data_analyze and feature_extraction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np                     # For mathematical calculations \n",
    "import seaborn as sns                  # For data visualization \n",
    "import matplotlib.pyplot as plt        # For plotting graphs \n",
    "%matplotlib inline \n",
    "import warnings                        # To ignore any warnings warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_csv(\"train_ctrUa4K.csv\") \n",
    "test=pd.read_csv(\"test_lAUu6dG.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Firstly, we will check the features present in our data and then we will look at their data types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We have similar features in the test dataset as the train dataset except the Loan_Status. We will predict the Loan_Status using the model built using the train data.\n",
    "\n",
    "#### Given below is the description for each variable.\n",
    "\n",
    "| Variable | Description |  \\\n",
    "Loan_ID\t    Unique Loan ID \\\n",
    "Gender\t    MALE/Female \\\n",
    "Married\t    Applicant married (Y/N) \\\n",
    "Dependents\tNumber of dependents \\\n",
    "Education\tApplicant Education (Graduate/Under Graduate) \\\n",
    "Self_Employed \t    Self employed (Y/N) \\\n",
    "ApplicantIncome\t    Applicant income \\\n",
    "CoapplicantIncome\tCoapplicant income \\\n",
    "LoanAmount\tLoan    amount in thousands \\\n",
    "Loan_Amount_Term\tTerm of loan in months \\\n",
    "Credit_History\t    Credit history meets guidelines \\\n",
    "Property_Area\t    Urban/ Semi Urban/ Rural \\\n",
    "Loan_Status\tLoan    approved (Y/N) \n"
   ]
  },
  {
   "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": [
    "### We can see there are three format of data types:\n",
    "1. object: Object format means variables are categorical. Categorical variables in our dataset are: Loan_ID, Gender, Married, Dependents, Education, Self_Employed, Property_Area, Loan_Status. \n",
    "\n",
    "2. int64: It represents the integer variables. ApplicantIncome is of this format.\n",
    "\n",
    "2. float64: It represents the variable which have some decimal values involved. They are also numerical variables. Numerical variables in our dataset are: CoapplicantIncome, LoanAmount, Loan_Amount_Term, and Credit_History"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let’s look at the shape of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((614, 13), (367, 12))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We have 614 rows and 13 columns in the train dataset and 367 rows and 12 columns in test dataset. Where 614 rows are total numbers of observations , 12 columns are number of features and last is labels(classifier)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Next Section\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In this section, we will do univariate analysis. It is the simplest form of analyzing data where we examine each variable individually. For categorical features we can use frequency table or bar plots which will calculate the number of each category in a particular variable. For numerical features, probability density plots can be used to look at the distribution of the variable.\n",
    "\n",
    "# Target Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    422\n",
       "N    192\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will first look at the target variable, i.e., Loan_Status. \n",
    "# As it is a categorical variable, let us look at its frequency table, percentage distribution and bar plot.\n",
    "\n",
    "train['Loan_Status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    0.687296\n",
       "N    0.312704\n",
       "Name: Loan_Status, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalize can be set to True to print proportions instead of number \n",
    "train['Loan_Status'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f177a29a3d0>"
      ]
     },
     "execution_count": 9,
