_since_last_record</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>mths_since_last_record:3-20</td>\n",
       "      <td>0.435920</td>\n",
       "      <td>3.756425e-04</td>\n",
       "      <td>mths_since_last_record</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>mths_since_last_record:21-31</td>\n",
       "      <td>0.357063</td>\n",
       "      <td>1.509320e-03</td>\n",
       "      <td>mths_since_last_record</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>mths_since_last_record:32-80</td>\n",
       "      <td>0.509914</td>\n",
       "      <td>2.355095e-09</td>\n",
       "      <td>mths_since_last_record</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>mths_since_last_record:81-86</td>\n",
       "      <td>0.171742</td>\n",
       "      <td>9.307974e-02</td>\n",
       "      <td>mths_since_last_record</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>mths_since_last_record:&gt;86</td>\n",
       "      <td>0.237603</td>\n",
       "      <td>4.738771e-03</td>\n",
       "      <td>mths_since_last_record</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>grade:G</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>grade</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>home_ownership:RENT_OTHER_NONE_ANY</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>home_ownership</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>2</td>\n",
       "      <td>addr_state:ND_NE_IA_NV_FL_HI_AL</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>addr_state</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>3</td>\n",
       "      <td>verification_status:Verified</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>verification_status</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>4</td>\n",
       "      <td>purpose:educ__sm_b__wedd__ren_en__mov__house</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>purpose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>5</td>\n",
       "      <td>initial_list_status:f</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>initial_list_status</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>6</td>\n",
       "      <td>term:60</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>term</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>7</td>\n",
       "      <td>emp_length:0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>emp_length</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>8</td>\n",
       "      <td>mths_since_issue_d:&gt;84</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mths_since_issue_d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>9</td>\n",
       "      <td>int_rate:&gt;20.281</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>int_rate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>10</td>\n",
       "      <td>mths_since_earliest_cr_line:&lt;140</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mths_since_earliest_cr_line</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>11</td>\n",
       "      <td>inq_last_6mths:&gt;6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>inq_last_6mths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>12</td>\n",
       "      <td>acc_now_delinq:0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>acc_now_delinq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>13</td>\n",
       "      <td>annual_inc:&lt;20K</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>annual_inc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>14</td>\n",
       "      <td>dti:&gt;35</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>15</td>\n",
       "      <td>mths_since_last_delinq:0-3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mths_since_last_delinq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>16</td>\n",
       "      <td>mths_since_last_record:0-2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mths_since_last_record</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     index                                  Feature name  Coefficients  \\\n",
       "0        0                                     Intercept     -1.385395   \n",
       "1        1                                       grade:A      1.121520   \n",
       "2        2                                       grade:B      0.881507   \n",
       "3        3                                       grade:C      0.687851   \n",
       "4        4                                       grade:D      0.500477   \n",
       "5        5                                       grade:E      0.325091   \n",
       "6        6                                       grade:F      0.137039   \n",
       "7        7                            home_ownership:OWN      0.093505   \n",
       "8        8                       home_ownership:MORTGAGE      0.105977   \n",
       "9        9                              addr_state:NM_VA      0.037053   \n",
       "10      10                                 addr_state:NY      0.057066   \n",
       "11      11                  addr_state:OK_TN_MO_LA_MD_NC      0.059627   \n",
       "12      12                                 addr_state:CA      0.063601   \n",
       "13      13                        addr_state:UT_KY_AZ_NJ      0.078480   \n",
       "14      14                     addr_state:AR_MI_PA_OH_MN      0.135479   \n",
       "15      15                     addr_state:RI_MA_DE_SD_IN      0.100868   \n",
       "16      16                           addr_state:GA_WA_OR      0.185784   \n",
       "17      17                              addr_state:WI_MT      0.240481   \n",
       "18      18                                 addr_state:TX      0.222372   \n",
       "19      19                              addr_state:IL_CT      0.261843   \n",
       "20      20                  addr_state:KS_SC_CO_VT_AK_MS      0.320744   \n",
       "21      21                  addr_state:WV_NH_WY_DC_ME_ID      0.522261   \n",
       "22      22              verification_status:Not Verified      0.085864   \n",
       "23      23           verification_status:Source Verified     -0.010907   \n",
       "24      24                           purpose:credit_card      0.301411   \n",
       "25      25                    purpose:debt_consolidation      0.197790   \n",
       "26      26                    purpose:oth__med__vacation      0.213355   \n",
       "27      27           purpose:major_purch__car__home_impr      0.265439   \n",
       "28      28                         initial_list_status:w      0.054413   \n",
       "29      29                                       term:36      0.078530   \n",
       "30      30                                  emp_length:1      0.099320   \n",
       "31      31                                emp_length:2-4      0.125188   \n",
       "32      32                                emp_length:5-6      0.090508   \n",
       "33      33                                emp_length:7-9      0.060713   \n",
       "34      34                                 emp_length:10      0.124822   \n",
       "35      35                        mths_since_issue_d:<38      1.076656   \n",
       "36      36                      mths_since_issue_d:38-39      0.874363   \n",
       "37      37                      mths_since_issue_d:40-41      0.773143   \n",
       "38      38                      mths_since_issue_d:42-48      0.572739   \n",
       "39      39                      mths_since_issue_d:49-52      0.411661   \n",
       "40      40                      mths_since_issue_d:53-64      0.166235   \n",
       "41      41                      mths_since_issue_d:65-84     -0.074976   \n",
       "42      42                               int_rate:<9.548      0.889336   \n",
       "43      43                         int_rate:9.548-12.025      0.559782   \n",
       "44      44                         int_rate:12.025-15.74      0.306816   \n",
       "45      45                         int_rate:15.74-20.281      0.113399   \n",
       "46      46           mths_since_earliest_cr_line:141-164      0.054006   \n",
       "47      47           mths_since_earliest_cr_line:165-247      0.034950   \n",
       "48      48           mths_since_earliest_cr_line:248-270      0.076711   \n",
       "49      49           mths_since_earliest_cr_line:271-352      0.119321   \n",
       "50      50              mths_since_earliest_cr_line:>352      0.127038   \n",
       "51      51                              inq_last_6mths:0      0.667286   \n",
       "52      52                            inq_last_6mths:1-2      0.520869   \n",
       "53      53                            inq_last_6mths:3-6      0.310262   \n",
       "54      54                            acc_now_delinq:>=1      0.193324   \n",
       "55      55                            annual_inc:20K-30K     -0.076546   \n",
       "56      56                            annual_inc:30K-40K     -0.013045   \n",
       "57      57                            annual_inc:40K-50K      0.081668   \n",
       "58      58                            annual_inc:50K-60K      0.148741   \n",
       "59      59                            annual_inc:60K-70K      0.224316   \n",
       "60      60                            annual_inc:70K-80K      0.298252   \n",
       "61      61                            annual_inc:80K-90K      0.374168   \n",
       "62      62                           annual_inc:90K-100K      0.393907   \n",
       "63      63                          annual_inc:100K-120K      0.472350   \n",
       "64      64                          annual_inc:120K-140K      0.563210   \n",
       "65      65                              annual_inc:>140K      0.499159   \n",
       "66      66                                     dti:<=1.4      0.245709   \n",
       "67      67                                   dti:1.4-3.5      0.361029   \n",
       "68      68                                   dti:3.5-7.7      0.379480   \n",
       "69      69                                  dti:7.7-10.5      0.314826   \n",
       "70      70                                 dti:10.5-16.1      0.235991   \n",
       "71      71                                 dti:16.1-20.3      0.134808   \n",
       "72      72                                 dti:20.3-21.7      0.122554   \n",
       "73      73                                 dti:21.7-22.4      0.087639   \n",
       "74      74                                   dti:22.4-35      0.045907   \n",
       "75      75                mths_since_last_delinq:Missing      0.116762   \n",
       "76      76                   mths_since_last_delinq:4-30      0.143818   \n",
       "77      77                  mths_since_last_delinq:31-56      0.189952   \n",
       "78      78                   mths_since_last_delinq:>=57      0.142870   \n",
       "79      79                mths_since_last_record:Missing      0.324814   \n",
       "80      80                   mths_since_last_record:3-20      0.435920   \n",
       "81      81                  mths_since_last_record:21-31      0.357063   \n",
       "82      82                  mths_since_last_record:32-80      0.509914   \n",
       "83      83                  mths_since_last_record:81-86      0.171742   \n",
       "84      84                    mths_since_last_record:>86      0.237603   \n",
       "85       0                                       grade:G      0.000000   \n",
       "86       1            home_ownership:RENT_OTHER_NONE_ANY      0.000000   \n",
       "87       2               addr_state:ND_NE_IA_NV_FL_HI_AL      0.000000   \n",
       "88       3                  verification_status:Verified      0.000000   \n",
       "89       4  purpose:educ__sm_b__wedd__ren_en__mov__house      0.000000   \n",
       "90       5                         initial_list_status:f      0.000000   \n",
       "91       6                                       term:60      0.000000   \n",
       "92       7                                  emp_length:0      0.000000   \n",
       "93       8                        mths_since_issue_d:>84      0.000000   \n",
       "94       9                              int_rate:>20.281      0.000000   \n",
       "95      10              mths_since_earliest_cr_line:<140      0.000000   \n",
       "96      11                             inq_last_6mths:>6      0.000000   \n",
       "97      12                              acc_now_delinq:0      0.000000   \n",
       "98      13                               annual_inc:<20K      0.000000   \n",
       "99      14                                       dti:>35      0.000000   \n",
       "100     15                    mths_since_last_delinq:0-3      0.000000   \n",
       "101     16                    mths_since_last_record:0-2      0.000000   \n",
       "\n",
       "          p_values        Original feature name  \n",
       "0              NaN                    Intercept  \n",
       "1     4.753387e-35                        grade  \n",
       "2     2.158571e-47                        grade  \n",
       "3     3.271838e-34                        grade  \n",
       "4     6.929364e-21                        grade  \n",
       "5     5.871894e-12                        grade  \n",
       "6     5.231950e-03                        grade  \n",
       "7     3.462640e-06               home_ownership  \n",
       "8     5.303227e-17               home_ownership  \n",
       "9     2.472817e-01                   addr_state  \n",
       "10    1.845927e-02                   addr_state  \n",
       "11    1.000094e-02                   addr_state  \n",
       "12    2.693792e-03                   addr_state  \n",
       "13    1.630257e-03                   addr_state  \n",
       "14    1.793406e-09                   addr_state  \n",
       "15    7.237427e-04                   addr_state  \n",
       "16    3.242749e-12                   addr_state  \n",
       "17    4.262594e-07                   addr_state  \n",
       "18    1.127032e-17                   addr_state  \n",
       "19    3.088720e-19                   addr_state  \n",
       "20    2.434110e-25                   addr_state  \n",
       "21    9.635088e-23                   addr_state  \n",
       "22    4.116527e-09          verification_status  \n",
       "23    4.167432e-01          verification_status  \n",
       "24    1.495937e-26                      purpose  \n",
       "25    2.746032e-14                      purpose  \n",
       "26    6.900118e-12                      purpose  \n",
       "27    1.846669e-17                      purpose  \n",
       "28    3.070754e-05          initial_list_status  \n",
       "29    1.936743e-08                         term  \n",
       "30    1.283870e-04                   emp_length  \n",
       "31    3.292712e-11                   emp_length  \n",
       "32    2.349932e-05                   emp_length  \n",
       "33    3.512433e-03                   emp_length  \n",
       "34    1.281702e-11                   emp_length  \n",
       "35   1.298182e-178           mths_since_issue_d  \n",
       "36   5.872595e-148           mths_since_issue_d  \n",
       "37   2.876952e-122           mths_since_issue_d  \n",
       "38    2.871610e-86           mths_since_issue_d  \n",
       "39    5.203951e-40           mths_since_issue_d  \n",
       "40    1.289234e-08           mths_since_issue_d  \n",
       "41    1.038774e-02           mths_since_issue_d  \n",
       "42    1.600893e-31                     int_rate  \n",
       "43    4.322923e-36                     int_rate  \n",
       "44    6.205030e-17                     int_rate  \n",
       "45    9.067425e-05                     int_rate  \n",
       "46    2.996284e-02  mths_since_earliest_cr_line  \n",
       "47    8.185699e-02  mths_since_earliest_cr_line  \n",
       "48    3.462911e-03  mths_since_earliest_cr_line  \n",
       "49    3.525173e-07  mths_since_earliest_cr_line  \n",
       "50    1.665873e-06  mths_since_earliest_cr_line  \n",
       "51    3.180519e-14               inq_last_6mths  \n",
       "52    2.981658e-09               inq_last_6mths  \n",
       "53    4.454631e-04               inq_last_6mths  \n",
