PD_Model_Estimation_accuracy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ebc8eeb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fb9ecb00",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3444: DtypeWarning: Columns (1) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    }
   ],
   "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": "code",
   "execution_count": 3,
   "id": "02b4e735",
   "metadata": {},
   "outputs": [
    {
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       "      <th>id</th>\n",
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       "<p>5 rows × 207 columns</p>\n",
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       "        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",
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       "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",
       "       addr_state:TX addr_state:UT addr_state:VA addr_state:VT  addr_state:WA  \\\n",
       "427211             0             0             0             0              0   \n",
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       "\n",
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       "\n",
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       "136020                     0  \n",
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       "36159                      0  \n",
       "\n",
       "[5 rows x 207 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_data_inputs_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4c096cf4",
   "metadata": {},
   "outputs": [
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  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cac65892",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['home_ownership:RENT_OTHER_NONE_ANY', 'addr_state:ND_NE_IA_NV_FL_HI_AL', 'addr_state:NM_VA', 'addr_state:OK_TN_MO_LA_MD_NC', 'addr_state:UT_KY_AZ_NJ', 'addr_state:AR_MI_PA_OH_MN', 'addr_state:RI_MA_DE_SD_IN', 'addr_state:GA_WA_OR', 'addr_state:WI_MT', 'addr_state:IL_CT', 'addr_state:KS_SC_CO_VT_AK_MS', 'addr_state:WV_NH_WY_DC_ME_ID', 'purpose:educ__sm_b__wedd__ren_en__mov__house', 'purpose:oth__med__vacation', 'purpose:major_purch__car__home_impr', 'term:36', 'term:60', 'emp_length:0', 'emp_length:1', 'emp_length:2-4', 'emp_length:5-6', 'emp_length:7-9', 'emp_length:10', 'mths_since_issue_d:<38', 'mths_since_issue_d:38-39', 'mths_since_issue_d:40-41', 'mths_since_issue_d:42-48', 'mths_since_issue_d:49-52', 'mths_since_issue_d:53-64', 'mths_since_issue_d:65-84', 'mths_since_issue_d:>84', 'int_rate:<9.548', 'int_rate:9.548-12.025', 'int_rate:12.025-15.74', 'int_rate:15.74-20.281', 'int_rate:>20.281', 'mths_since_earliest_cr_line:<140', 'mths_since_earliest_cr_line:141-164', 'mths_since_earliest_cr_line:165-247', 'mths_since_earliest_cr_line:248-270', 'mths_since_earliest_cr_line:271-352', 'mths_since_earliest_cr_line:>352', 'delinq_2yrs:0', 'delinq_2yrs:1-3', 'delinq_2yrs:>=4', 'inq_last_6mths:0', 'inq_last_6mths:1-2', 'inq_last_6mths:3-6', 'inq_last_6mths:>6', 'open_acc:0', 'open_acc:1-3', 'open_acc:4-12', 'open_acc:13-17', 'open_acc:18-22', 'open_acc:23-25', 'open_acc:26-30', 'open_acc:>=31', 'pub_rec:0-2', 'pub_rec:3-4', 'pub_rec:>=5', 'total_acc:<=27', 'total_acc:28-51', 'total_acc:>=52', 'acc_now_delinq:0', 'acc_now_delinq:>=1', 'total_rev_hi_lim:<=5K', 'total_rev_hi_lim:5K-10K', 'total_rev_hi_lim:10K-20K', 'total_rev_hi_lim:20K-30K', 'total_rev_hi_lim:30K-40K', 'total_rev_hi_lim:40K-55K', 'total_rev_hi_lim:55K-95K', 'total_rev_hi_lim:>95K', 'annual_inc:<20K', 'annual_inc:20K-30K', 'annual_inc:30K-40K', 'annual_inc:40K-50K', 'annual_inc:50K-60K', 'annual_inc:60K-70K', 'annual_inc:70K-80K', 'annual_inc:80K-90K', 'annual_inc:90K-100K', 'annual_inc:100K-120K', 'annual_inc:120K-140K', 'annual_inc:>140K', 'dti:<=1.4', 'dti:1.4-3.5', 'dti:3.5-7.7', 'dti:7.7-10.5', 'dti:10.5-16.1', 'dti:16.1-20.3', 'dti:20.3-21.7', 'dti:21.7-22.4', 'dti:22.4-35', 'dti:>35', 'mths_since_last_delinq:Missing', 'mths_since_last_delinq:0-3', 'mths_since_last_delinq:4-30', 'mths_since_last_delinq:31-56', 'mths_since_last_delinq:>=57', 'mths_since_last_record:Missing', 'mths_since_last_record:0-2', 'mths_since_last_record:3-20', 'mths_since_last_record:21-31', 'mths_since_last_record:32-80', 'mths_since_last_record:81-86', 'mths_since_last_record:>=86'] not in index\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_12004/1615269337.