     "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_Status'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Independent Variable (Categorical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
    "1. 80% applicants in the dataset are male. \n",
    "2. Around 65% of the applicants in the dataset are married. \n",
    "3. Around 15% applicants in the dataset are self employed. \n",
    "4. Around 85% applicants have repaid their debts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Independent Variable (Ordinal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(131)\n",
    "train['Dependents'].value_counts(normalize=True).plot.bar(figsize=(24,6), title= 'Dependents') \n",
    "plt.subplot(132) \n",
    "train['Education'].value_counts(normalize=True).plot.bar(title= 'Education') \n",
    "plt.subplot(133)\n",
    "train['Property_Area'].value_counts(normalize=True).plot.bar(title= 'Property_Area') \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Following inferences can be made from the above bar plots:\n",
    "\n",
    "1. Most of the applicants don’t have any dependents.\n",
    "2. Around 80% of the applicants are Graduate. \n",
    "3. Most of the applicants are from Semiurban area."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Independent Variable (Numerical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(121) \n",
    "sns.distplot(train['ApplicantIncome']); \n",
    "plt.subplot(122)\n",
    "train['ApplicantIncome'].plot.box(figsize=(16,5)) \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1) \n",
    "plt.subplot(121) \n",
    "sns.distplot(train['CoapplicantIncome']); \n",
    "plt.subplot(122) \n",
    "train['CoapplicantIncome'].plot.box(figsize=(16,5)) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let’s look at the distribution of LoanAmount variable.\n",
    "\n",
    "plt.figure(1) \n",
    "plt.subplot(121)\n",
    "df=train.dropna()\n",
    "sns.distplot(train['LoanAmount']); \n",
    "plt.subplot(122)\n",
    "train['LoanAmount'].plot.box(figsize=(16,5)) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Following inferences can be made from the above dist  plots\n",
    "1. Most of the data in the distribution of is towards left which means it is not normally distributed. We will try to make it normal in later sections as algorithms works better if the data is normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### After looking at every variable individually in univariate analysis, we will now explore them again with respect to the target variable.\n",
    "\n",
    "### Categorical Independent Variable vs Target Variable\n",
    "First of all we will find the relation between target variable and categorical independent variables. Let us look at the stacked bar plot now which will give us the proportion of approved and unapproved loans.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Loan_Status</th>\n",
       "      <th>N</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>37</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>150</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Loan_Status    N    Y\n",
       "Gender               \n",
       "Female        37   75\n",
       "Male         150  339"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Gender=pd.crosstab(train['Gender'],train['Loan_Status']) \n",
    "Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f177919f9d0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Gender.div(Gender.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### It can be inferred that the proportion of male and female applicants is more or less same for both approved and unapproved loans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Cl2lmxVDnHkJE7JB0DTAfaAlMj4jlkoZn26cAtwJdgB9IAtgREZWNV7aZNYa8nrocEfOAeTXWTcl5/WXgy4UtzcyKzVcqmlniQDCzxIFgZokDwcwSB4KZJQ4EM0scCGaWOBDMLHEgmFniQDCzxIFgZokDwcwSB4KZJQ4EM0scCGaWOBDMLHEgmFniQDCzxIFgZokDwcwSB4KZJQ4EM0scCGaWOBDMLHEgmFniQDCzxIFgZokDwcwSB4KZJQ4EM0scCGaWOBDMLHEgmFmSVyBIGihppaRVkkbW0i5JE7PtyyT1K3ypZtbY6gwESS2BycAgoDdwqaTeNboNAnpmv6qAHxa4TjMrgnz2EE4HVkXE6oh4D5gFXFSjz0XATyJjEdBZ0uEFrtXMGlmrPPp0A9bkLFcDZ+TRpxvwem4nSVVk9iAANktaWa9q9y+HARuKtTGNL9aWmo0D+fM7ak8N+QSCalkXDehDREwFpuaxzf2epCURUVnqOqxhmuvnl8+UoRo4Mme5HFjbgD5m1sTlEwiLgZ6SKiS1AYYAc2r0mQMMy55t6A9siojXaw5kZk1bnVOGiNgh6RpgPtASmB4RyyUNz7ZPAeYBFwCrgK3AFxuv5P1Gs5gaHcCa5eeniL+b6ptZM+UrFc0scSCYWeJAMLPEgWAGSDpGUtvs6wGSrpXUudR1FZsDoYAklUt6SNJ6SW9IekBSeanrsrw8ALwv6VhgGlAB/LS0JRWfA6Gw7iZzTcbhZC7dfiS7zpq+nRGxA7gY+F5E3EDmc2xWHAiFVRYRd0fEjuzXDKCs1EVZXrZLuhT4AjA3u651CespCQdCYW2QdLmkltmvy4GNpS7K8vJF4Ezg2xHxB0kVwL0lrqnofGFSAUnqDkwi8w8rgKeB6yLijyUtzPIiqT3QPSIO5Ltw98qBYAZIuhC4A2gTERWS+gLfiohPl7i0osrn9merg6Rb99IcETGmaMVYQ40m8zCgBQARsTQ7bWhWHAiFsaWWdQcBVwJdAAdC07cjIjZJuz3ao9ntPjsQCiAiJux6LakDcB2Zg1SzgAl7ep+VnqR5wNXAS5IuA1pK6glcS+YYULPiswwFIulQSf8BLCMTtP0i4qaIWFfi0mzvZpC5tf814CTgXTIXJG0iE+zNig8qFoCk24HPkrmHfnJEbC5xSVYPkg4CbgUGAvfwwVQhIuLOkhVWAp4yFMaNZP7P8u/ALTnzUJH5R9WxVIVZXraTOQ7UFjiYZnjsYBcHQgFEhKde+ylJA4E7yVxy3i8itpa4pJLylMGaNUlPAcMjYnmpa2kKHAhmlnhX18wSB4KZJQ4EM0scCM2MpJB0T85yq+wTnubu7X15jHuEpJ/V8z0zJH1uX7ZrheVAaH62ACdlb/UFOB/4c30GkNSq5nJErI0I/3Lv5xwIzdNjwD9mX18K3LerQdLpkp6W9EL2v8dn118h6b8lPQL8opblHpJeyvZtKel2SYslLZP0L9n1kjRJ0suSHgU+XMxv2urmQGieZgFDJLUD+gDP5rS9AvxDRHyEzOW8Y3PazgS+EBHn7GF5lyvJ/H3P04DTgKuytxJfDBwPnAxcBXy0sN+W7StfqdgMRcQyST3I7B3Mq9HcCfhx9o6/YPfnCj4REW/uZXmXTwB9co4PdAJ6Av8A3BcR7wNrJf1qn78ZKygHQvM1h8wTggaQeWbDLmOA/4mIi7OhsSCnreZzH2p7DgRk7uH4akTM322ldAHN+D6B/YGnDM3XdDKPCHuxxvpOfHCQ8YoGjj0f+Iqk1gCSjsveUbiQzFSlpaTDgbMbOL41Eu8hNFMRUQ18v5am28hMGUYADd2lvwvoATyvzK2f64HPAA8B5wAvAr8Dft3A8a2R+F4GM0s8ZTCzxIFgZokDwcwSB4KZJQ4EM0scCGaWOBDMLPl/n99Y5bN6/4wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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z88/VzfuBpvqWKUnqSC2BPhhY0GK7ubqvPWcAt29OUZKkzqvlTtFoY1+22THicCqB/t522scD4wGGDBlSY4mSpFrUcoXeDOzeYrsJWNi6U0SMBK4Gjs3MJW0NlJlTMnNUZo4aOHDgptQrSWpHLYE+CxgeEcMiog8wFpjeskNEDAFuAj6WmX+sf5mSpI50OOWSmWsi4mzgTqAnMDUz50XEhGr7ZOCrwADgOxEBsCYzR225siVJrdX0tMXMnAHMaLVvcovXnwQ+Wd/SJEmd4Z2iklQIA12SCmGgS1IhDHRJKoSBLkmFMNAlqRAGuiQVwkCXpEIY6JJUCANdkgphoEtSIQx0SSqEgS5JhTDQJakQBrokFcJAl6RCGOiSVAgDXZIKYaBLUiEMdEkqhIEuSYUw0CWpEAa6JBXCQJekQhjoklQIA12SCmGgS1IhDHRJKoSBLkmFMNAlqRAGuiQVwkCXpEIY6JJUiJoCPSJGR8QTETE/Ii5ooz0iYlK1fW5EHFj/UiVJG9NhoEdET+AqYAwwAjg5Ika06jYGGF79Mx74bp3rlCR1oJYr9IOA+Zn5dGauAqYBx7bqcyzww6y4H9gpInatc62SpI3oVUOfwcCCFtvNwME19BkMPN+yU0SMp3IFD7A8Ip7oVLWNsQvwYj0HjEvrOVqX4/msH89lfXWV8/mW9hpqCfRoY19uQh8ycwowpYZjbjMiYnZmjmp0HaXwfNaP57K+SjiftUy5NAO7t9huAhZuQh9J0hZUS6DPAoZHxLCI6AOMBaa36jMdGFdd7XIIsCwzn289kCRpy+lwyiUz10TE2cCdQE9gambOi4gJ1fbJwAzgw8B8YAXw8S1X8lbXpaaIugDPZ/14Luury5/PyHzdVLckqQvyTlFJKoSBLkmFMNAlqRC1rEPvViJibyp3vg6mspZ+ITA9Mx9vaGHq9qp/NwcDD2Tm8hb7R2fmHY2rTNsKr9BbiIjPU3m0QQC/p7JkM4Ab23oomTZdRJS0EmqLi4hzgV8A5wCPRkTLx298vTFVlSMi/r3RNdSDq1xaiIg/Avtm5upW+/sA8zJzeGMqK09EPJeZQxpdR1cREY8A787M5RExFPgZ8KPM/HZEPJyZ72hogV1cRDyUmV3+KbFOubzWOmA34H9a7d+12qZOiIi57TUBg7ZmLQXouX6aJTOfjYjDgJ9FxFto+9Eb6oYM9Nf6NPCriHiSvz1sbAiwJ3B2w6rqugYBHwL+3Gp/APdu/XK6tBci4oDMnANQvVI/GpgK7NfY0rqmiHiGyudkAewaEU9XX2dm7tHQ4jaRUy6tREQPKo8MHkzlX24zMCsz1za0sC4oIq4BfpCZv22j7ceZeUoDyuqSIqIJWJOZL7TRdmhm/q4BZRWjlGkrA11St1dKoLvKRZLgPxpdQD14hS6pW4qIg6jMl8+KiH2pfN7zh8yc0eDSNpmBLqnbiYgLqXwXci/gLiqfm/0XcCRwZ2Z+rYHlbTIDXVK3U13XfwDQF3gBaMrMv0REfyp34o5saIGbyDl0Sd3Rmsxcm5krgKcy8y8AmfkKXfieEwNdUne0KiK2q75+5/qdEbEjXTjQnXKR1O1ERN/MfLWN/bsAu2bmIw0oa7MZ6JJUCKdcJKkQBrokFcJA1zYtItZGxJyImBcR/x0R51Wft9Ooep6tzrNuynuPi4gR9a5JWs9A17bulcw8IDP3BY4CPgxc2OCaNtVxgIGuLcZAV5eRmYuA8cDZUdEzIr4VEbMiYm5EfAogIg6LiJkRcXNEPBYRk9df1UfEByPivoh4KCL+IyK2r+5/NiIuqu5/pPp1b0TEgIj4ZUQ8HBHfo8WzxyPitIj4ffU3iO9FRM/q/uUR8bXqbxT3R8SgiHgP8BHgW9X+b42Ic6v1zY2IaVv1ZKpIBrq6lMx8msrf2zcDZwDLMvNdwLuAMyNiWLXrQcD5VJ4V/lbg+OpUyZeBI6vfTjMbOK/F8C9W938X+Gx134XAb6tP4ptO5fn4RMQ+wEnAoZl5ALAWOLX6njcA92fm/sBM4MzMvLf6/s9Vf+N4CrgAeEf1rsQJdTtJ6rb8ggt1Reuvkj8IjIyIE6rbOwLDgVXA76vhT0TcCLwXWEllyuN3EQHQB7ivxbg3Vf/5IHB89fXfrX+dmbdFxPov6ziCyg0ps6pj9QcWVdtWAbe2GOuodn6OucANEXELcEuNP7vULgNdXUpE7EHlangRlWA/JzPvbNXnMCrfRNPS+m+muSszT25n+PU3mqzltf9ttHWzRgDXZeYX2mhbnX+7waP1WC39PZX/YXwE+EpE7JuZa9rpK3XIKRd1GRExEJgMXFkNzDuBf4qI3tX2vSLiDdXuB0XEsOrc+UnAb4H7gUMjYs9q/+0iYq8ODjuT6lRKRIwBdq7u/xVwQkS8udr2pur3e27My8AO1f49gN0z8/8B/wLsBGxfy3mQ2uMVurZ1/SNiDtAbWAP8CLii2nY1MBR4KCrzHouprCSBylTKJVTm0GcCN2fmuog4HbgxIvpW+30Z+ONGjn9Rtf9DVB6v+hxAZj4WEV8GflkN59XAWbz+C8ZbmgZ8PyLOBcYC11SfHRLAv2bm0hrOh9Qub/1XcapTLp/NzKMbXYu0NTnlIkmF8ApdkgrhFbokFcJAl6RCGOiSVAgDXZIKYaBLUiEMdEkqxP8HdnP1v3FasXQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now let us visualize the remaining categorical variables vs target variable.\n",