       "54    6.837618e-02               acc_now_delinq  \n",
       "55    6.885605e-02                   annual_inc  \n",
       "56    7.469306e-01                   annual_inc  \n",
       "57    4.307606e-02                   annual_inc  \n",
       "58    2.754270e-04                   annual_inc  \n",
       "59    8.151162e-08                   annual_inc  \n",
       "60    2.716401e-12                   annual_inc  \n",
       "61    3.701330e-17                   annual_inc  \n",
       "62    1.108341e-17                   annual_inc  \n",
       "63    3.990653e-25                   annual_inc  \n",
       "64    1.886763e-27                   annual_inc  \n",
       "65    8.322286e-26                   annual_inc  \n",
       "66    3.209346e-03                          dti  \n",
       "67    2.475348e-07                          dti  \n",
       "68    3.095375e-10                          dti  \n",
       "69    1.489433e-07                          dti  \n",
       "70    4.597990e-05                          dti  \n",
       "71    2.009874e-02                          dti  \n",
       "72    4.348893e-02                          dti  \n",
       "73    1.760704e-01                          dti  \n",
       "74    4.231849e-01                          dti  \n",
       "75    2.515592e-02       mths_since_last_delinq  \n",
       "76    6.337521e-03       mths_since_last_delinq  \n",
       "77    3.712932e-04       mths_since_last_delinq  \n",
       "78    8.926129e-03       mths_since_last_delinq  \n",
       "79    6.397001e-05       mths_since_last_record  \n",
       "80    3.756425e-04       mths_since_last_record  \n",
       "81    1.509320e-03       mths_since_last_record  \n",
       "82    2.355095e-09       mths_since_last_record  \n",
       "83    9.307974e-02       mths_since_last_record  \n",
       "84    4.738771e-03       mths_since_last_record  \n",
       "85             NaN                        grade  \n",
       "86             NaN               home_ownership  \n",
       "87             NaN                   addr_state  \n",
       "88             NaN          verification_status  \n",
       "89             NaN                      purpose  \n",
       "90             NaN          initial_list_status  \n",
       "91             NaN                         term  \n",
       "92             NaN                   emp_length  \n",
       "93             NaN           mths_since_issue_d  \n",
       "94             NaN                     int_rate  \n",
       "95             NaN  mths_since_earliest_cr_line  \n",
       "96             NaN               inq_last_6mths  \n",
       "97             NaN               acc_now_delinq  \n",
       "98             NaN                   annual_inc  \n",
       "99             NaN                          dti  \n",
       "100            NaN       mths_since_last_delinq  \n",
       "101            NaN       mths_since_last_record  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We create a new column, called 'Original feature name', which contains the value of the 'Feature name' column,\n",
    "# up to the column symbol.\n",
    "df_scorecard['Original feature name'] = df_scorecard['Feature name'].str.split(':').str[0]\n",
    "\n",
    "df_scorecard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we want a scorecard with score range  :\n",
    "\n",
    "$$ \\textbf{Minimum score} = 300 $$\n",
    "\n",
    "$$ \\textbf{Maximum score} = 850 $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_score = 300\n",
    "max_score = 850"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.5478236224860793"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the data by Original features,\n",
    "# and  aggregate further by min  and sum all the minimum values.\n",
    "\n",
    "min_sum_coef = df_scorecard.groupby('Original feature name')['Coefficients'].min().sum()\n",
    "min_sum_coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.6059676439951645"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Now the maximum values.\n",
    "max_sum_coef = df_scorecard.groupby('Original feature name')['Coefficients'].max().sum()\n",
    "max_sum_coef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the simple formula\n",
    "$$ variable_{-}score =  variable_{-}coef\\quad \\frac{( max_{-}score - min_{-}score )}{( max_{-}sum_{-}coef - min_{-}sum_{-}coef )} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>index</th>\n",
       "      <th>Feature name</th>\n",
       "      <th>Coefficients</th>\n",
       "      <th>p_values</th>\n",
       "      <th>Original feature name</th>\n",
       "      <th>Score - Calculation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.385395</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Intercept</td>\n",
       "      <td>-106.512392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.121520</td>\n",
       "      <td>4.753387e-35</td>\n",
       "      <td>grade</td>\n",
       "      <td>86.225013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.881507</td>\n",
       "      <td>2.158571e-47</td>\n",
       "      <td>grade</td>\n",
       "      <td>67.772288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.687851</td>\n",
       "      <td>3.271838e-34</td>\n",
       "      <td>grade</td>\n",
       "      <td>52.883554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.500477</td>\n",
       "      <td>6.929364e-21</td>\n",
       "      <td>grade</td>\n",
       "      <td>38.477842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index Feature name  Coefficients      p_values Original feature name  \\\n",
       "0      0    Intercept     -1.385395           NaN             Intercept   \n",
       "1      1      grade:A      1.121520  4.753387e-35                 grade   \n",
       "2      2      grade:B      0.881507  2.158571e-47                 grade   \n",
       "3      3      grade:C      0.687851  3.271838e-34                 grade   \n",
       "4      4      grade:D      0.500477  6.929364e-21                 grade   \n",
       "\n",
       "   Score - Calculation  \n",
       "0          -106.512392  \n",
       "1            86.225013  \n",
       "2            67.772288  \n",
       "3            52.883554  \n",
       "4            38.477842  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scorecard['Score - Calculation'] = df_scorecard['Coefficients'] * (max_score - min_score) / (max_sum_coef - min_sum_coef)\n",
    "df_scorecard.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " For the intercep score, \n",
    " \n",
    " $$ intercept_{-}score = \\frac{intercept_{-}coef - min_{-}sum_{-}coef}{( max_{-}sum_{-}coef - min_{-}sum_{-}coef )} ( max_{-}score - min_{-}score )$$\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Feature name</th>\n",
       "      <th>Coefficients</th>\n",
       "      <th>p_values</th>\n",
       "      <th>Original feature name</th>\n",
       "      <th>Score - Calculation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.385395</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Intercept</td>\n",
       "      <td>312.487864</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.121520</td>\n",
       "      <td>4.753387e-35</td>\n",
       "      <td>grade</td>\n",
       "      <td>86.225013</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.881507</td>\n",
       "      <td>2.158571e-47</td>\n",
       "      <td>grade</td>\n",
       "      <td>67.772288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.687851</td>\n",
       "      <td>3.271838e-34</td>\n",
       "      <td>grade</td>\n",
       "      <td>52.883554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.500477</td>\n",
       "      <td>6.929364e-21</td>\n",
       "      <td>grade</td>\n",
       "      <td>38.477842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index Feature name  Coefficients      p_values Original feature name  \\\n",
       "0      0    Intercept     -1.385395           NaN             Intercept   \n",
       "1      1      grade:A      1.121520  4.753387e-35                 grade   \n",
       "2      2      grade:B      0.881507  2.158571e-47                 grade   \n",
       "3      3      grade:C      0.687851  3.271838e-34                 grade   \n",
       "4      4      grade:D      0.500477  6.929364e-21                 grade   \n",
       "\n",
       "   Score - Calculation  \n",
       "0           312.487864  \n",
       "1            86.225013  \n",
       "2            67.772288  \n",
       "3            52.883554  \n",
       "4            38.477842  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scorecard['Score - Calculation'][0] = ((df_scorecard['Coefficients'][0] - min_sum_coef) / (max_sum_coef - min_sum_coef)) * (max_score - min_score) + min_score\n",
    "df_scorecard.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Feature name</th>\n",
       "      <th>Coefficients</th>\n",
       "      <th>p_values</th>\n",
       "      <th>Original feature name</th>\n",
       "      <th>Score - Calculation</th>\n",
       "      <th>Score - Preliminary</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.385395</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Intercept</td>\n",
       "      <td>312.487864</td>\n",
       "      <td>312.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.121520</td>\n",
       "      <td>4.753387e-35</td>\n",
       "      <td>grade</td>\n",
       "      <td>86.225013</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.881507</td>\n",
       "      <td>2.158571e-47</td>\n",
       "      <td>grade</td>\n",
       "      <td>67.772288</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.687851</td>\n",
       "      <td>3.271838e-34</td>\n",
       "      <td>grade</td>\n",
       "      <td>52.883554</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.500477</td>\n",
       "      <td>6.929364e-21</td>\n",
       "      <td>grade</td>\n",
       "      <td>38.477842</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index Feature name  Coefficients      p_values Original feature name  \\\n",
       "0      0    Intercept     -1.385395           NaN             Intercept   \n",
       "1      1      grade:A      1.121520  4.753387e-35                 grade   \n",
       "2      2      grade:B      0.881507  2.158571e-47                 grade   \n",
       "3      3      grade:C      0.687851  3.271838e-34                 grade   \n",
       "4      4      grade:D      0.500477  6.929364e-21                 grade   \n",
       "\n",
       "   Score - Calculation  Score - Preliminary  \n",
       "0           312.487864                312.0  \n",
       "1            86.225013                 86.0  \n",
       "2            67.772288                 68.0  \n",
       "3            52.883554                 53.0  \n",
       "4            38.477842                 38.0  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scorecard['Score - Preliminary'] = df_scorecard['Score - Calculation'].round()\n",
    "df_scorecard.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "299.0"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Groups the data by the values of the 'Original feature name' column.\n",
    "# Aggregates the data in the 'Coefficients' column, calculating their minimum.\n",
    "# Sums all minimum values.\n",
    "\n",
    "min_sum_score_prel = df_scorecard.groupby('Original feature name')['Score - Preliminary'].min().sum()\n",
    "min_sum_score_prel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "849.0"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Groups the data by the values of the 'Original feature name' column.\n",
    "# Aggregates the data in the 'Coefficients' column, calculating their maximum.\n",
    "# Sums all maximum values.\n",
    "\n",
    "max_sum_score_prel = df_scorecard.groupby('Original feature name')['Score - Preliminary'].max().sum()\n",
    "max_sum_score_prel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> We need to adjust so that the minimum and maximum score to be 300 and 850 respectively </center>\n",
    "\n",
    "Since we rounded, in our case, a simple way is to add 1 to the value minimum rounded value, and  the same to the maximum rounded value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -0.487864\n",
       "1   -0.225013\n",
       "2    0.227712\n",
       "3    0.116446\n",
       "4   -0.477842\n",
       "Name: Difference, dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scorecard['Difference'] = df_scorecard['Score - Preliminary'] - df_scorecard['Score - Calculation']\n",
    "df_scorecard['Difference'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scorecard['Score - Final'] = df_scorecard['Score - Preliminary']\n",
    "df_scorecard['Score - Final'][77] = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "850.0"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_sum_score_prel = df_scorecard.groupby('Original feature name')['Score - Final'].max().sum()\n",
    "max_sum_score_prel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Caclulating Credit Score\n",
    "\n",
    "<center>To calculate the  credit score of a borrower, we only have to sum the credit scores corresponding to the respective dummy variables categories </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
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       "      <th>grade:E</th>\n",
       "      <th>grade:F</th>\n",
       "      <th>grade:G</th>\n",
       "      <th>home_ownership:RENT_OTHER_NONE_ANY</th>\n",
       "      <th>home_ownership:OWN</th>\n",
       "      <th>home_ownership:MORTGAGE</th>\n",
       "      <th>addr_state:ND_NE_IA_NV_FL_HI_AL</th>\n",
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       "      <th>verification_status:Source Verified</th>\n",
       "      <th>verification_status:Verified</th>\n",
       "      <th>purpose:educ__sm_b__wedd__ren_en__mov__house</th>\n",
       "      <th>purpose:credit_card</th>\n",
       "      <th>purpose:debt_consolidation</th>\n",
       "      <th>purpose:oth__med__vacation</th>\n",
       "      <th>purpose:major_purch__car__home_impr</th>\n",
       "      <th>initial_list_status:f</th>\n",
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       "      <th>term:36</th>\n",
       "      <th>term:60</th>\n",
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       "      <th>mths_since_issue_d:42-48</th>\n",
       "      <th>mths_since_issue_d:49-52</th>\n",
       "      <th>mths_since_issue_d:53-64</th>\n",
       "      <th>mths_since_issue_d:65-84</th>\n",
       "      <th>mths_since_issue_d:&gt;84</th>\n",
       "      <th>int_rate:&lt;9.548</th>\n",
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       "      <th>int_rate:12.025-15.74</th>\n",
       "      <th>int_rate:15.74-20.281</th>\n",
       "      <th>int_rate:&gt;20.281</th>\n",
       "      <th>mths_since_earliest_cr_line:&lt;140</th>\n",
       "      <th>mths_since_earliest_cr_line:141-164</th>\n",
       "      <th>mths_since_earliest_cr_line:165-247</th>\n",
       "      <th>mths_since_earliest_cr_line:248-270</th>\n",
       "      <th>mths_since_earliest_cr_line:271-352</th>\n",
       "      <th>mths_since_earliest_cr_line:&gt;352</th>\n",
       "      <th>inq_last_6mths:0</th>\n",
       "      <th>inq_last_6mths:1-2</th>\n",
       "      <th>inq_last_6mths:3-6</th>\n",
       "      <th>inq_last_6mths:&gt;6</th>\n",
       "      <th>acc_now_delinq:0</th>\n",
       "      <th>acc_now_delinq:&gt;=1</th>\n",
       "      <th>annual_inc:&lt;20K</th>\n",
       "      <th>annual_inc:20K-30K</th>\n",
       "      <th>annual_inc:30K-40K</th>\n",
       "      <th>annual_inc:40K-50K</th>\n",
       "      <th>annual_inc:50K-60K</th>\n",
       "      <th>annual_inc:60K-70K</th>\n",
       "      <th>annual_inc:70K-80K</th>\n",
       "      <th>annual_inc:80K-90K</th>\n",
       "      <th>annual_inc:90K-100K</th>\n",
       "      <th>annual_inc:100K-120K</th>\n",
       "      <th>annual_inc:120K-140K</th>\n",
       "      <th>annual_inc:&gt;140K</th>\n",
