modelling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ebf9ed94-f8d9-4053-a8fd-98d4db1d1739",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from scipy import stats \n",
    "import matplotlib.pyplot as plt\n",
    "import hvplot.pandas\n",
    "\n",
    "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score, confusion_matrix, classification_report, \n",
    "    roc_auc_score, roc_curve, auc,\n",
    "    plot_confusion_matrix, plot_roc_curve\n",
    ")\n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Dense, Dropout, BatchNormalization \n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.metrics import AUC\n",
    "\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "pd.set_option('display.float', '{:.2f}'.format)\n",
    "pd.set_option('display.max_columns', 50)\n",
    "pd.set_option('display.max_rows', 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1cde780c-0c10-421c-ac17-6b0f9f7a26de",
   "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>Unnamed: 0</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>purpose</th>\n",
       "      <th>title</th>\n",
       "      <th>dti</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>application_type</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.44</td>\n",
       "      <td>329.48</td>\n",
       "      <td>B</td>\n",
       "      <td>B4</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>117000.00</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>vacation</td>\n",
       "      <td>vacation</td>\n",
       "      <td>26.24</td>\n",
       "      <td>1990-06-01</td>\n",
       "      <td>16.00</td>\n",
       "      <td>0</td>\n",
       "      <td>36369.00</td>\n",
       "      <td>41.80</td>\n",
       "      <td>25.00</td>\n",
       "      <td>w</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0174 Michelle Gateway\\r\\nMendozaberg, OK 22690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.99</td>\n",
       "      <td>265.68</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>Credit analyst</td>\n",
       "      <td>4 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>65000.00</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>debt consolidation</td>\n",
       "      <td>22.05</td>\n",
       "      <td>2004-07-01</td>\n",
       "      <td>17.00</td>\n",
       "      <td>0</td>\n",
       "      <td>20131.00</td>\n",
       "      <td>53.30</td>\n",
       "      <td>27.00</td>\n",
       "      <td>f</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1076 Carney Fort Apt. 347\\r\\nLoganmouth, SD 05113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>15600.00</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.49</td>\n",
       "      <td>506.97</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>Statistician</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>43057.00</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>credit card refinancing</td>\n",
       "      <td>12.79</td>\n",
       "      <td>2007-08-01</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0</td>\n",
       "      <td>11987.00</td>\n",
       "      <td>92.20</td>\n",
       "      <td>26.00</td>\n",
       "      <td>f</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>87025 Mark Dale Apt. 269\\r\\nNew Sabrina, WV 05113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7200.00</td>\n",
       "      <td>36 months</td>\n",
       "      <td>6.49</td>\n",
       "      <td>220.65</td>\n",
       "      <td>A</td>\n",
       "      <td>A2</td>\n",
       "      <td>Client Advocate</td>\n",
       "      <td>6 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>54000.00</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>2014-11-01</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>credit card refinancing</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2006-09-01</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0</td>\n",
       "      <td>5472.00</td>\n",
       "      <td>21.50</td>\n",
       "      <td>13.00</td>\n",
       "      <td>f</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>823 Reid Ford\\r\\nDelacruzside, MA 00813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>24375.00</td>\n",
       "      <td>60 months</td>\n",
       "      <td>17.27</td>\n",
       "      <td>609.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>Destiny Management Inc.</td>\n",
       "      <td>9 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>55000.00</td>\n",
       "      <td>Verified</td>\n",
       "      <td>2013-04-01</td>\n",
       "      <td>0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>credit card refinance</td>\n",
       "      <td>33.95</td>\n",
       "      <td>1999-03-01</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0</td>\n",
       "      <td>24584.00</td>\n",
       "      <td>69.80</td>\n",
       "      <td>43.00</td>\n",
       "      <td>f</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>679 Luna Roads\\r\\nGreggshire, VA 11650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  loan_amnt        term  int_rate  installment grade sub_grade  \\\n",
       "0           0   10000.00   36 months     11.44       329.48     B        B4   \n",