    "\n",
    "Married=pd.crosstab(train['Married'],train['Loan_Status'])\n",
    "Dependents=pd.crosstab(train['Dependents'],train['Loan_Status']) \n",
    "Education=pd.crosstab(train['Education'],train['Loan_Status']) \n",
    "Self_Employed=pd.crosstab(train['Self_Employed'],train['Loan_Status']) \n",
    "\n",
    "Married.div(Married.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))\n",
    "plt.show() \n",
    "\n",
    "Dependents.div(Dependents.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True) \n",
    "plt.show() \n",
    "\n",
    "Education.div(Education.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4)) \n",
    "plt.show() \n",
    "\n",
    "Self_Employed.div(Self_Employed.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Following inferences can be made from the above bar plots:\n",
    "\n",
    "1. Proportion of married applicants is higher for the approved loans.\n",
    "2. Distribution of applicants with 1 or 3+ dependents is similar across both the categories of Loan_Status.\n",
    "3. There is nothing significant we can infer from Self_Employed vs Loan_Status plot.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B0gDgTuAzEfHH4pdpZuXQ4gwhIrZKmgDcD3QGZkTEMknjs+0NwOVAH+C7kgC2RkRt6co2s1Io5JCBiJgLzM1b15Dz+nPA54pbmpmVm+9UNLPEgWBmiQPBzBIHgpklDgQzSxwIZpY4EMwscSCYWeJAMLPEgWBmiQPBzBIHgpklDgQzSxwIZpY4EMwscSCYWeJAMLPEgWBmiQPBzBIHgpklDgQzSxwIZpY4EMwscSCYWeJAMLPEgWBmiQPBzBIHgpklDgQzSxwIZpY4EMwscSCYWeJAMLOkoECQNErSCkmrJE1sol2S6rPtSyUNK36pZlZqLQaCpM7AVGA0MBg4XdLgvG6jgUHZnzrge0Wu08zKoJAZwlHAqoh4NiLeAGYBJ+f1ORm4NTIWAL0l7V3kWs2sxLoU0Kc/sDpnuREYXkCf/sALuZ0k1ZGZQQBslLSiVdV2LH2Bl8u1M11Vrj3tMt7N799+zTUUEghqYl20oQ8RMQ2YVsA+OzxJiyKittJ1WNvsqu9fIYcMjcC+OcvVwJo29DGzdq6QQFgIDJJUI6kbMAaYk9dnDjAue7VhBLAhIl7IH8jM2rcWDxkiYqukCcD9QGdgRkQskzQ+294AzAU+DqwCXgU+W7qSO4xd4tDoXWyXfP8U8Y5DfTPbRflORTNLHAhmljgQzCxxIJhZUsiNSVYgSf3I3KEZwJqIeLHCJZm1iq8yFIGkoUADsCfw1+zqamA98KWIeLxStVnhHOgOhKKQtAT4QkQ8mrd+BHBTRBxRmcqsEA70tzgQikDSyogY1Ezbqog4sNw1WeEc6G/xOYTiuFfSPcCtvPWpz32BccB9FavKCrVbfhgARMQCSbtVoqBK8QyhSCSNJvNciP5kPv3ZCMyJiLkVLcxaJKkeOICmA/25iJhQqdrKzYFghgN9OwdCiUmqyz4Hwqzd841JpdfUw2Osg8g+5WuX4ZOKRSLpYN6acgaZB8TMiYibKlqY7axdKtA9QygCSReTefisgMfIPFRGwB1NPbbeOpQ3Kl1AOfkcQhFI+iNwaERsyVvfDVjW3D0K1v5J+ktEDKh0HeXiQ4bi2AbsA/w5b/3e2TZrxyQtba4J6FfOWirNgVAc5wMPSlrJW9exBwAHArvMNewOrB/wMeAfeesFPFz+cirHgVAEEXGfpIPIfKlN7nXshRHxZkWLs0LcDeweEUvyGyTNK385leNzCGaW+CqDmSUOBDNLHAhmljgQOhhJ/yRplqQ/SXpa0tzsCc22jHWLpE9nX98saXD29aUFbLsxb/ksSTdmX4+XNG4H246U9OG21Gyl5UDoQCQJuAuYFxEHRMRg4FJyrpVL6tyWsSPicxHxdHaxxUBoYayGiLh1B11GAq0KBEm+IlYGDoSO5VhgS/br8wDIXirrLOk3km4HnpTUWdI1khZKWirpC5AJFEk3ZmcW9wDv2z6OpHmSaiVNAXpKWiJpZluKlDRJ0lezr8/L7m9pdmYzEBgPXJDdx9GS9pP0YLbPg5IGZLe9RdL1kn4DXCNppaSqbFsnSask9W1LjdY0p27HchiwuJm2o4DDIuK57Cf0NkTEByV1B34v6ZfAB4D3A4eTmVU8DczIHSQiJkqaEBFDW6ilZ/bRY9vtxTu/BBhgIlATEa9L6h0R6yU1ABsj4loASb8Abo2IH0o6G6gHPpnd/iDghIh4U9J6YCzwbeAE4A8R8XILdVoreIbw7vFYRDyXff1RMt/GvQR4FOgDDAL+BbgjIt6MiDXAr3dif69FxNDtP8DlzfRbCsyUdCawtZk+HwJuz77+EfDPOW3/m3Nz1wwyTzECOBv4QZurtyY5EDqWZcCRzbRtynkt4Ms5f7A1EfHLbFu570T7V2AqmboXF3guILfG9O+KiNXAi5KOA4YD9xazUHMgdDS/BrpL+vz2FZI+CByT1+9+4IuSumb7HJR9WOh8YEz2HMPeZM5JNGXL9m13hqROwL4R8Rvgv4DewO7AK0CvnK4PA2Oyr8cCv9vBsDcDtwGzfVt48TkQOpDI3Gd+CnBi9rLjMmASmYex5LqZzPmBxyU9BdxE5nzRXcBK4Enge8Bvm9nVNGBpW08q5ugM3CbpSeAJ4IaIWA/8Ajhl+0lF4Dzgs9lPHX4G+MoOxpxDJlR8uFAC/iyDdSiSaskEy9GVruXdyFcZrMPIPn3qi2QOK6wEPEOwZknqAzzYRNPxEbGu3PVY6TkQzCzxSUUzSxwIZpY4EMwscSCYWfL//bfehrp8WysAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# relationship between remaining categorical independent variables and Loan_Status.\n",
    "\n",
    "Credit_History=pd.crosstab(train['Credit_History'],train['Loan_Status']) \n",