       "      <th>dti:&lt;=1.4</th>\n",
       "      <th>dti:1.4-3.5</th>\n",
       "      <th>dti:3.5-7.7</th>\n",
       "      <th>dti:7.7-10.5</th>\n",
       "      <th>dti:10.5-16.1</th>\n",
       "      <th>dti:16.1-20.3</th>\n",
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       "      <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_delinq:Missing</th>\n",
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       "      <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",
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       "      <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>1</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>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>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>0</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",
       "      <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>0</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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>213591</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>1</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>0</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>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>1</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",
       "      <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>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>0</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",
       "      <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>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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>263083</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>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>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>0</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>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>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>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>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>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>0</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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>165001</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>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>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>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>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",
       "      <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>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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <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>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>0</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        grade:A  grade:B  grade:C  grade:D  grade:E  grade:F  grade:G  \\\n",
       "362514        0        0        1        0        0        0        0   \n",
       "288564        0        0        0        0        1        0        0   \n",
       "213591        0        0        1        0        0        0        0   \n",
       "263083        0        0        1        0        0        0        0   \n",
       "165001        1        0        0        0        0        0        0   \n",
       "\n",
       "        home_ownership:RENT_OTHER_NONE_ANY  home_ownership:OWN  \\\n",
       "362514                                   0                   0   \n",
       "288564                                   0                   0   \n",
       "213591                                   0                   0   \n",
       "263083                                   0                   0   \n",
       "165001                                   0                   0   \n",
       "\n",
       "        home_ownership:MORTGAGE  addr_state:ND_NE_IA_NV_FL_HI_AL  \\\n",
       "362514                        1                                0   \n",
       "288564                        1                                0   \n",
       "213591                        1                                0   \n",
       "263083                        1                                0   \n",
       "165001                        1                                0   \n",
       "\n",
       "        addr_state:NM_VA  addr_state:NY  addr_state:OK_TN_MO_LA_MD_NC  \\\n",
       "362514                 0              0                             0   \n",
       "288564                 0              0                             0   \n",
       "213591                 0              0                             0   \n",
       "263083                 0              0                             0   \n",
       "165001                 0              0                             0   \n",
       "\n",
       "        addr_state:CA  addr_state:UT_KY_AZ_NJ  addr_state:AR_MI_PA_OH_MN  \\\n",
       "362514              1                       0                          0   \n",
       "288564              0                       0                          0   \n",
       "213591              0                       1                          0   \n",
       "263083              0                       0                          1   \n",
       "165001              0                       0                          1   \n",
       "\n",
       "        addr_state:RI_MA_DE_SD_IN  addr_state:GA_WA_OR  addr_state:WI_MT  \\\n",
       "362514                          0                    0                 0   \n",
       "288564                          1                    0                 0   \n",
       "213591                          0                    0                 0   \n",
       "263083                          0                    0                 0   \n",
       "165001                          0                    0                 0   \n",
       "\n",
       "        addr_state:TX  addr_state:IL_CT  addr_state:KS_SC_CO_VT_AK_MS  \\\n",
       "362514              0                 0                             0   \n",
       "288564              0                 0                             0   \n",
       "213591              0                 0                             0   \n",
       "263083              0                 0                             0   \n",
       "165001              0                 0                             0   \n",
       "\n",
       "        addr_state:WV_NH_WY_DC_ME_ID  verification_status:Not Verified  \\\n",
       "362514                             0                                 0   \n",
       "288564                             0                                 0   \n",
       "213591                             0                                 0   \n",
       "263083                             0                                 0   \n",
       "165001                             0                                 0   \n",
       "\n",
       "        verification_status:Source Verified  verification_status:Verified  \\\n",
       "362514                                    0                             1   \n",
       "288564                                    0                             1   \n",
       "213591                                    0                             1   \n",
       "263083                                    1                             0   \n",
       "165001                                    0                             1   \n",
       "\n",
       "        purpose:educ__sm_b__wedd__ren_en__mov__house  purpose:credit_card  \\\n",
       "362514                                             0                    0   \n",
       "288564                                             0                    1   \n",
       "213591                                             0                    0   \n",
       "263083                                             0                    0   \n",
       "165001                                             0                    0   \n",
       "\n",
       "        purpose:debt_consolidation  purpose:oth__med__vacation  \\\n",
       "362514                           0                           0   \n",
       "288564                           0                           0   \n",
       "213591                           1                           0   \n",
       "263083                           1                           0   \n",
       "165001                           1                           0   \n",
       "\n",
       "        purpose:major_purch__car__home_impr  initial_list_status:f  \\\n",
       "362514                                    1                      1   \n",
       "288564                                    0                      0   \n",
       "213591                                    0                      1   \n",
       "263083                                    0                      0   \n",
       "165001                                    0                      1   \n",
       "\n",
       "        initial_list_status:w  term:36  term:60  emp_length:0  emp_length:1  \\\n",
       "362514                      0        0        1             1             0   \n",
       "288564                      1        0        1             0             0   \n",
       "213591                      0        1        0             0             1   \n",
       "263083                      1        0        1             0             0   \n",
       "165001                      0        1        0             0             0   \n",
       "\n",
       "        emp_length:2-4  emp_length:5-6  emp_length:7-9  emp_length:10  \\\n",
       "362514               0               0               0              0   \n",
       "288564               0               0               0              1   \n",
       "213591               0               0               0              0   \n",
       "263083               1               0               0              0   \n",
       "165001               0               0               0              1   \n",
       "\n",
       "        mths_since_issue_d:<38  mths_since_issue_d:38-39  \\\n",
       "362514                       0                         0   \n",
       "288564                       0                         1   \n",
       "213591                       0                         0   \n",
       "263083                       1                         0   \n",
       "165001                       0                         0   \n",
       "\n",
       "        mths_since_issue_d:40-41  mths_since_issue_d:42-48  \\\n",
       "362514                         1                         0   \n",
       "288564                         0                         0   \n",
       "213591                         0                         0   \n",
       "263083                         0                         0   \n",
       "165001                         0                         0   \n",
       "\n",
       "        mths_since_issue_d:49-52  mths_since_issue_d:53-64  \\\n",
       "362514                         0                         0   \n",
       "288564                         0                         0   \n",
       "213591                         0                         0   \n",
       "263083                         0                         0   \n",
       "165001                         0                         1   \n",
       "\n",
       "        mths_since_issue_d:65-84  mths_since_issue_d:>84  int_rate:<9.548  \\\n",
       "362514                         0                       0                0   \n",
       "288564                         0                       0                0   \n",
       "213591                         1                       0                0   \n",
       "263083                         0                       0                0   \n",
       "165001                         0                       0                1   \n",
       "\n",
       "        int_rate:9.548-12.025  int_rate:12.025-15.74  int_rate:15.74-20.281  \\\n",
       "362514                      0                      1                      0   \n",
       "288564                      0                      0                      0   \n",
       "213591                      0                      1                      0   \n",
       "263083                      0                      1                      0   \n",
       "165001                      0                      0                      0   \n",
       "\n",
       "        int_rate:>20.281  mths_since_earliest_cr_line:<140  \\\n",
       "362514                 0                                 0   \n",
       "288564                 1                                 0   \n",
       "213591                 0                                 0   \n",
       "263083                 0                                 1   \n",
       "165001                 0                                 0   \n",
       "\n",
       "        mths_since_earliest_cr_line:141-164  \\\n",
       "362514                                    0   \n",
       "288564                                    0   \n",
       "213591                                    0   \n",
       "263083                                    0   \n",
       "165001                                    0   \n",
       "\n",
       "        mths_since_earliest_cr_line:165-247  \\\n",
       "362514                                    1   \n",
       "288564                                    1   \n",
       "213591                                    1   \n",
       "263083                                    0   \n",
       "165001                                    0   \n",
       "\n",
       "        mths_since_earliest_cr_line:248-270  \\\n",
       "362514                                    0   \n",
       "288564                                    0   \n",
       "213591                                    0   \n",
       "263083                                    0   \n",
       "165001                                    0   \n",
       "\n",
       "        mths_since_earliest_cr_line:271-352  mths_since_earliest_cr_line:>352  \\\n",
       "362514                                    0                                 0   \n",
       "288564                                    0                                 0   \n",
       "213591                                    0                                 0   \n",
       "263083                                    0                                 0   \n",
       "165001                                    0                                 1   \n",
       "\n",
       "        inq_last_6mths:0  inq_last_6mths:1-2  inq_last_6mths:3-6  \\\n",
       "362514                 1                   0                   0   \n",
       "288564                 1                   0                   0   \n",
       "213591                 1                   0                   0   \n",
       "263083                 1                   0                   0   \n",
       "165001                 1                   0                   0   \n",
       "\n",
       "        inq_last_6mths:>6  acc_now_delinq:0  acc_now_delinq:>=1  \\\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",
       "        annual_inc:<20K  annual_inc:20K-30K  annual_inc:30K-40K  \\\n",
       "362514                0                   0                   0   \n",
       "288564                0                   0                   0   \n",
       "213591                0                   0                   0   \n",
       "263083                0                   0                   0   \n",
       "165001                0                   0                   0   \n",
       "\n",
       "        annual_inc:40K-50K  annual_inc:50K-60K  annual_inc:60K-70K  \\\n",
       "362514                   0                   0                   1   \n",
       "288564                   0                   1                   0   \n",
       "213591                   0                   0                   0   \n",
