Loan Prediction ML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "# import statsmodels.api as sm\n",
    "# import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "import pylab\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from sklearn.metrics import classification_report,roc_auc_score\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from xgboost import XGBClassifier\n",
    "import re\n",
    "from plot_metric.functions import BinaryClassification\n",
    "from catboost import CatBoostClassifier\n",
    "from mlxtend.evaluate import confusion_matrix\n",
    "from mlxtend.plotting import plot_confusion_matrix\n",
    "from sklearn.utils import class_weight\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install plot_metric\n",
    "# !pip install catboost --no-cache-dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the dataset\n",
    "dataset=pd.read_csv(\"D:/Data Science Project/Loan Prediction/credit_train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan ID</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Loan Status</th>\n",
       "      <th>Current Loan Amount</th>\n",
       "      <th>Term</th>\n",
       "      <th>Credit Score</th>\n",
       "      <th>Annual Income</th>\n",
       "      <th>Years in current job</th>\n",
       "      <th>Home Ownership</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Monthly Debt</th>\n",
       "      <th>Years of Credit History</th>\n",
       "      <th>Months since last delinquent</th>\n",
       "      <th>Number of Open Accounts</th>\n",
       "      <th>Number of Credit Problems</th>\n",
       "      <th>Current Credit Balance</th>\n",
       "      <th>Maximum Open Credit</th>\n",
       "      <th>Bankruptcies</th>\n",
       "      <th>Tax Liens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14dd8831-6af5-400b-83ec-68e61888a048</td>\n",
       "      <td>981165ec-3274-42f5-a3b4-d104041a9ca9</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>445412.0</td>\n",
       "      <td>Short Term</td>\n",
       "      <td>709.0</td>\n",
       "      <td>1167493.0</td>\n",
       "      <td>8 years</td>\n",
       "      <td>Home Mortgage</td>\n",
       "      <td>Home Improvements</td>\n",
       "      <td>5214.74</td>\n",
       "      <td>17.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>228190.0</td>\n",
       "      <td>416746.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4771cc26-131a-45db-b5aa-537ea4ba5342</td>\n",
       "      <td>2de017a3-2e01-49cb-a581-08169e83be29</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>262328.0</td>\n",
       "      <td>Short Term</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>Home Mortgage</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>33295.98</td>\n",
       "      <td>21.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>229976.0</td>\n",
       "      <td>850784.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4eed4e6a-aa2f-4c91-8651-ce984ee8fb26</td>\n",
       "      <td>5efb2b2b-bf11-4dfd-a572-3761a2694725</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>99999999.0</td>\n",
       "      <td>Short Term</td>\n",
       "      <td>741.0</td>\n",
       "      <td>2231892.0</td>\n",
       "      <td>8 years</td>\n",
       "      <td>Own Home</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>29200.53</td>\n",
       "      <td>14.9</td>\n",
       "      <td>29.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>297996.0</td>\n",
       "      <td>750090.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>77598f7b-32e7-4e3b-a6e5-06ba0d98fe8a</td>\n",
       "      <td>e777faab-98ae-45af-9a86-7ce5b33b1011</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>347666.0</td>\n",
       "      <td>Long Term</td>\n",
       "      <td>721.0</td>\n",
       "      <td>806949.0</td>\n",
       "      <td>3 years</td>\n",
       "      <td>Own Home</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>8741.90</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>256329.0</td>\n",
       "      <td>386958.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d4062e70-befa-4995-8643-a0de73938182</td>\n",
       "      <td>81536ad9-5ccf-4eb8-befb-47a4d608658e</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>176220.0</td>\n",
       "      <td>Short Term</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5 years</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>20639.70</td>\n",
       "      <td>6.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>253460.0</td>\n",
       "      <td>427174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Loan ID                           Customer ID  \\\n",
       "0  14dd8831-6af5-400b-83ec-68e61888a048  981165ec-3274-42f5-a3b4-d104041a9ca9   \n",
       "1  4771cc26-131a-45db-b5aa-537ea4ba5342  2de017a3-2e01-49cb-a581-08169e83be29   \n",
       "2  4eed4e6a-aa2f-4c91-8651-ce984ee8fb26  5efb2b2b-bf11-4dfd-a572-3761a2694725   \n",
       "3  77598f7b-32e7-4e3b-a6e5-06ba0d98fe8a  e777faab-98ae-45af-9a86-7ce5b33b1011   \n",
       "4  d4062e70-befa-4995-8643-a0de73938182  81536ad9-5ccf-4eb8-befb-47a4d608658e   \n",
       "\n",
       "  Loan Status  Current Loan Amount        Term  Credit Score  Annual Income  \\\n",
       "0  Fully Paid             445412.0  Short Term         709.0      1167493.0   \n",
       "1  Fully Paid             262328.0  Short Term           NaN            NaN   \n",
       "2  Fully Paid           99999999.0  Short Term         741.0      2231892.0   \n",
       "3  Fully Paid             347666.0   Long Term         721.0       806949.0   \n",
       "4  Fully Paid             176220.0  Short Term           NaN            NaN   \n",
       "\n",
       "  Years in current job Home Ownership             Purpose  Monthly Debt  \\\n",
       "0              8 years  Home Mortgage   Home Improvements       5214.74   \n",
       "1            10+ years  Home Mortgage  Debt Consolidation      33295.98   \n",
       "2              8 years       Own Home  Debt Consolidation      29200.53   \n",
       "3              3 years       Own Home  Debt Consolidation       8741.90   \n",
       "4              5 years           Rent  Debt Consolidation      20639.70   \n",
       "\n",
       "   Years of Credit History  Months since last delinquent  \\\n",
       "0                     17.2                           NaN   \n",
       "1                     21.1                           8.0   \n",
       "2                     14.9                          29.0   \n",
       "3                     12.0                           NaN   \n",
       "4                      6.1                           NaN   \n",
       "\n",
       "   Number of Open Accounts  Number of Credit Problems  Current Credit Balance  \\\n",
       "0                      6.0                        1.0                228190.0   \n",
       "1                     35.0                        0.0                229976.0   \n",
       "2                     18.0                        1.0                297996.0   \n",
       "3                      9.0                        0.0                256329.0   \n",
       "4                     15.0                        0.0                253460.0   \n",
       "\n",
       "   Maximum Open Credit  Bankruptcies  Tax Liens  \n",
       "0             416746.0           1.0        0.0  \n",
       "1             850784.0           0.0        0.0  \n",
       "2             750090.0           0.0        0.0  \n",
       "3             386958.0           0.0        0.0  \n",
       "4             427174.0           0.0        0.0  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exploratory Data Analysis\n",
    "dataset.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x280ca936160>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Checking the missing values\n",
    "sns.heatmap(dataset.isnull(),yticklabels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>Current Loan Amount</th>\n",
       "      <th>Credit Score</th>\n",
       "      <th>Annual Income</th>\n",
