credit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e15f3283",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries \n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9b872488",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ignore harmless warnings \n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "589ca922",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set to display all the columns in dataset\n",
    "\n",
    "pd.set_option(\"display.max_columns\", None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d1d02cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the application dataset \n",
    "\n",
    "df = pd.read_csv(r\"C:\\Users\\sanje\\OneDrive\\Desktop\\Application_Data.csv\", header=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ec4bd799",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy to back-up file\n",
    "df_BK = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2c6615fa",
   "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>Applicant_ID</th>\n",
       "      <th>Applicant_Gender</th>\n",
       "      <th>Owned_Car</th>\n",
       "      <th>Owned_Realty</th>\n",
       "      <th>Total_Children</th>\n",
       "      <th>Total_Income</th>\n",
       "      <th>Income_Type</th>\n",
       "      <th>Education_Type</th>\n",
       "      <th>Family_Status</th>\n",
       "      <th>Housing_Type</th>\n",
       "      <th>Owned_Mobile_Phone</th>\n",
       "      <th>Owned_Work_Phone</th>\n",
       "      <th>Owned_Phone</th>\n",
       "      <th>Owned_Email</th>\n",
       "      <th>Job_Title</th>\n",
       "      <th>Total_Family_Members</th>\n",
       "      <th>Applicant_Age</th>\n",
       "      <th>Years_of_Working</th>\n",
       "      <th>Total_Bad_Debt</th>\n",
       "      <th>Total_Good_Debt</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5008806</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>112500</td>\n",
       "      <td>Working                                       ...</td>\n",
       "      <td>Secondary / secondary special                 ...</td>\n",
       "      <td>Married                                       ...</td>\n",
       "      <td>House / apartment                             ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Security staff                                ...</td>\n",
       "      <td>2</td>\n",
       "      <td>59</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5008808</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>270000</td>\n",
       "      <td>Commercial associate                          ...</td>\n",
       "      <td>Secondary / secondary special                 ...</td>\n",
       "      <td>Single / not married                          ...</td>\n",
       "      <td>House / apartment                             ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sales staff                                   ...</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5008809</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>270000</td>\n",
       "      <td>Commercial associate                          ...</td>\n",
       "      <td>Secondary / secondary special                 ...</td>\n",
       "      <td>Single / not married                          ...</td>\n",
       "      <td>House / apartment                             ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sales staff                                   ...</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5008810</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>270000</td>\n",
       "      <td>Commercial associate                          ...</td>\n",
       "      <td>Secondary / secondary special                 ...</td>\n",
       "      <td>Single / not married                          ...</td>\n",
       "      <td>House / apartment                             ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sales staff                                   ...</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5008811</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>270000</td>\n",
       "      <td>Commercial associate                          ...</td>\n",
       "      <td>Secondary / secondary special                 ...</td>\n",
       "      <td>Single / not married                          ...</td>\n",
       "      <td>House / apartment                             ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sales staff                                   ...</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Applicant_ID Applicant_Gender  Owned_Car  Owned_Realty  Total_Children  \\\n",
       "0       5008806          M                1             1               0   \n",
       "1       5008808          F                0             1               0   \n",
       "2       5008809          F                0             1               0   \n",
       "3       5008810          F                0             1               0   \n",
       "4       5008811          F                0             1               0   \n",
       "\n",
       "   Total_Income                                        Income_Type  \\\n",