       "263083                   0                   0                   1   \n",
       "165001                   0                   0                   0   \n",
       "\n",
       "        annual_inc:70K-80K  annual_inc:80K-90K  annual_inc:90K-100K  \\\n",
       "362514                   0                   0                    0   \n",
       "288564                   0                   0                    0   \n",
       "213591                   0                   0                    0   \n",
       "263083                   0                   0                    0   \n",
       "165001                   0                   1                    0   \n",
       "\n",
       "        annual_inc:100K-120K  annual_inc:120K-140K  annual_inc:>140K  \\\n",
       "362514                     0                     0                 0   \n",
       "288564                     0                     0                 0   \n",
       "213591                     0                     0                 1   \n",
       "263083                     0                     0                 0   \n",
       "165001                     0                     0                 0   \n",
       "\n",
       "        dti:<=1.4  dti:1.4-3.5  dti:3.5-7.7  dti:7.7-10.5  dti:10.5-16.1  \\\n",
       "362514          0            0            0             1              0   \n",
       "288564          0            0            0             0              0   \n",
       "213591          0            0            0             0              1   \n",
       "263083          0            0            0             0              0   \n",
       "165001          0            0            0             1              0   \n",
       "\n",
       "        dti:16.1-20.3  dti:20.3-21.7  dti:21.7-22.4  dti:22.4-35  dti:>35  \\\n",
       "362514              0              0              0            0        0   \n",
       "288564              0              0              0            1        0   \n",
       "213591              0              0              0            0        0   \n",
       "263083              1              0              0            0        0   \n",
       "165001              0              0              0            0        0   \n",
       "\n",
       "        mths_since_last_delinq:Missing  mths_since_last_delinq:0-3  \\\n",
       "362514                               1                           0   \n",
       "288564                               0                           1   \n",
       "213591                               0                           0   \n",
       "263083                               1                           0   \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:0-2  mths_since_last_record:3-20  \\\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:21-31  mths_since_last_record:32-80  \\\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:81-86  mths_since_last_record:>86  \n",
       "362514                             0                           0  \n",
       "288564                             0                           0  \n",
       "213591                             0                           0  \n",
       "263083                             0                           0  \n",
       "165001                             0                           0  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs_test_with_ref_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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",
       "        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>index</th>\n",
       "      <th>Feature name</th>\n",
       "      <th>Coefficients</th>\n",
       "      <th>p_values</th>\n",
       "      <th>Original feature name</th>\n",
       "      <th>Score - Calculation</th>\n",
       "      <th>Score - Preliminary</th>\n",
       "      <th>Difference</th>\n",
       "      <th>Score - Final</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.385395</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Intercept</td>\n",
       "      <td>312.487864</td>\n",
       "      <td>312.0</td>\n",
       "      <td>-0.487864</td>\n",
       "      <td>312.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.121520</td>\n",
       "      <td>4.753387e-35</td>\n",
       "      <td>grade</td>\n",
       "      <td>86.225013</td>\n",
       "      <td>86.0</td>\n",
       "      <td>-0.225013</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.881507</td>\n",
       "      <td>2.158571e-47</td>\n",
       "      <td>grade</td>\n",
       "      <td>67.772288</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.227712</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.687851</td>\n",
       "      <td>3.271838e-34</td>\n",
       "      <td>grade</td>\n",
       "      <td>52.883554</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.116446</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.500477</td>\n",
       "      <td>6.929364e-21</td>\n",
       "      <td>grade</td>\n",
       "      <td>38.477842</td>\n",
       "      <td>38.0</td>\n",
       "      <td>-0.477842</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index Feature name  Coefficients      p_values Original feature name  \\\n",
       "0      0    Intercept     -1.385395           NaN             Intercept   \n",
       "1      1      grade:A      1.121520  4.753387e-35                 grade   \n",
       "2      2      grade:B      0.881507  2.158571e-47                 grade   \n",
       "3      3      grade:C      0.687851  3.271838e-34                 grade   \n",
       "4      4      grade:D      0.500477  6.929364e-21                 grade   \n",
       "\n",
       "   Score - Calculation  Score - Preliminary  Difference  Score - Final  \n",
       "0           312.487864                312.0   -0.487864          312.0  \n",
       "1            86.225013                 86.0   -0.225013           86.0  \n",
       "2            67.772288                 68.0    0.227712           68.0  \n",
       "3            52.883554                 53.0    0.116446           53.0  \n",
       "4            38.477842                 38.0   -0.477842           38.0  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scorecard.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs_test_with_ref_cat_w_intercept = inputs_test_with_ref_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# We insert a column in the dataframe, with an index of 0, that is, in the beginning of the dataframe.\n",
    "# The name of that column is 'Intercept', and its values are 1s.\n",
    "inputs_test_with_ref_cat_w_intercept.insert(0, 'Intercept', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Intercept</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>grade:G</th>\n",
       "      <th>home_ownership:RENT_OTHER_NONE_ANY</th>\n",
       "      <th>home_ownership:OWN</th>\n",
       "      <th>home_ownership:MORTGAGE</th>\n",
       "      <th>addr_state:ND_NE_IA_NV_FL_HI_AL</th>\n",
       "      <th>addr_state:NM_VA</th>\n",
       "      <th>addr_state:NY</th>\n",
       "      <th>addr_state:OK_TN_MO_LA_MD_NC</th>\n",
       "      <th>addr_state:CA</th>\n",
       "      <th>addr_state:UT_KY_AZ_NJ</th>\n",
       "      <th>addr_state:AR_MI_PA_OH_MN</th>\n",
       "      <th>addr_state:RI_MA_DE_SD_IN</th>\n",
       "      <th>addr_state:GA_WA_OR</th>\n",
       "      <th>addr_state:WI_MT</th>\n",
       "      <th>addr_state:TX</th>\n",
       "      <th>addr_state:IL_CT</th>\n",
       "      <th>addr_state:KS_SC_CO_VT_AK_MS</th>\n",
       "      <th>addr_state:WV_NH_WY_DC_ME_ID</th>\n",
       "      <th>verification_status:Not Verified</th>\n",
       "      <th>verification_status:Source Verified</th>\n",
       "      <th>verification_status:Verified</th>\n",
       "      <th>purpose:educ__sm_b__wedd__ren_en__mov__house</th>\n",
       "      <th>purpose:credit_card</th>\n",
       "      <th>purpose:debt_consolidation</th>\n",
       "      <th>purpose:oth__med__vacation</th>\n",
       "      <th>purpose:major_purch__car__home_impr</th>\n",
       "      <th>initial_list_status:f</th>\n",
       "      <th>initial_list_status:w</th>\n",
       "      <th>term:36</th>\n",
       "      <th>term:60</th>\n",
       "      <th>emp_length:0</th>\n",
       "      <th>emp_length:1</th>\n",
       "      <th>emp_length:2-4</th>\n",
       "      <th>emp_length:5-6</th>\n",
       "      <th>emp_length:7-9</th>\n",
       "      <th>emp_length:10</th>\n",
       "      <th>mths_since_issue_d:&lt;38</th>\n",
       "      <th>mths_since_issue_d:38-39</th>\n",
       "      <th>mths_since_issue_d:40-41</th>\n",
       "      <th>mths_since_issue_d:42-48</th>\n",
       "      <th>mths_since_issue_d:49-52</th>\n",
       "      <th>mths_since_issue_d:53-64</th>\n",
       "      <th>mths_since_issue_d:65-84</th>\n",
       "      <th>mths_since_issue_d:&gt;84</th>\n",
       "      <th>int_rate:&lt;9.548</th>\n",
       "      <th>int_rate:9.548-12.025</th>\n",
       "      <th>int_rate:12.025-15.74</th>\n",
       "      <th>int_rate:15.74-20.281</th>\n",
       "      <th>int_rate:&gt;20.281</th>\n",
       "      <th>mths_since_earliest_cr_line:&lt;140</th>\n",
       "      <th>mths_since_earliest_cr_line:141-164</th>\n",
       "      <th>mths_since_earliest_cr_line:165-247</th>\n",
       "      <th>mths_since_earliest_cr_line:248-270</th>\n",
       "      <th>mths_since_earliest_cr_line:271-352</th>\n",
       "      <th>mths_since_earliest_cr_line:&gt;352</th>\n",
       "      <th>inq_last_6mths:0</th>\n",
       "      <th>inq_last_6mths:1-2</th>\n",
       "      <th>inq_last_6mths:3-6</th>\n",
       "      <th>inq_last_6mths:&gt;6</th>\n",
       "      <th>acc_now_delinq:0</th>\n",
       "      <th>acc_now_delinq:&gt;=1</th>\n",
       "      <th>annual_inc:&lt;20K</th>\n",
       "      <th>annual_inc:20K-30K</th>\n",
       "      <th>annual_inc:30K-40K</th>\n",
       "      <th>annual_inc:40K-50K</th>\n",
       "      <th>annual_inc:50K-60K</th>\n",
       "      <th>annual_inc:60K-70K</th>\n",
       "      <th>annual_inc:70K-80K</th>\n",
       "      <th>annual_inc:80K-90K</th>\n",
       "      <th>annual_inc:90K-100K</th>\n",
       "      <th>annual_inc:100K-120K</th>\n",
       "      <th>annual_inc:120K-140K</th>\n",
       "      <th>annual_inc:&gt;140K</th>\n",
       "      <th>dti:&lt;=1.4</th>\n",
       "      <th>dti:1.4-3.5</th>\n",
       "      <th>dti:3.5-7.7</th>\n",
       "      <th>dti:7.7-10.5</th>\n",
       "      <th>dti:10.5-16.1</th>\n",
       "      <th>dti:16.1-20.3</th>\n",
       "      <th>dti:20.3-21.7</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_delinq:Missing</th>\n",
       "      <th>mths_since_last_delinq:0-3</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: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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>362514</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",
       "      <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>0</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>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>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>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>0</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",
       "      <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>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>288564</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <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>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>213591</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <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",
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       "      <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",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <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>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>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>0</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",
       "      <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>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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>263083</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <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",
       "    <tr>\n",
       "      <td>165001</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <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",
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       "      <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>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>0</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Intercept  grade:A  grade:B  grade:C  grade:D  grade:E  grade:F  \\\n",
       "362514          1        0        0        1        0        0        0   \n",
       "288564          1        0        0        0        0        1        0   \n",
       "213591          1        0        0        1        0        0        0   \n",
       "263083          1        0        0        1        0        0        0   \n",
       "165001          1        1        0        0        0        0        0   \n",
       "\n",
       "        grade:G  home_ownership:RENT_OTHER_NONE_ANY  home_ownership:OWN  \\\n",
       "362514        0                                   0                   0   \n",
       "288564        0                                   0                   0   \n",
       "213591        0                                   0                   0   \n",
       "263083        0                                   0                   0   \n",
       "165001        0                                   0                   0   \n",
       "\n",
       "        home_ownership:MORTGAGE  addr_state:ND_NE_IA_NV_FL_HI_AL  \\\n",
       "362514                        1                                0   \n",
       "288564                        1                                0   \n",
       "213591                        1                                0   \n",
       "263083                        1                                0   \n",
       "165001                        1                                0   \n",
       "\n",
       "        addr_state:NM_VA  addr_state:NY  addr_state:OK_TN_MO_LA_MD_NC  \\\n",
       "362514                 0              0                             0   \n",
       "288564                 0              0                             0   \n",
       "213591                 0              0                             0   \n",
       "263083                 0              0                             0   \n",
       "165001                 0              0                             0   \n",
       "\n",
       "        addr_state:CA  addr_state:UT_KY_AZ_NJ  addr_state:AR_MI_PA_OH_MN  \\\n",
       "362514              1                       0                          0   \n",
       "288564              0                       0                          0   \n",
       "213591              0                       1                          0   \n",
       "263083              0                       0                          1   \n",
       "165001              0                       0                          1   \n",
       "\n",
       "        addr_state:RI_MA_DE_SD_IN  addr_state:GA_WA_OR  addr_state:WI_MT  \\\n",
       "362514                          0                    0                 0   \n",
       "288564                          1                    0                 0   \n",
       "213591                          0                    0                 0   \n",
       "263083                          0                    0                 0   \n",
       "165001                          0                    0                 0   \n",
       "\n",
       "        addr_state:TX  addr_state:IL_CT  addr_state:KS_SC_CO_VT_AK_MS  \\\n",
       "362514              0                 0                             0   \n",