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m inputs_train_with_ref_cat= loan_data_inputs_train.loc[: , ['grade:A',\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;34m'grade:B'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;34m'grade:C'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;34m'grade:D'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;34m'grade:E'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    923\u001b[0m                 \u001b[1;32mwith\u001b[0m \u001b[0msuppress\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    924\u001b[0m                     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtakeable\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_takeable\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 925\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    926\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    927\u001b[0m             \u001b[1;31m# we by definition only have the 0th axis\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m   1107\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_multi_take\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1108\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1109\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple_same_dim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1110\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1111\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple_same_dim\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m    804\u001b[0m                 \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    805\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 806\u001b[1;33m             \u001b[0mretval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mretval\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    807\u001b[0m             \u001b[1;31m# We should never have retval.ndim < self.ndim, as that should\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    808\u001b[0m             \u001b[1;31m#  be handled by the _getitem_lowerdim call above.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1151\u001b[0m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Cannot index with multidimensional key\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1152\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1153\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_iterable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1154\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1155\u001b[0m             \u001b[1;31m# nested tuple slicing\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_iterable\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1091\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1092\u001b[0m         \u001b[1;31m# A collection of keys\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1093\u001b[1;33m         \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1094\u001b[0m         return self.obj._reindex_with_indexers(\n\u001b[0;32m   1095\u001b[0m             \u001b[1;33m{\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1312\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1313\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1314\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_read_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1315\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1316\u001b[0m         if needs_i8_conversion(ax.dtype) or isinstance(\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[1;34m(self, key, indexer, axis)\u001b[0m\n\u001b[0;32m   1375\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1376\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1377\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{not_found} not in index\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1378\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1379\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['home_ownership:RENT_OTHER_NONE_ANY', 'addr_state:ND_NE_IA_NV_FL_HI_AL', 'addr_state:NM_VA', 'addr_state:OK_TN_MO_LA_MD_NC', 'addr_state:UT_KY_AZ_NJ', 'addr_state:AR_MI_PA_OH_MN', 'addr_state:RI_MA_DE_SD_IN', 'addr_state:GA_WA_OR', 'addr_state:WI_MT', 'addr_state:IL_CT', 'addr_state:KS_SC_CO_VT_AK_MS', 'addr_state:WV_NH_WY_DC_ME_ID', 'purpose:educ__sm_b__wedd__ren_en__mov__house', 'purpose:oth__med__vacation', 'purpose:major_purch__car__home_impr', 'term:36', 'term:60', 'emp_length:0', 'emp_length:1', 'emp_length:2-4', 'emp_length:5-6', 'emp_length:7-9', 'emp_length:10', 'mths_since_issue_d:<38', 'mths_since_issue_d:38-39', 'mths_since_issue_d:40-41', 'mths_since_issue_d:42-48', 'mths_since_issue_d:49-52', 'mths_since_issue_d:53-64', 'mths_since_issue_d:65-84', 'mths_since_issue_d:>84', 'int_rate:<9.548', 'int_rate:9.548-12.025', 'int_rate:12.025-15.74', 'int_rate:15.74-20.281', 