    "Property_Area=pd.crosstab(train['Property_Area'],train['Loan_Status']) \n",
    "\n",
    "Credit_History.div(Credit_History.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4)) \n",
    "plt.show() \n",
    "\n",
    "Property_Area.div(Property_Area.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Following inferences can be made from the above bar plots:\n",
    "\n",
    "1. It seems people with credit history as 1 are more likely to get their loans approved.\n",
    "2. Proportion of loans getting approved in semiurban area is higher as compared to that in rural or urban areas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": [
    "## Numerical Independent Variable vs Target Variable\n",
    "\n",
    "bins=[0,2500,4000,6000,81000] \n",
    "group=['Low','Average','High', 'Very high']\n",
    "train['Income_bin']=pd.cut(train['ApplicantIncome'],bins,labels=group)\n",
    "\n",
    "Income_bin=pd.crosstab(train['Income_bin'],train['Loan_Status']) \n",
    "Income_bin.div(Income_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True) \n",
    "plt.xlabel('ApplicantIncome') \n",
    "P = plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Following inferences can be made from the above bar plots:\n",
    "\n",
    "1. Applicant income does not affect the chances of loan approval which contradicts our hypothesis in which we assumed that if the applicant income is high the chances of loan approval will also be high."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "#We will analyze the coapplicant income and loan amount variable in similar manner.\n",
    "\n",
    "bins=[0,1000,3000,42000]\n",
    "group=['Low','Average','High']\n",
    "train['Coapplicant_Income_bin']=pd.cut(train['CoapplicantIncome'],bins,labels=group)\n",
    "Coapplicant_Income_bin=pd.crosstab(train['Coapplicant_Income_bin'],train['Loan_Status'])\n",
    "Coapplicant_Income_bin.div(Coapplicant_Income_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True) \n",
    "plt.xlabel('CoapplicantIncome')\n",
    "P = plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Following inferences can be made from the above bar plots:\n",
    "\n",
    "1. It shows that if coapplicant’s income is less the chances of loan approval are high. But this does not look right. The possible reason behind this may be that most of the applicants don’t have any coapplicant so the coapplicant income for such applicants is 0 and hence the loan approval is not dependent on it. \n",
    "2. So we can make a new variable in which we will combine the applicant’s and coapplicant’s income to visualize the combined effect of income on loan approval.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "#Let us combine the Applicant Income and Coapplicant Income and see the combined effect of \n",
    "# Total Income on the Loan_Status.\n",
    "\n",
    "train['Total_Income']=train['ApplicantIncome']+train['CoapplicantIncome']\n",
    "bins=[0,2500,4000,6000,81000] \n",
    "group=['Low','Average','High', 'Very high']\n",
    "train['Total_Income_bin']=pd.cut(train['Total_Income'],bins,labels=group)\n",
    "\n",
    "Total_Income_bin=pd.crosstab(train['Total_Income_bin'],train['Loan_Status']) \n",
    "Total_Income_bin.div(Total_Income_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True) \n",
    "plt.xlabel('Total_Income')\n",
    "P = plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Following inferences can be made from the above bar plots:\n",
    "\n",