       "288564              0                 0                             0   \n",
       "213591              0                 0                             0   \n",
       "263083              0                 0                             0   \n",
       "165001              0                 0                             0   \n",
       "\n",
       "        addr_state:WV_NH_WY_DC_ME_ID  verification_status:Not Verified  \\\n",
       "362514                             0                                 0   \n",
       "288564                             0                                 0   \n",
       "213591                             0                                 0   \n",
       "263083                             0                                 0   \n",
       "165001                             0                                 0   \n",
       "\n",
       "        verification_status:Source Verified  verification_status:Verified  \\\n",
       "362514                                    0                             1   \n",
       "288564                                    0                             1   \n",
       "213591                                    0                             1   \n",
       "263083                                    1                             0   \n",
       "165001                                    0                             1   \n",
       "\n",
       "        purpose:educ__sm_b__wedd__ren_en__mov__house  purpose:credit_card  \\\n",
       "362514                                             0                    0   \n",
       "288564                                             0                    1   \n",
       "213591                                             0                    0   \n",
       "263083                                             0                    0   \n",
       "165001                                             0                    0   \n",
       "\n",
       "        purpose:debt_consolidation  purpose:oth__med__vacation  \\\n",
       "362514                           0                           0   \n",
       "288564                           0                           0   \n",
       "213591                           1                           0   \n",
       "263083                           1                           0   \n",
       "165001                           1                           0   \n",
       "\n",
       "        purpose:major_purch__car__home_impr  initial_list_status:f  \\\n",
       "362514                                    1                      1   \n",
       "288564                                    0                      0   \n",
       "213591                                    0                      1   \n",
       "263083                                    0                      0   \n",
       "165001                                    0                      1   \n",
       "\n",
       "        initial_list_status:w  term:36  term:60  emp_length:0  emp_length:1  \\\n",
       "362514                      0        0        1             1             0   \n",
       "288564                      1        0        1             0             0   \n",
       "213591                      0        1        0             0             1   \n",
       "263083                      1        0        1             0             0   \n",
       "165001                      0        1        0             0             0   \n",
       "\n",
       "        emp_length:2-4  emp_length:5-6  emp_length:7-9  emp_length:10  \\\n",
       "362514               0               0               0              0   \n",
       "288564               0               0               0              1   \n",
       "213591               0               0               0              0   \n",
       "263083               1               0               0              0   \n",
       "165001               0               0               0              1   \n",
       "\n",
       "        mths_since_issue_d:<38  mths_since_issue_d:38-39  \\\n",
       "362514                       0                         0   \n",
       "288564                       0                         1   \n",
       "213591                       0                         0   \n",
       "263083                       1                         0   \n",
       "165001                       0                         0   \n",
       "\n",
       "        mths_since_issue_d:40-41  mths_since_issue_d:42-48  \\\n",
       "362514                         1                         0   \n",
       "288564                         0                         0   \n",
       "213591                         0                         0   \n",
       "263083                         0                         0   \n",
       "165001                         0                         0   \n",
       "\n",
       "        mths_since_issue_d:49-52  mths_since_issue_d:53-64  \\\n",
       "362514                         0                         0   \n",
       "288564                         0                         0   \n",
       "213591                         0                         0   \n",
       "263083                         0                         0   \n",
       "165001                         0                         1   \n",
       "\n",
       "        mths_since_issue_d:65-84  mths_since_issue_d:>84  int_rate:<9.548  \\\n",
       "362514                         0                       0                0   \n",
       "288564                         0                       0                0   \n",
       "213591                         1                       0                0   \n",
       "263083                         0                       0                0   \n",
       "165001                         0                       0                1   \n",
       "\n",
       "        int_rate:9.548-12.025  int_rate:12.025-15.74  int_rate:15.74-20.281  \\\n",
       "362514                      0                      1                      0   \n",
       "288564                      0                      0                      0   \n",
       "213591                      0                      1                      0   \n",
       "263083                      0                      1                      0   \n",
       "165001                      0                      0                      0   \n",
       "\n",
       "        int_rate:>20.281  mths_since_earliest_cr_line:<140  \\\n",
       "362514                 0                                 0   \n",
       "288564                 1                                 0   \n",
       "213591                 0                                 0   \n",
       "263083                 0                                 1   \n",
       "165001                 0                                 0   \n",
       "\n",
       "        mths_since_earliest_cr_line:141-164  \\\n",
       "362514                                    0   \n",
       "288564                                    0   \n",
       "213591                                    0   \n",
       "263083                                    0   \n",
       "165001                                    0   \n",
       "\n",
       "        mths_since_earliest_cr_line:165-247  \\\n",
       "362514                                    1   \n",
       "288564                                    1   \n",
       "213591                                    1   \n",
       "263083                                    0   \n",
       "165001                                    0   \n",
       "\n",
       "        mths_since_earliest_cr_line:248-270  \\\n",
       "362514                                    0   \n",
       "288564                                    0   \n",
       "213591                                    0   \n",
       "263083                                    0   \n",
       "165001                                    0   \n",
       "\n",
       "        mths_since_earliest_cr_line:271-352  mths_since_earliest_cr_line:>352  \\\n",
       "362514                                    0                                 0   \n",
       "288564                                    0                                 0   \n",
       "213591                                    0                                 0   \n",
       "263083                                    0                                 0   \n",
       "165001                                    0                                 1   \n",
       "\n",
       "        inq_last_6mths:0  inq_last_6mths:1-2  inq_last_6mths:3-6  \\\n",
       "362514                 1                   0                   0   \n",
       "288564                 1                   0                   0   \n",
       "213591                 1                   0                   0   \n",
       "263083                 1                   0                   0   \n",
       "165001                 1                   0                   0   \n",
       "\n",
       "        inq_last_6mths:>6  acc_now_delinq:0  acc_now_delinq:>=1  \\\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",
       "        annual_inc:<20K  annual_inc:20K-30K  annual_inc:30K-40K  \\\n",
       "362514                0                   0                   0   \n",
       "288564                0                   0                   0   \n",
       "213591                0                   0                   0   \n",
       "263083                0                   0                   0   \n",
       "165001                0                   0                   0   \n",
       "\n",
       "        annual_inc:40K-50K  annual_inc:50K-60K  annual_inc:60K-70K  \\\n",
       "362514                   0                   0                   1   \n",
       "288564                   0                   1                   0   \n",
       "213591                   0                   0                   0   \n",
       "263083                   0                   0                   1   \n",
       "165001                   0                   0                   0   \n",
       "\n",
       "        annual_inc:70K-80K  annual_inc:80K-90K  annual_inc:90K-100K  \\\n",
       "362514                   0                   0                    0   \n",
       "288564                   0                   0                    0   \n",
       "213591                   0                   0                    0   \n",
       "263083                   0                   0                    0   \n",
       "165001                   0                   1                    0   \n",
       "\n",
       "        annual_inc:100K-120K  annual_inc:120K-140K  annual_inc:>140K  \\\n",
       "362514                     0                     0                 0   \n",
       "288564                     0                     0                 0   \n",
       "213591                     0                     0                 1   \n",
       "263083                     0                     0                 0   \n",
       "165001                     0                     0                 0   \n",
       "\n",
       "        dti:<=1.4  dti:1.4-3.5  dti:3.5-7.7  dti:7.7-10.5  dti:10.5-16.1  \\\n",
       "362514          0            0            0             1              0   \n",
       "288564          0            0            0             0              0   \n",
       "213591          0            0            0             0              1   \n",
       "263083          0            0            0             0              0   \n",
       "165001          0            0            0             1              0   \n",
       "\n",
       "        dti:16.1-20.3  dti:20.3-21.7  dti:21.7-22.4  dti:22.4-35  dti:>35  \\\n",
       "362514              0              0              0            0        0   \n",
       "288564              0              0              0            1        0   \n",
       "213591              0              0              0            0        0   \n",
       "263083              1              0              0            0        0   \n",
       "165001              0              0              0            0        0   \n",
       "\n",
       "        mths_since_last_delinq:Missing  mths_since_last_delinq:0-3  \\\n",
       "362514                               1                           0   \n",
       "288564                               0                           1   \n",
       "213591                               0                           0   \n",
       "263083                               1                           0   \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:0-2  mths_since_last_record:3-20  \\\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:21-31  mths_since_last_record:32-80  \\\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:81-86  mths_since_last_record:>86  \n",
       "362514                             0                           0  \n",
       "288564                             0                           0  \n",
       "213591                             0                           0  \n",
       "263083                             0                           0  \n",
       "165001                             0                           0  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs_test_with_ref_cat_w_intercept.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we keep only the columns with column names that are in df_scorecard['feature name'],\n",
    "inputs_test_with_ref_cat_w_intercept = inputs_test_with_ref_cat_w_intercept[df_scorecard['Feature name'].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Intercept</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>addr_state:OK_TN_MO_LA_MD_NC</th>\n",
       "      <th>addr_state:CA</th>\n",
       "      <th>addr_state:UT_KY_AZ_NJ</th>\n",
       "      <th>addr_state:AR_MI_PA_OH_MN</th>\n",
       "      <th>addr_state:RI_MA_DE_SD_IN</th>\n",
       "      <th>addr_state:GA_WA_OR</th>\n",
       "      <th>addr_state:WI_MT</th>\n",
       "      <th>addr_state:TX</th>\n",
       "      <th>addr_state:IL_CT</th>\n",
       "      <th>addr_state:KS_SC_CO_VT_AK_MS</th>\n",
       "      <th>addr_state:WV_NH_WY_DC_ME_ID</th>\n",
       "      <th>verification_status:Not Verified</th>\n",
       "      <th>verification_status:Source Verified</th>\n",
       "      <th>purpose:credit_card</th>\n",
       "      <th>purpose:debt_consolidation</th>\n",
       "      <th>purpose:oth__med__vacation</th>\n",
       "      <th>purpose:major_purch__car__home_impr</th>\n",
       "      <th>initial_list_status:w</th>\n",
       "      <th>term:36</th>\n",
       "      <th>emp_length:1</th>\n",
       "      <th>emp_length:2-4</th>\n",
       "      <th>emp_length:5-6</th>\n",
       "      <th>emp_length:7-9</th>\n",
       "      <th>emp_length:10</th>\n",
       "      <th>mths_since_issue_d:&lt;38</th>\n",
       "      <th>mths_since_issue_d:38-39</th>\n",
       "      <th>mths_since_issue_d:40-41</th>\n",
       "      <th>mths_since_issue_d:42-48</th>\n",
       "      <th>mths_since_issue_d:49-52</th>\n",
       "      <th>mths_since_issue_d:53-64</th>\n",
       "      <th>mths_since_issue_d:65-84</th>\n",
       "      <th>int_rate:&lt;9.548</th>\n",
       "      <th>int_rate:9.548-12.025</th>\n",
       "      <th>int_rate:12.025-15.74</th>\n",
       "      <th>int_rate:15.74-20.281</th>\n",
       "      <th>mths_since_earliest_cr_line:141-164</th>\n",
       "      <th>mths_since_earliest_cr_line:165-247</th>\n",
       "      <th>mths_since_earliest_cr_line:248-270</th>\n",
       "      <th>mths_since_earliest_cr_line:271-352</th>\n",
       "      <th>mths_since_earliest_cr_line:&gt;352</th>\n",
       "      <th>inq_last_6mths:0</th>\n",
       "      <th>inq_last_6mths:1-2</th>\n",
       "      <th>inq_last_6mths:3-6</th>\n",
       "      <th>acc_now_delinq:&gt;=1</th>\n",
       "      <th>annual_inc:20K-30K</th>\n",
       "      <th>annual_inc:30K-40K</th>\n",