'int_rate:>20.281', 'mths_since_earliest_cr_line:<140', 'mths_since_earliest_cr_line:141-164', 'mths_since_earliest_cr_line:165-247', 'mths_since_earliest_cr_line:248-270', 'mths_since_earliest_cr_line:271-352', 'mths_since_earliest_cr_line:>352', 'delinq_2yrs:0', 'delinq_2yrs:1-3', 'delinq_2yrs:>=4', 'inq_last_6mths:0', 'inq_last_6mths:1-2', 'inq_last_6mths:3-6', 'inq_last_6mths:>6', 'open_acc:0', 'open_acc:1-3', 'open_acc:4-12', 'open_acc:13-17', 'open_acc:18-22', 'open_acc:23-25', 'open_acc:26-30', 'open_acc:>=31', 'pub_rec:0-2', 'pub_rec:3-4', 'pub_rec:>=5', 'total_acc:<=27', 'total_acc:28-51', 'total_acc:>=52', 'acc_now_delinq:0', 'acc_now_delinq:>=1', 'total_rev_hi_lim:<=5K', 'total_rev_hi_lim:5K-10K', 'total_rev_hi_lim:10K-20K', 'total_rev_hi_lim:20K-30K', 'total_rev_hi_lim:30K-40K', 'total_rev_hi_lim:40K-55K', 'total_rev_hi_lim:55K-95K', 'total_rev_hi_lim:>95K', 'annual_inc:<20K', 'annual_inc:20K-30K', 'annual_inc:30K-40K', 'annual_inc:40K-50K', 'annual_inc:50K-60K', 'annual_inc:60K-70K', 'annual_inc:70K-80K', 'annual_inc:80K-90K', 'annual_inc:90K-100K', 'annual_inc:100K-120K', 'annual_inc:120K-140K', 'annual_inc:>140K', 'dti:<=1.4', 'dti:1.4-3.5', 'dti:3.5-7.7', 'dti:7.7-10.5', 'dti:10.5-16.1', 'dti:16.1-20.3', 'dti:20.3-21.7', 'dti:21.7-22.4', 'dti:22.4-35', 'dti:>35', 'mths_since_last_delinq:Missing', 'mths_since_last_delinq:0-3', 'mths_since_last_delinq:4-30', 'mths_since_last_delinq:31-56', 'mths_since_last_delinq:>=57', 'mths_since_last_record:Missing', 'mths_since_last_record:0-2', 'mths_since_last_record:3-20', 'mths_since_last_record:21-31', 'mths_since_last_record:32-80', 'mths_since_last_record:81-86', 'mths_since_last_record:>=86'] not in index\""
     ]
    }
   ],
   "source": [
    "inputs_train_with_ref_cat= loan_data_inputs_train.loc[: , ['grade:A',\n",
    "'grade:B',\n",
    "'grade:C',\n",
    "'grade:D',\n",
    "'grade:E',\n",
    "'grade:F',\n",
    "'grade:G',\n",
    "'home_ownership:RENT_OTHER_NONE_ANY',\n",
    "'home_ownership:OWN',\n",
    "'home_ownership:MORTGAGE',\n",
    "'addr_state:ND_NE_IA_NV_FL_HI_AL',\n",
    "'addr_state:NM_VA',\n",
    "'addr_state:NY',\n",
    "'addr_state:OK_TN_MO_LA_MD_NC',\n",
    "'addr_state:CA',\n",
    "'addr_state:UT_KY_AZ_NJ',\n",
    "'addr_state:AR_MI_PA_OH_MN',\n",
    "'addr_state:RI_MA_DE_SD_IN',\n",
    "'addr_state:GA_WA_OR',\n",
    "'addr_state:WI_MT',\n",
    "'addr_state:TX',\n",
    "'addr_state:IL_CT',\n",
    "'addr_state:KS_SC_CO_VT_AK_MS',\n",
    "'addr_state:WV_NH_WY_DC_ME_ID',\n",
    "'verification_status:Not Verified',\n",
    "'verification_status:Source Verified',\n",
    "'verification_status:Verified',\n",
    "'purpose:educ__sm_b__wedd__ren_en__mov__house',\n",
    "'purpose:credit_card',\n",
    "'purpose:debt_consolidation',\n",
    "'purpose:oth__med__vacation',\n",
    "'purpose:major_purch__car__home_impr',\n",
    "'initial_list_status:f',\n",
    "'initial_list_status:w',\n",
    "'term:36',\n",
    "'term:60',\n",
    "'emp_length:0',\n",
    "'emp_length:1',\n",
    "'emp_length:2-4',\n",
    "'emp_length:5-6',\n",
    "'emp_length:7-9',\n",
    "'emp_length:10',\n",
    "'mths_since_issue_d:<38',\n",
    "'mths_since_issue_d:38-39',\n",
    "'mths_since_issue_d:40-41',\n",
    "'mths_since_issue_d:42-48',\n",
    "'mths_since_issue_d:49-52',\n",
    "'mths_since_issue_d:53-64',\n",
    "'mths_since_issue_d:65-84',\n",
    "'mths_since_issue_d:>84',\n",
    "'int_rate:<9.548',\n",
    "'int_rate:9.548-12.025',\n",
    "'int_rate:12.025-15.74',\n",
    "'int_rate:15.74-20.281',\n",
    "'int_rate:>20.281',\n",
    "'mths_since_earliest_cr_line:<140',\n",
    "'mths_since_earliest_cr_line:141-164',\n",
    "'mths_since_earliest_cr_line:165-247',\n",
    "'mths_since_earliest_cr_line:248-270',\n",
    "'mths_since_earliest_cr_line:271-352',\n",
    "'mths_since_earliest_cr_line:>352',\n",
    "'delinq_2yrs:0',\n",
    "'delinq_2yrs:1-3',\n",
    "'delinq_2yrs:>=4',\n",
    "'inq_last_6mths:0',\n",
    "'inq_last_6mths:1-2',\n",
    "'inq_last_6mths:3-6',\n",
    "'inq_last_6mths:>6',\n",
    "'open_acc:0',\n",
    "'open_acc:1-3',\n",
    "'open_acc:4-12',\n",
    "'open_acc:13-17',\n",
    "'open_acc:18-22',\n",
    "'open_acc:23-25',\n",
    "'open_acc:26-30',\n",
    "'open_acc:>=31',\n",
    "'pub_rec:0-2',\n",
    "'pub_rec:3-4',\n",
    "'pub_rec:>=5',\n",
    "'total_acc:<=27',\n",
    "'total_acc:28-51',\n",
    "'total_acc:>=52',\n",
    "'acc_now_delinq:0',\n",
    "'acc_now_delinq:>=1',\n",
    "'total_rev_hi_lim:<=5K',\n",
    "'total_rev_hi_lim:5K-10K',\n",
    "'total_rev_hi_lim:10K-20K',\n",
    "'total_rev_hi_lim:20K-30K',\n",