       "0        112500  Working                                       ...   \n",
       "1        270000  Commercial associate                          ...   \n",
       "2        270000  Commercial associate                          ...   \n",
       "3        270000  Commercial associate                          ...   \n",
       "4        270000  Commercial associate                          ...   \n",
       "\n",
       "                                      Education_Type  \\\n",
       "0  Secondary / secondary special                 ...   \n",
       "1  Secondary / secondary special                 ...   \n",
       "2  Secondary / secondary special                 ...   \n",
       "3  Secondary / secondary special                 ...   \n",
       "4  Secondary / secondary special                 ...   \n",
       "\n",
       "                                       Family_Status  \\\n",
       "0  Married                                       ...   \n",
       "1  Single / not married                          ...   \n",
       "2  Single / not married                          ...   \n",
       "3  Single / not married                          ...   \n",
       "4  Single / not married                          ...   \n",
       "\n",
       "                                        Housing_Type  Owned_Mobile_Phone  \\\n",
       "0  House / apartment                             ...                   1   \n",
       "1  House / apartment                             ...                   1   \n",
       "2  House / apartment                             ...                   1   \n",
       "3  House / apartment                             ...                   1   \n",
       "4  House / apartment                             ...                   1   \n",
       "\n",
       "   Owned_Work_Phone  Owned_Phone  Owned_Email  \\\n",
       "0                 0            0            0   \n",
       "1                 0            1            1   \n",
       "2                 0            1            1   \n",
       "3                 0            1            1   \n",
       "4                 0            1            1   \n",
       "\n",
       "                                           Job_Title  Total_Family_Members  \\\n",
       "0  Security staff                                ...                     2   \n",
       "1  Sales staff                                   ...                     1   \n",
       "2  Sales staff                                   ...                     1   \n",
       "3  Sales staff                                   ...                     1   \n",
       "4  Sales staff                                   ...                     1   \n",
       "\n",
       "   Applicant_Age  Years_of_Working  Total_Bad_Debt  Total_Good_Debt  Status  \n",
       "0             59                 4               0               30       1  \n",
       "1             53                 9               0                5       1  \n",
       "2             53                 9               0                5       1  \n",
       "3             53                 9               0               27       1  \n",
       "4             53                 9               0               39       1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first 5 records\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0e3ff3a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25128 entries, 0 to 25127\n",
      "Data columns (total 21 columns):\n",
      " #   Column                Non-Null Count  Dtype \n",
      "---  ------                --------------  ----- \n",
      " 0   Applicant_ID          25128 non-null  int64 \n",
      " 1   Applicant_Gender      25128 non-null  object\n",
      " 2   Owned_Car             25128 non-null  int64 \n",
      " 3   Owned_Realty          25128 non-null  int64 \n",
      " 4   Total_Children        25128 non-null  int64 \n",
      " 5   Total_Income          25128 non-null  int64 \n",
      " 6   Income_Type           25128 non-null  object\n",
      " 7   Education_Type        25128 non-null  object\n",
      " 8   Family_Status         25128 non-null  object\n",
      " 9   Housing_Type          25128 non-null  object\n",
      " 10  Owned_Mobile_Phone    25128 non-null  int64 \n",
      " 11  Owned_Work_Phone      25128 non-null  int64 \n",
      " 12  Owned_Phone           25128 non-null  int64 \n",
      " 13  Owned_Email           25128 non-null  int64 \n",
      " 14  Job_Title             25128 non-null  object\n",
      " 15  Total_Family_Members  25128 non-null  int64 \n",
      " 16  Applicant_Age         25128 non-null  int64 \n",
      " 17  Years_of_Working      25128 non-null  int64 \n",
      " 18  Total_Bad_Debt        25128 non-null  int64 \n",
      " 19  Total_Good_Debt       25128 non-null  int64 \n",
      " 20  Status                25128 non-null  int64 \n",
      "dtypes: int64(15), object(6)\n",
      "memory usage: 4.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# Display the dataset information\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e30045a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Displaying Duplicate values with in dataset\n",