       "      <th>Monthly Debt</th>\n",
       "      <th>Years of Credit History</th>\n",
       "      <th>Months since last delinquent</th>\n",
       "      <th>Number of Open Accounts</th>\n",
       "      <th>Number of Credit Problems</th>\n",
       "      <th>Current Credit Balance</th>\n",
       "      <th>Maximum Open Credit</th>\n",
       "      <th>Bankruptcies</th>\n",
       "      <th>Tax Liens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000000e+05</td>\n",
       "      <td>80846.000000</td>\n",
       "      <td>8.084600e+04</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>46859.000000</td>\n",
       "      <td>100000.00000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>1.000000e+05</td>\n",
       "      <td>9.999800e+04</td>\n",
       "      <td>99796.000000</td>\n",
       "      <td>99990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.176045e+07</td>\n",
       "      <td>1076.456089</td>\n",
       "      <td>1.378277e+06</td>\n",
       "      <td>18472.412336</td>\n",
       "      <td>18.199141</td>\n",
       "      <td>34.901321</td>\n",
       "      <td>11.12853</td>\n",
       "      <td>0.168310</td>\n",
       "      <td>2.946374e+05</td>\n",
       "      <td>7.607984e+05</td>\n",
       "      <td>0.117740</td>\n",
       "      <td>0.029313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.178394e+07</td>\n",
       "      <td>1475.403791</td>\n",
       "      <td>1.081360e+06</td>\n",
       "      <td>12174.992609</td>\n",
       "      <td>7.015324</td>\n",
       "      <td>21.997829</td>\n",
       "      <td>5.00987</td>\n",
       "      <td>0.482705</td>\n",
       "      <td>3.761709e+05</td>\n",
       "      <td>8.384503e+06</td>\n",
       "      <td>0.351424</td>\n",
       "      <td>0.258182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.080200e+04</td>\n",
       "      <td>585.000000</td>\n",
       "      <td>7.662700e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.796520e+05</td>\n",
       "      <td>705.000000</td>\n",
       "      <td>8.488440e+05</td>\n",
       "      <td>10214.162500</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.126700e+05</td>\n",
       "      <td>2.734380e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.122460e+05</td>\n",
       "      <td>724.000000</td>\n",
       "      <td>1.174162e+06</td>\n",
       "      <td>16220.300000</td>\n",
       "      <td>16.900000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.098170e+05</td>\n",
       "      <td>4.678740e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.249420e+05</td>\n",
       "      <td>741.000000</td>\n",
       "      <td>1.650663e+06</td>\n",
       "      <td>24012.057500</td>\n",
       "      <td>21.700000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>14.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.679588e+05</td>\n",
       "      <td>7.829580e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>7510.000000</td>\n",
       "      <td>1.655574e+08</td>\n",
       "      <td>435843.280000</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>76.00000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.287897e+07</td>\n",
       "      <td>1.539738e+09</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Current Loan Amount  Credit Score  Annual Income   Monthly Debt  \\\n",
       "count         1.000000e+05  80846.000000   8.084600e+04  100000.000000   \n",
       "mean          1.176045e+07   1076.456089   1.378277e+06   18472.412336   \n",
       "std           3.178394e+07   1475.403791   1.081360e+06   12174.992609   \n",
       "min           1.080200e+04    585.000000   7.662700e+04       0.000000   \n",
       "25%           1.796520e+05    705.000000   8.488440e+05   10214.162500   \n",
       "50%           3.122460e+05    724.000000   1.174162e+06   16220.300000   \n",
       "75%           5.249420e+05    741.000000   1.650663e+06   24012.057500   \n",
       "max           1.000000e+08   7510.000000   1.655574e+08  435843.280000   \n",
       "\n",
       "       Years of Credit History  Months since last delinquent  \\\n",
       "count            100000.000000                  46859.000000   \n",
       "mean                 18.199141                     34.901321   \n",
       "std                   7.015324                     21.997829   \n",
       "min                   3.600000                      0.000000   \n",
       "25%                  13.500000                     16.000000   \n",
       "50%                  16.900000                     32.000000   \n",
       "75%                  21.700000                     51.000000   \n",
       "max                  70.500000                    176.000000   \n",
       "\n",
       "       Number of Open Accounts  Number of Credit Problems  \\\n",
       "count             100000.00000              100000.000000   \n",
       "mean                  11.12853                   0.168310   \n",
       "std                    5.00987                   0.482705   \n",
       "min                    0.00000                   0.000000   \n",
       "25%                    8.00000                   0.000000   \n",
       "50%                   10.00000                   0.000000   \n",
       "75%                   14.00000                   0.000000   \n",
       "max                   76.00000                  15.000000   \n",
       "\n",
       "       Current Credit Balance  Maximum Open Credit  Bankruptcies     Tax Liens  \n",
       "count            1.000000e+05         9.999800e+04  99796.000000  99990.000000  \n",
       "mean             2.946374e+05         7.607984e+05      0.117740      0.029313  \n",
       "std              3.761709e+05         8.384503e+06      0.351424      0.258182  \n",
       "min              0.000000e+00         0.000000e+00      0.000000      0.000000  \n",
       "25%              1.126700e+05         2.734380e+05      0.000000      0.000000  \n",
       "50%              2.098170e+05         4.678740e+05      0.000000      0.000000  \n",
       "75%              3.679588e+05         7.829580e+05      0.000000      0.000000  \n",
       "max              3.287897e+07         1.539738e+09      7.000000     15.000000  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100514 entries, 0 to 100513\n",
      "Data columns (total 19 columns):\n",
      "Loan ID                         100000 non-null object\n",
      "Customer ID                     100000 non-null object\n",
      "Loan Status                     100000 non-null object\n",
      "Current Loan Amount             100000 non-null float64\n",
      "Term                            100000 non-null object\n",
      "Credit Score                    80846 non-null float64\n",
      "Annual Income                   80846 non-null float64\n",
      "Years in current job            95778 non-null object\n",
      "Home Ownership                  100000 non-null object\n",
      "Purpose                         100000 non-null object\n",
      "Monthly Debt                    100000 non-null float64\n",
      "Years of Credit History         100000 non-null float64\n",
      "Months since last delinquent    46859 non-null float64\n",
      "Number of Open Accounts         100000 non-null float64\n",
      "Number of Credit Problems       100000 non-null float64\n",
      "Current Credit Balance          100000 non-null float64\n",
      "Maximum Open Credit             99998 non-null float64\n",
      "Bankruptcies                    99796 non-null float64\n",
      "Tax Liens                       99990 non-null float64\n",
      "dtypes: float64(12), object(7)\n",
      "memory usage: 14.6+ MB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan ID                          0.511372\n",
       "Customer ID                      0.511372\n",
       "Loan Status                      0.511372\n",
       "Current Loan Amount              0.511372\n",
       "Term                             0.511372\n",
       "Credit Score                    19.567423\n",