       "      <th>annual_inc:40K-50K</th>\n",
       "      <th>annual_inc:50K-60K</th>\n",
       "      <th>annual_inc:60K-70K</th>\n",
       "      <th>annual_inc:70K-80K</th>\n",
       "      <th>annual_inc:80K-90K</th>\n",
       "      <th>annual_inc:90K-100K</th>\n",
       "      <th>annual_inc:100K-120K</th>\n",
       "      <th>annual_inc:120K-140K</th>\n",
       "      <th>annual_inc:&gt;140K</th>\n",
       "      <th>dti:&lt;=1.4</th>\n",
       "      <th>dti:1.4-3.5</th>\n",
       "      <th>dti:3.5-7.7</th>\n",
       "      <th>dti:7.7-10.5</th>\n",
       "      <th>dti:10.5-16.1</th>\n",
       "      <th>dti:16.1-20.3</th>\n",
       "      <th>dti:20.3-21.7</th>\n",
       "      <th>dti:21.7-22.4</th>\n",
       "      <th>dti:22.4-35</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",
       "      <th>grade:G</th>\n",
       "      <th>home_ownership:RENT_OTHER_NONE_ANY</th>\n",
       "      <th>addr_state:ND_NE_IA_NV_FL_HI_AL</th>\n",
       "      <th>verification_status:Verified</th>\n",
       "      <th>purpose:educ__sm_b__wedd__ren_en__mov__house</th>\n",
       "      <th>initial_list_status:f</th>\n",
       "      <th>term:60</th>\n",
       "      <th>emp_length:0</th>\n",
       "      <th>mths_since_issue_d:&gt;84</th>\n",
       "      <th>int_rate:&gt;20.281</th>\n",
       "      <th>mths_since_earliest_cr_line:&lt;140</th>\n",
       "      <th>inq_last_6mths:&gt;6</th>\n",
       "      <th>acc_now_delinq:0</th>\n",
       "      <th>annual_inc:&lt;20K</th>\n",
       "      <th>dti:&gt;35</th>\n",
       "      <th>mths_since_last_delinq:0-3</th>\n",
       "      <th>mths_since_last_record:0-2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>362514</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <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>1</td>\n",
       "      <td>1</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",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>288564</td>\n",
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       "      <td>0</td>\n",
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       "      <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>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>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>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",
       "      <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>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>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>0</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>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",
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       "      <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>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>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>213591</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>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>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>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>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",
       "      <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>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>0</td>\n",
       "      <td>0</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",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>263083</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>1</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>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>1</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>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>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>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>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>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>0</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",
       "      <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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>165001</td>\n",
       "      <td>1</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>1</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>0</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>0</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>1</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>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>0</td>\n",
       "      <td>1</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>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>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>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",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Intercept  grade:A  grade:B  grade:C  grade:D  grade:E  grade:F  \\\n",
       "362514          1        0        0        1        0        0        0   \n",
       "288564          1        0        0        0        0        1        0   \n",
       "213591          1        0        0        1        0        0        0   \n",
       "263083          1        0        0        1        0        0        0   \n",
       "165001          1        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  addr_state:OK_TN_MO_LA_MD_NC  addr_state:CA  \\\n",
       "362514              0                             0              1   \n",
       "288564              0                             0              0   \n",
       "213591              0                             0              0   \n",
       "263083              0                             0              0   \n",
       "165001              0                             0              0   \n",
       "\n",
       "        addr_state:UT_KY_AZ_NJ  addr_state:AR_MI_PA_OH_MN  \\\n",
       "362514                       0                          0   \n",
       "288564                       0                          0   \n",
       "213591                       1                          0   \n",
       "263083                       0                          1   \n",
       "165001                       0                          1   \n",
       "\n",
       "        addr_state:RI_MA_DE_SD_IN  addr_state:GA_WA_OR  addr_state:WI_MT  \\\n",
       "362514                          0                    0                 0   \n",
       "288564                          1                    0                 0   \n",
       "213591                          0                    0                 0   \n",
       "263083                          0                    0                 0   \n",
       "165001                          0                    0                 0   \n",
       "\n",
       "        addr_state:TX  addr_state:IL_CT  addr_state:KS_SC_CO_VT_AK_MS  \\\n",
       "362514              0                 0                             0   \n",
       "288564              0                 0                             0   \n",
       "213591              0                 0                             0   \n",
       "263083              0                 0                             0   \n",
       "165001              0                 0                             0   \n",
       "\n",
       "        addr_state:WV_NH_WY_DC_ME_ID  verification_status:Not Verified  \\\n",
       "362514                             0                                 0   \n",
       "288564                             0                                 0   \n",
       "213591                             0                                 0   \n",
       "263083                             0                                 0   \n",
       "165001                             0                                 0   \n",
       "\n",
       "        verification_status:Source Verified  purpose:credit_card  \\\n",
       "362514                                    0                    0   \n",
       "288564                                    0                    1   \n",
       "213591                                    0                    0   \n",
       "263083                                    1                    0   \n",
       "165001                                    0                    0   \n",
       "\n",
       "        purpose:debt_consolidation  purpose:oth__med__vacation  \\\n",
       "362514                           0                           0   \n",
       "288564                           0                           0   \n",
       "213591                           1                           0   \n",
       "263083                           1                           0   \n",
       "165001                           1                           0   \n",
       "\n",
       "        purpose:major_purch__car__home_impr  initial_list_status:w  term:36  \\\n",
       "362514                                    1                      0        0   \n",
       "288564                                    0                      1        0   \n",
       "213591                                    0                      0        1   \n",
       "263083                                    0                      1        0   \n",
       "165001                                    0                      0        1   \n",
       "\n",
       "        emp_length:1  emp_length:2-4  emp_length:5-6  emp_length:7-9  \\\n",
       "362514             0               0               0               0   \n",
       "288564             0               0               0               0   \n",
       "213591             1               0               0               0   \n",
       "263083             0               1               0               0   \n",
       "165001             0               0               0               0   \n",
       "\n",
       "        emp_length:10  mths_since_issue_d:<38  mths_since_issue_d:38-39  \\\n",
       "362514              0                       0                         0   \n",
       "288564              1                       0                         1   \n",
       "213591              0                       0                         0   \n",
       "263083              0                       1                         0   \n",
       "165001              1                       0                         0   \n",
       "\n",
       "        mths_since_issue_d:40-41  mths_since_issue_d:42-48  \\\n",
       "362514                         1                         0   \n",
       "288564                         0                         0   \n",
       "213591                         0                         0   \n",
       "263083                         0                         0   \n",
       "165001                         0                         0   \n",
       "\n",
       "        mths_since_issue_d:49-52  mths_since_issue_d:53-64  \\\n",
       "362514                         0                         0   \n",
       "288564                         0                         0   \n",
       "213591                         0                         0   \n",
       "263083                         0                         0   \n",
       "165001                         0                         1   \n",
       "\n",
       "        mths_since_issue_d:65-84  int_rate:<9.548  int_rate:9.548-12.025  \\\n",
       "362514                         0                0                      0   \n",
       "288564                         0                0                      0   \n",
       "213591                         1                0                      0   \n",
       "263083                         0                0                      0   \n",
       "165001                         0                1                      0   \n",
       "\n",
       "        int_rate:12.025-15.74  int_rate:15.74-20.281  \\\n",
       "362514                      1                      0   \n",
       "288564                      0                      0   \n",
       "213591                      1                      0   \n",
       "263083                      1                      0   \n",
       "165001                      0                      0   \n",
       "\n",
       "        mths_since_earliest_cr_line:141-164  \\\n",
       "362514                                    0   \n",
       "288564                                    0   \n",
       "213591                                    0   \n",
       "263083                                    0   \n",
       "165001                                    0   \n",
       "\n",
       "        mths_since_earliest_cr_line:165-247  \\\n",
       "362514                                    1   \n",
       "288564                                    1   \n",
       "213591                                    1   \n",
       "263083                                    0   \n",
       "165001                                    0   \n",
       "\n",
       "        mths_since_earliest_cr_line:248-270  \\\n",
       "362514                                    0   \n",
       "288564                                    0   \n",
       "213591                                    0   \n",
       "263083                                    0   \n",
       "165001                                    0   \n",
       "\n",
       "        mths_since_earliest_cr_line:271-352  mths_since_earliest_cr_line:>352  \\\n",
       "362514                                    0                                 0   \n",
       "288564                                    0                                 0   \n",
       "213591                                    0                                 0   \n",
       "263083                                    0                                 0   \n",
       "165001                                    0                                 1   \n",
       "\n",
       "        inq_last_6mths:0  inq_last_6mths:1-2  inq_last_6mths:3-6  \\\n",
       "362514                 1                   0                   0   \n",
       "288564                 1                   0                   0   \n",
       "213591                 1                   0                   0   \n",
       "263083                 1                   0                   0   \n",
       "165001                 1                   0                   0   \n",
       "\n",
       "        acc_now_delinq:>=1  annual_inc:20K-30K  annual_inc:30K-40K  \\\n",
       "362514                   0                   0                   0   \n",
       "288564                   0                   0                   0   \n",
       "213591                   0                   0                   0   \n",
       "263083                   0                   0                   0   \n",
       "165001                   0                   0                   0   \n",
       "\n",
       "        annual_inc:40K-50K  annual_inc:50K-60K  annual_inc:60K-70K  \\\n",
       "362514                   0                   0                   1   \n",
       "288564                   0                   1                   0   \n",
       "213591                   0                   0                   0   \n",
       "263083                   0                   0                   1   \n",
       "165001                   0                   0                   0   \n",
       "\n",
       "        annual_inc:70K-80K  annual_inc:80K-90K  annual_inc:90K-100K  \\\n",
       "362514                   0                   0                    0   \n",
       "288564                   0                   0                    0   \n",
       "213591                   0                   0                    0   \n",
       "263083                   0                   0                    0   \n",
       "165001                   0                   1                    0   \n",
       "\n",
       "        annual_inc:100K-120K  annual_inc:120K-140K  annual_inc:>140K  \\\n",
       "362514                     0                     0                 0   \n",
       "288564                     0                     0                 0   \n",