    "\n",
    "df_dup = df[df.duplicated(keep='last')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "77e39fb2",
   "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>Applicant_ID</th>\n",
       "      <th>Applicant_Gender</th>\n",
       "      <th>Owned_Car</th>\n",
       "      <th>Owned_Realty</th>\n",
       "      <th>Total_Children</th>\n",
       "      <th>Total_Income</th>\n",
       "      <th>Income_Type</th>\n",
       "      <th>Education_Type</th>\n",
       "      <th>Family_Status</th>\n",
       "      <th>Housing_Type</th>\n",
       "      <th>Owned_Mobile_Phone</th>\n",
       "      <th>Owned_Work_Phone</th>\n",
       "      <th>Owned_Phone</th>\n",
       "      <th>Owned_Email</th>\n",
       "      <th>Job_Title</th>\n",
       "      <th>Total_Family_Members</th>\n",
       "      <th>Applicant_Age</th>\n",
       "      <th>Years_of_Working</th>\n",
       "      <th>Total_Bad_Debt</th>\n",
       "      <th>Total_Good_Debt</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Applicant_ID, Applicant_Gender, Owned_Car, Owned_Realty, Total_Children, Total_Income, Income_Type, Education_Type, Family_Status, Housing_Type, Owned_Mobile_Phone, Owned_Work_Phone, Owned_Phone, Owned_Email, Job_Title, Total_Family_Members, Applicant_Age, Years_of_Working, Total_Bad_Debt, Total_Good_Debt, Status]\n",
       "Index: []"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the duplicate records\n",
    "\n",
    "df_dup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c96f8d9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25128, 21)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the shape of the dataset\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0dea6b6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Applicant_ID            25128\n",
       "Applicant_Gender            2\n",
       "Owned_Car                   2\n",
       "Owned_Realty                2\n",
       "Total_Children              6\n",
       "Total_Income              195\n",
       "Income_Type                 5\n",
       "Education_Type              5\n",
       "Family_Status               5\n",
       "Housing_Type                6\n",
       "Owned_Mobile_Phone          1\n",
       "Owned_Work_Phone            2\n",
       "Owned_Phone                 2\n",
       "Owned_Email                 2\n",
       "Job_Title                  18\n",
       "Total_Family_Members        7\n",
       "Applicant_Age              48\n",
       "Years_of_Working           43\n",
       "Total_Bad_Debt             33\n",
       "Total_Good_Debt            61\n",
       "Status                      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the unique values of the all the variables\n",
    "\n",
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "900e2196",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Applicant_ID            0\n",
       "Applicant_Gender        0\n",
       "Owned_Car               0\n",
       "Owned_Realty            0\n",
       "Total_Children          0\n",
       "Total_Income            0\n",
       "Income_Type             0\n",
       "Education_Type          0\n",
       "Family_Status           0\n",
       "Housing_Type            0\n",
       "Owned_Mobile_Phone      0\n",
       "Owned_Work_Phone        0\n",
       "Owned_Phone             0\n",
       "Owned_Email             0\n",
       "Job_Title               0\n",
       "Total_Family_Members    0\n",
       "Applicant_Age           0\n",
       "Years_of_Working        0\n",
       "Total_Bad_Debt          0\n",
       "Total_Good_Debt         0\n",
       "Status                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the missing values information of variables\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f30c421d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Applicant_Gender\n",
       "F          15627\n",
       "M           9501\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Applicant_Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "88b174e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace 'Gender' variable and convert to integer value.\n",
    "\n",
    "df['Applicant_Gender'] = df['Applicant_Gender'].str.replace('M', '0')\n",
    "df['Applicant_Gender'] = df['Applicant_Gender'].str.replace('F', '1')\n",
    "df['Applicant_Gender'] = df['Applicant_Gender'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ceac8aca",
   "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>Applicant_ID</th>\n",
       "      <th>Applicant_Gender</th>\n",
       "      <th>Owned_Car</th>\n",
       "      <th>Owned_Realty</th>\n",
       "      <th>Total_Children</th>\n",
       "      <th>Total_Income</th>\n",
       "      <th>Income_Type</th>\n",
       "      <th>Education_Type</th>\n",
       "      <th>Family_Status</th>\n",
       "      <th>Housing_Type</th>\n",
       "      <th>Owned_Mobile_Phone</th>\n",
       "      <th>Owned_Work_Phone</th>\n",
       "      <th>Owned_Phone</th>\n",
       "      <th>Owned_Email</th>\n",
       "      <th>Job_Title</th>\n",
       "      <th>Total_Family_Members</th>\n",
       "      <th>Applicant_Age</th>\n",