       "Annual Income                   19.567423\n",
       "Years in current job             4.711781\n",
       "Home Ownership                   0.511372\n",
       "Purpose                          0.511372\n",
       "Monthly Debt                     0.511372\n",
       "Years of Credit History          0.511372\n",
       "Months since last delinquent    53.380624\n",
       "Number of Open Accounts          0.511372\n",
       "Number of Credit Problems        0.511372\n",
       "Current Credit Balance           0.511372\n",
       "Maximum Open Credit              0.513361\n",
       "Bankruptcies                     0.714328\n",
       "Tax Liens                        0.521320\n",
       "dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().mean()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "## Visualizing the target column\n",
    "dataset['Loan Status'].value_counts().plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x280cca6e278>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Correlation Matrix Heatmap\n",
    "plt.figure(figsize=(16,5))\n",
    "plt.figure(figsize = (16,5))\n",
    "sns.heatmap(dataset.corr(),annot=True,linewidths=0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Dropping the highly correlated and unneccesary columns\n",
    "dataset.drop(columns=['Loan ID','Customer ID','Bankruptcies'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Splitting into train and test\n",
    "X=dataset.drop(columns=['Loan Status'])\n",
    "y=dataset['Loan Status']\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=X_train\n",
    "train['Loan Status']=y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Current Loan Amount              0.515925\n",
       "Term                             0.515925\n",
       "Credit Score                    19.599483\n",
       "Annual Income                   19.599483\n",
       "Years in current job             4.717236\n",
       "Home Ownership                   0.515925\n",
       "Purpose                          0.515925\n",
       "Monthly Debt                     0.515925\n",
       "Years of Credit History          0.515925\n",
       "Months since last delinquent    53.377677\n",
       "Number of Open Accounts          0.515925\n",
       "Number of Credit Problems        0.515925\n",
       "Current Credit Balance           0.515925\n",
       "Maximum Open Credit              0.517347\n",
       "Tax Liens                        0.521611\n",
       "Loan Status                      0.515925\n",
       "dtype: float64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().mean()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(columns=['Months since last delinquent'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>Current Loan Amount</th>\n",
       "      <th>Term</th>\n",
       "      <th>Credit Score</th>\n",
       "      <th>Annual Income</th>\n",
       "      <th>Years in current job</th>\n",
       "      <th>Home Ownership</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Monthly Debt</th>\n",
       "      <th>Years of Credit History</th>\n",
       "      <th>Number of Open Accounts</th>\n",
       "      <th>Number of Credit Problems</th>\n",
       "      <th>Current Credit Balance</th>\n",
       "      <th>Maximum Open Credit</th>\n",
       "      <th>Tax Liens</th>\n",
       "      <th>Loan Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>93088</th>\n",
       "      <td>42900.0</td>\n",
       "      <td>Short Term</td>\n",
       "      <td>740.0</td>\n",
       "      <td>509485.0</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>8873.57</td>\n",
       "      <td>16.7</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45733.0</td>\n",
       "      <td>212630.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74764</th>\n",
       "      <td>220616.0</td>\n",
       "      <td>Short Term</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8 years</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Take a Trip</td>\n",
       "      <td>29574.83</td>\n",
       "      <td>26.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>161747.0</td>\n",
       "      <td>210672.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99536</th>\n",
       "      <td>766458.0</td>\n",
       "      <td>Long Term</td>\n",
       "      <td>698.0</td>\n",
       "      <td>1872355.0</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>Home Mortgage</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>33234.23</td>\n",
       "      <td>17.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>307363.0</td>\n",
       "      <td>453376.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91232</th>\n",
       "      <td>494010.0</td>\n",
       "      <td>Long Term</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6 years</td>\n",
       "      <td>Home Mortgage</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>12896.63</td>\n",
       "      <td>16.2</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252757.0</td>\n",
       "      <td>542982.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72952</th>\n",
       "      <td>325578.0</td>\n",
       "      <td>Short Term</td>\n",
       "      <td>721.0</td>\n",
       "      <td>1306193.0</td>\n",
       "      <td>4 years</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>23947.22</td>\n",
       "      <td>13.7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3610.0</td>\n",
       "      <td>190454.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Current Loan Amount        Term  Credit Score  Annual Income  \\\n",
       "93088              42900.0  Short Term         740.0       509485.0   \n",
       "74764             220616.0  Short Term           NaN            NaN   \n",
       "99536             766458.0   Long Term         698.0      1872355.0   \n",
       "91232             494010.0   Long Term           NaN            NaN   \n",
       "72952             325578.0  Short Term         721.0      1306193.0   \n",
       "\n",
       "      Years in current job Home Ownership             Purpose  Monthly Debt  \\\n",
       "93088            10+ years           Rent  Debt Consolidation       8873.57   \n",
       "74764              8 years           Rent         Take a Trip      29574.83   \n",
       "99536            10+ years  Home Mortgage  Debt Consolidation      33234.23   \n",
       "91232              6 years  Home Mortgage  Debt Consolidation      12896.63   \n",
       "72952              4 years           Rent  Debt Consolidation      23947.22   \n",
       "\n",
       "       Years of Credit History  Number of Open Accounts  \\\n",
       "93088                     16.7                     11.0   \n",
       "74764                     26.2                      8.0   \n",
       "99536                     17.5                      7.0   \n",
       "91232                     16.2                     11.0   \n",
       "72952                     13.7                     10.0   \n",
       "\n",
       "       Number of Credit Problems  Current Credit Balance  Maximum Open Credit  \\\n",
       "93088                        1.0                 45733.0             212630.0   \n",
       "74764                        1.0                161747.0             210672.0   \n",
       "99536                        0.0                307363.0             453376.0   \n",
       "91232                        1.0                252757.0             542982.0   \n",
       "72952                        1.0                  3610.0             190454.0   \n",
       "\n",
       "       Tax Liens Loan Status  \n",
       "93088        0.0  Fully Paid  \n",
       "74764        0.0  Fully Paid  \n",
       "99536        0.0  Fully Paid  \n",
       "91232        0.0  Fully Paid  \n",
       "72952        0.0  Fully Paid  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Checking the distribution\n",
    "sns.distplot(train['Tax Liens'],hist=False,rug=False)\n",
    "plt.ylabel('Probability Distribution')\n",