       "1           1    8000.00   36 months     11.99       265.68     B        B5   \n",
       "2           2   15600.00   36 months     10.49       506.97     B        B3   \n",
       "3           3    7200.00   36 months      6.49       220.65     A        A2   \n",
       "4           4   24375.00   60 months     17.27       609.33     C        C5   \n",
       "\n",
       "                 emp_title emp_length home_ownership  annual_inc  \\\n",
       "0                Marketing  10+ years           RENT   117000.00   \n",
       "1          Credit analyst     4 years       MORTGAGE    65000.00   \n",
       "2             Statistician   < 1 year           RENT    43057.00   \n",
       "3          Client Advocate    6 years           RENT    54000.00   \n",
       "4  Destiny Management Inc.    9 years       MORTGAGE    55000.00   \n",
       "\n",
       "  verification_status     issue_d  loan_status             purpose  \\\n",
       "0        Not Verified  2015-01-01            1            vacation   \n",
       "1        Not Verified  2015-01-01            1  debt_consolidation   \n",
       "2     Source Verified  2015-01-01            1         credit_card   \n",
       "3        Not Verified  2014-11-01            1         credit_card   \n",
       "4            Verified  2013-04-01            0         credit_card   \n",
       "\n",
       "                     title   dti earliest_cr_line  open_acc  pub_rec  \\\n",
       "0                 vacation 26.24       1990-06-01     16.00        0   \n",
       "1       debt consolidation 22.05       2004-07-01     17.00        0   \n",
       "2  credit card refinancing 12.79       2007-08-01     13.00        0   \n",
       "3  credit card refinancing  2.60       2006-09-01      6.00        0   \n",
       "4    credit card refinance 33.95       1999-03-01     13.00        0   \n",
       "\n",
       "   revol_bal  revol_util  total_acc initial_list_status application_type  \\\n",
       "0   36369.00       41.80      25.00                   w       INDIVIDUAL   \n",
       "1   20131.00       53.30      27.00                   f       INDIVIDUAL   \n",
       "2   11987.00       92.20      26.00                   f       INDIVIDUAL   \n",
       "3    5472.00       21.50      13.00                   f       INDIVIDUAL   \n",
       "4   24584.00       69.80      43.00                   f       INDIVIDUAL   \n",
       "\n",
       "   mort_acc  pub_rec_bankruptcies  \\\n",
       "0      0.00                  0.00   \n",
       "1      1.00                  0.00   \n",
       "2      0.00                  0.00   \n",
       "3      0.00                  0.00   \n",
       "4      1.00                  0.00   \n",
       "\n",
       "                                             address  \n",
       "0     0174 Michelle Gateway\\r\\nMendozaberg, OK 22690  \n",
       "1  1076 Carney Fort Apt. 347\\r\\nLoganmouth, SD 05113  \n",
       "2  87025 Mark Dale Apt. 269\\r\\nNew Sabrina, WV 05113  \n",
       "3            823 Reid Ford\\r\\nDelacruzside, MA 00813  \n",
       "4             679 Luna Roads\\r\\nGreggshire, VA 11650  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('new_data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "88a050e8-9368-49c2-850b-94d9623602f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Length of the data: (396030, 28)\n"
     ]
    }
   ],
   "source": [
    "# The length of the data\n",
    "print(f\"The Length of the data: {data.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "49ed2f7b-a352-4001-8103-e0cede6fb540",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'emp_title': number of missing values '22927' ==> '5.789%'\n",
      "'emp_length': number of missing values '18301' ==> '4.621%'\n",
      "'title': number of missing values '1755' ==> '0.443%'\n",
      "'revol_util': number of missing values '276' ==> '0.070%'\n",
      "'mort_acc': number of missing values '37795' ==> '9.543%'\n",
      "'pub_rec_bankruptcies': number of missing values '535' ==> '0.135%'\n"
     ]
    }
   ],
   "source": [
    "# Missing values\n",
    "for column in data.columns:\n",
    "    if data[column].isna().sum() != 0:\n",
    "        missing = data[column].isna().sum()\n",
    "        portion = (missing / data.shape[0]) * 100\n",
    "        print(f\"'{column}': number of missing values '{missing}' ==> '{portion:.3f}%'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df3953b3-b31d-4cae-a608-7382f397faf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173105"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.emp_title.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "706aca15-72fb-4eca-9eda-184c39a2198b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('emp_title', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "34961e42-3f1a-4221-b154-f28352575a38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['10+ years', '4 years', '< 1 year', '6 years', '9 years',\n",
       "       '2 years', '3 years', '8 years', '7 years', '5 years', '1 year',\n",
       "       nan], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.emp_length.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c0d781c0-0e98-4f34-84c6-e21d65b81ea7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10+ years years in this position:\n",