       "213591                     0                     0                 1   \n",
       "263083                     0                     0                 0   \n",
       "165001                     0                     0                 0   \n",
       "\n",
       "        dti:<=1.4  dti:1.4-3.5  dti:3.5-7.7  dti:7.7-10.5  dti:10.5-16.1  \\\n",
       "362514          0            0            0             1              0   \n",
       "288564          0            0            0             0              0   \n",
       "213591          0            0            0             0              1   \n",
       "263083          0            0            0             0              0   \n",
       "165001          0            0            0             1              0   \n",
       "\n",
       "        dti:16.1-20.3  dti:20.3-21.7  dti:21.7-22.4  dti:22.4-35  \\\n",
       "362514              0              0              0            0   \n",
       "288564              0              0              0            1   \n",
       "213591              0              0              0            0   \n",
       "263083              1              0              0            0   \n",
       "165001              0              0              0            0   \n",
       "\n",
       "        mths_since_last_delinq:Missing  mths_since_last_delinq:4-30  \\\n",
       "362514                               1                            0   \n",
       "288564                               0                            0   \n",
       "213591                               0                            0   \n",
       "263083                               1                            0   \n",
       "165001                               0                            0   \n",
       "\n",
       "        mths_since_last_delinq:31-56  mths_since_last_delinq:>=57  \\\n",
       "362514                             0                            0   \n",
       "288564                             0                            0   \n",
       "213591                             1                            0   \n",
       "263083                             0                            0   \n",
       "165001                             1                            0   \n",
       "\n",
       "        mths_since_last_record:Missing  mths_since_last_record:3-20  \\\n",
       "362514                               1                            0   \n",
       "288564                               1                            0   \n",
       "213591                               1                            0   \n",
       "263083                               1                            0   \n",
       "165001                               1                            0   \n",
       "\n",
       "        mths_since_last_record:21-31  mths_since_last_record:32-80  \\\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:81-86  mths_since_last_record:>86  grade:G  \\\n",
       "362514                             0                           0        0   \n",
       "288564                             0                           0        0   \n",
       "213591                             0                           0        0   \n",
       "263083                             0                           0        0   \n",
       "165001                             0                           0        0   \n",
       "\n",
       "        home_ownership:RENT_OTHER_NONE_ANY  addr_state:ND_NE_IA_NV_FL_HI_AL  \\\n",
       "362514                                   0                                0   \n",
       "288564                                   0                                0   \n",
       "213591                                   0                                0   \n",
       "263083                                   0                                0   \n",
       "165001                                   0                                0   \n",
       "\n",
       "        verification_status:Verified  \\\n",
       "362514                             1   \n",
       "288564                             1   \n",
       "213591                             1   \n",
       "263083                             0   \n",
       "165001                             1   \n",
       "\n",
       "        purpose:educ__sm_b__wedd__ren_en__mov__house  initial_list_status:f  \\\n",
       "362514                                             0                      1   \n",
       "288564                                             0                      0   \n",
       "213591                                             0                      1   \n",
       "263083                                             0                      0   \n",
       "165001                                             0                      1   \n",
       "\n",
       "        term:60  emp_length:0  mths_since_issue_d:>84  int_rate:>20.281  \\\n",
       "362514        1             1                       0                 0   \n",
       "288564        1             0                       0                 1   \n",
       "213591        0             0                       0                 0   \n",
       "263083        1             0                       0                 0   \n",
       "165001        0             0                       0                 0   \n",
       "\n",
       "        mths_since_earliest_cr_line:<140  inq_last_6mths:>6  acc_now_delinq:0  \\\n",
       "362514                                 0                  0                 1   \n",
       "288564                                 0                  0                 1   \n",
       "213591                                 0                  0                 1   \n",
       "263083                                 1                  0                 1   \n",
       "165001                                 0                  0                 1   \n",
       "\n",
       "        annual_inc:<20K  dti:>35  mths_since_last_delinq:0-3  \\\n",
       "362514                0        0                           0   \n",
       "288564                0        0                           1   \n",
       "213591                0        0                           0   \n",
       "263083                0        0                           0   \n",
       "165001                0        0                           0   \n",
       "\n",
       "        mths_since_last_record:0-2  \n",
       "362514                           0  \n",
       "288564                           0  \n",
       "213591                           0  \n",
       "263083                           0  \n",
       "165001                           0  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs_test_with_ref_cat_w_intercept.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "scorecard_scores = df_scorecard['Score - Final']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(93257, 102)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs_test_with_ref_cat_w_intercept.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(102,)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scorecard_scores.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "scorecard_scores = scorecard_scores.values.reshape(102, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(102, 1)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scorecard_scores.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores = inputs_test_with_ref_cat_w_intercept.dot(scorecard_scores)\n",
    "# Here we multiply the values of each row of the dataframe by the values of each column of the variable,\n",
    "# which is an argument of the 'dot' method, and sum them. It's essentially the sum of the products."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>577.0</td>\n",
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       "    <tr>\n",
       "      <td>263083</td>\n",
       "      <td>630.0</td>\n",
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       "      <td>165001</td>\n",
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       "            0\n",
       "362514  610.0\n",
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     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "y_scores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>571.0</td>\n",
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       "    <tr>\n",
       "      <td>296284</td>\n",
       "      <td>676.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61777</td>\n",
       "      <td>694.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91763</td>\n",
       "      <td>661.0</td>\n",
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       "      <td>167512</td>\n",
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       "            0\n",
       "115     571.0\n",
       "296284  676.0\n",
       "61777   694.0\n",
       "91763   661.0\n",
       "167512  649.0"
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     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "y_scores.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From Credit Score to PD\n",
    "\n",
    "We have  already seen how to go from PD to credit score, the converse is done applying the formula\n",
    "\n",
    "$$ sum_{-}of_{-}coef_{-}from_{-}score = \\frac{( total_{-}score - min_{-}score )}{( max_{-}score - min_{-}score )} ( max_{-}sum_{-}coef - min_{-}sum_{-}coef) + min_{-}sum_{-}coef$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum_coef_from_score = ((y_scores - min_score) / (max_score - min_score)) * (max_sum_coef - min_sum_coef) + min_sum_coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.923035</td>\n",
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       "    <tr>\n",
       "      <td>288564</td>\n",
       "      <td>0.847730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>213591</td>\n",
       "      <td>0.886461</td>\n",
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       "    <tr>\n",
       "      <td>263083</td>\n",
       "      <td>0.939599</td>\n",
       "    </tr>\n",
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       "      <td>165001</td>\n",
       "      <td>0.968745</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0\n",
       "362514  0.923035\n",
       "288564  0.847730\n",
       "213591  0.886461\n",
       "263083  0.939599\n",
       "165001  0.968745"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat_proba_from_score = np.exp(sum_coef_from_score) / (np.exp(sum_coef_from_score) + 1)\n",
    "y_hat_proba_from_score.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.92416892, 0.84854678, 0.88559808, 0.94057941, 0.96870126])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat_test_proba[0: 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.372626\n",
       "1    0.391437\n",
       "2    0.393127\n",
       "3    0.448663\n",
       "4    0.455010\n",
       "Name: y_hat_test_proba, dtype: float64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actual_predicted_probs['y_hat_test_proba'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting Cut-offs\n",
    "\n",
    "Let look at the ROC curve we plot earlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "tr = 0.9\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": 122,
   "metadata": {
    "scrolled": true
   },
   "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",
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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>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",
       "      <td>0</td>\n",
       "      <td>7375</td>\n",
       "      <td>2815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>35811</td>\n",
       "      <td>47256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted      0      1\n",
       "Actual                 \n",
       "0           7375   2815\n",
       "1          35811  47256"
      ]
     },
     "execution_count": 122,
     "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'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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",
       "      <td>0</td>\n",
       "      <td>0.079083</td>\n",
       "      <td>0.030185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.384003</td>\n",
       "      <td>0.506729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted         0         1\n",
       "Actual                       \n",
       "0          0.079083  0.030185\n",
       "1          0.384003  0.506729"
      ]
     },
     "execution_count": 123,
     "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]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5858112527745907"
      ]
     },
     "execution_count": 124,
     "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]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.        , 0.        , 0.        , ..., 0.99950932, 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([1.99266112, 0.99266112, 0.99073202, ..., 0.48797074, 0.39312673,\n",
       "        0.37262574]))"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_curve(df_actual_predicted_probs['loan_data_targets_test'], df_actual_predicted_probs['y_hat_test_proba'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = roc_curve(df_actual_predicted_probs['loan_data_targets_test'], df_actual_predicted_probs['y_hat_test_proba'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ROC curve')"
      ]
     },
     "execution_count": 129,
     "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",
    "plt.plot(fpr, fpr, linestyle = '--', color = 'k')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.99266112, 0.99266112, 0.99073202, ..., 0.48797074, 0.39312673,\n",
       "       0.37262574])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17240,)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thresholds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cutoffs = pd.concat([pd.DataFrame(thresholds), pd.DataFrame(fpr), pd.DataFrame(tpr)], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cutoffs.columns = ['thresholds', 'fpr', 'tpr']\n",
    "# We name the columns of the dataframe 'thresholds', 'fpr', and 'tpr'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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>thresholds</th>\n",
       "      <th>fpr</th>\n",
       "      <th>tpr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.992661</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.992661</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.990732</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.990661</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.989775</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   thresholds       fpr       tpr\n",
       "0    1.992661  0.000000  0.000000\n",
       "1    0.992661  0.000000  0.000012\n",
       "2    0.990732  0.000000  0.000120\n",
       "3    0.990661  0.000098  0.000120\n",
       "4    0.989775  0.000098  0.000445"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cutoffs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the first row threshold to 1\n",
    "df_cutoffs['thresholds'][0] = 1 - 1 / np.power(10, 16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For setting cutoffs, \n",
    " - a common way is based on estimating probability to be good\n",
    " - An other is using credit scores using the formula\n",
    " \n",
    " $$ score_{-}from_{-}prob = \\Big( \\ln\\frac{prob_{-}good}{1- prob_{-}good } - min_{-}score_{-}coef \\Big) \\frac{(max_{-}score - min_{-}score )}{( max_{-}sum_{-}coef - min_{-}sum_{-}coef )} + min_{-}score$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cutoffs['Score'] = ((np.log(df_cutoffs['thresholds'] / (1 - df_cutoffs['thresholds'])) - min_sum_coef) * ((max_score - min_score) / (max_sum_coef - min_sum_coef)) + min_score).round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>thresholds</th>\n",
       "      <th>fpr</th>\n",
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       "      <td>796.0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.990732</td>\n",