    "plt.title('Normal Distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(columns='Current Loan Amount',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>Term</th>\n",
       "      <th>Credit Score</th>\n",
       "      <th>Annual Income</th>\n",
       "      <th>Years in current job</th>\n",
       "      <th>Home Ownership</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Monthly Debt</th>\n",
       "      <th>Years of Credit History</th>\n",
       "      <th>Number of Open Accounts</th>\n",
       "      <th>Number of Credit Problems</th>\n",
       "      <th>Current Credit Balance</th>\n",
       "      <th>Maximum Open Credit</th>\n",
       "      <th>Tax Liens</th>\n",
       "      <th>Loan Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>93088</th>\n",
       "      <td>Short Term</td>\n",
       "      <td>740.0</td>\n",
       "      <td>509485.0</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>8873.57</td>\n",
       "      <td>16.7</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45733.0</td>\n",
       "      <td>212630.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74764</th>\n",
       "      <td>Short Term</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8 years</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Take a Trip</td>\n",
       "      <td>29574.83</td>\n",
       "      <td>26.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>161747.0</td>\n",
       "      <td>210672.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99536</th>\n",
       "      <td>Long Term</td>\n",
       "      <td>698.0</td>\n",
       "      <td>1872355.0</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>Home Mortgage</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>33234.23</td>\n",
       "      <td>17.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>307363.0</td>\n",
       "      <td>453376.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91232</th>\n",
       "      <td>Long Term</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6 years</td>\n",
       "      <td>Home Mortgage</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>12896.63</td>\n",
       "      <td>16.2</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252757.0</td>\n",
       "      <td>542982.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72952</th>\n",
       "      <td>Short Term</td>\n",
       "      <td>721.0</td>\n",
       "      <td>1306193.0</td>\n",
       "      <td>4 years</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>23947.22</td>\n",
       "      <td>13.7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3610.0</td>\n",
       "      <td>190454.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Term  Credit Score  Annual Income Years in current job  \\\n",
       "93088  Short Term         740.0       509485.0            10+ years   \n",
       "74764  Short Term           NaN            NaN              8 years   \n",
       "99536   Long Term         698.0      1872355.0            10+ years   \n",
       "91232   Long Term           NaN            NaN              6 years   \n",
       "72952  Short Term         721.0      1306193.0              4 years   \n",
       "\n",
       "      Home Ownership             Purpose  Monthly Debt  \\\n",
       "93088           Rent  Debt Consolidation       8873.57   \n",
       "74764           Rent         Take a Trip      29574.83   \n",
       "99536  Home Mortgage  Debt Consolidation      33234.23   \n",
       "91232  Home Mortgage  Debt Consolidation      12896.63   \n",
       "72952           Rent  Debt Consolidation      23947.22   \n",
       "\n",
       "       Years of Credit History  Number of Open Accounts  \\\n",
       "93088                     16.7                     11.0   \n",
       "74764                     26.2                      8.0   \n",
       "99536                     17.5                      7.0   \n",
       "91232                     16.2                     11.0   \n",
       "72952                     13.7                     10.0   \n",
       "\n",
       "       Number of Credit Problems  Current Credit Balance  Maximum Open Credit  \\\n",
       "93088                        1.0                 45733.0             212630.0   \n",
       "74764                        1.0                161747.0             210672.0   \n",
       "99536                        0.0                307363.0             453376.0   \n",
       "91232                        1.0                252757.0             542982.0   \n",
       "72952                        1.0                  3610.0             190454.0   \n",
       "\n",
       "       Tax Liens Loan Status  \n",
       "93088        0.0  Fully Paid  \n",
       "74764        0.0  Fully Paid  \n",
       "99536        0.0  Fully Paid  \n",
       "91232        0.0  Fully Paid  \n",
       "72952        0.0  Fully Paid  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "# Q-Q plot\n",
    "sm.qqplot(train['Years of Credit History'],dist=\"norm\",line='45')\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "train['Term'].value_counts().plot(kind='bar')\n",
    "plt.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "train['Purpose'].value_counts().plot(kind='bar')\n",
    "plt.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handling Missing values\n",
    "train[\"Term\"].fillna(train[\"Term\"].mode()[0], inplace = True) \n",
    "train = train.dropna(axis=0, subset=['Credit Score'])\n",
    "train['Years in current job'].fillna(train[\"Years in current job\"].mode()[0],inplace=True)\n",
    "train = train.dropna(axis=0, subset=['Maximum Open Credit'])\n",
    "train = train.dropna(axis=0, subset=['Tax Liens'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Term                         0.0\n",
       "Credit Score                 0.0\n",
       "Annual Income                0.0\n",
       "Years in current job         0.0\n",
       "Home Ownership               0.0\n",
       "Purpose                      0.0\n",
       "Monthly Debt                 0.0\n",
       "Years of Credit History      0.0\n",
       "Number of Open Accounts      0.0\n",
       "Number of Credit Problems    0.0\n",
       "Current Credit Balance       0.0\n",
       "Maximum Open Credit          0.0\n",
       "Tax Liens                    0.0\n",
       "Loan Status                  0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().mean()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x280cc196e80>"
      ]
     },
     "execution_count": 115,
     "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": [
    "# Univariate Outlier Detection\n",
    "sns.boxplot(data=train.loc[:,'Monthly Debt'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x280cced4ba8>"
      ]
     },
     "execution_count": 116,
     "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": [
    "sns.scatterplot(data=train['Monthly Debt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Computing IQR\n",
    "# Q1 = X_train.quantile(0.25)\n",
    "# Q3 = X_train.quantile(0.75)\n",
    "# IQR = Q3 - Q1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (train < (Q1 - 1.5 * IQR)) | (train> (Q3 + 1.5 * IQR))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing outliers\n",
    "# train = train[~((train < (Q1 - 1.5 * IQR)) |(train> (Q3 + 1.5 * IQR))).any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56566, 14)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Term                         0.0\n",
       "Credit Score                 0.0\n",
       "Annual Income                0.0\n",
       "Years in current job         0.0\n",
       "Home Ownership               0.0\n",
       "Purpose                      0.0\n",
       "Monthly Debt                 0.0\n",
       "Years of Credit History      0.0\n",
       "Number of Open Accounts      0.0\n",
       "Number of Credit Problems    0.0\n",
       "Current Credit Balance       0.0\n",
       "Maximum Open Credit          0.0\n",
       "Tax Liens                    0.0\n",