       "      <th>Years_of_Working</th>\n",
       "      <th>Total_Bad_Debt</th>\n",
       "      <th>Total_Good_Debt</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25124</th>\n",
       "      <td>5150483</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>157500</td>\n",
       "      <td>Working                                       ...</td>\n",
       "      <td>Secondary / secondary special                 ...</td>\n",
       "      <td>Married                                       ...</td>\n",
       "      <td>House / apartment                             ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Core staff                                    ...</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25125</th>\n",
       "      <td>5150484</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>157500</td>\n",
       "      <td>Working                                       ...</td>\n",
       "      <td>Secondary / secondary special                 ...</td>\n",
       "      <td>Married                                       ...</td>\n",
       "      <td>House / apartment                             ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Core staff                                    ...</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25126</th>\n",
       "      <td>5150485</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>157500</td>\n",
       "      <td>Working                                       ...</td>\n",
       "      <td>Secondary / secondary special                 ...</td>\n",
       "      <td>Married                                       ...</td>\n",
       "      <td>House / apartment                             ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Core staff                                    ...</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25127</th>\n",
       "      <td>5150487</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202500</td>\n",
       "      <td>Working                                       ...</td>\n",
       "      <td>Secondary / secondary special                 ...</td>\n",
       "      <td>Married                                       ...</td>\n",
       "      <td>House / apartment                             ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Drivers                                       ...</td>\n",
       "      <td>2</td>\n",
       "      <td>54</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Applicant_ID  Applicant_Gender  Owned_Car  Owned_Realty  \\\n",
       "25124       5150483                 1          1             1   \n",
       "25125       5150484                 1          1             1   \n",
       "25126       5150485                 1          1             1   \n",
       "25127       5150487                 0          1             0   \n",
       "\n",
       "       Total_Children  Total_Income  \\\n",
       "25124               1        157500   \n",
       "25125               1        157500   \n",
       "25126               1        157500   \n",
       "25127               0        202500   \n",
       "\n",
       "                                             Income_Type  \\\n",
       "25124  Working                                       ...   \n",
       "25125  Working                                       ...   \n",
       "25126  Working                                       ...   \n",
       "25127  Working                                       ...   \n",
       "\n",
       "                                          Education_Type  \\\n",
       "25124  Secondary / secondary special                 ...   \n",
       "25125  Secondary / secondary special                 ...   \n",
       "25126  Secondary / secondary special                 ...   \n",
       "25127  Secondary / secondary special                 ...   \n",
       "\n",
       "                                           Family_Status  \\\n",
       "25124  Married                                       ...   \n",
       "25125  Married                                       ...   \n",
       "25126  Married                                       ...   \n",
       "25127  Married                                       ...   \n",
       "\n",
       "                                            Housing_Type  Owned_Mobile_Phone  \\\n",
       "25124  House / apartment                             ...                   1   \n",
       "25125  House / apartment                             ...                   1   \n",
       "25126  House / apartment                             ...                   1   \n",
       "25127  House / apartment                             ...                   1   \n",
       "\n",
       "       Owned_Work_Phone  Owned_Phone  Owned_Email  \\\n",
       "25124                 0            0            0   \n",
       "25125                 0            0            0   \n",
       "25126                 0            0            0   \n",
       "25127                 0            0            0   \n",
       "\n",
       "                                               Job_Title  \\\n",
       "25124  Core staff                                    ...   \n",
