Loan_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset Information\n",
    "\n",
    "Dream Housing finance company deals in all home loans. They have presence across all urban, semi urban and rural areas. Customer first apply for loan after that company validates the customer eligibility for loan. Company wants to automate the loan eligibility process(real time) base on customer details provided while filling online application form. These details are Gender, Marital status, Education, No. of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specifically target these customers.\n",
    "\n",
    "This is a standard supervised classification task. A classification problem where we have to predict whether a loan would be approved or not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"train.csv\")\n",
    "test = pd.read_csv(\"test.csv\")\n",
    "train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001015</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5720</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001022</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3076</td>\n",
       "      <td>1500</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001031</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5000</td>\n",
       "      <td>1800</td>\n",
       "      <td>208.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001035</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2340</td>\n",
       "      <td>2546</td>\n",
       "      <td>100.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001051</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3276</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001015   Male     Yes          0      Graduate            No   \n",
       "1  LP001022   Male     Yes          1      Graduate            No   \n",
       "2  LP001031   Male     Yes          2      Graduate            No   \n",
       "3  LP001035   Male     Yes          2      Graduate            No   \n",
       "4  LP001051   Male      No          0  Not Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5720                  0       110.0             360.0   \n",
       "1             3076               1500       126.0             360.0   \n",
       "2             5000               1800       208.0             360.0   \n",
       "3             2340               2546       100.0             360.0   \n",
       "4             3276                  0        78.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area  \n",
       "0             1.0         Urban  \n",
       "1             1.0         Urban  \n",
       "2             1.0         Urban  \n",
       "3             NaN         Urban  \n",
       "4             1.0         Urban  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>600.00000</td>\n",
       "      <td>564.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5403.459283</td>\n",
       "      <td>1621.245798</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>342.00000</td>\n",
       "      <td>0.842199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6109.041673</td>\n",
       "      <td>2926.248369</td>\n",
       "      <td>85.587325</td>\n",
       "      <td>65.12041</td>\n",
       "      <td>0.364878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2877.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3812.500000</td>\n",
       "      <td>1188.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5795.000000</td>\n",
       "      <td>2297.250000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>480.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       614.000000         614.000000  592.000000         600.00000   \n",
       "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
       "std        6109.041673        2926.248369   85.587325          65.12041   \n",
       "min         150.000000           0.000000    9.000000          12.00000   \n",
       "25%        2877.500000           0.000000  100.000000         360.00000   \n",
       "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
       "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
       "max       81000.000000       41667.000000  700.000000         480.00000   \n",
       "\n",
       "       Credit_History  \n",
       "count      564.000000  \n",
       "mean         0.842199  \n",
       "std          0.364878  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            614 non-null    object \n",
      " 1   Gender             601 non-null    object \n",
      " 2   Married            611 non-null    object \n",
      " 3   Dependents         599 non-null    object \n",
      " 4   Education          614 non-null    object \n",
      " 5   Self_Employed      582 non-null    object \n",
      " 6   ApplicantIncome    614 non-null    int64  \n",
      " 7   CoapplicantIncome  614 non-null    float64\n",
      " 8   LoanAmount         592 non-null    float64\n",
