Loan Predict Problem.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#    Loan Prediction solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, I have used different classifications algorithms to demonstrate that it's very important to understand which algorithm/model is best suitable and for which problem.\n",
    "- **LogisticRegression**\n",
    "- **Naive Bayes**\n",
    "- **KNeighbors**\n",
    "- **Random Forest**\n",
    "- **SVM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import svm\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestRegressor \n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1 - Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading the dataset\n",
    "\n",
    "train=pd.read_csv(r\"B:/Projects/Loan prediction problem/train.csv\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "**Displaying first 5 rows to get an overview of data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "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",
       "    </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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 13)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              614\n",
       "Gender               601\n",
       "Married              611\n",
       "Dependents           599\n",
       "Education            614\n",
       "Self_Employed        582\n",
       "ApplicantIncome      614\n",
       "CoapplicantIncome    614\n",
       "LoanAmount           592\n",
       "Loan_Amount_Term     600\n",
       "Credit_History       564\n",
       "Property_Area        614\n",
       "Loan_Status          614\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               object\n",
       "Gender                object\n",
       "Married               object\n",
       "Dependents            object\n",
       "Education             object\n",
       "Self_Employed         object\n",
       "ApplicantIncome        int64\n",
       "CoapplicantIncome    float64\n",
       "LoanAmount           float64\n",
       "Loan_Amount_Term     float64\n",
       "Credit_History       float64\n",
       "Property_Area         object\n",
       "Loan_Status           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2 - Data Analysis and Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Finding missing values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "149"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 13)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Drop unnecessary rows/data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train = train.drop('Loan_ID', axis=1)\n",
    "train = train.dropna(how='any', subset=['LoanAmount', 'Married', 'Self_Employed', 'Loan_Amount_Term', 'Credit_History'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(502, 12)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Gender'] = train['Gender'].fillna(value='Can\\'t say')\n",
    "train['Dependents'] = train['Dependents'].fillna(value=0)\n",
    "train['Loan_Amount_Term'] = train['Loan_Amount_Term'].fillna(value=train['Loan_Amount_Term'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset has been cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
       "       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term', 'Credit_History', 'Property_Area', 'Loan_Status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Now we need to `encode` our values from categorical to numerical data so that it would be easier for data modelling.**\n",
    "### - We will use `LabelEncoder` for that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender :  ['Male' 'Female' \"Can't say\"]\n",
      "Gender :  [2 1 0]\n",
      "\n",
      "Married :  ['Yes' 'No']\n",
      "Married :  [1 0]\n",
      "\n",
      "Education :  ['Graduate' 'Not Graduate']\n",
      "Education :  [0 1]\n",
      "\n",
      "Self_Employed :  ['No' 'Yes']\n",
      "Self_Employed :  [0 1]\n",
      "\n",
      "Property_Area :  ['Rural' 'Urban' 'Semiurban']\n",
      "Property_Area :  [0 2 1]\n",
      "\n",
      "Loan_Status :  ['N' 'Y']\n",
      "Loan_Status :  [0 1]\n"
     ]
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "\n",
    "print('Gender : ',train['Gender'].unique())\n",
    "train['Gender'] = le.fit_transform(train['Gender'])\n",
    "print('Gender : ',train['Gender'].unique())\n",
    "print('')\n",
    "print('Married : ',train['Married'].unique())\n",
    "train['Married'] = le.fit_transform(train['Married'])\n",
    "print('Married : ',train['Married'].unique())\n",
    "print('')\n",
    "print('Education : ',train['Education'].unique())\n",
    "train['Education'] = le.fit_transform(train['Education'])\n",
    "print('Education : ',train['Education'].unique())\n",
    "print('')\n",
    "print('Self_Employed : ',train['Self_Employed'].unique())\n",
    "train['Self_Employed'] = le.fit_transform(train['Self_Employed'])\n",
    "print('Self_Employed : ',train['Self_Employed'].unique())\n",
    "print('')\n",
    "print('Property_Area : ',train['Property_Area'].unique())\n",
    "train['Property_Area'] = le.fit_transform(train['Property_Area'])\n",
    "print('Property_Area : ',train['Property_Area'].unique())\n",
    "print('')\n",
    "print('Loan_Status : ',train['Loan_Status'].unique())\n",
    "train['Loan_Status'] = le.fit_transform(train['Loan_Status'])\n",
    "print('Loan_Status : ',train['Loan_Status'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                 int32\n",
       "Married                int32\n",
       "Dependents            object\n",
       "Education              int32\n",
       "Self_Employed          int32\n",
       "ApplicantIncome        int64\n",
       "CoapplicantIncome    float64\n",
       "LoanAmount           float64\n",
       "Loan_Amount_Term     float64\n",
       "Credit_History       float64\n",
       "Property_Area          int32\n",
       "Loan_Status            int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting all object types to integer types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Gender'] = train['Gender'].astype('int')\n",
    "train['Married'] = train['Married'].astype('int')\n",
    "train['Education'] = train['Education'].astype('int')\n",
    "train['Self_Employed'] = train['Self_Employed'].astype('int')\n",
    "train['Property_Area'] = train['Property_Area'].astype('int')\n",
    "train['Property_Area'] = train['Property_Area'].astype('int')\n",
    "train['Loan_Status'] = train['Loan_Status'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Dependents'] = train['Dependents'].replace('3+',3)\n",