    "'total_rev_hi_lim:30K-40K',\n",
    "'total_rev_hi_lim:40K-55K',\n",
    "'total_rev_hi_lim:55K-95K',\n",
    "'total_rev_hi_lim:>95K',\n",
    "'annual_inc:<20K',\n",
    "'annual_inc:20K-30K',\n",
    "'annual_inc:30K-40K',\n",
    "'annual_inc:40K-50K',\n",
    "'annual_inc:50K-60K',\n",
    "'annual_inc:60K-70K',\n",
    "'annual_inc:70K-80K',\n",
    "'annual_inc:80K-90K',\n",
    "'annual_inc:90K-100K',\n",
    "'annual_inc:100K-120K',\n",
    "'annual_inc:120K-140K',\n",
    "'annual_inc:>140K',\n",
    "'dti:<=1.4',\n",
    "'dti:1.4-3.5',\n",
    "'dti:3.5-7.7',\n",
    "'dti:7.7-10.5',\n",
    "'dti:10.5-16.1',\n",
    "'dti:16.1-20.3',\n",
    "'dti:20.3-21.7',\n",
    "'dti:21.7-22.4',\n",
    "'dti:22.4-35',\n",
    "'dti:>35',\n",
    "'mths_since_last_delinq:Missing',\n",
    "'mths_since_last_delinq:0-3',\n",
    "'mths_since_last_delinq:4-30',\n",
    "'mths_since_last_delinq:31-56',\n",
    "'mths_since_last_delinq:>=57',\n",
    "'mths_since_last_record:Missing',\n",
    "'mths_since_last_record:0-2',\n",
    "'mths_since_last_record:3-20',\n",
    "'mths_since_last_record:21-31',\n",
    "'mths_since_last_record:32-80',\n",
    "'mths_since_last_record:81-86',\n",
    "'mths_since_last_record:>=86',\n",
    "]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ddba683",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": null,
   "id": "12f61fdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs_train = inputs_train_with_ref_cat.drop(ref_categories, axis = 1) \n",
    "inputs_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f252178b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4b17da4",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70055583",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7c33530",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg.fit(inputs_train, loan_data_targets_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec2cfcc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1cf2f9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16100a0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_name = inputs_train.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41d43f56",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": null,
   "id": "24b6c70f",
   "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):#,**kwargs):\n",
    "        self.model = linear_model.LogisticRegression(*args,**kwargs)#,**args)\n",
    "\n",
    "    def fit(self,X,y):\n",
    "        self.model.fit(X,y)\n",
    "        \n",
    "        #### Get p-values for the fitted model ####\n",
    "        denom = (2.0 * (1.0 + np.cosh(self.model.decision_function(X))))\n",
    "        denom = np.tile(denom,(X.shape[1],1)).T\n",
    "        F_ij = np.dot((X / denom).T,X)\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",
    "        \n",
    "        self.coef_ = self.model.coef_\n",
    "        self.intercept_ = self.model.intercept_\n",
    "        self.p_values = p_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20306adf",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = LogisticRegression_with_p_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efbb3764",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg.fit(inputs_train, loan_data_targets_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c69470b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": null,
   "id": "7a00e3b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_values = reg.p_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c45ecdfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_values = np.append(np.nan, np.array(p_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "415cdd60",
   "metadata": {},
   "outputs": [],
   "source": [
    "summary_table['p_values'] = p_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "360c3f85",
   "metadata": {},
   "outputs": [],
   "source": [
    "summary_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db621906",
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1492bbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": null,
   "id": "4c1a7f16",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs_train = inputs_train_with_ref_cat.drop(ref_categories, axis = 1)\n",
    "inputs_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "651305ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg2 = LogisticRegression_with_p_values()\n",
    "reg2.fit(inputs_train, loan_data_targets_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b05604e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_name = inputs_train.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0932fdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": null,