       "Loan Status                  0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().mean()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Normal Distribution')"
      ]
     },
     "execution_count": 122,
     "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": [
    "sns.distplot(train['Monthly Debt'],hist=False,rug=False)\n",
    "plt.ylabel('Probability Distribution')\n",
    "plt.title('Normal Distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Term</th>\n",
       "      <th>Credit Score</th>\n",
       "      <th>Annual Income</th>\n",
       "      <th>Years in current job</th>\n",
       "      <th>Home Ownership</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Monthly Debt</th>\n",
       "      <th>Years of Credit History</th>\n",
       "      <th>Number of Open Accounts</th>\n",
       "      <th>Number of Credit Problems</th>\n",
       "      <th>Current Credit Balance</th>\n",
       "      <th>Maximum Open Credit</th>\n",
       "      <th>Tax Liens</th>\n",
       "      <th>Loan Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>93088</th>\n",
       "      <td>Short Term</td>\n",
       "      <td>740.0</td>\n",
       "      <td>509485.0</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>8873.57</td>\n",
       "      <td>16.7</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45733.0</td>\n",
       "      <td>212630.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99536</th>\n",
       "      <td>Long Term</td>\n",
       "      <td>698.0</td>\n",
       "      <td>1872355.0</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>Home Mortgage</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>33234.23</td>\n",
       "      <td>17.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>307363.0</td>\n",
       "      <td>453376.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72952</th>\n",
       "      <td>Short Term</td>\n",
       "      <td>721.0</td>\n",
       "      <td>1306193.0</td>\n",
       "      <td>4 years</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>23947.22</td>\n",
       "      <td>13.7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3610.0</td>\n",
       "      <td>190454.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43913</th>\n",
       "      <td>Short Term</td>\n",
       "      <td>723.0</td>\n",
       "      <td>951748.0</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>15307.35</td>\n",
       "      <td>12.8</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54739.0</td>\n",
       "      <td>507012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38357</th>\n",
       "      <td>Short Term</td>\n",
       "      <td>745.0</td>\n",
       "      <td>1394657.0</td>\n",
       "      <td>8 years</td>\n",
       "      <td>Home Mortgage</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>11970.76</td>\n",
       "      <td>18.6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>85063.0</td>\n",
       "      <td>325028.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Term  Credit Score  Annual Income Years in current job  \\\n",
       "93088  Short Term         740.0       509485.0            10+ years   \n",
       "99536   Long Term         698.0      1872355.0            10+ years   \n",
       "72952  Short Term         721.0      1306193.0              4 years   \n",
       "43913  Short Term         723.0       951748.0            10+ years   \n",
       "38357  Short Term         745.0      1394657.0              8 years   \n",
       "\n",
       "      Home Ownership             Purpose  Monthly Debt  \\\n",
       "93088           Rent  Debt Consolidation       8873.57   \n",
       "99536  Home Mortgage  Debt Consolidation      33234.23   \n",
       "72952           Rent  Debt Consolidation      23947.22   \n",
       "43913           Rent  Debt Consolidation      15307.35   \n",
       "38357  Home Mortgage  Debt Consolidation      11970.76   \n",
       "\n",
       "       Years of Credit History  Number of Open Accounts  \\\n",
       "93088                     16.7                     11.0   \n",
       "99536                     17.5                      7.0   \n",
       "72952                     13.7                     10.0   \n",
       "43913                     12.8                     15.0   \n",
       "38357                     18.6                      9.0   \n",
       "\n",
       "       Number of Credit Problems  Current Credit Balance  Maximum Open Credit  \\\n",
       "93088                        1.0                 45733.0             212630.0   \n",
       "99536                        0.0                307363.0             453376.0   \n",
       "72952                        1.0                  3610.0             190454.0   \n",
       "43913                        1.0                 54739.0             507012.0   \n",
       "38357                        1.0                 85063.0             325028.0   \n",
       "\n",
       "       Tax Liens Loan Status  \n",
       "93088        0.0  Fully Paid  \n",
       "99536        0.0  Fully Paid  \n",
       "72952        0.0  Fully Paid  \n",
       "43913        0.0  Fully Paid  \n",
       "38357        0.0  Fully Paid  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J/mJT41X14dnqpY8Mg375tSQ3t+UAL23rG+5N9BvdtSY9dWHZrwK/ydN3Lj2KwZlvmkHuJuqRJC/e1HhVfXe2epEmk+SrwB9W1SNtfQfgX6pqabedzW1uGfSIv+z1LPEi4PGh9ceBhd200h+GgaRx82kGdyr9IoMLz97I4C6mmkHuJpI0dpIcwM9/Et9GN7DTluW9iXooyRuS+P9e4+y5wMNV9VFgbZK9u25orvMXQj8tA+5M8qEkL++6GWlYklOAdwPvaaXn8PQH3WiGGAY9VFV/BLwS+Dbwj0muSrKinbUhde2NwNHA/wFU1b34eQYzzjDoqap6GPg8cB6wB4N/gDck+dNOG5MGF0cW7XO6240VNcMMgx5KclQ7U+NyBpvgB1bVkcB+wF922pwE5yf5BLBTkrcB/w78Q8c9zXmeTdRDSc4FPllVG13VmeSwqrqsg7akpyT5feBwBlfHr6qqSztuac4zDCRJXnTWJ0keoe2H3VBq6xvuTbRjJ41JQ0b8nAI8BKwG3lVVd81+V3OfYdAjVeUZGXo2+DBwL/DPDP5QWQb8EnAHsBI4uLPO5jB3E/VIkl02NV5V62erF2kySa6pqldPqF1dVUuSfLOq9uuqt7nMLYN+uZ6ndwtNVMBLZrcdaaSfJXkzcEFbf9PQmH+9zhC3DCSNlSQvAT4K/BaDX/5XA38OfB94VVV9vcP25iy3DHooye+Mqo861VSaTUnmAcdU1VGTTDEIZohbBj2U5EtDq9sBBwLXV9WhHbUkPSXJlVV1cNd99I1hIJLsBXyoqo7vuhcpyWnAC4DP0e5PBFBVN3TWVA8YBiJJgJur6te77kVKcsWIcrnlOrM8ZtBDSf6Op8/K2ArYH/hmdx1JT6uqQ7ruoY/cMuihJMuHVp8A7q6qb3TVjzRRktcD+zI4pgVAVb2vu47mPrcMeiTJi6rqe1V1Tte9SJNJ8vcMPunsEOCTDK4zuLbTpnrAW1j3y79uWEjy+S4bkTbhoKo6AXigqt7L4HqDvTruac4zDPpl+MpjrzbWuPpxe340yS8DPwX8DOQZ5m6ifqlJlqVxcnGSnYC/AW5g8LP6yW5bmvs8gNwjSZ5kcN52gO2BRzcM4S2sNYaSbAtsV1UPdd3LXGcYSBo7SQ4CFjK096Kqzu2soR5wN5GksZLk08BLgZuAJ1u5AMNgBrllIGmsJLkd2Kf85TSrPJtI0rj5FoNPNtMscjeRpLHQ7qZbwA7AbUmuBR7bMF5VR3fVWx8YBpLGxd923UCfGQaSxsX3gd0n3ierfRjT97tpqT88ZiBpXHwEeGRE/dE2phlkGEgaFwur6uaJxapazeCaA80gw0DSuNhuE2Pbz1oXPWUYSBoX1yV528RikhOB6zvop1e86EzSWEiyO/BF4HGe/uW/GNgGeGNV/U9XvfWBYSBprCQ5BHhFW721qi7vsp++MAwkSR4zkCQZBpIkDANJEoaBJAnDQJIE/D9iFj9iaSJceAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Loan Status'].value_counts().plot(kind='bar')\n",
    "plt.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ## Feature Encoding\n",
    "# le=LabelEncoder()\n",
    "# train['Term']=le.fit_transform(train['Term'])\n",
    "\n",
    "# # Target Column\n",
    "# train['Loan Status']=le.fit_transform(train['Loan Status'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One Hot Encoding\n",
    "# train=pd.get_dummies(data=train,columns=['Home Ownership'],prefix_sep='-')\n",
    "# train=pd.get_dummies(data=train,columns=['Purpose'],prefix_sep='-')\n",
    "# train=pd.get_dummies(data=train,columns=['Years in current job'],prefix_sep='-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train.drop(columns=['_renewable_energy'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train.drop(columns=['_< 1 year'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Term                          object\n",
       "Credit Score                 float64\n",
       "Annual Income                float64\n",
       "Years in current job          object\n",
       "Home Ownership                object\n",
       "Purpose                       object\n",
       "Monthly Debt                 float64\n",
       "Years of Credit History      float64\n",