    "train['Dependents'] = train['Dependents'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['ApplicantIncome'] = train['ApplicantIncome']/1000\n",
    "train['CoapplicantIncome'] = train['CoapplicantIncome']/1000\n",
    "train['LoanAmount'] = train['LoanAmount']/1000\n",
    "train['Loan_Amount_Term'] = train['Loan_Amount_Term']/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                 int32\n",
       "Married                int32\n",
       "Dependents             int32\n",
       "Education              int32\n",
       "Self_Employed          int32\n",
       "ApplicantIncome      float64\n",
       "CoapplicantIncome    float64\n",
       "LoanAmount           float64\n",
       "Loan_Amount_Term     float64\n",
       "Credit_History       float64\n",
       "Property_Area          int32\n",
       "Loan_Status            int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3 - Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230fa054668>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4u6p6crJdnXmcupE0Fkn+Bngzi3/24Peq6ukJt3TG8o5e0lgkeQ54FjjKL/+Z8rD4y9hfm0hjZyCDXpIa5wtTktQ4g16SGmfQS1LjDHpJatz/AzYQiMLUMKdRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Loan_Status'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Gender\", y=\"LoanAmount\", data=train, color='aqua');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Male takes more loan than female or others**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Married\", y=\"LoanAmount\", data=train, color='yellow');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Education\", y=\"LoanAmount\", data=train, color='green');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Self_Employed\", y=\"LoanAmount\", data=train, color='orange');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = train.plot.scatter('ApplicantIncome','LoanAmount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230fa260748>"
      ]
     },
     "execution_count": 24,
     "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.regplot('ApplicantIncome','LoanAmount', data=train, color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**It is clearly visible from above regplot that:**\n",
    "- People with low income tend to take loan more\n",
    "- Also, people with high income take hogh amound of loan for big investments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1) \n",
    "plt.subplot(221) \n",
    "train['Gender'].value_counts(normalize=True).plot.bar(figsize=(20,10), title= 'Gender') \n",
    "plt.subplot(222) \n",
    "train['Married'].value_counts(normalize=True).plot.bar(title= 'Married') \n",
    "plt.subplot(223) \n",
    "train['Self_Employed'].value_counts(normalize=True).plot.bar(title= 'Self_Employed') \n",
    "plt.subplot(224) \n",
    "train['Credit_History'].value_counts(normalize=True).plot.bar(title= 'Credit_History') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**It can be inferred from the above bar plots that:**\n",
    "\n",
    "- 80% applicants in the dataset are male.<br></br>\n",
    "- Around 65% of the applicants in the dataset are married.<br></br>\n",
    "- Around 15% applicants in the dataset are self employed.<br></br>\n",
    "- Around 85% applicants have repaid their debts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230f9d90b70>"
      ]
     },
     "execution_count": 26,
     "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['ApplicantIncome'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**It can be inferred that most of the data in the distribution of applicant income is towards left which means it is not normally distributed**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matrix = train.corr() \n",
    "f, ax = plt.subplots(figsize=(9, 6)) \n",
    "sns.heatmap(matrix, vmax=.8, square=True, cmap=\"BuPu\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "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>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>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5417</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       "1       2        1           1          0              0             4583   \n",
       "2       2        1           0          0              1             3000   \n",
       "3       2        1           0          1              0             2583   \n",
       "4       2        0           0          0              0             6000   \n",
       "5       2        1           2          0              1             5417   \n",
       "\n",
       "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "1             1508.0       128.0             360.0             1.0   \n",
       "2                0.0        66.0             360.0             1.0   \n",
       "3             2358.0       120.0             360.0             1.0   \n",
       "4                0.0       141.0             360.0             1.0   \n",
       "5             4196.0       267.0             360.0             1.0   \n",
       "\n",
       "   Property_Area  Loan_Status  \n",
       "1              0            0  \n",
       "2              2            1  \n",
       "3              2            1  \n",
       "4              2            1  \n",
       "5              2            1  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4 - Data Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train-Test-Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train['Loan_Status']\n",
    "x = train.drop(['Loan_Status'],axis=1)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=1/3, random_state=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. ` Logistic Regression`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Applying R squared method to evaluate the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75.59523809523809"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(x_test, y_test)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.13173652694611"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(x_train, y_train)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. ` Naive Bayes`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb = GaussianNB()\n",
    "nb.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8323353293413174"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. `KNN`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=10, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.625"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. `Random Forest`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\n",
       "           oob_score=False, random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create regressor object \n",