   "id": "a9e49528",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_values = reg2.p_values\n",
    "p_values = np.append(np.nan,np.array(p_values))\n",
    "summary_table['p_values'] = p_values\n",
    "summary_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd39a3a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a36563a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(reg2, open('pd_model.sav', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3879ccb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Out-of-sample validation (test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8b24cd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": null,
   "id": "bd11c007",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": null,
   "id": "7b293245",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs_test = inputs_test_with_ref_cat.drop(ref_categories, axis = 1)\n",
    "inputs_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9536c92b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat_test = reg2.model.predict(inputs_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb5f1517",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "486451df",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat_test_proba = reg2.model.predict_proba(inputs_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2b3b06e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat_test_proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f1253d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat_test_proba[:][:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0b3a00f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat_test_proba = y_hat_test_proba[: ][: , 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03397b72",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat_test_proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdf30e89",
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_data_targets_test_temp = loan_data_targets_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fdfc444",
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_data_targets_test_temp.reset_index(drop = True, inplace = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "954be9b6",
   "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": null,
   "id": "d94aa40a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19f156ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs.columns = ['loan_data_targets_test', 'y_hat_test_proba']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ed65a89",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs.index = loan_data_inputs_test.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57346433",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb6bfb80",
   "metadata": {},
   "outputs": [],
   "source": [
    "#accuracy and area under the curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69a8122e",
   "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": null,
   "id": "e70e76ba",
   "metadata": {},
   "outputs": [],
   "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": null,
   "id": "9d354869",
   "metadata": {},
   "outputs": [],
   "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": null,
   "id": "67b06244",
   "metadata": {},
   "outputs": [],
   "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": null,
   "id": "3569b7fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57cd75f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "roc_curve(df_actual_predicted_probs['loan_data_targets_test'], df_actual_predicted_probs['y_hat_test_proba'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9acf5868",
   "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": null,
   "id": "ecc934a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4361b75",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(fpr, tpr)\n",
    "# We plot the false positive rate along the x-axis and the true positive rate along the y-axis,\n",
    "# thus plotting the ROC curve.\n",
    "plt.plot(fpr, fpr, linestyle = '--', color = 'k')\n",
    "# We plot a seconary diagonal line, with dashed line style and black color.\n",
    "plt.xlabel('False positive rate')\n",
    "# We name the x-axis \"False positive rate\".\n",
    "plt.ylabel('True positive rate')\n",
    "# We name the x-axis \"True positive rate\".\n",
    "plt.title('ROC curve')\n",
    "# We name the graph \"ROC curve\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e98781a",
   "metadata": {},
   "outputs": [],
   "source": [