    "1. We can see that Proportion of loans getting approved for applicants having low Total_Income is very less as compared to that of applicants with Average, High and Very High Income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAElCAYAAADp4+XfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAbrElEQVR4nO3de5xVdb3/8debO8pFReKnDAgSpqiANCmllteT9NOjdVRQDC2TB780Mz395GgFHovQ46VQi0OCaGJKJzVS0qxzFMswQQlE48ghiwlTIBlFQG6f88fe0Hacy5ph1l4zs97Px2Mezrrstd/DrnnP+q6bIgIzM8uvdlkHMDOzbLkIzMxyzkVgZpZzLgIzs5xzEZiZ5VyHrAM01v777x8DBgzIOoaZWauyePHidRHRu7Zlra4IBgwYwKJFi7KOYWbWqkj6U13LPDRkZpZzLgIzs5xzEZiZ5VyrO0ZgZlZq27ZtVFVVsWXLlqyjtAhdunShoqKCjh07Jn6Ni8DMWrWqqiq6d+/OgAEDkJR1nExFBOvXr6eqqoqBAwcmfp2HhsysVduyZQu9evXKfQkASKJXr16N3jtKrQgkzZL0hqQX61guSdMkrZS0VNKItLKYWdvmEvi7pvxbpLlHMBs4rZ7lo4DBxa/xwPdTzGJmZnVIrQgiYgHwt3pWORO4JwoWAvtIOiCtPGZmVrssDxb3BVaXTFcV571Wc0VJ4ynsNdC/f/+yhNttcs/yvl+5Ta7OOkG6/Pm1Xkk/u0/OhTXvHRPvNvhYNr7ymxRC1W7nzp1cMekm/vM3zyGJLp07MXf6DQzs35cp02ZyzeUXv/9FBx71nskpU6ZwzTXXlCnxe2V5sLi2gaxaH5cWETMiojIiKnv3rvVWGWZmmXlg3i9Y89e1LP3lAyz71VweuvNm9unRHYApt81KtI0pU6akGbFeWRZBFdCvZLoCWJNRFjNr45a8uIKRp49j6Cnn8umLr+LNDW8B8IM5D/KRT13AsFNG80+X/DObNm8G4KIrJnH512/kY/94EQd/9Az+45Ff1rnt115fxwF99qddu8Kv1IoD+7DvPj2YOGUam7e8y/BTxzD2smsBOOvzV/Lh087n8MMPZ8aMGQBMnDiRzZs3M3z4cMaOHcurr77KEUccsXv7N910E5MnTwZg2rRpDBkyhKFDhzJmzJhm+bfJsgjmAeOKZw+NBKoj4n3DQmZmzWHcFV/nhmu/zNJfzuXIQz/Idbf8OwCfGXUSz82/l9//8gEO++BAZv7op7tf89rr6/j1w7N45O7vMvHb0+rc9rlnnMrPnljA8FPHcNV1t/DCi38AYOo1l9O1S2eWPHE/c27/FgCzbp7E4sfuY9GiRUybNo3169czdepUunbtypIlS5gzZ069P8fUqVN54YUXWLp0KdOnT9/TfxYg3dNHfwT8FviQpCpJF0uaIGlCcZX5wCpgJfAD4ItpZTGzfKt+6202VG/kEx/9MAAXnnM6C559AYAXV/wPx3/68xx58rnMeejnLF/xP7tfd9ZpJ9CuXTuGHHIwr6+t+9yXigP7sGLBQ3z7X75Eu3bi5NET+NXTz9a67rRZP2LYKaMZOXIkq1ev5pVXXmnUzzJ06FDGjh3LvffeS4cOzXOYN7WDxRFxXgPLA7g0rfc3M0vioq9M4uGZtzDs8EOY/cA8nvzt4t3LOnfqtPv7wq+sunXu3IlRJx3LqJOOpU/vXjz8+JOcfPwx71nnyWcW8cunf8dvfzabvQZ9jBNOOKHWi786dOjAzp07d0+XrvPoo4+yYMEC5s2bx/XXX8/y5cv3uBB8ZbGZtXk9e3Rn357defrZ5wH44U8e5RMjC9ewvr1xEwf02Z9t27Yx56GfN2n7zy97mTV/XQsUziBa+tIrHFRROBu+Y8cObNu2DYDqtzeyb8/u7NW1K3/4wx9YuHDh7m107Nhx93p9+vThjTfeYP369bz77rs88sgju7e9evVqTjzxRG688UY2bNjAxo0bm5S5lO81ZGZtzqbNW6j48N+vZ71y/AXc/Z1/ZcLEb7FpyxYO7l/BXbdMBuD6r/4/jjl9HAdVHMCRh36QtzduavT7vbHub1zy1et5d2vhF/nRww/nsotGAzB+7GcYespoRhx5GLNunsT0H/4HQ085lw8dPpyRI0fu3sb48eMZOnQoI0aMYM6cOXzjG9/gmGOOYeDAgRx66KEA7NixgwsuuIDq6moigq985Svss88+Tf1n2k0N7e60NJWVlVHWJ5T5PPTWzZ9f65Xws3v5k3M57KAPpBwmBTWuI2hOL7/8Mocddth75klaHBGVta3voSEzs5zz0JCZWULLXn6Fz17+9ffM69y5E88+ck9GiZqHi8DMLKEjDxvMkifuzzpGs/PQkJlZzrkIzMxyzkVgZpZzPkZgZrk3YFrz3u/y1csPbHAdSVx55ZXcfPPNQOHGchs3btx9c7ly8h6BmVkGOnfuzIMPPsi6deuyjuIiMDPLQocOHRg/fjy33npr1lFcBGZmWbn00kuZM2cO1dXZXiHuIjAzy0iPHj0YN24c06bV/ayDcnARmJll6IorrmDmzJm88847mWVwEZiZZWi//fbj3HPPZebMmZll8OmjZpZ7SU73TNNVV13F7bffntn7uwjMzDJQ+kCZPn36sGlT45+D0Fw8NGRmlnMuAjOznHMRmJnlnIvAzCznXARmZjnnIjAzyzmfPmpmNuOE5t3e+CfrXRwRHH/ccVx77bWMGjUKgLlz5zJr1iwee+yx5s2SgIvAzKzMJDF9+nTOOeccTjzxRHbs2MG1116bSQmAi8DMLBNHHHEEZ5xxBjfccAPvvPMO48aNY9CgQZlkcRGYmWVk0qRJjBgxgk6dOrFo0aLMcrgIzMwysvfeezN69Gi6detG586dM8vhs4bMzDLUrl072rXL9lexi8DMLOc8NGRmNv7JrBNkKtUikHQa8F2gPXBnREytsbwncC/Qv5jlpoi4K81MZmYtyeTJk7OOkN7QkKT2wB3AKGAIcJ6kITVWuxR4KSKGAScAN0vqlFYmMzN7vzSPERwNrIyIVRGxFbgfOLPGOgF0lySgG/A3YHuKmczMrIY0i6AvsLpkuqo4r9TtwGHAGmAZ8OWI2FlzQ5LGS1okadHatWvTymtmrVIQEVmHaDGa8m+RZhGolnk1E34SWAIcCAwHbpfU430vipgREZURUdm7d+/mT2pmrVaX6lWsf2e7y4BCCaxfv54uXbo06nVpHiyuAvqVTFdQ+Mu/1OeAqVH4BFdK+iNwKPC7FHOZWRtS8fwNVHE1a3seTO1/f7ZQ1S+nstkuXbpQUVHRqNekWQTPAYMlDQT+AowBzq+xzp+Bk4GnJfUBPgSsSjGTmbUxHbduYODCf8k6RuNNrs46wW6pFUFEbJd0GfA4hdNHZ0XEckkTisunA9cDsyUto1DlV0fEurQymZnZ+6V6HUFEzAfm15g3veT7NcA/pJnBzMzq51tMmJnlnIvAzCznXARmZjnnIjAzyzkXgZlZzrkIzMxyzkVgZpZzLgIzs5xzEZiZ5ZyLwMws51wEZmY55yIwM8s5F4GZWc65CMzMcs5FYGaWcy4CM7OccxGYmeWci8DMLOdcBGZmOeciMDPLOReBmVnOuQjMzHLORWBmlnMuAjOznHMRmJnlnIvAzCznEhWBCi6Q9I3idH9JR6cbzczMyiHpHsH3gI8C5xWn3wbuSCWRmZmVVYeE6x0TESMkvQAQEW9K6pRiLjMzK5OkewTbJLUHAkBSb2BnaqnMzKxskhbBNOAh4AOSvgX8GpiSWiozMyubRENDETFH0mLgZEDAWRHxcqrJzMysLJKeNbQf8AbwI+A+4HVJHRO87jRJKyStlDSxjnVOkLRE0nJJTzUmvJmZ7bmkB4ufB/oBb1LYI9gHeE3SG8AlEbG45guKxxTuAE4FqoDnJM2LiJdK1tmHwhlJp0XEnyV9YI9+GjMza7SkxwgeAz4VEftHRC9gFDAX+CKFX+S1ORpYGRGrImIrcD9wZo11zgcejIg/A0TEG439AczMbM8kLYLKiHh810RE/AL4eEQsBDrX8Zq+wOqS6arivFKHAPtKelLSYknjEuYxM7NmknRo6G+SrqbwVz3AaODN4vBPXaeRqpZ5Ucv7f5jCQeiuwG8lLYyI/37PhqTxwHiA/v37J4xsZmZJJN0jOB+oAB4Gfgr0L85rD5xbx2uqKBxX2KUCWFPLOo9FxDsRsQ5YAAyruaGImBERlRFR2bt374SRzcwsiaSnj64DvlTH4pV1zH8OGCxpIPAXYAyF8ij1U+B2SR2ATsAxwK1JMpmZWfNIVATFK4n/P3A40GXX/Ig4qa7XRMR2SZcBj1PYc5gVEcslTSgunx4RL0t6DFhKYYjpzoh4sck/jZmZNVrSYwRzgAeA04EJwIXA2oZeFBHzgfk15k2vMf1vwL8lzGFmZs0s6TGCXhExE9gWEU9FxOeBkSnmMjOzMkm6R7Ct+N/XJP1fCgd9K9KJZGZm5ZS0CL4pqSdwFXAb0AO4IrVUZmZWNkmL4M2IqAaqgRMBJB2bWiozMyubpMcIbks4z8zMWpl69wgkfRT4GNBb0pUli3pQOCXUzMxauYaGhjoB3YrrdS+Z/xZwdlqhzMysfOotgoh4CnhK0uyI+FOZMpmZWRklPVjcWdIMYEDpa+q7stjMzFqHpEXwY2A6cCewI704ZmZWbkmLYHtEfD/VJGZmlomkp4/+TNIXJR0gab9dX6kmMzOzski6R3Bh8b9fLZkXwMHNG8fMzMot6fMIBqYdxMzMspFoaEjSXpK+VjxzCEmDJZ2ebjQzMyuHpMcI7gK2UrjKGAqPmPxmKonMzKyskhbBoIi4keLtqCNiM7U/nN7MzFqZpEWwVVJXCgeIkTQIeDe1VGZmVjZJzxqaBDwG9JM0BzgWuCitUGZmVj5Jzxp6QtLzFB5PKeDLEbEu1WRmZlYWSc8a+jSFq4sfjYhHgO2Szko3mpmZlUPSYwSTik8oAyAiNlAYLjIzs1YuaRHUtl7S4wtmZtaCJS2CRZJukTRI0sGSbgUWpxnMzMzKI2kRfInCBWUPAHOBzcClaYUyM7PyaXB4R1J74KcRcUoZ8piZWZk1uEcQETuATZJ6liGPmZmVWdIDvluAZZKeAN7ZNTMiLk8llZmZlU3SIni0+GVmZm1M0iuL7y7ea6h/RKxIOZOZmZVR0iuLzwCWULjfEJKGS5qXZjAzMyuPpKePTgaOBjYARMQSwE8tMzNrA5IWwfbSW0wURXOHMTOz8ktaBC9KOh9oX3xM5W3AMw29SNJpklZIWilpYj3rfUTSDklnJ8xjZmbNpDFXFh9O4WE09wHVwBX1vaB4IdodwChgCHCepCF1rHcD8Hjy2GZm1lzqPWtIUhdgAvBBYBnw0YjYnnDbRwMrI2JVcVv3A2cCL9VY70vAT4CPNCK3mZk1k4b2CO4GKimUwCjgpkZsuy+wumS6qjhvN0l9gU8D0+vbkKTxkhZJWrR27dpGRDAzs4Y0dB3BkIg4EkDSTOB3jdh2bQ+3r3mA+TvA1RGxQ6pt9eKLImYAMwAqKyt9kNrMrBk1VATbdn0TEdvr+2VdiyqgX8l0BbCmxjqVwP3F7e4PfErS9oh4uDFvZGZmTddQEQyT9FbxewFdi9MCIiJ61PPa54DBkgYCfwHGAOeXrhARu69FkDQbeMQlYGYAA7bcl3WEVL2adYAS9RZBRLRv6oaLexCXUTgbqD0wKyKWS5pQXF7vcQEzMyuPVB83GRHzgfk15tVaABFxUZpZzMysdkmvIzAzszbKRWBmlnMuAjOznHMRmJnlnIvAzCznXARmZjnnIjAzyzkXgZlZzrkIzMxyzkVgZpZzLgIzs5xzEZiZ5ZyLwMws51wEZmY5l+ptqNsCPxzDzNo67xGYmeWci8DMLOc8NGRtmof2zBrmPQIzs5xzEZiZ5ZyLwMws51wEZmY55yIwM8s5F4GZWc65CMzMcs5FYGaWcy4CM7OccxGYmeWci8DMLOdcBGZmOeciMDPLOReBmVnOpVoEkk6TtELSSkkTa1k+VtLS4tczkoalmcfMzN4vtSKQ1B64AxgFDAHOkzSkxmp/BD4REUOB64EZaeUxM7PapblHcDSwMiJWRcRW4H7gzNIVIuKZiHizOLkQqEgxj5mZ1SLNIugLrC6ZrirOq8vFwM9rWyBpvKRFkhatXbu2GSOamVmaRaBa5kWtK0onUiiCq2tbHhEzIqIyIip79+7djBHNzCzNZxZXAf1KpiuANTVXkjQUuBMYFRHrU8xjZma1SHOP4DlgsKSBkjoBY4B5pStI6g88CHw2Iv47xSxmZlaH1PYIImK7pMuAx4H2wKyIWC5pQnH5dOAbQC/ge5IAtkdEZVqZzMzs/dIcGiIi5gPza8ybXvL9F4AvpJnBzMzq5yuLzcxyzkVgZpZzLgIzs5xzEZiZ5ZyLwMws51wEZmY55yIwM8s5F4GZWc65CMzMcs5FYGaWcy4CM7OccxGYmeWci8DMLOdcBGZmOeciMDPLOReBmVnOuQjMzHLORWBmlnMuAjOznHMRmJnlnIvAzCznXARmZjnnIjAzyzkXgZlZzrkIzMxyzkVgZpZzLgIzs5xzEZiZ5ZyLwMws51wEZmY55yIwM8s5F4GZWc65CMzMci7VIpB0mqQVklZKmljLckmaVly+VNKINPOYmdn7pVYEktoDdwCjgCHAeZKG1FhtFDC4+DUe+H5aeczMrHZp7hEcDayMiFURsRW4HzizxjpnAvdEwUJgH0kHpJjJzMxq6JDitvsCq0umq4BjEqzTF3itdCVJ4ynsMQBslLSieaO2KPsD68r1ZrqhXO+UG/78Wq+2/tkdVNeCNItAtcyLJqxDRMwAZjRHqJZO0qKIqMw6hzWNP7/WK8+fXZpDQ1VAv5LpCmBNE9YxM7MUpVkEzwGDJQ2U1AkYA8yrsc48YFzx7KGRQHVEvFZzQ2Zmlp7UhoYiYruky4DHgfbArIhYLmlCcfl0YD7wKWAlsAn4XFp5WpFcDIG1Yf78Wq/cfnaKeN+QvJmZ5YivLDYzyzkXgZlZzrkIzMxyzkXQAkjqknUGM8svHyxuASStBF4HngYWAL+JiOpsU1lSko4DBkfEXZJ6A90i4o9Z57JkJPWlcNXt7rMoI2JBdonKz0XQQkjqDxwPHEvhlNoNETE821TWEEmTgErgQxFxiKQDgR9HxLEZR7MEJN0AjAZeAnYUZ0dE/GN2qcovzVtMWEKSKigUwPHAMGA58OtMQ1lSnwaOAp4HiIg1krpnG8ka4SwKJf5u1kGy5CJoGf5M4UrsKRExIesw1ihbIyIkBYCkvbMOZI2yCugIuAgsc0cBxwHnFx/g8wrwVETMzDaWJTBX0r9TuIX6JcDngR9knMkaIOk2Cje43AQskfQrSsogIi7PKlsWfIyghZDUjUIZHA9cQGGcckCmoSwRSacC/0DhbrqPR8QTGUeyBki6sL7lEXF3ubK0BC6CFkDSIqAz8AyFYwMLIuJP2aYys7xwEbQAknpHxNqsc1jjSXqb9z9DoxpYBFwVEavKn8qSkrSMuj+/b0bE+vKnKj8fI2gZtkq6Bfh4cfop4F99LUGrcAuFZ2jcR2FoaAzwf4AVwCzghMySWRI/p3Da6H3F6TEUPsdqYDZwRjaxyst7BC2ApJ8ALwK7xiU/CwyLiM9kl8qSkPRsRBxTY97CiBgp6fcRMSyrbNYwSb+pec3HrnmSlkXEkVllKyffYqJlGBQRkyJiVfHrOuDgrENZIjslnSupXfHr3JJl/iur5esmaXeRSzoa6Fac3J5NpPLz0FDLsFnScRHxawBJxwKbM85kyYwFvgt8j8Iv/oXABZK6ApdlGcwS+QIwq3jWnoC3gC8Urwf5dqbJyshDQy2ApGHAPUDP4qw3gQsjYml2qczyQ1JPCr8PN2SdJQsughZEUg+AiHhL0hUR8Z2sM1n9ineOvRg4HNh9F9mI+HxmoaxBki6IiHslXVnb8oi4pdyZsuRjBC1IRLwVEW8VJ2v9H6i1OD+kcJbQJymc7VUBvJ1pIkti161AutfxlSveI2ihJK2OiH5Z57D6SXohIo6StDQihkrqSOHq4pOyzmaWlA8Wt1xu6NZhW/G/GyQdAfwVGJBdHEtC0rT6luftXkMuggzVcVUqFM5e6FrmONY0MyTtC3wNmEfh1MOvZxvJElhc8v11wKSsgrQEHhoyayJJ7YCzI2Ju1lms6XYN72WdI0s+WGzWRBGxE18r0Bbk/q9hF4HZnnlC0j9L6idpv11fWYcyawwPDZntAUm1PaQ+IsK3CGnBahyf24vCA2qgcHwuIqJHJsEy4iIwM8s5Dw2Z7QFJe0n6mqQZxenBkk7POpdZY7gIzPbMXcBW4GPF6Srgm9nFMWs8F4HZnhkUETdSvLAsIjZTGGc2azVcBGZ7ZmvxltMBIGkQ8G62kcwax1cWm+2ZycBjQD9Jc4BjgYuyDGTWWD5ryGwPSeoFjKQwJLQwItZlHMmsUbxHYLYHJM0DfgTMi4h3ss5j1hQ+RmC2Z24GjgdekvRjSWcXH1Zj1mp4aMisGUhqD5wEXAKclrcrU61189CQ2R4qnjV0BjAaGAHMzjSQWSN