       "Number of Open Accounts      float64\n",
       "Number of Credit Problems    float64\n",
       "Current Credit Balance       float64\n",
       "Maximum Open Credit          float64\n",
       "Tax Liens                    float64\n",
       "Loan Status                   object\n",
       "dtype: object"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train['Term']=train['Term'].astype('category')\n",
    "# train['Loan Status']=train['Loan Status'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Term                          object\n",
       "Credit Score                 float64\n",
       "Annual Income                float64\n",
       "Years in current job          object\n",
       "Home Ownership                object\n",
       "Purpose                       object\n",
       "Monthly Debt                 float64\n",
       "Years of Credit History      float64\n",
       "Number of Open Accounts      float64\n",
       "Number of Credit Problems    float64\n",
       "Current Credit Balance       float64\n",
       "Maximum Open Credit          float64\n",
       "Tax Liens                    float64\n",
       "Loan Status                   object\n",
       "dtype: object"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    " # Handling Imbalanced Classes of target column\n",
    "X_train_new=train.drop(columns=['Loan Status'])\n",
    "y_train_new=train['Loan Status']\n",
    "\n",
    "# sm = SMOTE(random_state=42)\n",
    "\n",
    "# # ros = RandomOverSampler(random_state=0)\n",
    "# # X_res, y_res = ros.fit_resample(X_train_new, y_train_new)\n",
    "\n",
    "# # # rus = RandomUnderSampler(random_state=42)\n",
    "# X_res, y_res = sm.fit_resample(X_train_new, y_train_new)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y_res=pd.Series(y_res)\n",
    "# y_res.value_counts().plot(kind='bar')\n",
    "# plt.plot()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Current Loan Amount              0.500746\n",
       "Term                             0.500746\n",
       "Credit Score                    19.492621\n",
       "Annual Income                   19.492621\n",
       "Years in current job             4.699055\n",
       "Home Ownership                   0.500746\n",
       "Purpose                          0.500746\n",
       "Monthly Debt                     0.500746\n",
       "Years of Credit History          0.500746\n",
       "Months since last delinquent    53.387498\n",
       "Number of Open Accounts          0.500746\n",
       "Number of Credit Problems        0.500746\n",
       "Current Credit Balance           0.500746\n",
       "Maximum Open Credit              0.504062\n",
       "Tax Liens                        0.520643\n",
       "dtype: float64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " ## Handling Missing values in Test Data\n",
    "X_test.isnull().mean()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test=X_test\n",
    "test['Loan Status']=y_test\n",
    "test=test.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fully Paid     44481\n",
       "Charged Off    12085\n",
       "Name: Loan Status, dtype: int64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Loan Status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Current Loan Amount             0.0\n",
       "Term                            0.0\n",
       "Credit Score                    0.0\n",
       "Annual Income                   0.0\n",
       "Years in current job            0.0\n",
       "Home Ownership                  0.0\n",
       "Purpose                         0.0\n",
       "Monthly Debt                    0.0\n",
       "Years of Credit History         0.0\n",
       "Months since last delinquent    0.0\n",
       "Number of Open Accounts         0.0\n",
       "Number of Credit Problems       0.0\n",
       "Current Credit Balance          0.0\n",
       "Maximum Open Credit             0.0\n",
       "Tax Liens                       0.0\n",
       "Loan Status                     0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().mean()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ## Feature Encoding\n",
    "# le=LabelEncoder()\n",
    "# test['Term']=le.fit_transform(test['Term'])\n",
    "\n",
    "# # Target Column\n",
    "# test['Loan Status']=le.fit_transform(test['Loan Status'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One Hot Encoding\n",
    "# test=pd.get_dummies(data=test,columns=['Home Ownership'],prefix_sep='-')\n",
    "# test=pd.get_dummies(data=test,columns=['Purpose'],prefix_sep='-',)\n",
    "# test=pd.get_dummies(data=test,columns=['Years in current job'],prefix_sep='-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test.drop(columns=['_renewable_energy'],inplace=True)\n",
    "# test.drop(columns=['_< 1 year'],inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test['Term']=test['Term'].astype('category')\n",
    "# test['Loan Status']=test['Loan Status'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.drop(columns=['Current Loan Amount'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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>Term</th>\n",
       "      <th>Credit Score</th>\n",
       "      <th>Annual Income</th>\n",
       "      <th>Years in current job</th>\n",
       "      <th>Home Ownership</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Monthly Debt</th>\n",
       "      <th>Years of Credit History</th>\n",
       "      <th>Months since last delinquent</th>\n",
       "      <th>Number of Open Accounts</th>\n",
       "      <th>Number of Credit Problems</th>\n",
       "      <th>Current Credit Balance</th>\n",
       "      <th>Maximum Open Credit</th>\n",
       "      <th>Tax Liens</th>\n",
       "      <th>Loan Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>83837</th>\n",
       "      <td>Short Term</td>\n",
       "      <td>744.0</td>\n",
       "      <td>1090752.0</td>\n",
       "      <td>2 years</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>16997.59</td>\n",
       "      <td>18.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>377036.0</td>\n",
       "      <td>537636.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57656</th>\n",
       "      <td>Long Term</td>\n",
       "      <td>680.0</td>\n",
       "      <td>2211657.0</td>\n",
       "      <td>4 years</td>\n",
       "      <td>Home Mortgage</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>44601.74</td>\n",
       "      <td>14.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>213921.0</td>\n",
       "      <td>509652.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50783</th>\n",
       "      <td>Short Term</td>\n",
       "      <td>750.0</td>\n",
       "      <td>6500375.0</td>\n",
       "      <td>6 years</td>\n",
       "      <td>Home Mortgage</td>\n",
       "      <td>Home Improvements</td>\n",
       "      <td>32231.03</td>\n",
       "      <td>20.6</td>\n",
       "      <td>81.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53713.0</td>\n",
       "      <td>482130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38528</th>\n",
       "      <td>Short Term</td>\n",
       "      <td>738.0</td>\n",
       "      <td>1281873.0</td>\n",
       "      <td>3 years</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>16984.86</td>\n",
       "      <td>25.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>116109.0</td>\n",
       "      <td>344696.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74032</th>\n",
       "      <td>Short Term</td>\n",
       "      <td>741.0</td>\n",
       "      <td>814834.0</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>Home Mortgage</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>22475.86</td>\n",
       "      <td>25.7</td>\n",
       "      <td>75.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>170582.0</td>\n",
       "      <td>247192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Charged Off</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Term  Credit Score  Annual Income Years in current job  \\\n",