      " 9   Loan_Amount_Term   600 non-null    float64\n",
      " 10  Credit_History     564 non-null    float64\n",
      " 11  Property_Area      614 non-null    object \n",
      " 12  Loan_Status        614 non-null    object \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 62.5+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               13\n",
       "Married               3\n",
       "Dependents           15\n",
       "Education             0\n",
       "Self_Employed        32\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           22\n",
       "Loan_Amount_Term     14\n",
       "Credit_History       50\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find the null values\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fill the missing values for numerical terms\n",
    "train['LoanAmount'] = train['LoanAmount'].fillna(train['LoanAmount'].mean())\n",
    "train['Loan_Amount_Term'] = train['Loan_Amount_Term'].fillna(train['Loan_Amount_Term'].mean())\n",
    "train['Credit_History'] = train['Credit_History'].fillna(train['Credit_History'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fill the missing values for Categorical terms- mode\n",
    "train['Gender'] = train['Gender'].fillna(train['Gender'].mode()[0])\n",
    "train['Married'] = train['Married'].fillna(train['Married'].mode()[0])\n",
    "train['Dependents'] = train['Dependents'].fillna(train['Dependents'].mode()[0])\n",
    "train['Self_Employed'] = train['Self_Employed'].fillna(train['Self_Employed'].mode()[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              0\n",
       "Gender               0\n",
       "Married              0\n",
       "Dependents           0\n",
       "Education            0\n",
       "Self_Employed        0\n",
       "ApplicantIncome      0\n",
       "CoapplicantIncome    0\n",
       "LoanAmount           0\n",
       "Loan_Amount_Term     0\n",
       "Credit_History       0\n",
       "Property_Area        0\n",
       "Loan_Status          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ef5530acd0>"
      ]
     },
     "execution_count": 10,
     "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": [
    "#Categorical attribute visualization\n",
    "sns.countplot(train['Gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ef552d2ac0>"
      ]
     },
     "execution_count": 11,
     "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.countplot(train['Married'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ef5541f8e0>"
      ]
     },
     "execution_count": 12,
     "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.countplot(train['Dependents'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ef5548b670>"
      ]
     },
     "execution_count": 13,
     "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.countplot(train['Education'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ef554c4a60>"
      ]
     },
     "execution_count": 14,
     "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.countplot(train['Self_Employed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ef5551ec70>"
      ]
     },
     "execution_count": 15,
     "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.countplot(train['Property_Area'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ef5556be50>"
      ]
     },
     "execution_count": 16,
     "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.countplot(train['Loan_Status'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ef555b3bb0>"
      ]
     },
     "execution_count": 17,
     "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": [
    "#Numerical attribute visualization\n",
    "sns.distplot(train['ApplicantIncome'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ef55695a00>"
      ]
     },
     "execution_count": 18,
     "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['CoapplicantIncome'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ef5555bbb0>"
      ]
     },
     "execution_count": 19,
     "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['LoanAmount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ajha48\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ef5582c970>"
      ]
     },
     "execution_count": 20,
     "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['Loan_Amount_Term'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ajha48\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ef558b0b80>"
      ]
     },
     "execution_count": 21,
     "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['Credit_History'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Creation of new arttribute\n",
    "train['Total_Income'] = train['ApplicantIncome'] + train['CoapplicantIncome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>Total_Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>5849.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "      <td>6091.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>4941.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>6000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0  146.412162             360.0   \n",