      "1   0.82\n",
      "0   0.18\n",
      "Name: loan_status, dtype: float64\n",
      "==========================================\n",
      "4 years years in this position:\n",
      "1   0.81\n",
      "0   0.19\n",
      "Name: loan_status, dtype: float64\n",
      "==========================================\n",
      "< 1 year years in this position:\n",
      "1   0.79\n",
      "0   0.21\n",
      "Name: loan_status, dtype: float64\n",
      "==========================================\n",
      "6 years years in this position:\n",
      "1   0.81\n",
      "0   0.19\n",
      "Name: loan_status, dtype: float64\n",
      "==========================================\n",
      "9 years years in this position:\n",
      "1   0.80\n",
      "0   0.20\n",
      "Name: loan_status, dtype: float64\n",
      "==========================================\n",
      "2 years years in this position:\n",
      "1   0.81\n",
      "0   0.19\n",
      "Name: loan_status, dtype: float64\n",
      "==========================================\n",
      "3 years years in this position:\n",
      "1   0.80\n",
      "0   0.20\n",
      "Name: loan_status, dtype: float64\n",
      "==========================================\n",
      "8 years years in this position:\n",
      "1   0.80\n",
      "0   0.20\n",
      "Name: loan_status, dtype: float64\n",
      "==========================================\n",
      "7 years years in this position:\n",
      "1   0.81\n",
      "0   0.19\n",
      "Name: loan_status, dtype: float64\n",
      "==========================================\n",
      "5 years years in this position:\n",
      "1   0.81\n",
      "0   0.19\n",
      "Name: loan_status, dtype: float64\n",
      "==========================================\n",
      "1 year years in this position:\n",
      "1   0.80\n",
      "0   0.20\n",
      "Name: loan_status, dtype: float64\n",
      "==========================================\n",
      "nan years in this position:\n",
      "Series([], Name: loan_status, dtype: float64)\n",
      "==========================================\n"
     ]
    }
   ],
   "source": [
    "for year in data.emp_length.unique():\n",
    "    print(f\"{year} years in this position:\")\n",
    "    print(f\"{data[data.emp_length == year].loan_status.value_counts(normalize=True)}\")\n",
    "    print('==========================================')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ae6f5fcf-fb41-4639-b9c8-4deee1a1e823",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('emp_length', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1b6c866b-f164-4005-9110-4809f5049f1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "debt consolidation         168108\n",
       "credit card refinancing     51781\n",
       "home improvement            17117\n",
       "other                       12993\n",
       "consolidation                5583\n",
       "Name: title, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.title.value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d3e11834-ccd8-4a0c-b050-b94556bcaacd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "debt_consolidation    234507\n",
       "credit_card            83019\n",
       "home_improvement       24030\n",
       "other                  21185\n",
       "major_purchase          8790\n",
       "Name: purpose, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.purpose.value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d90b6acc-d29b-4a3a-9908-98b9a60b7f79",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('title', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1a246e42-3fd9-47fe-9519-251c094669fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.00    218458\n",
       "0.00    139777\n",
       "Name: mort_acc, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.mort_acc.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "36063481-8cf0-45fd-8bf3-1533f6c6b65b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37795"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.mort_acc.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d7881b9a-247e-4cfc-b78f-f5a2619dd878",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_acc_avg = data.groupby(by='total_acc').mean().mort_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "64c83b6f-4a3e-4112-ad62-9636a0e5fdbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_mort_acc(total_acc, mort_acc):\n",
    "    if np.isnan(mort_acc):\n",
    "        return total_acc_avg[total_acc].round()\n",
    "    else:\n",
    "        return mort_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d6598686-573e-4b94-9b68-a5c1f4a81649",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['mort_acc'] = data.apply(lambda x: fill_mort_acc(x['total_acc'], x['mort_acc']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5bc8ce51-f01f-4fba-aea3-ed4d616140ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'revol_util': number of missing values '276' ==> '0.070%'\n",
      "'pub_rec_bankruptcies': number of missing values '535' ==> '0.135%'\n"
     ]
    }