    "AUROC = roc_auc_score(df_actual_predicted_probs['loan_data_targets_test'], df_actual_predicted_probs['y_hat_test_proba'])\n",
    "AUROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c53278e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs = df_actual_predicted_probs.sort_values('y_hat_test_proba')\n",
    "# Sorts a dataframe by the values of a specific column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3bdfe26",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82667625",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5c6c24b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs = df_actual_predicted_probs.reset_index()\n",
    "# We reset the index of a dataframe and overwrite it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2422e508",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0496129b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs['Cumulative N Population'] = df_actual_predicted_probs.index + 1\n",
    "df_actual_predicted_probs['Cumulative N Good'] = df_actual_predicted_probs['loan_data_targets_test'].cumsum()\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": null,
   "id": "0f141d38",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65a77eaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs['Cumulative Perc Population'] = df_actual_predicted_probs['Cumulative N Population'] / (df_actual_predicted_probs.shape[0])\n",
    "# We calculate the cumulative percentage of all observations.\n",
    "df_actual_predicted_probs['Cumulative Perc Good'] = df_actual_predicted_probs['Cumulative N Good'] / df_actual_predicted_probs['loan_data_targets_test'].sum()\n",
    "# We calculate cumulative percentage of 'good'.\n",
    "df_actual_predicted_probs['Cumulative Perc Bad'] = df_actual_predicted_probs['Cumulative N Bad'] / (df_actual_predicted_probs.shape[0] - df_actual_predicted_probs['loan_data_targets_test'].sum())\n",
    "# We calculate the cumulative percentage of 'bad'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac460d32",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b07ff481",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_predicted_probs.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14945c5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot Gini\n",
    "plt.plot(df_actual_predicted_probs['Cumulative Perc Population'], df_actual_predicted_probs['Cumulative Perc Bad'])\n",
    "# We plot the cumulative percentage of all along the x-axis and the cumulative percentage 'good' along the y-axis,\n",
    "# thus plotting the Gini curve.\n",
    "plt.plot(df_actual_predicted_probs['Cumulative Perc Population'], df_actual_predicted_probs['Cumulative Perc Population'], linestyle = '--', color = 'k')\n",
    "# We plot a seconary diagonal line, with dashed line style and black color.\n",
    "plt.xlabel('Cumulative % Population')\n",
    "# We name the x-axis \"Cumulative % Population\".\n",
    "plt.ylabel('Cumulative % Bad')\n",
    "# We name the y-axis \"Cumulative % Bad\".\n",
    "plt.title('Gini')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d75e990c",
   "metadata": {},
   "outputs": [],
   "source": [
    "Gini = AUROC * 2 - 1\n",
    "# Here we calculate Gini from AUROC.\n",
    "Gini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71f95e70",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot KS\n",
    "plt.plot(df_actual_predicted_probs['y_hat_test_proba'], df_actual_predicted_probs['Cumulative Perc Bad'], color = 'r')\n",
    "# We plot the predicted (estimated) probabilities along the x-axis and the cumulative percentage 'bad' along the y-axis,\n",
    "# colored in red.\n",
    "plt.plot(df_actual_predicted_probs['y_hat_test_proba'], df_actual_predicted_probs['Cumulative Perc Good'], color = 'b')\n",
    "# We plot the predicted (estimated) probabilities along the x-axis and the cumulative percentage 'good' along the y-axis,\n",
    "# colored in red.\n",
    "plt.xlabel('Estimated Probability for being Good')\n",
    "# We name the x-axis \"Estimated Probability for being Good\".\n",
    "plt.ylabel('Cumulative %')\n",
    "# We name the y-axis \"Cumulative %\".\n",
    "plt.title('Kolmogorov-Smirnov')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "beb1a019",
   "metadata": {},
   "outputs": [],
   "source": [
    "KS = max(df_actual_predicted_probs['Cumulative Perc Bad'] - df_actual_predicted_probs['Cumulative Perc Good'])\n",
    "# We calculate KS from the data. It is the maximum of the difference between the cumulative percentage of 'bad'\n",
    "# and the cumulative percentage of 'good'.\n",
    "KS"
   ]
  }
 ],
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