       "1             4583             1508.0  128.000000             360.0   \n",
       "2             3000                0.0   66.000000             360.0   \n",
       "3             2583             2358.0  120.000000             360.0   \n",
       "4             6000                0.0  141.000000             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  Total_Income  \n",
       "0             1.0         Urban           Y        5849.0  \n",
       "1             1.0         Rural           N        6091.0  \n",
       "2             1.0         Urban           Y        3000.0  \n",
       "3             1.0         Urban           Y        4941.0  \n",
       "4             1.0         Urban           Y        6000.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apply log transformation to the attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ef5598ba60>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V21Q1BnwHuHGa/f4U+CHQlcH6jJlVW3eU5tpSgoHsnMhrOpuaqgCsC8YsiHRCfQVwctL9dnfbWSKyAng7cPdsLyQid4jIDhHZ0d3dPd9ajTlHW3ckZ06SptSWFbK6zsarm4WRTqhP1wSaOtP/vwAfU9VZOwlV9R5V3aKqW+rr69Ot0ZhpxRNJTvSNZPXsjDPZ1FTNruP9qC2aYTIsnVBvB1ZOut8IdEzZZwvwHRE5BtwEfElE3paRCo2Zwcn+USYSmlMnSVM2N1fTG41xrHfE61KMz6QT6k8DLSKyWkTCwM3A/ZN3UNXVqrpKVVcBPwA+pKr3ZbxaYyY52uOMfMnGxabnsrm5GrB+dZN5c4a6qsaBu3BGtRwAvqeq+0TkThG5c6ELNGYmqQUnVufIFAGTtSwpo7zQJvcymZfWDEiq+iDw4JRt054UVdXbL7wsY+Z2pDtKVUkBNaVhr0uZt0BAuLLZ6Vc3JpPsilKTs3Jx5Mtkm5uqeb5rmMHRCa9LMT5ioW5yVltPNCdHvqRsbq5GFXafHPC6FOMjFuomJw2PTdA9PJ6TI19SLl9ZSUDsZKnJLAt1k5OOuhN55XL3S3lRAeuXVli/uskoC3WTk1IjX3K5+wVgc3MVz5zoJ5G0i5BMZliom5zU1h0hIC8s5pyrNjdXE40lONQ57HUpxics1E1OOtITpbG6hMJQ0OtSLsjmphoAdtpKSCZDLNRNTmrrjub0SdKUlTXF1JcXWr+6yRgLdZNzkknlaE8kZxabno2IsLmp2kbAmIyxUDc559TAKGMTSVoacj/UwelXP9E3QtfwmNelGB+wUDc5p7XLmcjroiX+CPVN7uReu47bRUjmwlmom5xzuMsZKXJRjg9nTLlsRQXhYIBddrLUZICFusk5h89EqCsrpDoHJ/KaTmEoyEsaK61f3WREWrM0GpNNWrsjlBeF+Nb2E16XkjGbm6u59/fHGI8ncn6YpvGWtdRNTlFVWs9EWFJe6HUpGbWpqZpYIslzpwa9LsXkOGupm5zSNTzO8Hg8p0N9uk8YkfE4Avy+tZfNzTWLX5TxDWupm5xy+Iwz8mVJRZHHlWRWWWGI5VXFPPZ8t9elmBxnoW5ySmrkSy631GfSsqSMZ04OMDRmi2aY82ehbnJKa1eEiqIQZYX+6zlsaSgnkVQeb+3xuhSTwyzUTU453BWhpaEcEfG6lIxrqimhrDDEb5+3UDfnz0Ld5JTWrggtPrmSdKpgQNi6tpbHnu9G1eZXN+fHQt3kjN7IOH3RmG+mB5jOdevqOTUwyhF3ERBj5stC3eQMv835Mp1XrqsHsFEw5rxZqJuc8fwZZ+RLS0O5x5UsnJU1JaypK+XRg11el2JylIW6yRkHO4cpLwqxvNJfY9SnetPGZTx+pIeuIZuK18yfhbrJGYc6h7l4qT9Hvkz29itXkFT4ye4Or0sxOchC3eQEVeXQmWHWL/Vv10vKmvoyLl9ZxQ93tXtdislBFuomJ3QMjjE8Fmf90gqvS1kUf3DlCg52DnPg9JDXpZgcY6FucsKhTifcLs6DljrAWy5fTigg/PiZU16XYnKMhbrJCQc7nZEv63w88mWymtIw29bXc98zp0gk7UIkkz4LdZMTDnUOs7yyiMriAq9LWTTv3LKSruFxfvqsnTA16bNQNznhUGd+nCSd7PpLGljXUMYXf91K0lrrJk0W6ibrxeJJWrsieXOSNCUQEP7kVRfx/JkID+/r9LockyMs1E3Wa+uJEE9q3pwknezNG5ezpq6Uf3201Sb5Mmnx36TUxjdSy77tPjkAwJHuiK8Wm57OdP++TU3V/GBXO7/cf4bXXbrUg6pMLrGWusl6Z4bGCAjU+3C1o3RcvrKKurIw//fBA4zHE16XY7KchbrJep2DY9SVFRIK5OfbNRgQ3rxxOcd6R/jy7456XY7Jcvn5W2JySsfgKCuqir0uw1PrGsq54dKlfP7Rw5waGPW6HJPF0gp1EblBRA6JSKuIfHyax98jIs+6X4+LyOWZL9Xko6GxCYbH4izP81AH+Ju3bADgUw/s97gSk83mDHURCQJfBN4AbABuEZENU3Y7CrxSVTcCnwLuyXShJj+ddlulFuqwoqqYu151EQ/t67RFNMyM0mmpXwW0qmqbqsaA7wA3Tt5BVR9X1X737pNAY2bLNPnq1IAzp7jf51BP1x9ft4ZVtSV88v59dtLUTCudUF8BnJx0v93dNpMPAD+/kKKMSekYGKWuLExhQdDrUrJCYSjIJ996KW09Ub7yX3bS1JwrnVCfbkWCaa+CEJFX4YT6x2Z4/A4R2SEiO7q77eOjmVvHwKh1vUyxbf0SXrehgc8/0monTc050gn1dmDlpPuNwDkzDInIRuDLwI2q2jvdC6nqPaq6RVW31NfXn0+9Jo9Ex+MMjE6wvNJCfaq/efMGkqr83c/spKl5sXSuKH0aaBGR1cAp4Gbg1sk7iEgT8CPgvar6fMarNHmpY9Bpha6otlCHc682vbalngf3dvK3D+znE2+ZOnbB5Ks5W+qqGgfuAh4GDgDfU9V9InKniNzp7vYJoBb4kojsFpEdC1axyRsdZ0+SWqhP59qWOmpKw9y/p4NYPOl1OSZLpDVOXVUfVNV1qrpWVf/O3Xa3qt7t3v6gqlar6hXu15aFLNrkh46BUapLCigO20nS6RQEA7xl4zJ6IuN89fd20tQ47IpSk7VODdiVpHNZv7SC9Q3lfPHXrQyOTHhdjskCFuomKw2OTtAXjdnIlzS87tIGhsfi3PO7I16XYrKAhbrJSqnpdhurSzyuJPstqyzmLZcv56v/dYzu4XGvyzEes1A3WWnnsT4EWFljLfV0fOS1LcQSSb70m1avSzEes0UyTFbaeaKfZZVFFIbsJGk6nmzr48qVVXz9iePUlxVSVRI++9itVzd5WJlZbNZSN1knnkjyzIkBmmpLvS4lp7z64iUAPHqwy+NKjJcs1E3WOdg5zEgsQXOt9afPR1VJmKtW1bDrRD89Eetbz1cW6ibr7DzuTPjZXGOhPl/b1tcTDAiPHDjjdSnGIxbqJuvsOO70p0/uFzbpKS8qYOvaOp5tH6RzcMzrcowHLNRN1tl5rI9NzdVel5Gzrm2po7AgwM+fO43qtBOqGh+zUDdZpWNglI7BMbZYqJ+3knCI11zcwOGuCAc7h70uxywyC3WTVVL96VuaazyuJLe9bE0t9WWFPLj3tK2QlGcs1E1WebKtl9JwkIuXlXtdSk4LBoQ3bVxGbzTGvb8/5nU5ZhFZqJusoar85lA3Wy+qoyBob80Lta6hnEuWlvPZXz3PIeuGyRv2m2OyxpHuCKcGRtm23lbFypS3XbmCssICPvTNnUTH416XYxaBhbrJGr855Kxb+8p1FuqZUl5UwL/ecgVHe6L81Y/32miYPGChbrLGb5/v5qIlZTYzY4ZtXVvHR69fx327O/jET/YxkbBVkvzMJvQyWWEkFmd7Wx/vu6bZ61J86UPbLmJ4LM6/P9bGke4IX7h1EzWldnGXH1lL3WSFJ470Eksk2bZ+idel+FIgIPzlGy/hn955OTuO9fPyTz/Kx37wLE8d7WMkZn3tfmItdZMVfnOom+KCIC9dbRcdLaSbNjdy2YoK7v39MX6yu4Pv7jiJCDRWF7O+oZyWhnKuXFnFK9fX27THOcpC3XhOVfn1oS62rq21IFkEFy+t4NPv2MhfvekSPv3zg5wZGuPM0DjPtg/y6MEukgpFBQE2rqhi2/r6c+bgsfnZs5uFuvHcU0f7aO8f5aPXr/O6lLxSXlTApcsruXR55dlt8WSStu