   ],
   "source": [
    "for column in data.columns:\n",
    "    if data[column].isna().sum() != 0:\n",
    "        missing = data[column].isna().sum()\n",
    "        portion = (missing / data.shape[0]) * 100\n",
    "        print(f\"'{column}': number of missing values '{missing}' ==> '{portion:.3f}%'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d27fba49-fdc0-4d50-af80-b0088b82503a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6180aa97-f133-46ce-b4e4-5bc1e674fcb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(395219, 25)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "63cf0089-90a9-4c1e-aca0-7ab89a5afe09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['term', 'grade', 'sub_grade', 'home_ownership', 'verification_status', 'issue_d', 'purpose', 'earliest_cr_line', 'initial_list_status', 'application_type', 'address']\n"
     ]
    }
   ],
   "source": [
    "#Categorical Variables and Dummy Variables\n",
    "\n",
    "print([column for column in data.columns if data[column].dtype == object])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "188faa4d-9a94-4e65-9ddd-8600e80be336",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' 36 months', ' 60 months'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.term.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "27b15774-e4ad-4dcd-9b44-3b52880ad4ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "term_values = {' 36 months': 36, ' 60 months': 60}\n",
    "data['term'] = data.term.map(term_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9d531982-501e-4f9a-a1d6-b413493b0a7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([36, 60], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.term.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "285ef3a7-83df-449e-b327-013e44c6349b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('grade', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7cabd2e4-6698-46ea-80d6-f2fbef19d3e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies = ['sub_grade', 'verification_status', 'purpose', 'initial_list_status', \n",
    "           'application_type', 'home_ownership']\n",
    "data = pd.get_dummies(data, columns=dummies, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e723e8fb-d2b1-41dc-ad5e-2703abb85728",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0174 Michelle Gateway\\r\\nMendozaberg, OK 22690\n",
       "1    1076 Carney Fort Apt. 347\\r\\nLoganmouth, SD 05113\n",
       "2    87025 Mark Dale Apt. 269\\r\\nNew Sabrina, WV 05113\n",
       "3              823 Reid Ford\\r\\nDelacruzside, MA 00813\n",
       "4               679 Luna Roads\\r\\nGreggshire, VA 11650\n",
       "Name: address, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.address.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a678d6d5-66dc-4e23-a198-934f12227efa",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['zip_code'] = data.address.apply(lambda x: x[-5:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f9572d77-3cbb-4ade-bdf6-08170f970808",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70466    56880\n",
       "22690    56413\n",
       "30723    56402\n",
       "48052    55811\n",
       "00813    45725\n",
       "29597    45393\n",
       "05113    45300\n",
       "11650    11210\n",
       "93700    11126\n",
       "86630    10959\n",
       "Name: zip_code, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.zip_code.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fd99030c-df02-4be9-b3b6-f3906a3446e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.get_dummies(data, columns=['zip_code'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6921edec-9ea0-4ddf-9750-cbcaaa42c8f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('address', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "62378123-4e1e-4dc3-83aa-ea2af9637f49",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('issue_d', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "54c03db2-8e66-4f93-8603-8a87589716ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('earliest_cr_line', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "677b9bcc-937e-43bb-93e4-967bb0f0e9ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weight of positive values 0.19615200686201828\n",
      "Weight of negative values 0.8038479931379817\n"
     ]
    }
   ],
   "source": [
    "# Train Test Split\n",
    "\n",
    "w_p = data.loan_status.value_counts()[0] / data.shape[0]\n",
    "w_n = data.loan_status.value_counts()[1] / data.shape[0]\n",
    "\n",
    "print(f\"Weight of positive values {w_p}\")\n",
    "print(f\"Weight of negative values {w_n}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "281ee705-8277-4d57-87a1-f08aa312cafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(264796, 79)\n",
      "(130423, 79)\n"
     ]
    }
   ],
   "source": [
    "train, test = train_test_split(data, test_size=0.33, random_state=42)\n",
    "\n",
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1aa7c0d0-cedb-479e-8426-3836970c3e1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(264796, 79)\n",
      "(262143, 79)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "train = train[train['annual_inc'] <= 250000]\n",