    "regressor = RandomForestRegressor(n_estimators = 100, random_state = 0) \n",
    "  \n",
    "# fit the regressor with x and y data \n",
    "regressor.fit(x_train, y_train)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_test = regressor.predict(x_test)\n",
    "y_pred_train = regressor.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18117587301587323"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(regressor.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.897424518557423"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(regressor.score(x_train,y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. `Support Vector Machines (SVM)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = svm.SVC(kernel ='linear', C = 1).fit(x_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='linear', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7559523809523809"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8383233532934131"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All models together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shray\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\Shray\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "models = {'LR':LogisticRegression(), 'NB':GaussianNB(), 'KNN':KNeighborsClassifier(n_neighbors=10), 'RF':RandomForestRegressor(n_estimators = 100),'SVM':svm.SVC()}\n",
    "accuracy_list = {}\n",
    "for key,value in models.items():\n",
    "    model = value\n",
    "    model.fit(x_train, y_train)\n",
    "    accuracy_list.update({key:model.score(x_test, y_test)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LR': 0.7559523809523809,\n",
       " 'NB': 0.75,\n",
       " 'KNN': 0.625,\n",
       " 'RF': 0.16983619047619047,\n",
       " 'SVM': 0.7142857142857143}"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model is : `LR` \n"
     ]
    }
   ],
   "source": [
    "print('Best model is : `{}` '.format(max(accuracy_list, key=accuracy_list.get)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional knowledge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the domain knowledge, we can come up with new features that might affect the target variable. We will create the following three new features:\n",
    "\n",
    "- **Total Income** - As discussed during bivariate analysis we will combine the Applicant Income and Coapplicant Income. If the total income is high, chances of loan approval might also be high.\n",
    "- **EMI** - EMI is the monthly amount to be paid by the applicant to repay the loan. Idea behind making this variable is that people who have high EMI’s might find it difficult to pay back the loan. We can calculate the EMI by taking the ratio of loan amount with respect to loan amount term.\n",
    "- **Balance Income** - This is the income left after the EMI has been paid. Idea behind creating this variable is that if this value is high, the chances are high that a person will repay the loan and hence increasing the chances of loan approval.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Total_Income']=(train['ApplicantIncome']+train['CoapplicantIncome'] )/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['EMI']=train['LoanAmount']/train['Loan_Amount_Term'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Balance Income']=(train['Total_Income']-(train['EMI']*1000))/1000\n",
    "# Multiply with 1000 to make the units equal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now drop the variables which we used to create these new features. Reason for doing this is, the correlation between those old features and these new features will be very high and logistic regression assumes that the variables are not highly correlated. We also wants to remove the noise from the dataset, so removing correlated features will help in reducing the noise too.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=train.drop(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term'], axis=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "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>Total_Income</th>\n",
       "      <th>EMI</th>\n",
       "      <th>Balance Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</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>0.006091</td>\n",
       "      <td>0.355556</td>\n",
       "      <td>-0.355549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</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>0.003000</td>\n",
       "      <td>0.183333</td>\n",
       "      <td>-0.183330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</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>0.004941</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>-0.333328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</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>0.006000</td>\n",
       "      <td>0.391667</td>\n",
       "      <td>-0.391661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.009613</td>\n",
       "      <td>0.741667</td>\n",
       "      <td>-0.741657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Married  Dependents  Education  Self_Employed  Credit_History  \\\n",
       "1       2        1           1          0              0             1.0   \n",
       "2       2        1           0          0              1             1.0   \n",
       "3       2        1           0          1              0             1.0   \n",
       "4       2        0           0          0              0             1.0   \n",
       "5       2        1           2          0              1             1.0   \n",
       "\n",
       "   Property_Area  Loan_Status  Total_Income       EMI  Balance Income  \n",
       "1              0            0      0.006091  0.355556       -0.355549  \n",
       "2              2            1      0.003000  0.183333       -0.183330  \n",
       "3              2            1      0.004941  0.333333       -0.333328  \n",
       "4              2            1      0.006000  0.391667       -0.391661  \n",
       "5              2            1      0.009613  0.741667       -0.741657  "
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train['Loan_Status']\n",
    "x = train.drop(['Loan_Status'],axis=1)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=1/3, random_state=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shray\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7559523809523809"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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   "metadata": {},
   "outputs": [],
   "source": []
  }
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