       "25125  Core staff                                    ...   \n",
       "25126  Core staff                                    ...   \n",
       "25127  Drivers                                       ...   \n",
       "\n",
       "       Total_Family_Members  Applicant_Age  Years_of_Working  Total_Bad_Debt  \\\n",
       "25124                     3             30                 5               0   \n",
       "25125                     3             30                 5               0   \n",
       "25126                     3             30                 5               0   \n",
       "25127                     2             54                 6               0   \n",
       "\n",
       "       Total_Good_Debt  Status  \n",
       "25124               18       1  \n",
       "25125               13       1  \n",
       "25126                2       1  \n",
       "25127               30       1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d390d866",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Applicant_ID', 'Applicant_Gender', 'Owned_Car', 'Owned_Realty',\n",
       "       'Total_Children', 'Total_Income', 'Income_Type', 'Education_Type',\n",
       "       'Family_Status', 'Housing_Type', 'Owned_Mobile_Phone',\n",
       "       'Owned_Work_Phone', 'Owned_Phone', 'Owned_Email', 'Job_Title',\n",
       "       'Total_Family_Members', 'Applicant_Age', 'Years_of_Working',\n",
       "       'Total_Bad_Debt', 'Total_Good_Debt', 'Status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display all the variables\n",
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ad49b30b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identify the variables for scaling (MinMaxScalar)\n",
    "\n",
    "cols = ['Applicant_ID', 'Applicant_Gender', 'Owned_Car', 'Owned_Realty', 'Total_Children', 'Total_Income', 'Owned_Mobile_Phone',\n",
    "       'Owned_Phone', \n",
    "       'Total_Family_Members', 'Applicant_Age', 'Years_of_Working',\n",
    "       'Total_Bad_Debt', 'Total_Good_Debt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "73ca3c2e",
   "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>Applicant_ID</th>\n",
       "      <th>Applicant_Gender</th>\n",
       "      <th>Owned_Car</th>\n",
       "      <th>Owned_Realty</th>\n",
       "      <th>Total_Children</th>\n",
       "      <th>Total_Income</th>\n",
       "      <th>Owned_Mobile_Phone</th>\n",
       "      <th>Owned_Work_Phone</th>\n",
       "      <th>Owned_Phone</th>\n",
       "      <th>Owned_Email</th>\n",
       "      <th>Total_Family_Members</th>\n",
       "      <th>Applicant_Age</th>\n",
       "      <th>Years_of_Working</th>\n",
       "      <th>Total_Bad_Debt</th>\n",
       "      <th>Total_Good_Debt</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5008806</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>112500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>59</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5008808</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>270000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5008809</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>270000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5008810</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>270000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5008811</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>270000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Applicant_ID  Applicant_Gender  Owned_Car  Owned_Realty  Total_Children  \\\n",
       "0       5008806                 0          1             1               0   \n",
       "1       5008808                 1          0             1               0   \n",
       "2       5008809                 1          0             1               0   \n",
       "3       5008810                 1          0             1               0   \n",
       "4       5008811                 1          0             1               0   \n",
       "\n",
       "   Total_Income  Owned_Mobile_Phone  Owned_Work_Phone  Owned_Phone  \\\n",
       "0        112500                   1                 0            0   \n",
       "1        270000                   1                 0            1   \n",
       "2        270000                   1                 0            1   \n",
       "3        270000                   1                 0            1   \n",
       "4        270000                   1                 0            1   \n",
       "\n",
       "   Owned_Email  Total_Family_Members  Applicant_Age  Years_of_Working  \\\n",
       "0            0                     2             59                 4   \n",
       "1            1                     1             53                 9   \n",
       "2            1                     1             53                 9   \n",
       "3            1                     1             53                 9   \n",
       "4            1                     1             53                 9   \n",
       "\n",
       "   Total_Bad_Debt  Total_Good_Debt  Status  \n",
       "0               0               30       1  \n",