    "train = train[train['dti'] <= 50]\n",
    "train = train[train['open_acc'] <= 40]\n",
    "train = train[train['total_acc'] <= 80]\n",
    "train = train[train['revol_util'] <= 120]\n",
    "train = train[train['revol_bal'] <= 250000]\n",
    "print(train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "81b64f45-d4d2-4033-9d7c-5f481a18a54d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalizing the data\n",
    "\n",
    "X_train, y_train = train.drop('loan_status', axis=1), train.loan_status\n",
    "X_test, y_test = test.drop('loan_status', axis=1), test.loan_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "de12667e-9a92-4e45-8c4d-836d66bebea5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0          int64\n",
       "loan_amnt         float64\n",
       "term                int64\n",
       "int_rate          float64\n",
       "installment       float64\n",
       "                   ...   \n",
       "zip_code_30723      uint8\n",
       "zip_code_48052      uint8\n",
       "zip_code_70466      uint8\n",
       "zip_code_86630      uint8\n",
       "zip_code_93700      uint8\n",
       "Length: 78, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f9638250-26d7-4036-a990-a82234748e4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e9c32033-eec9-4b6c-87b3-80d1c573f038",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_score(true, pred, train=True):\n",
    "    if train:\n",
    "        clf_report = pd.DataFrame(classification_report(true, pred, output_dict=True))\n",
    "        print(\"Train Result:\\n================================================\")\n",
    "        print(f\"Accuracy Score: {(accuracy_score(true, pred) * 100)}%\")\n",
    "        print(\"_______________________________________________\")\n",
    "        print(f\"CLASSIFICATION REPORT:\\n{clf_report}\")\n",
    "        print(\"_______________________________________________\")\n",
    "        print(f\"Confusion Matrix: \\n {confusion_matrix(true, pred)}\\n\")\n",
    "        \n",
    "    elif train==False:\n",
    "        clf_report = pd.DataFrame(classification_report(true, pred, output_dict=True))\n",
    "        print(\"Test Result:\\n================================================\")        \n",
    "        print(f\"Accuracy Score: {(accuracy_score(true, pred) * 100)}%\")\n",
    "        print(\"_______________________________________________\")\n",
    "        print(f\"CLASSIFICATION REPORT:\\n{clf_report}\")\n",
    "        print(\"_______________________________________________\")\n",
    "        print(f\"Confusion Matrix: \\n {confusion_matrix(true, pred)}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8e558b48-a516-4e02-86ab-e87e79bff731",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.array(X_train).astype(np.float32)\n",
    "X_test = np.array(X_test).astype(np.float32)\n",
    "y_train = np.array(y_train).astype(np.float32)\n",
    "y_test = np.array(y_test).astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "982df807-4ecc-4f7e-885b-0812956da221",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:16:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "Train Result:\n",
      "================================================\n",
      "Accuracy Score: 89.52976047424497%\n",
      "_______________________________________________\n",
      "CLASSIFICATION REPORT:\n",
      "               0.0       1.0  accuracy  macro avg  weighted avg\n",
      "precision     0.95      0.89      0.90       0.92          0.90\n",
      "recall        0.50      0.99      0.90       0.74          0.90\n",
      "f1-score      0.65      0.94      0.90       0.80          0.88\n",
      "support   51665.00 210478.00      0.90  262143.00     262143.00\n",
      "_______________________________________________\n",
      "Confusion Matrix: \n",
      " [[ 25671  25994]\n",
      " [  1453 209025]]\n",
      "\n",
      "Test Result:\n",
      "================================================\n",
      "Accuracy Score: 88.92986666462204%\n",
      "_______________________________________________\n",
      "CLASSIFICATION REPORT:\n",
      "               0.0       1.0  accuracy  macro avg  weighted avg\n",
      "precision     0.92      0.89      0.89       0.90          0.89\n",
      "recall        0.48      0.99      0.89       0.73          0.89\n",
      "f1-score      0.63      0.94      0.89       0.78          0.87\n",
      "support   25480.00 104943.00      0.89  130423.00     130423.00\n",
      "_______________________________________________\n",
      "Confusion Matrix: \n",
      " [[ 12142  13338]\n",
      " [  1100 103843]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#XGBoost\n",
    "xgb_clf = XGBClassifier(use_label_encoder=False)\n",
    "\n",
    "xgb_clf.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = xgb_clf.predict(X_train)\n",
    "y_test_pred = xgb_clf.predict(X_test)\n",
    "\n",
    "print_score(y_train, y_train_pred, train=True)\n",
    "print_score(y_test, y_test_pred, train=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2d24fa8d-aa43-4ddc-9717-b428c3e5e737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp = plot_confusion_matrix(\n",