       "83837  Short Term         744.0      1090752.0              2 years   \n",
       "57656   Long Term         680.0      2211657.0              4 years   \n",
       "50783  Short Term         750.0      6500375.0              6 years   \n",
       "38528  Short Term         738.0      1281873.0              3 years   \n",
       "74032  Short Term         741.0       814834.0             < 1 year   \n",
       "\n",
       "      Home Ownership             Purpose  Monthly Debt  \\\n",
       "83837           Rent  Debt Consolidation      16997.59   \n",
       "57656  Home Mortgage  Debt Consolidation      44601.74   \n",
       "50783  Home Mortgage   Home Improvements      32231.03   \n",
       "38528           Rent  Debt Consolidation      16984.86   \n",
       "74032  Home Mortgage  Debt Consolidation      22475.86   \n",
       "\n",
       "       Years of Credit History  Months since last delinquent  \\\n",
       "83837                     18.8                           5.0   \n",
       "57656                     14.5                          15.0   \n",
       "50783                     20.6                          81.0   \n",
       "38528                     25.0                          51.0   \n",
       "74032                     25.7                          75.0   \n",
       "\n",
       "       Number of Open Accounts  Number of Credit Problems  \\\n",
       "83837                      9.0                        0.0   \n",
       "57656                     11.0                        0.0   \n",
       "50783                     10.0                        0.0   \n",
       "38528                     15.0                        1.0   \n",
       "74032                     10.0                        0.0   \n",
       "\n",
       "       Current Credit Balance  Maximum Open Credit  Tax Liens  Loan Status  \n",
       "83837                377036.0             537636.0        0.0   Fully Paid  \n",
       "57656                213921.0             509652.0        0.0   Fully Paid  \n",
       "50783                 53713.0             482130.0        0.0   Fully Paid  \n",
       "38528                116109.0             344696.0        0.0   Fully Paid  \n",
       "74032                170582.0             247192.0        0.0  Charged Off  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Term                            0.0\n",
       "Credit Score                    0.0\n",
       "Annual Income                   0.0\n",
       "Years in current job            0.0\n",
       "Home Ownership                  0.0\n",
       "Purpose                         0.0\n",
       "Monthly Debt                    0.0\n",
       "Years of Credit History         0.0\n",
       "Months since last delinquent    0.0\n",
       "Number of Open Accounts         0.0\n",
       "Number of Credit Problems       0.0\n",
       "Current Credit Balance          0.0\n",
       "Maximum Open Credit             0.0\n",
       "Tax Liens                       0.0\n",
       "Loan Status                     0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().mean()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test=test.drop(columns=['Loan Status'])\n",
    "y_test=test['Loan Status']\n",
    "X_test.drop(columns=['Months since last delinquent'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_res=pd.DataFrame(X_res)\n",
    "# X_res.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_res=pd.DataFrame(X_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_res.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Computing class weights\n",
    "# cw = list(class_weight.compute_class_weight('balanced',\n",
    "#                                              y_train_new,\n",
    "#                                              y_train_new))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "# class_weights={'Fully Paid':0.63,'Charged Off':2.35}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Checking for any categorical features\n",
    "cate_features_index = np.where(X_train_new.dtypes != float)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Fitting the Classifier to the training data\n",
    "# classifier=LogisticRegression(max_iter=200)\n",
    "# classifier=SVC()\n",
    "# classifier=DecisionTreeClassifier(max_depth=20,max_leaf_nodes=20)\n",
    "# classifier=RandomForestClassifier(n_estimators=800,max_depth=9)\n",
    "# classifier=XGBClassifier(silent=False, \n",
    "#                       scale_pos_weight=1,\n",
    "#                       learning_rate=0.01,  \n",
    "#                       colsample_bytree = 0.4,\n",
    "#                       subsample = 0.8,\n",
    "#                       objective='binary:logistic', \n",
    "#                       n_estimators=1000, \n",
    "#                       reg_alpha = 0.3,\n",
    "#                       max_depth=4, \n",
    "#                       gamma=10)\n",
    "classifier = CatBoostClassifier(iterations=2000, learning_rate=0.01, l2_leaf_reg=3.5, depth=9, rsm=0.98, loss_function= 'Logloss', eval_metric='AUC',use_best_model=True,random_seed=42,class_weights=[2.35,0.63])\n",
    "\n",
    "classifier.fit(X_train_new,y_train_new,cat_features=cate_features_index,eval_set=(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_test['Term'] = pd.to_numeric(X_test['Term'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_test=np.array(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "# regex = re.compile(r\"\\[|\\]|<\", re.IGNORECASE)\n",
    "\n",
    "# X_test.columns = [regex.sub(\"_\", col) if any(x in str(col) for x in set(('[', ']', '<'))) else col for col in X_test.columns.values]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GridSearch Hyperparameter Tuning\n",
    "# grid_values = {'n_estimators': [100, 200,400,800],'max_depth':[1,2,3,6,4]}\n",
    "# grid_clf_acc = GridSearchCV(classifier, param_grid = grid_values,scoring = 'accuracy')\n",
    "# grid_clf_acc.fit(X_res, y_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grid_clf_acc.__best__params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Predicting the training set results\n",
    "y_pred_train=classifier.predict(X_train_new)\n",
    "# y_pred_train = classifier.predict_proba(X_train_new)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Predicting the Test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_test=classifier.predict(X_test)\n",
    "# y_pred_test = classifier.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8094968709118552\n"
     ]
    }
   ],
   "source": [
    "# Checking the training accuracy\n",
    "print(accuracy_score(y_train_new,y_pred_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.781733746130031\n"
     ]
    }
   ],
   "source": [
    "# Checking the test accuracy\n",
    "print(accuracy_score(y_test,y_pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      " Charged Off       0.55      0.63      0.58     12085\n",
      "  Fully Paid       0.89      0.86      0.88     44481\n",
      "\n",
      "    accuracy                           0.81     56566\n",
      "   macro avg       0.72      0.74      0.73     56566\n",
      "weighted avg       0.82      0.81      0.81     56566\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# # Training classification report\n",
    "print(classification_report(y_train_new,y_pred_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      " Charged Off       0.47      0.50      0.49      2268\n",
      "  Fully Paid       0.87      0.86      0.86      8714\n",
      "\n",
      "    accuracy                           0.78     10982\n",
      "   macro avg       0.67      0.68      0.67     10982\n",
      "weighted avg       0.79      0.78      0.78     10982\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Testing classification report\n",
    "print(classification_report(y_test,y_pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Training confusion matrix\n",
    "class_names = ['ChargedOff','Fullypaid']\n",