       "1               0                5       1  \n",
       "2               0                5       1  \n",
       "3               0               27       1  \n",
       "4               0               39       1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df = df.drop(['Family_Status', 'Housing_Type','Income_Type', 'Education_Type','Job_Title'], axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ff808665",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identify the independent and Target (dependent) variables\n",
    "\n",
    "IndepVar = []\n",
    "for col in df.columns:\n",
    "    if col != 'Status':\n",
    "        IndepVar.append(col)\n",
    "\n",
    "TargetVar = 'Status'\n",
    "\n",
    "x = df[IndepVar]\n",
    "y = df[TargetVar]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cb0b7f25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17589, 15), (7539, 15), (17589,), (7539,))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the data into train and test (random sampling)\n",
    "\n",
    "from sklearn.model_selection import train_test_split \n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# Display the shape for train & test data\n",
    "\n",
    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2b6d961b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling the features by using MinMaxScaler\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "mmscaler = MinMaxScaler(feature_range=(0, 1))\n",
    "\n",
    "x_train[cols] = mmscaler.fit_transform(x_train[cols])\n",
    "x_train = pd.DataFrame(x_train)\n",
    "\n",
    "x_test[cols] = mmscaler.fit_transform(x_test[cols])\n",
    "x_test = pd.DataFrame(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0a7be44",
   "metadata": {},
   "source": [
    "## Decision tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fb60719a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To build the decisiom tree model with random sampling\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "ModelDT=DecisionTreeClassifier()\n",
    "#ModelDT=DecisionTreeClassifier(criterion='girl',splitter='best',)\n",
    "\n",
    "\n",
    "ModelDT.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5559592d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=ModelDT.predict(x_test)\n",
    "y_pred_prob=ModelDT.predict_proba(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d4450728",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot Decision Tree\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import tree\n",
    "plt.figure(figsize=(20,5))\n",
    "tree.plot_tree(ModelDT);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d20ca73d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix : \n",
      " [[7490    6]\n",
      " [   3   40]]\n",
      "Outcome values : \n",
      " 7490 6 3 40\n"
     ]
    }
   ],
   "source": [
    "# 'Confusion matrix' and 'classification report' in sklearn\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# Actual values\n",
    "\n",
    "actual = y_test\n",
    "\n",
    "# Predicted values\n",
    "\n",
    "predicted = y_pred\n",
    "\n",
    "# Confusion matrix\n",
    "\n",
    "matrix = confusion_matrix(actual,predicted, labels=[1,0], sample_weight=None, normalize=None)\n",
    "print('Confusion matrix : \\n', matrix)\n",
    "\n",
    "# Outcome values order in sklearn\n",
    "\n",
    "tp, fn, fp, tn = confusion_matrix(actual,predicted,labels=[1,0]).reshape(-1)\n",
    "print('Outcome values : \\n', tp, fn, fp, tn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a363cbec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00      7496\n",
      "           0       0.87      0.93      0.90        43\n",
      "\n",
      "    accuracy                           1.00      7539\n",
      "   macro avg       0.93      0.96      0.95      7539\n",
      "weighted avg       1.00      1.00      1.00      7539\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification report for precision, recall f1-score and accuracy\n",
    "\n",
    "C_Report = classification_report(actual,predicted,labels=[1,0])\n",
    "\n",
    "print('Classification report : \\n', C_Report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3367007a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating the metrics\n",
    "\n",
    "sensitivity = round(tp/(tp+fn), 3);\n",
    "specificity = round(tn/(tn+fp), 3);\n",
    "accuracy = round((tp+tn)/(tp+fp+tn+fn), 3);\n",
    "balanced_accuracy = round((sensitivity+specificity)/2, 3);\n",
    "\n",
    "precision = round(tp/(tp+fp), 3);\n",
    "f1Score = round((2*tp/(2*tp + fp + fn)), 3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bcfd722c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 99.9 %\n",
      "Precision : 100.0 %\n",
      "Recall : 99.9 %\n",
      "F1 Score : 0.999\n",
      "Specificity or True Negative Rate : 93.0 %\n",
      "Balanced Accuracy : 96.4 %\n",
      "MCC : 0.899\n"
     ]
    }
   ],
   "source": [
    "# Matthews Correlation Coefficient (MCC). Range of values of MCC lie between -1 to +1. \n",
    "# A model with a score of +1 is a perfect model and -1 is a poor model\n",
    "\n",
    "from math import sqrt\n",