    "    xgb_clf, X_test, y_test, \n",
    "    cmap='Blues', values_format='d', \n",
    "    display_labels=['Default', 'Fully-Paid']\n",
    ")\n",
    "\n",
    "# disp = plot_roc_curve(xgb_clf, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d61459e8-46e7-4c33-afb0-596801434777",
   "metadata": {},
   "outputs": [],
   "source": [
    "scores_dict = {}\n",
    "\n",
    "scores_dict['XGBoost'] = {\n",
    "        'Train': roc_auc_score(y_train, xgb_clf.predict(X_train)),\n",
    "        'Test': roc_auc_score(y_test, xgb_clf.predict(X_test)),\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e0e40c3a-340e-40d2-9acf-31310af3b57b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Result:\n",
      "================================================\n",
      "Accuracy Score: 100.0%\n",
      "_______________________________________________\n",
      "CLASSIFICATION REPORT:\n",
      "               0.0       1.0  accuracy  macro avg  weighted avg\n",
      "precision     1.00      1.00      1.00       1.00          1.00\n",
      "recall        1.00      1.00      1.00       1.00          1.00\n",
      "f1-score      1.00      1.00      1.00       1.00          1.00\n",
      "support   51665.00 210478.00      1.00  262143.00     262143.00\n",
      "_______________________________________________\n",
      "Confusion Matrix: \n",
      " [[ 51665      0]\n",
      " [     0 210478]]\n",
      "\n",
      "Test Result:\n",
      "================================================\n",
      "Accuracy Score: 88.90763132269615%\n",
      "_______________________________________________\n",
      "CLASSIFICATION REPORT:\n",
      "               0.0       1.0  accuracy  macro avg  weighted avg\n",
      "precision     0.95      0.88      0.89       0.92          0.90\n",
      "recall        0.45      0.99      0.89       0.72          0.89\n",
      "f1-score      0.62      0.94      0.89       0.78          0.87\n",
      "support   25480.00 104943.00      0.89  130423.00     130423.00\n",
      "_______________________________________________\n",
      "Confusion Matrix: \n",
      " [[ 11583  13897]\n",
      " [   570 104373]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Random Forest Classifier\n",
    "\n",
    "rf_clf = RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "rf_clf.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = rf_clf.predict(X_train)\n",
    "y_test_pred = rf_clf.predict(X_test)\n",
    "\n",
    "print_score(y_train, y_train_pred, train=True)\n",
    "print_score(y_test, y_test_pred, train=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d0fe4b5f-1ce9-429b-b448-8e80ea897581",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp = plot_confusion_matrix(rf_clf, X_test, y_test, \n",
    "                             cmap='Blues', values_format='d', \n",
    "                             display_labels=['Default', 'Fully-Paid'])\n",
    "\n",
    "# disp = plot_roc_curve(xgb_clf, X_test, y_test)\n",
    "# plot_roc_curve(rf_clf, X_test, y_test, ax=disp.ax_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a1862d92-22be-4407-ba41-181c5be6d00b",
   "metadata": {},
   "outputs": [],
   "source": [
    "scores_dict['Random Forest'] = {\n",
    "        'Train': roc_auc_score(y_train, rf_clf.predict(X_train)),\n",
    "        'Test': roc_auc_score(y_test, rf_clf.predict(X_test)),\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "cbc2e6df-296c-46d8-b49d-d650012e0e83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Result:\n",
      "================================================\n",
      "Accuracy Score: 86.35439435727828%\n",
      "_______________________________________________\n",
      "CLASSIFICATION REPORT:\n",
      "               0.0       1.0  accuracy  macro avg  weighted avg\n",
      "precision     0.69      0.90      0.86       0.79          0.86\n",
      "recall        0.56      0.94      0.86       0.75          0.86\n",
      "f1-score      0.62      0.92      0.86       0.77          0.86\n",
      "support   51665.00 210478.00      0.86  262143.00     262143.00\n",
      "_______________________________________________\n",
      "Confusion Matrix: \n",
      " [[ 29098  22567]\n",
      " [ 13204 197274]]\n",
      "\n",
      "Test Result:\n",
      "================================================\n",
      "Accuracy Score: 86.39503768507089%\n",
      "_______________________________________________\n",
      "CLASSIFICATION REPORT:\n",
      "               0.0       1.0  accuracy  macro avg  weighted avg\n",
      "precision     0.68      0.90      0.86       0.79          0.86\n",
      "recall        0.57      0.94      0.86       0.75          0.86\n",
      "f1-score      0.62      0.92      0.86       0.77          0.86\n",
      "support   25480.00 104943.00      0.86  130423.00     130423.00\n",
      "_______________________________________________\n",
      "Confusion Matrix: \n",
      " [[14424 11056]\n",
      " [ 6688 98255]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Naive Bayes\n",
    "\n",
    "nb_clf = GaussianNB()\n",
    "\n",
    "nb_clf.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = nb_clf.predict(X_train)\n",