4ou08OsOtEP7vbB7j+kgauWVtLQMTDak26LNSN536ws52ywhA3XLbU61LyXigQYF1DOesayrn+kgZ+sucUP9t7mgOnh3jP1c0Uh+2TVLazUDeeio7H+dne07xl43JKwvZ2XAjf2n7ivJ5XXRrmtmtWsetEP/c908E9vzvCbdesymxxJuNs9Ivx1IN7TzMSS/DOLY1el2KmISJsbq7htq2rGBiZ4N8fa+Nk34jXZZlZWKgbT/1gZzur60rZbFPtZrWLlpTxx9euIRZP8odf2U7XkC3Aka0s1I1njvdG2X60j5s2NyJ2Ei7rLa8q5vatq+geHue9X3mKgZGY1yWZaVioG898/tFWwqEAN222rpdcsbKmhP943xaO9kS54+s7ba72LGRnpownDnUO88Nd7bx8bR2PHOjyuhwzD8d7R3j7phV89+mTvOvuJ3jXlpVnP2nZGHbvWagbT3zm4YOUFYbYZjMy5qTLG6voj8b4xf4z1JSGuX6DDUfNFhbqZtE9dbSPXx3o4i9ev56SQnsL5qpXrqunLxrj14e6qS4Js2WVLUGYDaxP3SyqyHicv75vL0vKC/mjl6/2uhxzAUSEG69YwUVLyrhv9ylauyJel2SwUDeLKJlUPvLd3RzpjvLZd19hVyf6QDAg3HpVE/XlhXxz+3H2dQx6XVLes1A3i+azv3qeX+4/w1+/6RJeflGd1+WYDCkqCHLbNasoKgjy3q88RWuXrYfqJQt1s+BGYwn+8kfP8vlHW3nXlkZu37rK65JMhlWVhPnAK1Y7Lff/2M6xnqjXJeUtO0tl5mWmeUSmG8qWTCqPH+nlb3+6j8NdET60bS0fuX6dXWjkU3VlhXzzg1dz8z1P8o5/e5x73reZzc0XdvJ0tnlrbPjk9CzUTUYNjkywp32Ancf7+cnuUxzrHaG+vJCv/9FVXNtiwxf9bl1DOd+/8xo+cO/T3HLPdv7hppfw9ivt4rLFZKGepy60BZRUpS8So31ghJP9ozywp4Mj3RG6hsfP7vPSVdV8+LUtDI3GOdk3et6zBZrcsra+jB9/6OXc+Y2dfOS7e3hgz2k+8eYNrKorTev5yaRyrDfKnvYBHni2g8GRCSYSSRKqlIZDVBSFqC0rpKWhjPVLy6koKljgf1FusVA3c0oklY6BUfZ1DPLwvk7a+0c4NTDK2ISzKn1BULh0eSXXratnbX0ZGxsreUlj5dlfNgvz/FNdGuYbH7yae39/jM89cpjXffYxXndpA298yTKuWVNLZXEBgYAwHk9wZnCcA51DPNs+wJ6Tg+xpH2B4zFk3tSAo1JSGCQcDiAgdA6McHJtgIqHcv6cDgBVVxVyyrJyLl1Zw8bJyVtWWUlMapqY0TFFB/o2wElWdeyeRG4DPAUHgy6r66SmPi/v4G4ER4HZV3TXba27ZskV37NhxvnVnjKry/544zkRCURRBCIgzBlfEabUG3duT+4IXu69vPn3ZAIOjE5zqH6VjYJRTA6O0949wom+E9v5R+qMxeiIxEklFBAIihEMBwqEAhaEAjdXFBEQYm0gwPBanvX+UWMIJ8IDA0soiGqtKaKwuZkV1MUvKi3jvNc3zrt3kh6GxCX59sIvWrgi9UWcSsGBAKA0HGRp7YdHrgMDSiiIaq533VmN1CfXlhQQDLz4Ho6oMjcW5eGk5+08PcbBzmIOnh2jriZJIvjjPwsEAJYVBCkMBCoIBQoEA4ZAQCgQoCAoFwYD7JYQm3T67LeBse8NLllJdEqa2LEx1SficmhaTiOxU1S0zPj5XqItIEHgeuB5oB54GblHV/ZP2eSPwpzihfjXwOVW9erbXvdBQV1USSSWeVGKJJPGEMpFIEosnGRydoH8kRv/IBP3RGH3RGD2RcfcrRvfwOAMjMcbjScbjybSPGRDnzRgQIalKwA37YCBASThIaThIaWGIjY1V1JaGqS4NU1NaQE1pITUl4WnGZSuR8QQDIzEGRyecuqMT9EXH6YnG6IvE6I2OMzAyQXQ8TlKdbo+kOn98ggGhrChEOOiEcVKV8XiS4bE4kfEXrxBfGArQVOP8stSUFtI5OEooGEDd1xyPJ4nFE8TiSapKwiRU3X9TiMaaYlbXlrJ+aTnPtg9SEDx30NRsf8gs1A3Au7Y08tSxPvZ3DNEXjREZj1NbWsjSykLauqMsryqe9r01k6nvubGJBK1dEU4NjPLQ3k6isTgjsQTR8Tjj8SQTiSQTCSWefOG2893Jj3hy7gYugAhOwLufBmrLwtSWFlJTGqauLExNaaG7LUxtWSGlhUFCgcDZxuKFmivU0+l+uQpoVdU29wW/A9wI7J+0z43A19X5C/GkiEyEk6UAAAm9SURBVFSJyDJVPX0BtU/r53tP8+Hv7mYikSSNDxlnVZcUUFdWSH15IVesrKK6pICiAucv+MHOYUJB54eeVOcPRirsNjZWkkhCQvXsH5KEKvtPDTkBCyQSykgsTjSWoHt4nF/u76R/ZOKcVkO6KopC1JU5b4zVdaVUFYc52hslIM6niNRakfGksrquxA3kJAERigoClIRDrKgqZnmV05JeUVVMbWmYQODCP2kcOG1jkM35CQUDbF1bx9a1516jkIk//EUFQS5bUcllKyrpjcx/WuCkqhPuieQLDcWkE/7XttTRPxKjNxKjNxqjNzJOX9S5f6hzmN5oLwMjE3MeIxhwGmN3XLuGP3/9+vP5Z84pnVBfAZycdL8dpzU+1z4rgBeFuojcAdzh3o2IyKF5VXsBjs/8UB3Qs1h1XKBFqfU9F/6cXPmZWp2ZNWud5/O+ms0Fvp6nP9O/cL/SMF2dM/d1kl6oT/d5YWoTNJ19UNV7gHvSOOaiEZEds32UySa5UqvVmVlWZ+blSq3nU2c6HVjtwMpJ9xuBjvPYxxhjzAJLJ9SfBlpEZLWIhIGbgfun7HM/8D5xvAwYXIj+dGOMMbObs/tFVeMichfwMM6Qxq+q6j4RudN9/G7gQZyRL604Qxrfv3AlZ1xWdQfNIVdqtTozy+rMvFypdd51pjVO3RhjTG6wWRqNMcZHLNSNMcZH8jrUReSYiOwVkd0i4v2cBTNwL+b6gYgcFJEDInKN1zVNJSLr3Z9j6mtIRP7M67qmIyIfEZF9IvKciHxbRIq8rmkmIvJht8592fTzFJGvikiXiDw3aVuNiPxSRA6736u9rNGtabo63+n+PJMikjXDGmeo9TPu7/2zIvJjEama63XyOtRdr1LVK7J8zOrngIdU9WLgcuCAx/WcQ1UPuT/HK4DNOCfMf+xxWecQkRXAfwe2qOplOCf/b/a2qumJyGXAH+Nc1X058GYRafG2qrPuBW6Ysu3jwCOq2gI84t732r2cW+dzwB8Ajy16NbO7l3Nr/SVwmapuxJmu5S/nehEL9SwnIhXAdcBXAFQ1pqoD3lY1p9cAR1R1lgt5PRUCikUkBJSQvddUXAI8qaojqhoHfgu83eOaAFDVx4C+KZtvBL7m3v4a8LZFLWoa09WpqgdUddGuZk/XDLX+wv2/B3gS5xqgWeV7qCvwCxHZ6U5hkI3WAN3Af4rIMyLyZRFJb2Jq79wMfNvrIqajqqeAfwJO4ExjMaiqv/C2qhk9B1wnIrUiUoIzbHjlHM/xUkPq+hT3+xKP6/GbPwJ+PtdO+R7qL1fVTcAbgD8Rkeu8LmgaIWAT8G+qeiUQJTs+1k7LvUDtrcD3va5lOm4/743AamA5UCoif+htVdNT1QPAP+B8BH8I2APEZ32S8SUR+Suc//tvzrVvXoe6qna437tw+n+v8raiabUD7aq63b3/A5yQz1ZvAHap6hmvC5nBa4GjqtqtqhPAj4CtHtc0I1X9iqpuUtXrcD6aH/a6plmcEZFlAO73Lo/r8QURuQ14M/AeTePCorwNdREpFZHy1G3gdTgfd7OKqnYCJ0UkNU/na3jxtMfZ5haytOvFdQJ4mYiUuIu7vIYsPPGcIiJL3O9NOCf3svlnez9wm3v7NuAnHtbiC+4CRR8D3qqqI2k9J1+vKBWRNbwwOiMEfEtV/87DkmYkIlcAXwbCQBvwflXt97aqc7n9vieBNao66HU9MxGR/wO8G+fj7DPAB1V1fPZneUNEfgfUAhPAR1X1EY9LAkBEvg1sw5ka9gzwv4H7gO8BTTh/PN+pqlNPpi6qGersAz4P1AMDwG5Vfb1XNabMUOtfAoVAr7vbk6p656yvk6+hbowxfpS33S/GGONHFurGGOMjFurGGOMjFurGGOMjFurGGOMjFuom40Tk7SKiInLxBbzGvSJyk3v7yyKyIXMVgoj8ryn3I5l8/QslIp8UkT/3ug6TeyzUzUK4BfgvMjT7oap+UFUzfcHV/5p7F2Nyj4W6ySgRKQNeDnwAN9RFZJuIPObOB71fRO4WkYD7WERE/llEdonIIyJSP81r/iY177WI3ODuu0dEHnG3XSUij7sTnj2euvpWRG4XkR+JyEPuHN//6G7/NM4sjbtF5JtTjrXNPV5q/vpvuleeIiIvdV9/j4g8JSLlIlIkIv8pzrz8z4jIqyYd+z4ReUBEjorIXSLyUXefJ0Wkxt1vrVvfThH53WyfbsTxGXHmV98rIu92twdE5EvizBH+UxF5MPUpx+QhVbUv+8rYF/CHwFfc24/jzFOzDRjDmXEyiDNB1U3uPoozpwXAJ4AvuLfvnbTPb4AtOFcAngRWu9tr3O8VQMi9/Vrgh+7t23GuwK0EioDjwEr3sciUuiPu923AIM4UpwHgCeAVvHA170snHxP4H8B/utsuxrmSssg9ditQ7tY9CNzp7vdZ4M/c248ALe7tq4FH3dufBP58So3vcH92QaDBPdYy4Cacxd8DwFKgP/Wzs6/8+wrNkvfGnI9bgH9xb3/Hvf8z4ClVbYOzl0O/AmdysiTwXXf/b+BMsDWTlwGPqepRAH3hEvRK4GvuAhIKFEx6ziPqTlkgIvuBZpw/DLN5SlXb3efsBlbhhPJpVX3aPfaQ+/grcC45R1UPishxYJ37Or9W1WFgWEQGgQfc7XuBje6nmq3A990PA+BcEj6TVwDfVtUEzuRZvwVe6m7/vqomgU4R+fUc/z7jYxbqJmNEpBZ4NXCZiChOi1JxWpFT56OYaX6K2eatkBke/xROgL5dRFbhtOxTJs/pkiC99/x0z5np2DLNtuleJznpftJ9zQAwoM5qUemY6Viz1WDyjPWpm0y6Cfi6qjar6ipVXQkcxWlJXiUiq92+9HfjnEgF5z2Y6v+9ddL26TwBvFJEVoOzJqa7vRI45d6+Pc1aJ0SkYO7dzjoILBeRl7rHLhdn5aTHgPe429bhTGaV1qo6bmv/qIi8032+iMjlszzlMeDdIhJ0zz1cBzyF8zN7h9u33oDThWTylIW6yaRbOHdd0h/ihPUTwKdxpjc+Omm/KHCpiOzEaeX/7UwvrqrdwB3Aj0RkDy902/wj8Pci8nucTwfpuAd4duqJ0lmOHcP5Y/R599i/xOk7/xIQFJG9bj236/xmfHwP8AH3NffhLOCR8tci0p76wvmZPYuzWMajwP9UZ2rmH+LMu/8c8O/AdpzuIpOHbJZGs+BEZBvOSb83T/NYRFXLFr8qfxGRMlWNuF1gT+Gs6tXpdV1m8VmfujH+8FMRqcIZpfMpC/T8ZS11Y4zxEetTN8YYH7FQN8YYH7FQN8YYH7FQN8YYH7FQN8YYH/n/xhE3Xw8z5NkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['ApplicantIncomeLog'] = np.log(train['ApplicantIncome'])\n",
    "sns.distplot(train['ApplicantIncomeLog'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ef55a5cd30>"
      ]
     },
     "execution_count": 25,
     "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": [
    "train['Total_Income_Log'] = np.log(train['Total_Income'])\n",
    "sns.distplot(train['Total_Income_Log'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ajha48\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py:679: RuntimeWarning: divide by zero encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "train['CoapplicantIncomeLog'] = np.log(train['CoapplicantIncome'])\n",
    "#sns.distplot(train['CoapplicantIncomeLog'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ef559669a0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CV9UQcDPwKLAb+IGqNovITSJyU+yY3cAjwEvAc8CdqrozfWUb45zugSAAc0vyHa5kYpUlfvw+D209w06XYjIkqREdVX0YeHjMfbePuf3vwL+nrjRjslN3bHu3uSV+hyuZmEeE2vICm+mSQ+xKUWOm6FQLvTi7W+gQ7Uc/1jtMRMfOYzBuZIFuzBR1B4KUFfjw+7L/41NfXkgwHLFNo3NE9v9EGpNlugIjVM6C1jlAbUV0Kd2j1o+eEyzQjZkCVaWzf4Sa0tkR6DWlBfg8Yv3oOcIC3Zgp6AoEGRoNUz1LAt3rEeaXF9BmV4zmBAt0Y6ZgX0d0oauastkR6AC15YUc7RlGbWDU9SzQjZmC+CqGNaUFDleSvLqKAoZGw/QM2qbRbmeBbswU7OsIkO/zUFaQnYtyjSd+xWib9aO7ngW6MVPQ0hGgpjQfkfHWrMtO88sL8Eh0CQDjbhboxkzBvo4A1bOouwUgz+uhprSAdpu66HoW6MYkqXdwdFZNWUxUW15ga6PnAAt0Y5LU0hmb4TILA72uopD+4RB9wzYw6mYW6MYkqSU+w6VsdnW5AKeWzz1qA6OuZoFuTJL2HY/OcKkoynO6lCmrLY8tAdBr/ehuZoFuTJJaOgMsri7BM4tmuMQV5HmZU5Rnge5yFujGJGnf8QBLakqcLmPaassLLdBdzgLdmCQMBkO09QyxdFYHegHdgREGgyGnSzFpYoFuTBL2Ho8OiC6dN7sDXYE9x/qdLsWkiQW6MUnY0dYLwMq6cocrmb7a2BIAu472OVyJSRcLdGOSsLO1l4qiPBrmFDpdyrRVFOVRkOdhtwW6a1mgG5OEHW29rK4vn1VruIwlIswvK2RXuwW6W1mgGzOJ4dEwe4/3s6p+9na3xNWWF/DysX4iEVsb3Y0s0I2ZxJ5j/YQiymqXBPpgMMwrJwadLsWkgQW6MZPY2R4dEHVFoMeWALB+dHeyQDdmEjvbeikvnN0DonE1pfl4PWL96C5lgW7MJNwwIBqX5/WwpLqE5thfHcZdLNCNmcBIKMyeY+4YEI1bVV/OjrY+2zTahZIKdBG5TET2iEiLiNw6wXHnikhYRK5OXYnGOGfvsQCjYXcMiMatri+jKzDC8b4Rp0sxKTZpoIuIF7gNuBxYAVwnIitOc9y/Ao+mukhjnBK/QtRVgd4QfS/x92bcI5kW+gagRVUPqGoQuA+4apzjPgHcD3SksD5jHPXC4ZNUFOXRWDn7B0TjVtSW4xHY0drjdCkmxZIJ9HrgSMLt1th9p4hIPfBO4PaJTiQiN4rIVhHZ2tnZOdVajckoVeXpli7esHiuKwZE4wr9XpbUlFgL3YWSCfTxfpLHjqZ8BfiMqoYnOpGq3qGq61V1fXV1dbI1GuOIg10DtPcOc/6SKqdLSbnV9RU2MOpCyQR6K9CYcLsBaB9zzHrgPhE5BFwNfENE3pGSCo1xyNMtXQC80ZWBbgOjbuRL4pgtwFIRWQi0AdcC1yceoKoL49+LyN3Az1T1Jyms05iMe3JfFw1zCllQWeR0KSmXODA6v3z2bXptxjdpC11VQ8DNRGev7AZ+oKrNInKTiNyU7gKNcUIoHOGZA928cUmVq/rP42xg1J2SaaGjqg8DD4+5b9wBUFX94MzLMsZZ//nLvfQPR7dqu+fZww5Xk3o2MOpOdqWoMeNo6YxuObeoevZuOTcZGxh1Hwt0Y8bR0hGgrryAkvyk/oidldY0lNMVGKGtZ8jpUkyKWKAbM8aJgSCHuwdZUuPe1jnAuU2VAGw5dMLhSkyqWKAbM8aD29sIq7K2scLpUtJq+fxSygp8PHfQAt0tLNCNGeNHz7dSV1FAbbl7Lvcfj9cjnNtUybMW6K5hgW5Mgt1H+9jZ1sfrFsxxupSM2LCwkgOdA3T22wVGbmCBbkyC+7e1kucV1ja4u7slbsNC60d3Ewt0Y2JGwxF+sr2NN59ZQ7GLZ7ckWlVfTpHfa/3oLmGBbkzMb3YfpysQ5OpzGic/2CXyvB7OOWOO9aO7hAW6MUAkonz1Ny00zS3iTctzayXQDU2VvHysj97BUadLMTNkgW4M8EjzMXYf7eOTFy/F582tj8WGhZWoWj+6G+TWT64x4whHlC//ai9Lakq4cm395E9wmbWNFRTmeXl8r202NttZoJuc99CL7ezrCHDLxUvxety3suJkCvK8vPnMGh7ZeYxwxNZ1mc0s0E1OGwqG+Y9f7uHM+aW8bVWt0+U45oo1tXQFgjx7sNvpUswMWKCbnHbbb1toPTnE565ciScHW+dxb1peQ2Gel5+/dNTpUswMWKCbnLW/M8A3n9jPH51dz3mL5jpdjqMK/V7ecla02yUUjjhdjpkmC3STk1SVv//pTgrzvHz2bWc5XU5WuGJ1Ld0DQbvIaBbLjcvhjBnjwRfbebqlm8+/YxXVpflOl5NRp9uBKRiKUOT38rMdR3mDCzfGzgXWQjc5p294lH/6+W7WNJRz/YYFTpeTNfw+D5esmMdDL7bTMxh0uhwzDdZCNznnS7/cS1dghG99YH1OTlOcyE0XLubBF9u5/XcHuPXyM1/12ER7q16/0X4xZgNroZucsrOtl+8+c4j3bjyDNTmyouJUnFVbxpVr67h700E6+oadLsdMkQW6yRmqyucebGZOkZ+/fOtyp8vJWp+6eBmhsPJfj7U4XYqZIgt0kzMeeukoW185yacvXU55UZ7T5WStpqpi/vjcRu597jB7j/c7XY6ZAgt0kxOGgmG++PBuVtSW8e71ubM87nTd8palVBT5+dC3t9DRb10vs4UNihpXiw/k/ebl47T3DnPFmjq+v+WIw1Vlv5qyAu764Hqu+eZmbrh7K9//6HlOl2SSYC1043q9Q6M8sbeTVXVlLKwqdrqcWWNNQwVfv/5smtt7+dC3tzAYDDldkplEUoEuIpeJyB4RaRGRW8d5/D0i8lLsa5OIrE19qcZMz6PNx1CFy3N48a3pestZ8/jyNet44XAPt/9uP10B20w6m00a6CLiBW4DLgdWANeJyIoxhx0ELlTVNcDngTtSXagx03H4xCDbj/TwxqVVzCn2O13OrHTVunq+95GNDAbD/Pfj+znQGXC6JHMaybTQNwAtqnpAVYPAfcBViQeo6iZVPRm7uRloSG2ZxkxdJKL87KV2Sgt8XLgst7aVS7Vzmyr504uWUFLg466nD7LVdjfKSskEej2QOIrUGrvvdG4AfjHeAyJyo4hsFZGtnZ2dyVdpzDTc/3wrrSeHuHTlfPJ9XqfLmfUqi/3cdMFiFlWX8MALbTy+x3Y4yjbJBPp410aPu62JiLyJaKB/ZrzHVfUOVV2vquurq63FZNKnZzDIv/ziZRZUFrGu0a4ITZVCv5cPvL6JtQ3l/HLXcTYfsA0xskkygd4KJE7cbQDaxx4kImuAO4GrVNX+lY2j/vWRPfQOjXLVujo8Yuu1pJLXI1x9TiNnzi/loRfb2X6kx+mSTEwy89C3AEtFZCHQBlwLXJ94gIgsAB4A3qeqe1NepTFT8Pzhk9z73GE+/MaF1JYXOl3OrDLRAlyJvB7hug0L+M6mQ9z/fCvXb1xgfwllgUlb6KoaAm4GHgV2Az9Q1WYRuUlEbood9vfAXOAbIrJdRLamrWJjJjA8Guaz9+9gXlk+t1yyzOlyXC3P6+H6jQsoK/Dxsf/bZlMas0BS89BV9WFVXaaqi1X1n2P33a6qt8e+/7CqzlHVdbGv9eks2pjT+ddHXmbP8X6++K41lOTbhdDpVuT38Z6NZ3BiIMjN9zxv29c5zK4UNa7xxN5Ovv30IT7w+jN40/Iap8vJGXUVhXzhnavZfOAEX/n1PqfLyWkW6MYVOvqG+YsfvsjSmhLbI9QB7zqngXef08Btj7ewqaXL6XJylgW6mfUGgyE+/N2tBIZDfO26synIsznnTvjHq1aysKqYW76/nW7rT3eEBbqZ1SIR5VPf386Otl6+dt3ZnFVb5nRJOavI7+O/rjubnsFRPv2jl1Ad93IVk0YW6GbWUlU+91AzjzYf52+vWMElK+Y5XVLOW1lXzt9ccRaPvdzBXU8fcrqcnGPTAMysFIkof/vTndzz7GFuvGARf3J+k9Ml5bTE+es+j3BWbRlf+PluTgSCfPoy2+4vU6yFbmad0XCEz9z/Evc8e5iPXbSYz15+JmJXg2YNEeFdZ9dTUuDjvi2H6RsedbqknGEtdJM1JrpK8fqNC4DoZhUf/97zPNXSxSffspRbLl7Kvc/ZDkTZpijfxzXrG7nzqQP8xQ9e5JvvPQePx37pppu10M2s0dIR4I++8TTPHuzm365ew6cuWWYt8yzWVFXM21bX8qtdx7ntty1Ol5MTrIVusp6qcu9zh/nHh5op8vv43xs2ct6iuU6XZZLw+kVz8XmEL/16L2fVlnGxDVynlQW6yWpDwTA/fqGVne19LK4u5t3nNHKgc4ADnQNOl2aSICL8yx+tYX/nADff+zz3fuQ8zl4wx+myXMu6XEzWOtQ1wNce28euo31cunI+Hzp/IWWFeU6XZaao0O/lrg+eS01pATd8ZysHu+yXcbpYoJusE44ov959nP958gBej/DRCxZz4bJqW9d8Fqsuzec7f7IBgPfe+SyvdFuop4MFuskqPYNB7nzqAI+93MG6xgo+8aYlNFYWOV2WSYGFVcV89082MBgM8e7bn6Glo9/pklzHAt1kjR1tvXztsX0c7R3m3ec08O71jeTbuiyusqq+nPtufD0KXPPNzWx7xTabTiVxar2F9evX69attg+GiS6u9f8e2sV9W47QMKeQa9Y3Mrck3+myTBp1BUb4zqZD9A2P8rkrV3L9hgVTmoKazDULbiUi206354TNcjGOam7v5c/ufYEDXQNcuKyai8+ah9cuQHG9qpJ8/vSiJTzZ0snf/HgnWw6e4HNXrqSiyO90abOaBbrJqHjLSlXZtL+bR5qPUeT38qE3LGRJTYnD1ZlMKvR7+dYHzuW/HtvH1x9r4amWbj5/1UouWzXfLhibJgt0k3GBkRD3b2tlz/F+zpxfyrte10CxbReXk7we4ZaLl3HJinl8+ocv8bHvPc/ZCyr4y7cu5w2L51qwT5F9ikxG7Tvezw+3tTI8GuYP19Ry3iL70Jrosrs/vfl8frStla/9Zh/vufNZltaU8I6z63nzmTUsqi4m3/f7AfKIKgMjIfqGQvQNjxIYCeHzCEV+Ly0dARZXF+fkz5UNipqMGB4N86Vf7eWOJw5QU5rPNec2Ulte6HRZJguNhiO8cLiH1pODbH3lJAAegfllBQAEwxFODASJTBBdVSV+Xr+4iitW1/KmM6tf9ctgtrNBUeOoTfu7+Jsf7+Rg1wAbmip52+pa/D6bMWvGl+f1sGFhJV+5dh2tJwfZ9spJ9ncO0HpiEI9HyPN6aO8Zoqwwj/ICH6UFeZQW+AiFlcHRME1zi9h8oJunWrp46MV2ygp8vG11LVetq2fjwkpXr/poLXSTNi0d/Xz51/v4+UtHWVBZxBfeuZrDJwadLsu4XHzaYigc4en93fz0hTYeaT7GYDBMeWEeaxvKWdtYwfyygld1y8yW6Y7WQjcZE4kozxzo5r4tR/j5S+0U5nn5szcv4WMXLaHQ751w/rAxqeTzerhwWTUXLqvmn4IhfrXrON/47X6eauniiX1dVBTlsbSmlKU1JSyudscMKwt0c0o4oniEKQ0mhSNKe88QL7b28Mz+bh7f00lbzxClBT4+/AeL+OgFi+wiIeO4Ir+Pq9bVMzASZmAkxM72XvYdD/BSaw9bDp3AI/DwzqP8wdIqLlhWzdqGill5PYQFeg5IbBWrKh39I+zrCHC0Z4iIKm09Q/QPhxgJRQDI93koyPOiqvi8HnwewecRvB7B6/EgAsX5XvqGQhztHWI0HO22K8n3sWFhJX912XIuXTmfArts32Sh4nwfGxfOZePCuYQjypETg+zrCLCvo5+v/nofX/n1PgryPCypLmHZvFLOrC3jxgsWOV12UizQc8DwaJj9nQH2Hg+w73g/PUPRPR7LC/NYXV/OJSvKKSvwUeT3EVZlZDTM8GiY5vY+QhElFI4QiijhiBKKKKpKTWkBi6p8XLGmlqa5RSyfX8aqujJ8XhvsNLOH1yM0VRXTVFXMJSvmMTgSoqUzEA344zXRcr8AAAkMSURBVP3sbO9DXmjj17uO89aV87h05fysXiwuqUFREbkM+CrgBe5U1S+OeVxij78NGAQ+qKrPT3TObBkUVVXuefbwabsZMj1Qkoo1KgaDIV5q7WXbKyd5Ym8nWw6dIKLRlvfi6hKWzytl6bwSu8zamAmoKsf6hmlu76O9Z4iXj0VXh1w+r5Q/WFrFG5dWcfaCOZRneI3+iQZFJw10EfECe4FLgFZgC3Cdqu5KOOZtwCeIBvpG4KuqunGi88400FWjLcbRsDIaiTAaijAaVoKhCL1Do5wcDEa/BoKcHBylKzAS+wrSHRihZ2iUwWCYYKybwSPg93ko9vsozvdR5PdS7PexfuEcqorzqSz2U1niZ26xnyK/j0K/lwKfh0K/F39Cq1SB+P9SRVGN7rozEAxF+++CIQLDoVO1nRgcjf03evtg1wADIyEGg2HCEcXnjXZ1+DweygvzKPJ7KSnwUZIf/Sr0e4lElGA4QlcgSNvJIY72Dp2ao3tWbRk1pfksnVfCGZXFs7Jf0BinXb9xAa90D/DL5uM8vreDLYdOnsqORdXFnDm/lEVVJSyoLKKiKI+KIj9zYv8t8nvxeaOf4VR8/mY6y2UD0KKqB2Inuw+4CtiVcMxVwHc1+tths4hUiEitqh6dYe2v8YsdR/nk97czGo6Q7IxLEZhT5KeqxE9VST6rGyqYU5RHod9Lvs/LzrZewhFlJBRhMBhicCRM79Ao7T1D7GjrJRiOpPptvKq2isI85hT7qSzyU1Hkp76ikCK/F49HCIej3RyhiLKgsojBYIjASIj+4RDHeocZDIbxeoQ8rzCnyM+5TXNYMLeBdY3lnN04hznFfptZYkwKnDG3mI9csIiPXLCIoWCY5w+fZPuRHrYf6WH30X4ebT5OeKKrnYh+3vM8Hm68YBF/eenylNeYTKDXA0cSbrcSbYVPdkw98KpAF5EbgRtjNwMismdK1SavCuhKvONQml4oi7zmPecAe8+5wfH3/J4Un+/TX4BPn/7hyd7vGad7IJlAH+9vhLG/hpI5BlW9A7gjidecERHZero/SdzK3nNusPfsfjN5v8lMSWgFGhNuNwDt0zjGGGNMGiUT6FuApSKyUET8wLXAg2OOeRB4v0SdB/Smo//cGGPM6U3a5aKqIRG5GXiU6LTFu1S1WURuij1+O/Aw0RkuLUSnLX4ofSUnJe3dOlnI3nNusPfsftN+v44tzmWMMSa17LI+Y4xxCQt0Y4xxCVcFuog0ishvRWS3iDSLyCedrindRKRARJ4TkRdj7/kfna4pE0TEKyIviMjPnK4lE0TkkIjsEJHtIuL8mhkZELtA8Uci8nLsM/16p2tKJxFZHvv3jX/1icgtUzqHm/rQRaQWqFXV50WkFNgGvCNxmQK3ia2jU6yqARHJA54CPqmqmx0uLa1E5M+B9UCZqr7d6XrSTUQOAetVNWcuKhKR7wBPquqdsRl2Rara43RdmRBbcqUN2KiqryT7PFe10FX1aHxRMFXtB3YTvWLVtTQqELuZF/tyz2/pcYhIA3AFcKfTtZj0EJEy4ALgWwCqGsyVMI95C7B/KmEOLgv0RCLSBJwNPOtsJekX637YDnQAv1JVt7/nrwB/BaRvkZ3so8AvRWRbbAkNt1sEdALfjnWt3SkixU4XlUHXAvdO9UmuDHQRKQHuB25R1T6n60k3VQ2r6jqiV+huEJFVTteULiLydqBDVbc5XUuGna+qrwMuBz4uIhc4XVCa+YDXAf+tqmcDA8CtzpaUGbHupSuBH071ua4L9Fg/8v3A91T1AafryaTYn6SPA5c5XEo6nQ9cGetTvg94s4j8n7MlpZ+qtsf+2wH8mOgqqG7WCrQm/LX5I6IBnwsuB55X1eNTfaKrAj02QPgtYLeqfsnpejJBRKpFpCL2fSFwMfCys1Wlj6p+VlUbVLWJ6J+lj6nqex0uK61EpDg2yE+s2+GtwE5nq0ovVT0GHBGR+Bqzb+HVS3a72XVMo7sF3LcF3fnA+4AdsT5lgL9W1YcdrCndaoHvxEbFPcAPVDUnpvLlkHnAj2O7avmAe1T1EWdLyohPAN+LdUEcwPklRdJORIqIbib00Wk9303TFo0xJpe5qsvFGGNymQW6Mca4hAW6Mca4hAW6Mca4hAW6Mca4hAW6cYSIBCY/asav8SkRGRaR8nS/1iR1/PWY22l/7yY3WaAbN7uO6J6473S4jr+e/BBjZs4C3WQNEVknIptF5CUR+bGIzInd/xER2RJb8/3+2MUXiMjdIvI1EdkkIgdE5OqEcy0GSoC/JRrs8fs/KCI/EZGHROSgiNwsIn8eWwBqs4hUTlLL4yKyPvZ9VWwJgvh5HxCRR0Rkn4j8W+z+LwKFsfWtvzeN935u7L5nROTfRcTVV4iambFAN9nku8BnVHUNsAP4h9j9D6jquaq6luiSyDckPKcWeCPwduCLCffHL59+ElguIjUJj60Crie6Hso/A4OxBaCeAd4/SS0TWQdcA6wGrhGRRlW9FRhS1XWq+p5pvPdvAzep6uuBcBI1mBxmgW6yQqyfu0JVfxe76ztE18MGWCUiT4rIDuA9wMqEp/5EVSOxTUzmJdx/LXCfqkaAB4B3Jzz2W1XtV9VOoBd4KHb/DqBpklom8htV7VXVYaLrjpyRxHNO+95ja/SUquqm2P33JHM+k7vctpaLcae7ie489aKIfBC4KOGxkYTvBUBE1gBLgV/F1j+JrwVy2zjPiSTcjjD5ZyLE7xtCBWMeSzxvOIlzTUZm+HyTY6yFbrKCqvYCJ0XkD2J3vQ+It1hLgaOxpZEn6raIuw74nKo2xb7qgHoRSarFPEkth4BzYt9fTXJGY7VP6fVU9STQLyLnxe6/NsnXMznKWujGKUUi0ppw+0vAB4DbY4Oeiavr/R3RnadeIdotUjrJua8luqZ0oh/H7k92jenT1fIfwA9E5H3AY0me6w7gJRF5PtaPPpX3fgPwPyIyQHSt+94kX9PkIFtt0ZgsJiIl8T1jReRWopugf9LhskyWsha6MdntChH5LNHP6ivAB50tx2Qza6EbY4xL2KCoMca4hAW6Mca4hAW6Mca4hAW6Mca4hAW6Mca4xP8HYFIFei1EVeMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['LoanAmountLog'] = np.log(train['LoanAmount'])\n",
    "sns.distplot(train['LoanAmountLog'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ajha48\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ef55c10520>"
      ]
     },
     "execution_count": 28,
     "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": [
    "train['Loan_Amount_Term_Log'] = np.log(train['Loan_Amount_Term'])\n",
    "sns.distplot(train['Loan_Amount_Term_Log'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Coorelation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matrix=train.corr()\n",
    "f,ax = plt.subplots(figsize=(15,10))\n",
    "sns.heatmap(matrix,annot = True, cmap=\"BuPu\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>ApplicantIncomeLog</th>\n",
       "      <th>Total_Income_Log</th>\n",
       "      <th>LoanAmountLog</th>\n",
       "      <th>Loan_Amount_Term_Log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>8.674026</td>\n",
       "      <td>8.674026</td>\n",
       "      <td>4.986426</td>\n",
       "      <td>5.886104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "      <td>8.430109</td>\n",
       "      <td>8.714568</td>\n",
       "      <td>4.852030</td>\n",
       "      <td>5.886104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>8.006368</td>\n",
       "      <td>8.006368</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>5.886104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>7.856707</td>\n",
       "      <td>8.505323</td>\n",
       "      <td>4.787492</td>\n",
       "      <td>5.886104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>5.886104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender Married Dependents     Education Self_Employed  Credit_History  \\\n",
       "0   Male      No          0      Graduate            No             1.0   \n",
       "1   Male     Yes          1      Graduate            No             1.0   \n",
       "2   Male     Yes          0      Graduate           Yes             1.0   \n",
       "3   Male     Yes          0  Not Graduate            No             1.0   \n",
       "4   Male      No          0      Graduate            No             1.0   \n",
       "\n",
       "  Property_Area Loan_Status  ApplicantIncomeLog  Total_Income_Log  \\\n",
       "0         Urban           Y            8.674026          8.674026   \n",
       "1         Rural           N            8.430109          8.714568   \n",
       "2         Urban           Y            8.006368          8.006368   \n",
       "3         Urban           Y            7.856707          8.505323   \n",
       "4         Urban           Y            8.699515          8.699515   \n",
       "\n",
       "   LoanAmountLog  Loan_Amount_Term_Log  \n",
       "0       4.986426              5.886104  \n",
       "1       4.852030              5.886104  \n",
       "2       4.189655              5.886104  \n",
       "3       4.787492              5.886104  \n",
       "4       4.948760              5.886104  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop unnecessary columns\n",
    "cols = ['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term', 'Total_Income', 'Loan_ID', 'CoapplicantIncomeLog']\n",
    "train = train.drop(columns=cols, axis = 1)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Label Incoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "cols = ['Gender', 'Married', 'Education', 'Self_Employed', 'Property_Area', 'Loan_Status', 'Dependents']\n",
    "#cols =['Dependents']\n",
    "le = LabelEncoder()\n",
    "for col in cols:\n",
    "    train[col] = le.fit_transform(train[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>ApplicantIncomeLog</th>\n",
       "      <th>Total_Income_Log</th>\n",
       "      <th>LoanAmountLog</th>\n",
       "      <th>Loan_Amount_Term_Log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8.674026</td>\n",
       "      <td>8.674026</td>\n",