    "\n",
    "mx = (tp+fp) * (tp+fn) * (tn+fp) * (tn+fn)\n",
    "MCC = round(((tp * tn) - (fp * fn)) / sqrt(mx), 3)\n",
    "\n",
    "print('Accuracy :', round(accuracy*100, 2),'%')\n",
    "print('Precision :', round(precision*100, 2),'%')\n",
    "print('Recall :', round(sensitivity*100,2), '%')\n",
    "print('F1 Score :', f1Score)\n",
    "print('Specificity or True Negative Rate :', round(specificity*100,2), '%')\n",
    "print('Balanced Accuracy :', round(balanced_accuracy*100, 2),'%')\n",
    "print('MCC :', MCC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f3ed6904",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "roc_auc_score: 0.965\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Area under ROC curve \n",
    "\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "\n",
    "print('roc_auc_score:', round(roc_auc_score(actual, predicted), 3))\n",
    "\n",
    "# ROC Curve\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "model_roc_auc = roc_auc_score(actual, predicted)\n",
    "fpr, tpr, thresholds = roc_curve(actual, ModelDT.predict_proba(x_test)[:,1])\n",
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "79b8bffe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "plt.plot(fpr, tpr, label= 'Classification Model' % model_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show() \n",
    "print('-----------------------------------------------------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2878be97",
   "metadata": {},
   "source": [
    "## Random Forest Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6bfc8a0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "ModelRF=RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "65d48b2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ModelRF.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8f5c0915",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=ModelRF.predict(x_test)\n",
    "y_pred_prob=ModelRF.predict_proba(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "073db0ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix : \n",
      " [[7496    0]\n",
      " [  26   17]]\n",
      "Outcome values : \n",
      " 7496 0 26 17\n"
     ]
    }
   ],
   "source": [
    "# 'Confusion matrix' and 'classification report' in sklearn\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# Actual values\n",
    "\n",
    "actual = y_test\n",
    "\n",
    "# Predicted values\n",
    "\n",
    "predicted = y_pred\n",
    "\n",
    "# Confusion matrix\n",
    "\n",
    "matrix = confusion_matrix(actual,predicted, labels=[1,0], sample_weight=None, normalize=None)\n",
    "print('Confusion matrix : \\n', matrix)\n",
    "\n",
    "# Outcome values order in sklearn\n",
    "\n",
    "tp, fn, fp, tn = confusion_matrix(actual,predicted,labels=[1,0]).reshape(-1)\n",
    "print('Outcome values : \\n', tp, fn, fp, tn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "688e5c2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00      7496\n",
      "           0       1.00      0.40      0.57        43\n",
      "\n",
      "    accuracy                           1.00      7539\n",
      "   macro avg       1.00      0.70      0.78      7539\n",
      "weighted avg       1.00      1.00      1.00      7539\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification report for precision, recall f1-score and accuracy\n",
    "\n",
    "C_Report = classification_report(actual,predicted,labels=[1,0])\n",
    "\n",
    "print('Classification report : \\n', C_Report)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "56077a46",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating the metrics\n",
    "\n",
    "sensitivity = round(tp/(tp+fn), 3);\n",
    "specificity = round(tn/(tn+fp), 3);\n",
    "accuracy = round((tp+tn)/(tp+fp+tn+fn), 3);\n",
    "balanced_accuracy = round((sensitivity+specificity)/2, 3);\n",
    "\n",
    "precision = round(tp/(tp+fp), 3);\n",
    "f1Score = round((2*tp/(2*tp + fp + fn)), 3);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "14e38530",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 99.7 %\n",
      "Precision : 99.7 %\n",
      "Recall : 100.0 %\n",
      "F1 Score : 0.998\n",
      "Specificity or True Negative Rate : 39.5 %\n",
      "Balanced Accuracy : 69.8 %\n",
      "MCC : 0.628\n"
     ]
    }
   ],
   "source": [
    "# Matthews Correlation Coefficient (MCC). Range of values of MCC lie between -1 to +1. \n",
    "# A model with a score of +1 is a perfect model and -1 is a poor model\n",
    "\n",
    "from math import sqrt\n",
    "\n",
    "mx = (tp+fp) * (tp+fn) * (tn+fp) * (tn+fn)\n",
    "MCC = round(((tp * tn) - (fp * fn)) / sqrt(mx), 3)\n",
    "\n",
    "print('Accuracy :', round(accuracy*100, 2),'%')\n",
    "print('Precision :', round(precision*100, 2),'%')\n",
    "print('Recall :', round(sensitivity*100,2), '%')\n",
    "print('F1 Score :', f1Score)\n",
    "print('Specificity or True Negative Rate :', round(specificity*100,2), '%')\n",
    "print('Balanced Accuracy :', round(balanced_accuracy*100, 2),'%')\n",
    "print('MCC :', MCC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e54e9578",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}