    "cm=confusion_matrix(y_target=y_train_new,y_predicted=y_pred_train)\n",
    "fig,ax=plot_confusion_matrix(conf_mat=cm,cmap='Dark2',class_names=class_names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BR65t4YYCLkUdt9mUkq6dV5vXaYctUGn2v0mHLTeiZn7V2j8kAiKI5SuJCGqXraC0Z5P+/tgnNGTIEGbPns28efMAOPbYY7nvvvuIiHWu26lTJwYNGsTYsWMBqKmpYenSpQWtt6W0mpZcQySdRtZa+07++mHguoh4qpHlbwNGRsSD+es/A3dHxEMNLR8R1ZLGAVtL6gY8CPQCyoEL633OoojoJknADcD+wNuAmm1n24Alz7xE5z22XfW6ZnYlsy79X9SpIxVf+RwdB2+KykrpecoXmHXxrahjOWUDetHz5IOKWHX7sfvuuzNhwgQAdtppJxYsWMC77777keW23HJLLrzwQiorKxk5ciQzZsygb9++VFVVceqpp7LJJpswffp07r77blasWNHSu9HsWlNLrrOkyfnP/R/zM24BvgYgqQewJ/BoYwtL6gIcQHaYvAw4KiJ2AfYDfpGHWn1HAdsAOwKn55/fLlSNGgclJXT+zHYAlPboyoDrzqT/pV+jxwn7M//3o6hdupyormHx6Mn0v/Q0NvjltynfpD9Vj4wvcvXpKy0tZeedd2bixImUl5dzyCGH8NBDH/3bPn36dH784x9zxRVXMHr0aM4888xV62+22WaMGTOGK6+8kuXLl/PFL36xpXejIFpTyNU/XD3q43xARIwha5X1B04E/hIR1Q0supWkycBY4JGI+CtZq+wqSS8BTwAbAwPWWO/zwJ0RURMR7wN/b6wWSWdIel7S87WLlnyc3Wk1Fo99maUvTaXXGV+mLvdVXkZpt+zQtsPADSjt35PqD+ax8p1ZAJT174UkOu8+hBVvvle02tuLHXbYgenTp1NVVUW/fv3o06cPF110EVdeeSW9evXiwgsvpKKigmXLlrF8+XIAXnnlFUpLS+natSvz589n/vz5TJs2DYAXXniBzTbbrIh71Hxa9eEqUM3qQdypCevcBpwEnAB8vZFl6s7J1XcS0A/YNSJW5hc2Gtreuk9wABExHBgO0GHghk1apzVa9vJbLHr0n/S94L8o6Vi+an7NwiWUdOuESkqonrWA6pnzKevXk1hZzcr351CzcAmlFV1YPuVtyjfsU8Q9aB/qH6q+//77nH/++aveu/LKK7nqqqtYvHgxFRUVLFy4EICBAwdSUlLC4sWLAZg/fz4DBgxg5syZDBkyhBkzZrT8jhRAaw+5acBZkkrIWlZ7NGGdPwLPAR9ExJT12FYPYFYecPsBmzewzNPANyX9CehPdlh7x3pso1Wb97uHWP76dGoXLWXGeTdRccTeVD06HlbWMPcXdwMfdhVZ8e93WPjAP6CkBJWU0POUgynJW3YVh+/FnGv+DKWllPWpoOewQ4u5W8krLy9n22235fbbb1/nsrvssgv77LMPNTU1rFy5kptvvnnVe3fddRfDhg2jtLSUOXPmMGLEiEKW3WLUlCsvLaGhbiD5ObHbgaHAK2SHj5dGxFNr60Ii6W/AAxHxuwa2M5Cs28cOa8zvC4wiu+gwGdgL+FJETGvkwsO/81VvX1cXkg4DN4z+l5za1P8U1gocOn5+sUuw9XDfffcxe/bsBi8EtpqWXEP93CJL4JMaWX5gQ+vmFxMGAXc2st40YIcG5s8BPru22vJ6vtP4XphZa9OaLjx8YpIOBF4DboiIymLXY2bF12pacs0hIp4A0rgkZGbNIqmWnJnZmhxyZpY0h5yZJc0hZ2ZJc8iZWdIccmaWNIecmSXNIWdmSXPImVnSHHJmljSHnJklzSFnZklzyJlZ0hxyZpY0h5yZJc0hZ2ZJc8iZWdIccmaWNIecmSXNIWdmSXPImVnSHHJmljSHnJklzSFnZklzyJlZ0hxyZpY0h5yZJc0hZ2ZJc8iZWdIccmaWNIecmSXNIWdmSXPImVnSHHJmljSHnJklzSFnZklzyJlZ0hxyZpY0h5yZJc0hZ2ZJc8iZWdIccmaWNIecmSXNIWdmSXPImVnSHHJmljSHnJklTRFR7BqSJ2k28J9i11EAfYE5xS7C1kuq39nmEdGvoTcccvaxSXo+InYrdh3WdO3xO/PhqpklzSFnZklzyNknMbzYBdh6a3ffmc/JmVnS3JIzs6Q55KxoJA2SdHyx67C0OeSsKCQNAO4B3pTUqdj12EdJqpDUYN+ztsQhZy0uD7VuQBnQB7hEUu/iVmX1SeoGXAMcl/9BarMcctaiJO0I/DUipgJvAX8DnouIeZLKilud1YmIRcCDwC7AIZJ6Frmkj80hZy0mD7GzgaclbQRUAXcAP5c0ICKqJZUWtUij3ncwF9gY+ClwoqT+xavq4/NfTmtJAbwAfB4YCXwzIl6W9EvgOUm7RcRsSWURUV3UStuxiKiR9GngD8BJwP7AfsAKSX+JiAVFLXA9uSVnLSYiaoA3gX3JDlXn5/PPBe4GXpPU3wHX8iRtLemcerOGAK9ExIsR8SvgLuAHwCmSNihKkR+TQ84KStIQSVdLul3SccAmwJeA2cA3Je0MEBE/BG4Dti9ete3aXGBCfhoBYAJQIml3gIi4D5gI7A20qVMKvuPBCkbS9sCdZIc9pcAAYCvgBuBl4GrgXWBUREyqt57C/2O2GEml+SFqKdkjwe6LiLMlXQOsIGt9vw5cDFwSEROKWO56c8hZQUjqAIwAHouI/83n9QcOAo4BLiU7XL2K7ND15xFRVZxqrY6kDYFxZH+YrgJOJzsf1w+4PiJGFbG8j8UXHqxQaoGuZC21utbZLEn/B2wN7BERN0u6DOjkgGt5dS1mSXsBGwBzI+IpSZ8BngdKIuKnwO8l9Y2IOW2xle1zclYQ+cWDfwEV+azSfP4c4B3geEkdIuKNiHi5SGW2W/khakg6BLgV6Ak8KulrETET2BU4R9L/5KvMBWhrAQcOOWtmklTv5RvAtZK2zvvA1b33AfBmRKxo+QrbN0ld88laSX3JThscDkwH3gMuknRORMwiu8J6P7TNcKvjw1VrFpK6A0vqTmBHRE1E3CJpc+BxSd8EFkgqAa4DLihqwe2QpG2BayRVAU8A9wGHAZsDV0fEIElfBh6UVJmfS53VFg9R63PI2SeW3/JzPtnN9rflrbayiKiOiIskzQSOArYFlgA/iYhRbf2Xpy2RtB3ZxYQbyAaz+QywOCLuyTv+vpov+gEwCvh33bpt/Tvy1VX7xCR1BoaRtQgmAvfmLbpVdy5IKgc6AGURUemAazn5le7RwIqI2C+fdzGwaUScLmkX4BxgJbAH2Z0o41L5jnxOzj6R/NB0KdlFhoFkXQ6OqAu4uvsgI2JlRCyOiMr8dZv/5Wkr8nOf3yXr3HtRPnsmUJNPTwZ+DzwLfD8ixuXrJfEduSVnn5ik/cgOgy4EjiO7EjcOuCdv0SXRImjrJO0E/JbslEEH4MSIeL+B5ZL6vtySs49NUkl+xfSzwF0R8QBZS+5Nshu7j85bdMn8wrRlEfEScCawIfB8XcCt+eSX1L4vh5x9bBFRm/9CvAZ8TtLgiFgM3Aj0J3vaSJt8PE8q1nzqch50/wXsJumKfF5NQ+umwiFn66Wur5ukvSR9V9LewPtk53OOyh+KuRnZDfi/behwyFpGfkX1pHx6VWstD7rvAQdL2mqNvo3JccjZesl7yX8JuJns1q3hwHbAP8i6JN1K9tik4RExpWiFGsCWwAX546tWa61FxGRg/4iYmtrh6Zp84cGaLP+L3x24luwJIhuSXZXbP7+vsYysD1ZpRLyX2gnstiK/XW5FPn0VMIPsFEJy59uawi05W6e6w5nILCS7/efnwPXAYXnAHQHsFhEfRMR7dcsXreh2RNIW+dOV6w5Rr5B0Xv72c8C2+XcXqR+aNsQhZ+uU/3LsL+nafNa/yfrE/SwipucPVryWrFuCtbxZwFcl/Zqsv+J4YGdJI8la3sdJOg3a5x8e39Zljar3KJ49gIOBH0haFBE/zS8wnJD/8mwF/CAini5mve2NpC2AgyJieD49GSAivgfcJ+lksoZMJdlTRf7YHk8hOOSsUfWeNXY78FWyK6jX533fLpS0Gdmz4T6IiFfb4y9QkfUArs7Pwd0oaSjwgqTuEfH1iLgdQNKrwAhJe0TEc0WtuAgcctao/J7UDmR3LjyTzxsHvC6JiLiY7BE9QPs8FCqW/Ha6yZIOBO7OX/86vw91oqTfRsSZABHxvKQxZFfBHXJmAJIOAA4ApgD7SyrP7z+dJekm4GuSFkTEL4tbafuU3y63SUS8IOlY4N78D09d0L2Rf2ffkDQA2IjsXF2745Czj5A0mOwe1BsjGxf1ALKRnE4ie9LIxmT3qXpkrSLIn8nXB7hF2TioN9cLutqIuCH/DnfNV5lFdp/q0mLVXEwOOVsl7xXfHbilbhZARHw9v7J6PjAY+CawAzCoroVXjHrbq4ioBWZLuoVsHNQVETEiD7q/5edMfwWMkVSSL98uAw7cGdj46FMnJO1K1tn3AeC2vG9c3TPhyoC9yJ46cmxEvFKEktsVZYM5L46IKmVP9z00Iq7L3zuC7Kb7uhbdp4AeEfFU8SpuXdxPrp2r101kX0mXSfoC8ApZq+0Y4ERJFZA9E44s5DYGjnHAFZ6kIcDTwKfyWdsAO0j6fv7dPQj8BfiFpFMiYlJkI261u06/jXHItXP17kW9kez2n0uBK/Lps8me+Htyfh6IyIYOvM33pRZefvfCb4Fr6/og5o+zuoesb+L380VHA2PJBuwmX86HaDmHXDsnaSPgBLIRm6YCvYFOwA/Jxkw9HZiYn9cBVp0TsgLKTw08BCyKbECgMkn3KxuPYTTwGLCLpL+Rjah1TURMKmLJrZbPybUz+WGM6geVshG1OpONeH8I2VXTP5EdBv0oPHRgUeS3yz0CXAbsDbwfEefl75WRjWp/IDA18keW20f56mo7IqlTRCwDQtJuZKOmj4mI/+Svl0bEXElzgAnAzQ644omICcoGf34c+FdEnAirPWVkBnBbMWtsC9ySayeUDRv4G+BnQDlwBzCHrLPvnyPiaUljgRXAJsDZEfHXYtVrH5K0M/AU2f3Bf8jn+Ra6JnJLrh1QNiRdGfA82aFPOVk3hKmSLgWOkbQc2I/s8GdmREwsVr22uoh4UdJBwKOSukbE/zjgms4tucRJ2gY4F7iX7DlwnwcuB06LiEfz7iHfJ+sWcof7V7Ve+UWHJ8jOmb7rC0BN45ZcwvIuCHeTjZz+Un7f6btktwR9W9L8iHhW0q/I+sXNKmK5tg4R8U9JG9d1zramcUsuUXkL7X6y8223rvFeH7IRmw4AfpmfjyuNxEdtSkG9zts+J9dEbsmlaynZ4elfYNV9qbX5U7Dn5v2rAP5b0kuAWwdtQF2wOeCazp2B09WV7FagvWHV2JrK9SJ7fPm9wJkRscDndyxVDrlERcQCspvoj86fGAvZ6YkA9iEbj3NRRPynWDWatQSHXNruJ+sw+i1J+wO1+ePMrwLujohFRa3OrAX4wkPi8qfCHgecBbxAdmP31RHxgE9eW3vgkGsn8rCrBTpGxLsOOGsvHHJmljSfkzOzpDnkzCxpDjkzS5pDzsyS5pAzs6Q55Cw5khbl/24kaeQ6lj1HUpf1/Px9JT3c1PlrLHOapBvXc3vTJPVdn3XsQw45axPyBwysl4h4PyKOWcdi5wDrFXLWtjjkrKgkDZT0mqQRkl6SNLKuZZW3YC6W9AxwrKStJP1N0kRJ/8jHJEXSFpKelTRB0uVrfPYr+XSppOskvZxv57uSzgY2AkZLGp0v94X8s16QdK+kbvn8L+Z1PgN8pQn7tYekcZIm5f9uU+/tTfP9eF3SJfXWOVnSc5ImS/r9xwl2a0BE+Mc/RfshexpKAHvlr28lG8sAYBrww3rLPgkMyqc/Dfw9n34IOCWf/jbZgwfqPvuVfPpMssdOleWve9fbRt98ui/ZQM5d89cXABeTDdH4DjAIENm4pw83sC/71s0HKupt60CyEe4BTiO7n7gP2QhprwC7AdsCo4DyfLnf1NunVTX6Z/1//Dw5aw3eiYix+fTtZINaX5e/vhsgb1HtCdyrDweH75j/uxdwdD59G3BNA9s4EPhdRFQDRMS8Bpb5DLAdMDbfRgfgWWAI8HZEvJHXcjtwxjr2qQcwQtIgshAvr/fe4xExN/+s+8geh1UN7ApMyLfdGT+puVk45OEUmooAAAFcSURBVKw1WPPewvqvF+f/lgALImIoDVvX/Ylq4jKPRz7036qZ2aOq1vf+x8uB0RFxlKSBZKNt1WlofwWMiIgfred2bB18Ts5ag80kfTafPhF4Zs0FIhvX4G1Jx8Kqp3/unL89Fjghnz6pkW08RvbIqbJ8/d75/Cqgez49HthL0tb5Ml0kDQZeA7aQtFW9GtelB9mTmSE7RK3vIEm9JXUGjszrf5Js1LT+dfUpG/TbPiGHnLUG/wJOzR/D3hv4bSPLnQQMk/Qi2XixR+Tzv0c2MM8EsnBpyC3AdOClfP3/yucPB/4qaXREzCYLpDvzWsYDQyIbkPsM4JH8wkNTHjR6LfAzZWPZrnkB4Rmyw+rJZOfqno+IV4ELgcfybT8ObNiE7dg6+CkkVlT5odzDEbFDkUuxRLklZ2ZJc0vOzJLmlpyZJc0hZ2ZJc8iZWdIccmaWNIecmSXNIWdmSft/A7IzGptqg2gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Testing confusion matrix\n",
    "class_names = ['Charged Off','Fully Paid']\n",
    "cm=confusion_matrix(y_target=y_test,y_predicted=y_pred_test)\n",
    "fig,ax=plot_confusion_matrix(conf_mat=cm,cmap='Dark2',class_names=class_names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checking the roc_auc score for training data\n",
    "# print(roc_auc_score(y_train,y_pred_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "## ROC Curve for training data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Visualisation with plot_metric\n",
    "# bc = BinaryClassification(y_train_new, y_pred_train, labels=[\"Class 1\", \"Class 2\"])\n",
    "# # # Figures\n",
    "# plt.figure(figsize=(5,5))\n",
    "# bc.plot_roc_curve()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ROC CURVE for test data\n",
    "# Visualisation with plot_metric\n",
    "# bc = BinaryClassification(y_test, y_pred_test, labels=[\"Class 1\", \"Class 2\"])\n",
    "\n",
    "# # Figures\n",
    "# plt.figure(figsize=(5,5))\n",
    "# bc.plot_roc_curve()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the model\n",
    "filename = 'catboost.pkl'\n",
    "pickle.dump(classifier, open(filename, 'wb'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}