       "      <td>4.986426</td>\n",
       "      <td>5.886104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.430109</td>\n",
       "      <td>8.714568</td>\n",
       "      <td>4.852030</td>\n",
       "      <td>5.886104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8.006368</td>\n",
       "      <td>8.006368</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>5.886104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7.856707</td>\n",
       "      <td>8.505323</td>\n",
       "      <td>4.787492</td>\n",
       "      <td>5.886104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>5.886104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Married  Dependents  Education  Self_Employed  Credit_History  \\\n",
       "0       1        0           0          0              0             1.0   \n",
       "1       1        1           1          0              0             1.0   \n",
       "2       1        1           0          0              1             1.0   \n",
       "3       1        1           0          1              0             1.0   \n",
       "4       1        0           0          0              0             1.0   \n",
       "\n",
       "   Property_Area  Loan_Status  ApplicantIncomeLog  Total_Income_Log  \\\n",
       "0              2            1            8.674026          8.674026   \n",
       "1              0            0            8.430109          8.714568   \n",
       "2              2            1            8.006368          8.006368   \n",
       "3              2            1            7.856707          8.505323   \n",
       "4              2            1            8.699515          8.699515   \n",
       "\n",
       "   LoanAmountLog  Loan_Amount_Term_Log  \n",
       "0       4.986426              5.886104  \n",
       "1       4.852030              5.886104  \n",
       "2       4.189655              5.886104  \n",
       "3       4.787492              5.886104  \n",
       "4       4.948760              5.886104  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train-Test-Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Specify input and output attributes\n",
    "x = train.drop(columns=['Loan_Status'], axis = 1)\n",
    "y = train['Loan_Status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a function for reuse it\n",
    "from sklearn.model_selection import cross_val_score\n",
    "def classify(model, x, y):\n",
    "    x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=42)\n",
    "    model.fit(x_train, y_train)\n",
    "    print(\"Accuracy is\", model.score(x_test,y_test)*100)\n",
    "    #cross validation-it is used for better validation of model\n",
    "    score = cross_val_score(model, x, y, cv = 5)\n",
    "    print(\"Cross Validation iss\", np.mean(score)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 78.86178861788618\n",
      "Cross Validation iss 80.9462881514061\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()\n",
    "classify(model,x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 66.66666666666666\n",
      "Cross Validation iss 70.52645608423298\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model = DecisionTreeClassifier()\n",
    "classify(model,x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 77.23577235772358\n",
      "Cross Validation iss 79.15900306544049\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier,ExtraTreesClassifier\n",
    "model = RandomForestClassifier()\n",
    "classify(model,x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 74.79674796747967\n",
      "Cross Validation iss 77.52898840463814\n"
     ]
    }
   ],
   "source": [
    "model = ExtraTreesClassifier()\n",
    "classify(model,x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter tuining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 78.86178861788618\n",
      "Cross Validation iss 80.13194722111156\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier(n_estimators = 155, min_samples_split=20,max_depth=7,max_features=1)\n",
    "classify(model,x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Confusion Matrix\n",
    "\n",
    "A confusion matrix is a summary of prediction results on a classification problem. The number of correct and incorrect prediction are summarized with count values and brokes down by each class. It gives us insight not only the errors being made by a classifier but more importantly the types of errors that are being made.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier()\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[18, 25],\n",
       "       [ 4, 76]], dtype=int64)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred = model.predict(x_test)\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ef57915100>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm, annot =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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