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       "      <td>0.000445</td>\n",
       "      <td>771.0</td>\n",
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      ],
      "text/plain": [
       "   thresholds       fpr       tpr   Score\n",
       "0    1.000000  0.000000  0.000000  3243.0\n",
       "1    0.992661  0.000000  0.000012   796.0\n",
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       "3    0.990661  0.000098  0.000120   778.0\n",
       "4    0.989775  0.000098  0.000445   771.0"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cutoffs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cutoffs['Score'][0] = max_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.000445</td>\n",
       "      <td>771.0</td>\n",
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      ],
      "text/plain": [
       "   thresholds       fpr       tpr  Score\n",
       "0    1.000000  0.000000  0.000000  850.0\n",
       "1    0.992661  0.000000  0.000012  796.0\n",
       "2    0.990732  0.000000  0.000120  778.0\n",
       "3    0.990661  0.000098  0.000120  778.0\n",
       "4    0.989775  0.000098  0.000445  771.0"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cutoffs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
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       "       thresholds       fpr       tpr  Score\n",
       "17235    0.493179  0.999411  0.999964  417.0\n",
       "17236    0.492400  0.999509  0.999964  417.0\n",
       "17237    0.487971  0.999509  0.999976  415.0\n",
       "17238    0.393127  1.000000  0.999976  386.0\n",
       "17239    0.372626  1.000000  1.000000  379.0"
      ]
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     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cutoffs.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> Number of Approved + Number of Rejected = Total</center>\n",
    "<center>Approval Rate = Number of Approved /Total</center>\n",
    "<center>Rejection Rate = Number of Rejected/Total </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "def n_approved(p):\n",
    "    return np.where(df_actual_predicted_probs['y_hat_test_proba'] >= p, 1, 0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cutoffs['N Approved'] = df_cutoffs['thresholds'].apply(n_approved)\n",
    "\n",
    "df_cutoffs['N Rejected'] = df_actual_predicted_probs['y_hat_test_proba'].shape[0] - df_cutoffs['N Approved']\n",
    "\n",
    "df_cutoffs['Approval Rate'] = df_cutoffs['N Approved'] / df_actual_predicted_probs['y_hat_test_proba'].shape[0]\n",
    "\n",
    "df_cutoffs['Rejection Rate'] = 1 - df_cutoffs['Approval Rate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.000118</td>\n",
       "      <td>0.999882</td>\n",
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       "      <td>4</td>\n",
       "      <td>0.989775</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000445</td>\n",
       "      <td>771.0</td>\n",
       "      <td>38</td>\n",
       "      <td>93219</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.999593</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   thresholds       fpr       tpr  Score  N Approved  N Rejected  \\\n",
       "0    1.000000  0.000000  0.000000  850.0           0       93257   \n",
       "1    0.992661  0.000000  0.000012  796.0           1       93256   \n",
       "2    0.990732  0.000000  0.000120  778.0          10       93247   \n",
       "3    0.990661  0.000098  0.000120  778.0          11       93246   \n",
       "4    0.989775  0.000098  0.000445  771.0          38       93219   \n",
       "\n",
       "   Approval Rate  Rejection Rate  \n",
       "0       0.000000        1.000000  \n",
       "1       0.000011        0.999989  \n",
       "2       0.000107        0.999893  \n",
       "3       0.000118        0.999882  \n",
       "4       0.000407        0.999593  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cutoffs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
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       "      <td>8</td>\n",
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       "      <td>17237</td>\n",
       "      <td>0.487971</td>\n",
       "      <td>0.999509</td>\n",
       "      <td>0.999976</td>\n",
       "      <td>415.0</td>\n",
       "      <td>93250</td>\n",
       "      <td>7</td>\n",
       "      <td>0.999925</td>\n",
       "      <td>0.000075</td>\n",
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       "      <td>17238</td>\n",
       "      <td>0.393127</td>\n",
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       "      <td>0.999976</td>\n",
       "      <td>386.0</td>\n",
       "      <td>93255</td>\n",
       "      <td>2</td>\n",
       "      <td>0.999979</td>\n",
       "      <td>0.000021</td>\n",
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       "      <td>17239</td>\n",
       "      <td>0.372626</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>379.0</td>\n",
       "      <td>93257</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
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      ],
      "text/plain": [
       "       thresholds       fpr       tpr  Score  N Approved  N Rejected  \\\n",
       "17235    0.493179  0.999411  0.999964  417.0       93248           9   \n",
       "17236    0.492400  0.999509  0.999964  417.0       93249           8   \n",
       "17237    0.487971  0.999509  0.999976  415.0       93250           7   \n",
       "17238    0.393127  1.000000  0.999976  386.0       93255           2   \n",
       "17239    0.372626  1.000000  1.000000  379.0       93257           0   \n",
       "\n",
       "       Approval Rate  Rejection Rate  \n",
       "17235       0.999903        0.000097  \n",
       "17236       0.999914        0.000086  \n",
       "17237       0.999925        0.000075  \n",
       "17238       0.999979        0.000021  \n",
       "17239       1.000000        0.000000  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cutoffs.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting the <b>cut-offs</b> at no less than 95% will give a <b>reject rate</b> and <b>approval rate</b> of approximatively <b>21%</b> and <b>79%</b> respectively according to the table below.\n",
    "\n",
    "The closest credit score associated is $\\mathbf{645}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>thresholds</th>\n",
       "      <th>fpr</th>\n",
       "      <th>tpr</th>\n",
       "      <th>Score</th>\n",
       "      <th>N Approved</th>\n",
       "      <th>N Rejected</th>\n",
       "      <th>Approval Rate</th>\n",
       "      <th>Rejection Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1189</td>\n",
       "      <td>0.950089</td>\n",
       "      <td>0.057802</td>\n",
       "      <td>0.227467</td>\n",
       "      <td>646.0</td>\n",
       "      <td>19484</td>\n",
       "      <td>73773</td>\n",
       "      <td>0.208928</td>\n",
       "      <td>0.791072</td>\n",
       "    </tr>\n",
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       "      <td>1190</td>\n",
       "      <td>0.950034</td>\n",
       "      <td>0.057802</td>\n",
       "      <td>0.227840</td>\n",
       "      <td>645.0</td>\n",
       "      <td>19515</td>\n",
       "      <td>73742</td>\n",
       "      <td>0.209260</td>\n",
       "      <td>0.790740</td>\n",
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       "    <tr>\n",
       "      <td>1191</td>\n",
       "      <td>0.950032</td>\n",
       "      <td>0.057900</td>\n",
       "      <td>0.227840</td>\n",
       "      <td>645.0</td>\n",
       "      <td>19516</td>\n",
       "      <td>73741</td>\n",
       "      <td>0.209271</td>\n",
       "      <td>0.790729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1192</td>\n",
       "      <td>0.950006</td>\n",
       "      <td>0.057900</td>\n",
       "      <td>0.228033</td>\n",
       "      <td>645.0</td>\n",
       "      <td>19532</td>\n",
       "      <td>73725</td>\n",
       "      <td>0.209443</td>\n",
       "      <td>0.790557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1193</td>\n",
       "      <td>0.950006</td>\n",
       "      <td>0.057998</td>\n",
       "      <td>0.228033</td>\n",
       "      <td>645.0</td>\n",
       "      <td>19533</td>\n",
       "      <td>73724</td>\n",
       "      <td>0.209453</td>\n",
       "      <td>0.790547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      thresholds       fpr       tpr  Score  N Approved  N Rejected  \\\n",
       "1189    0.950089  0.057802  0.227467  646.0       19484       73773   \n",
       "1190    0.950034  0.057802  0.227840  645.0       19515       73742   \n",
       "1191    0.950032  0.057900  0.227840  645.0       19516       73741   \n",
       "1192    0.950006  0.057900  0.228033  645.0       19532       73725   \n",
       "1193    0.950006  0.057998  0.228033  645.0       19533       73724   \n",
       "\n",
       "      Approval Rate  Rejection Rate  \n",
       "1189       0.208928        0.791072  \n",
       "1190       0.209260        0.790740  \n",
       "1191       0.209271        0.790729  \n",
       "1192       0.209443        0.790557  \n",
       "1193       0.209453        0.790547  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cutoffs[df_cutoffs['thresholds']>=0.95].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting the <b>cut-offs</b> at no less than 94% will give a <b>reject rate</b> and <b>approval rate</b> of approximatively <b>28%</b> and <b>72%</b> respectively,\n",
    "according to the table below\n",
    "\n",
    "The closest credit score associated is $\\mathbf{631}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>thresholds</th>\n",
       "      <th>fpr</th>\n",
       "      <th>tpr</th>\n",
       "      <th>Score</th>\n",
       "      <th>N Approved</th>\n",
       "      <th>N Rejected</th>\n",
       "      <th>Approval Rate</th>\n",
       "      <th>Rejection Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1784</td>\n",
       "      <td>0.940089</td>\n",
       "      <td>0.088420</td>\n",
       "      <td>0.298169</td>\n",
       "      <td>631.0</td>\n",
       "      <td>25669</td>\n",
       "      <td>67588</td>\n",
       "      <td>0.275250</td>\n",
       "      <td>0.724750</td>\n",
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       "    <tr>\n",
       "      <td>1785</td>\n",
       "      <td>0.940048</td>\n",
       "      <td>0.088420</td>\n",
       "      <td>0.298410</td>\n",
       "      <td>631.0</td>\n",
       "      <td>25689</td>\n",
       "      <td>67568</td>\n",
       "      <td>0.275465</td>\n",
       "      <td>0.724535</td>\n",
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       "      <td>1786</td>\n",
       "      <td>0.940047</td>\n",
       "      <td>0.088420</td>\n",
       "      <td>0.298434</td>\n",
       "      <td>631.0</td>\n",
       "      <td>25691</td>\n",
       "      <td>67566</td>\n",
       "      <td>0.275486</td>\n",
       "      <td>0.724514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1787</td>\n",
       "      <td>0.940008</td>\n",
       "      <td>0.088420</td>\n",
       "      <td>0.298843</td>\n",
       "      <td>631.0</td>\n",
       "      <td>25725</td>\n",
       "      <td>67532</td>\n",
       "      <td>0.275851</td>\n",
       "      <td>0.724149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1788</td>\n",
       "      <td>0.940002</td>\n",
       "      <td>0.088518</td>\n",
       "      <td>0.298843</td>\n",
       "      <td>631.0</td>\n",
       "      <td>25726</td>\n",
       "      <td>67531</td>\n",
       "      <td>0.275861</td>\n",
       "      <td>0.724139</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      thresholds       fpr       tpr  Score  N Approved  N Rejected  \\\n",
       "1784    0.940089  0.088420  0.298169  631.0       25669       67588   \n",
       "1785    0.940048  0.088420  0.298410  631.0       25689       67568   \n",
       "1786    0.940047  0.088420  0.298434  631.0       25691       67566   \n",
       "1787    0.940008  0.088420  0.298843  631.0       25725       67532   \n",
       "1788    0.940002  0.088518  0.298843  631.0       25726       67531   \n",
       "\n",
       "      Approval Rate  Rejection Rate  \n",
       "1784       0.275250        0.724750  \n",
       "1785       0.275465        0.724535  \n",
       "1786       0.275486        0.724514  \n",
       "1787       0.275851        0.724149  \n",
       "1788       0.275861        0.724139  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cutoffs[df_cutoffs['thresholds']>=0.94].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting the <b>cut-offs credit score</b> at 670 points, the probability pf being good is <b>96%</b> respectively, according to the table below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>thresholds</th>\n",
       "      <th>fpr</th>\n",
       "      <th>tpr</th>\n",
       "      <th>Score</th>\n",
       "      <th>N Approved</th>\n",
       "      <th>N Rejected</th>\n",
       "      <th>Approval Rate</th>\n",
       "      <th>Rejection Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>561</td>\n",
       "      <td>0.963023</td>\n",
       "      <td>0.026987</td>\n",
       "      <td>0.139093</td>\n",
       "      <td>670.0</td>\n",
       "      <td>11829</td>\n",
       "      <td>81428</td>\n",
       "      <td>0.126843</td>\n",
       "      <td>0.873157</td>\n",
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       "    <tr>\n",
       "      <td>562</td>\n",
       "      <td>0.962994</td>\n",
       "      <td>0.026987</td>\n",
       "      <td>0.139261</td>\n",
       "      <td>670.0</td>\n",
       "      <td>11843</td>\n",
       "      <td>81414</td>\n",
       "      <td>0.126993</td>\n",
       "      <td>0.873007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>563</td>\n",
       "      <td>0.962990</td>\n",
       "      <td>0.027085</td>\n",
       "      <td>0.139261</td>\n",
       "      <td>670.0</td>\n",
       "      <td>11844</td>\n",
       "      <td>81413</td>\n",
       "      <td>0.127004</td>\n",
       "      <td>0.872996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>564</td>\n",
       "      <td>0.962976</td>\n",
       "      <td>0.027085</td>\n",
       "      <td>0.139442</td>\n",
       "      <td>670.0</td>\n",
       "      <td>11859</td>\n",
       "      <td>81398</td>\n",
       "      <td>0.127165</td>\n",
       "      <td>0.872835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>565</td>\n",
       "      <td>0.962975</td>\n",
       "      <td>0.027184</td>\n",
       "      <td>0.139442</td>\n",
       "      <td>670.0</td>\n",
       "      <td>11860</td>\n",
       "      <td>81397</td>\n",
       "      <td>0.127175</td>\n",
       "      <td>0.872825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     thresholds       fpr       tpr  Score  N Approved  N Rejected  \\\n",
       "561    0.963023  0.026987  0.139093  670.0       11829       81428   \n",
       "562    0.962994  0.026987  0.139261  670.0       11843       81414   \n",
       "563    0.962990  0.027085  0.139261  670.0       11844       81413   \n",
       "564    0.962976  0.027085  0.139442  670.0       11859       81398   \n",
       "565    0.962975  0.027184  0.139442  670.0       11860       81397   \n",
       "\n",
       "     Approval Rate  Rejection Rate  \n",
       "561       0.126843        0.873157  \n",
       "562       0.126993        0.873007  \n",
       "563       0.127004        0.872996  \n",
       "564       0.127165        0.872835  \n",
       "565       0.127175        0.872825  "
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cutoffs[df_cutoffs['Score'] >= 670].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving files as csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs_train_with_ref_cat.to_csv('inputs_train_with_ref_cat.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scorecard.to_csv('df_scorecard.csv')"
   ]
  }
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