    "y_test_pred = nb_clf.predict(X_test)\n",
    "\n",
    "print_score(y_train, y_train_pred, train=True)\n",
    "print_score(y_test, y_test_pred, train=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "33163b09-c88b-4d27-a102-fda061cab7ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ku2IiItstzReTcZ3PpKC5SuqH3TfDV0HPEXWVnEzUhyMiUmHFNCanpaA+7slEgTrn8c9LOefA30qrUiIixRXX1nQ6CpqrpHlZVkREpKQYkBnXDuw0pPXlpJm1BlqR0rft7iNLq1IiIsWV3LCd3nDA64GuRIF7DNHMVx8RTYQiIlLhmCV7zcl0RpX0Ixqr+LO7DwAOAuqUaq1ERIqppL6crIjS6SpZ7+6bzSzLzGoTfY+/e2EXiYiUpx3y5WSKSWZWF/gX0UiTNURfT4qIVFgJjttpzVVyQdh92MzeIprw+8vSrZaIyPYzsx1zVEnqOml5nXP3KaVTJRGR4ttRu0r+r4BzDnQv4brEQqUMo95OVcq7GlIE9ToMLu8qSBFsnL2o8ExpSGfkRVwV9AFOt7KsiIhISTF23Ba3iEhsJbiLW4FbRJLHTJ+8i4jEToLjduH992HdtDPM7LpwvIeZHVL6VRMR2X5J/nIynRevDwKHAaeG49XAA6VWIxGRYopWwLG0tjhKp6vkUHdvZ2ZTAdx9RVgwU0SkwtohhwOm+M3MMonGbmNmjYDNpVorEZFiimljOi3pBO57gVeBnc3sJqLZAq8p1VqJiBTDDvvJew53f9rMJhNN7WpAX3efVeo1ExEphgTH7bQWUtgDWAe8nprm7gtLs2IiItsr5+VkUqXTVfIfti4aXA1oDswG9i/FeomIFEuC43ZaXSUHpB6HWQMvyCe7iEj5sx28qyQ3d59iZoeWRmVEREqKJXi54HT6uC9LOcwA2gE/llqNRESKyYBKCR7InU6Lu1bKfhZRn/fLpVMdEZGSscNO6xo+vKnl7leUUX1ERIotGlVS3rUoPQUtXVbJ3bPM7PCyrJCISLHFeAKpdBTU4v6cqD97mpmNBl4E1uacdPdXSrluIiLbbUcfx10NWEa0xmTOeG4HFLhFpEIyIHMHfTm5cxhRMp2tATuHl2qtRESKxcjYQYcDZgI1Ic+nV+AWkQorWiy4vGtRegoK3D+5+/Ayq4mISEnZgb+cTPBji0jS7agvJ3uUWS1ERErQDttV4u7Ly7IiIiIlKckLKSR4wIyI7KiMKLils6VVnlmmmU01szfCcXMzm2Bmc83s+Zx1eM2sajieG843SynjbyF9tpkdnZLeM6TNNbOh6dRHgVtEkseiuUrS2dJ0MZC68tetwF3uvjewAhgY0gcCK0L6XSEfZtYKOIVoHYOewIPhH4NM4AGgF9AKODXkLZACt4gkkqW5FVqOWVPgWOCRcGxEHyS+FLI8AfQN+8eHY8L5HiH/8cBz7r7R3ecBc4FDwjbX3b9z903AcyFvgYo8H7eISEVXxKXLGprZpJTjEe4+IuX4buBKts6U2gBY6e5Z4fh7oEnYbwIsAghzPa0K+ZsAn6WUmXrNolzpha53oMAtIolUhFeTS929fZ5lmPUGFrv7ZDPrWiIVKwEK3CKSQEZGyYwqORw4zsyOIZq3qTZwD1A3ZwZVoCnwQ8j/A7A78L2ZVQLqEM31lJOeI/Wa/NLzpT5uEUmckhpV4u5/c/em7t6M6OXi++5+OvAB0C9k6w+MCvujwzHh/Pvu7iH9lDDqpDnQkmgG1olAyzBKpUq4x+jCnk8tbhFJpFJeAeevwHNmdiMwFXg0pD8KPGlmc4HlRIEYd59hZi8AM4lWErvQ3bNDPQcDY4nmh3rM3WcUdnMFbhFJpJIO2+4+DhgX9r8jGhGSO88G4KR8rr8JuCmP9DHAmKLURYFbRJLHduA1J0VE4siATAVuEZF4SW7YVuAWkYRKcINbgVtEkicaDpjcyK3ALSKJpBa3iEisGKYWt4hIfGhUiYhI3Ji6SkREYkeBW0QkZtTHLSISI9FCCuVdi9KjwC0iiVSEFXBiR4FbRBJJXSUSS6tWr2PIjc8w69ufMIP7rj2dQw7cixHPj+ORF8eTmWEc+cfWDB/Sl9+yshly49N88fUisrM3c/Ixh3DZgKMBePCZ93nytU/AjFZ778YD151BtaqVy/npkuO8U7rSv28nMGPkax/z8LPjaL1PE+4cegrVqlYmK2szV9z6PFNmLuCknu25+KwjMTPWrNvA5bc8z/Q50YIpX4waxpp1G8nevJmsrM10738bAH899xjO6tuJZSvXAPD3B0bzziczy+15y4K6SraTmWUDX6Uk9XX3+fnkPRto7+6DzewGYI2735HmfboSrT4xD6hKtJLysALyDwc+dPd38yjnCnfvnc5942Do/71Ej8Na8cSt57DptyzWb9jE+EnfMOa/XzH+maFUrVKZJctXA/Dau1PYuCmLT567mnUbNtHxTzfS7+j2VKqUyT+f/y+fPX811atVYcDfHuWVtydzWp+O5fx0ybBfi13p37cTPfrfzqasbF669wLGjp/OsIv6ctsjb/LuJzM5slMrhg3pS5/z72HBj8s49ry7WbV6PUd0asVdV53KkQO2/q/S5/x7WL5q7e/u89CzH3D/U++V5aOVM32As73Wu3ubUiw/1Xh3721mOwHTzOx1d5+SV0Z3v66M6lSuVq1ZzydTv+XB688EoErlSlSpXInHXh7PJf2PpGqVqMXcqH60cLWZsW79JrKystmwYRNVKmdSa6dqrN/4W5S28TcqV8pk3YZN7NKoTrk9V9Ls02wXJk2fz/qNvwHw8ZS59OnWBneotVM1AGrXrM7PS1YB8PmX87ZcO/Greey2c90yr3MsJHwcd5muOWlm882sYdhvb2bjCsjbwsympBy3TD3Oi7uvBSYDe5vZdWY20cymm9kIC7Oqm9njZtYv7Pc0s69Duf9T/CesOBb+sIyGdWty4bCn6HL6LQy58WnWrt/I3AWL+XTatxxx9u0cO+hupsxYAMDxPdpSo3oV9u11NQf0uY7Bp/egXp2d2G3nulx0Rg8O6HMt+/a6mto7Vad7x/3K+emSY9a3P3JYm72pV2cnqletzJGd9qdJ43pcdedLDB/Sl+lv/J3hF5/A8AdG/e7aM4/vxLspXR7uziv3D+aDkVfS/4TDt8l77kld+OiZv3HftadTp1b1Un+uisDS3OKoNAN3dTObFrZXi3qxu38LrDKzNiFpAPDvgq4xswZAR2AGcL+7d3D31kB1oHeuvNWAfwF9gIOBXQood5CZTTKzSUuWLinqo5SLrOxsvpi9iD/368yHTw+lRrWq3P34O2Rlb2bFr2t5599XMPzivgy46jHcnckz5pOZkcGsN29i2qhhPPD0+8z/fikrf13HmA+/YtqoYcx68ybWbdjE82M+L+/HS4xv5v/CPSPf4ZX7LuSley9k+jffk715M38+sTNX3fkKrXtfy9V3vcy9156+zXV/PLglZxx3GDfcvzWg9zr3LrqeeSsnXfwg5/TrTKe2LQB47OXxtD3hBjqffgu/LP2VGy9JVBslTzmfvKezxVFpBu717t4mbCdsZxmPAAPMLBM4GXgmn3ydzWwq8DZwS1hss5uZTTCzr4DuwP65rtkXmOfuc8IqzE/lVwl3H+Hu7d29faOGjbbzUcrWbjvXY7ed69K+dTMAjuvRhi9mL6LJznXp060NZsbB+zcjw4xlK9fw0luT6NGpFZUrZdKofi0OPWgvps5ayLjPv2bP3RrQsF4tKlfKpE+3g7b5dV2K76nRn9LtrNs49ry7Wbl6Hd8uXMypvQ/l9Q+mAfDau1Np12rPLfn333s37r3mNE6/YgQrUvqzfwrdKUtXrOGNcV/Sbv9mACxZvprNmx1354nXPubg/beWlWgJbnKXaVcJ0erGOfeslkb+l4FeRK3lye6+zMxOSGnJtw/5xrt7W3c/2N0fDq3pB4F+7n4AUcs6nfslRuOGtWnSuB5z5v8CwIcTZ/OH5rtwTNcDGT/pGwDmLviFTb9l0aBuTZruUp/xE2cDsHb9RiZNn0/LZo1pukt9Jn01j3UbNuHu/HfibP7QvHG5PVcSNaxXE4CmjevRu9tBvPjWJH5asorD27UEoEuHffhu0ZIteUbedi7nXz+Sbxcu3lJGjWpVqFmj6pb97h33Zda3PwLQuEHtLfl6dz2IWd/+VCbPVd4szf/iqKyHA84n6pZ4EzixsMzuvsHMxgIPAQND2qvAlq6XMBokt5wgvdTMagL9gJdy5fkaaGZmLUK3zKlFeZA4uO2Kkxh03eNs+i2bZk0a8sB1Z1CjehUGD3+aw06+iSqVM3nohjMxM845qQuDhz/FYX+6EQdO69OR1i2bAHBcj7Z0PeNWMjMzOPAPTX/XfyrFM/LWc6hXZyeysrL5y20v8Oua9Vxy0zP84/J+VMrMYMOmLC65+VkA/nJOL+rX2Yk7/noywJZhf40a1OKp284FILNSJi+/NYn3Pp0FwLAhfTlgn6a4Owt/Ws6loayki2kvSFos6iUohYLN1rh7zVxpnYFHgV+Jlrlv7+5dCxoOaGYdiYLunu6encd9upLHMD4zu5EoGP8MfAMscPcbzOxx4A13f8nMegJ3A+uA8UCLwoYDHnxwe/94wqQi/E1IeavXYXB5V0GKYOPsF9i8bnGxwu5+B7T1kaPGpZX3kBZ1J7t7+8JzVhyl1uLOHbRD2nhgnzzSHwceD/s35Dr9R+DfeQXtkH8c0T8CudOvAa7JI/3slP23iPq6RSRpEtzirtBfTobRKC2IXi6KiKTFTHOVlJtijEYRkR1ccsN2BQ/cIiLbLcGRW4FbRBIovkP90qHALSKJlOAubgVuEUkeQ4FbRCR21FUiIhIzanGLiMRMguO2AreIJFCMZ/5LhwK3iCSS+rhFRGJEiwWLiMSRAreISLwkuaukrFfAEREpE2bpbYWXY7ub2QdmNtPMZpjZxSG9vpm9Y2Zzwp/1QrqZ2b1mNtfMvjSzdill9Q/555hZ/5T0g83sq3DNvTmLm+dHgVtEEqkEl5zMAi5391ZEi5FfaGatgKHAe+7eEngvHEO03GLLsA0iWsELM6sPXA8cChwCXJ8T7EOec1Ou61lQhRS4RSSZSihyu/tP7j4l7K8GZgFNgOOBJ0K2J4C+Yf94YKRHPgPqmtmuwNHAO+6+3N1XAO8APcO52u7+WVi4fGRKWXlSH7eIJE4RF1JoaGap6xGOcPcReZdrzYC2wASgsbvnrLz8M5CzinYTYFHKZd+HtILSv88jPV8K3CKSSEV4Nbk0nTUnw8LjLwOXuPuvqd3Q7u5mVjoL+OZBXSUikkwl2MltZpWJgvbT7v5KSP4ldHMQ/lwc0n8Adk+5vGlIKyi9aR7p+VLgFpEEsrT/K7SkqGn9KDDL3e9MOTUayBkZ0h8YlZJ+Vhhd0hFYFbpUxgJHmVm98FLyKGBsOPermXUM9zorpaw8qatERBKpBGcHPBw4E/jKzKaFtKuAW4AXzGwgsAD4Uzg3BjgGmAusAwYAuPtyM/s7MDHkG+7uy8P+BcDjQHXgzbDlS4FbRBKnJBdScPePyL9TpUce+R24MJ+yHgMeyyN9EtA63TopcItIIiX5y0kFbhFJJC2kICISMwmO2wrcIpJAac5DElcK3CKSUMmN3ArcIpI4WkhBRCSG1FUiIhIzGg4oIhI3yY3bCtwikkwJjtsK3CKSPOkuSxZXCtwikkiFLNsYawrcIpJIyQ3bCtwiklAJbnArcItIEqW3SEJcKXCLSOKU5HzcFZECt4gkkgK3iEjMqKtERCRONI5bRCReDA0HFBGJnwRHbgVuEUkk9XGLiMSMFlIQEYkbBW4RkXhRV4mISIwk/ctJc/fyrkOsmNkSYEF516MUNASWlnclpEiS+jPb090bFacAM3uL6O8nHUvdvWdx7lfWFLgFADOb5O7ty7sekj79zHZcGeVdARERKRoFbhGRmFHglhwjyrsCUmT6me2g1MctIhIzanGLiMSMAreISMwocCeAmWWb2TQzm2FmX5jZ5WZW6M/WzG4P19y+nfddE/5sZmanbU8ZSZfys8nZmhWQ92wzuz/s32BmVxThPl3NbFW4xywzu76Q/MPN7Ih8ynkj3ftK+dCXk8mw3t3bAJjZzsAzQG2gwP95gUFAfXfPLub9mwGnhfvKtrb8bMrAeHfvbWY7AdPM7HV3n5JXRne/rozqJKVALe6EcffFRAF5sEUyQ8t6opl9aWbnAZjZaKAmMNnMTjazPmY2wcymmtm7ZtY45Num5Wdm0/NoNd4CdA6tvUvL5EFjzMzmm1nDsN/ezMYVkLeFmU1JOW6ZepwXd18LTAb2NrPrws9+upmNMIs+BDezx82sX9jvaWZfh3L/p/hPKKVNgTuB3P07IBPYGRgIrHL3DkAH4Fwza+7uxxFag+7+PPAR0NHd2wLPAVcW4ZZDiVp7bdz9rhJ9mPirntJN8mpRL3b3b4FVZtYmJA0A/l3QNWbWAOgIzADud/cO7t4aqA70zpW3GvAvoA9wMLBLUesoZU9dJcl3FHBgTusKqAO0BOblytcUeN7MdgWq5HFetk9JdJU8Agwws8uAk4FD8snX2cymApuBW9x9hpmdaGZXAjWA+kTB/PWUa/YF5rn7HAAze4roNzapwBS4E8jM9gKygcVEE6Vd5O5jC7nsPuBOdx9tZl2BG0J6Ftv+ZlatRCu7Y0r9O03n7/NlovcV7wOT3X2ZmZ3A1ncY54Q/x7v7lhZ1aE0/CLR390VmdkOa95MKTl0lCWNmjYCHiX5FdmAs8L9mVjmc3ye8vMqtDvBD2O+fkj4faBeubQc0z+Pa1UCtEnmAHcN8om4JgBMLy+zuG4h+jg8Ruknc/dXQNdXG3Sflc2lOkF5qZjWBfnnk+RpoZmYtwvGp6T2ClCcF7mTI6UedAbwLvA0MC+ceAWYCU8xsOvBP8v5N6wbgRTObzLZThb4M1A9lDwa+yePaL4HsMBRRLycLNwy4x8wmEf1mlI6nibpA3k73Ju6+kqj/ejpR4J+YR54NRF0j/wkvJxenW76UH33yLhIDYWRPHXe/trzrIuVPfdwiFVwYjdIC6F7edZGKQS1uEZGYUR+3iEjMKHCLiMSMAreISMwocEuJSpkNb7qZvWhmNYpRVup8Go+YWasC8nY1s07bcY8t84akk54rz5oi3qtIM/6J5EeBW0pazvwnrYFNwPmpJ81su0Yyufs57j6zgCxdgSIHbpE4UuCW0jSeaIa6rmY2PsxIOLOAGQvNzO43s9lm9i7RJFmEc+PMrH3Y72lmU8IHP++F2QrPBy4Nrf3OZtbIzF4O95hoZoeHaxuY2dsWzUP+CNGUAAUys9fMbHK4ZlCuc3eF9PfCV6s5M/q9Fa4Zb2b7lsjfpkigcdxSKkLLuhfwVkhqB7R293kh+K1y9w5mVhX42MzeBtoCfwBaAY2Jvvh8LFe5jYi+BuwSyqrv7svN7GFgjbvfEfI9A9zl7h+Z2R5EXw7uRzS/x0fuPtzMjiWaPbEwfw73qA5MNLOX3X0ZsBMwyd0vNbPrQtmDiRbxPd/d55jZoUTzhWgMtpQYBW4padXNbFrYHw88StSF8bm758w4mN+MhV2AZ8PCDj+a2ft5lN8R+DCnLHdfnk89jgBamW1pUNcO83V0Icw57e7/MbMVaTzTkDCpE8Duoa7LiD5Bfz6kPwW8Eu7RiWj6gJzrq6ZxD5G0KXBLSfvdNKYhgK1NTSKPGQvN7JgSrEcG0fziG/KoS9rCTIlHAIe5+zqLFj3Ib4Y9D/ddWYar3sgOSH3cUh7ym7HwQ+Dk0Ae+K9Atj2s/A7qYWfNwbf2QnnuGwreBi3IObOtCBB8SLbOGmfUC6hVS1zrAihC09yVq8efIYOuMe6cRdcH8Cswzs5PCPczMDirkHiJFosAt5SG/GQtfBeaEcyOBT3Nf6O5LiGaze8XMvmBrV8XrwAk5LyeBIUD78PJzJltHtwwjCvwziLpMFhZS17eASmY2i2iJts9Szq0FDgnP0B0YHtJPBwaG+s0Ajk/j70QkbZqrREQkZtTiFhGJGQVuEZGYUeAWEYkZBW4RkZhR4BYRiRkFbhGRmFHgFhGJmf8PTAkak4l0z3IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp = plot_confusion_matrix(nb_clf, X_test, y_test, \n",
    "                             cmap='Blues', values_format='d', \n",
    "                             display_labels=['Default', 'Fully-Paid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "cdd8ede3-e8a5-4652-b674-4bbe52cfe135",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x2090305e308>"
      ]
     },
     "execution_count": 55,
     "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": [
    "disp = plot_roc_curve(xgb_clf, X_test, y_test)\n",
    "plot_roc_curve(rf_clf, X_test, y_test, ax=disp.ax_)\n",
    "plot_roc_curve(nb_clf, X_test, y_test, ax=disp.ax_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "856e7745-3bdc-4c57-bb03-5b8471c41f95",
   "metadata": {},
   "outputs": [],
   "source": [
    "scores_dict['Naive Bayes'] = {\n",
    "        # 'Train': roc_auc_score(y_train, nb_clf.predict(X_train)),\n",
    "        'Test': roc_auc_score(y_test, nb_clf.predict(X_test)),\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d21d59e5-2674-48ee-aecc-5407f1041f00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RANDOM FOREST                  roc_auc_score: 0.725\n",
      "XGBOOST                        roc_auc_score: 0.733\n",
      "NAIVE BAYES                    roc_auc_score: 0.751\n"
     ]
    }
   ],
   "source": [
    "# Comparing Models Prerformance\n",
    "\n",
    "ml_models = {\n",
    "    'Random Forest': rf_clf, \n",
    "    'XGBoost': xgb_clf, \n",
    "    'Naive Bayes': nb_clf\n",
    "}\n",
    "\n",
    "for model in ml_models:\n",
    "    print(f\"{model.upper():{30}} roc_auc_score: {roc_auc_score(y_test, ml_models[model].predict(X_test)):.3f}\")"
   ]
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      "text/plain": [
       ":Bars   [index,Variable]   (value)"
      ]
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   "source": [
    "scores_df = pd.DataFrame(scores_dict)\n",
    "scores_df.hvplot.barh(\n",
    "    width=500, height=400, \n",
    "    title=\"ROC Scores of ML Models\", xlabel=\"ROC Scores\", \n",
    "    alpha=0.4, legend='top'\n",
    ")"
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