5aMhsD0h6AHiZwt7A7cCFQPtMQ5k1kovAbM/cBZwDvFX8/joKxWDWanhoyKwJJB0CjAHOA9YDD1A45nZipsHMmsAHi82aQNJO4Gng4ohYWZy3yrefttbIQ0NmTfNPFB5U/1+SfiDpZHyPIWulvEdgtgck7Q2cRWGI6CTgbuChiPhFpsHMGsFFYNZMio+oPAcYHREnZZ3HLCkXgZlZzvkYgZlZzrkIzMxyzkVgbY6kjWV4j69I2iKpZ9rv1UCOa7J8f2sbfIzA2hxJGyOiW8rv8TsKTyKbGRGz03yvBnKk/rNa2+c9AssFScMlLZS0VNJDkvYtzr9E0nOSfi/pJ5L2Ks6fLWmapGckrZJ0dsm2BgHdgK9ROG101/yLJD0s6WeS/ijpMklXSnqh+N77NZDlSUmVxe/3l/RqyXYflPSYpFck3VicPxXoKmlJ8eloZk3iIrC8uAe4OiKGAsuAScX5D0bERyJiGIV7BF1c8poDgOOA04GpJfPPo/AwmqeBD0n6QMmyI4DzgaOBbwGbIuIo4LfAuAay1Gc4hbubHgmMltQvIiYCmyNieESMTfKPYFYbF4G1ecVx/H0i4qnirLuBjxe/P0LS05KWAWOBw0te+nBE7IyIl4A+JfPHAPdHxE7gQQrXDuzyXxHxdkSsBaqBnxXnLwMGNJClPr+KiOqI2AK8BByU4DVmifimc5Z3s4GzIuL3ki4CTihZ9m7J9wKQNBQYDDwhCaATsAq4o5bX7CyZ3knD/3/bzt//OKv5lLPS7e5IsC2zxLxHYG1eRFQDb0o6vjjrs8Cuv8i7A69J6khhj6Ah5wGTI2JA8etAoK+kRH+hN5DlVeDDxe/PJpltxexmTea/Kqwt2ktSVcn0LRQeGDO9eDB4FfC54rKvA88Cf6IwfNO9gW2PAUbVmPdQcf7rCfPVleUmYK6kzwL/mXBbM4Clkp73cQJrKp8+amaWcx4aMjPLOReBmVnOuQjMzHLORWBmlnMuAjOznHMRmJnlnIvAzCzn/hdyscGVBJrdNwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let’s visualize the Loan amount variable.\n",
    "\n",
    "bins=[0,100,200,700]\n",
    "group=['Low','Average','High'] \n",
    "train['LoanAmount_bin']=pd.cut(train['LoanAmount'],bins,labels=group)\n",
    "LoanAmount_bin=pd.crosstab(train['LoanAmount_bin'],train['Loan_Status'])\n",
    "LoanAmount_bin.div(LoanAmount_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True) \n",
    "plt.xlabel('LoanAmount')\n",
    "P = plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen that the proportion of approved loans is higher for Low and Average Loan Amount as compared to that of High Loan Amount which supports our hypothesis in which we considered that the chances of loan approval will be high when the loan amount is less.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let’s drop the bins which we created for the exploration part. We will change the 3+ in dependents variable to 3 \n",
    "#to make it a numerical variable.\n",
    "\n",
    "train=train.drop(['Income_bin', 'Coapplicant_Income_bin', 'LoanAmount_bin', 'Total_Income_bin', 'Total_Income'],\n",
    "                 axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Dependents'].replace('3+', 3,inplace=True) \n",
    "test['Dependents'].replace('3+', 3,inplace=True) \n",
    "train['Loan_Status'].replace('N', 0,inplace=True) \n",
    "train['Loan_Status'].replace('Y', 1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": [
    "#Now lets look at the correlation between all the numerical variables.\n",
    "#We will use the heat map to visualize the correlation. Heatmaps visualize data through variations in coloring. \n",
    "#The variables with darker color means their correlation is more.\n",
    "\n",
    "matrix = train.corr()\n",
    "f, ax = plt.subplots(figsize=(9, 6)) \n",
    "sns.heatmap(matrix, vmax=.8, square=True, cmap=\"BuPu\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Next Section\n",
    "\n",
    "## Missing value imputation\n",
    "\n",
    "After exploring all the variables in our data, we can now impute the missing values and treat the outliers because missing data and outliers can have adverse effect on the model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can consider these methods to fill the missing values:\n",
    "\n",
    "1. For numerical variables: imputation using mean or median\n",
    "2. For categorical variables: imputation using mode\n",
    "\n",
    "There are very less missing values in Gender, Married, Dependents, Credit_History and Self_Employed features so we can fill them using the mode of the features.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Gender'].fillna(train['Gender'].mode()[0], inplace=True) \n",
    "train['Married'].fillna(train['Married'].mode()[0], inplace=True)\n",
    "train['Dependents'].fillna(train['Dependents'].mode()[0], inplace=True) \n",
    "train['Self_Employed'].fillna(train['Self_Employed'].mode()[0], inplace=True) \n",
    "train['Credit_History'].fillna(train['Credit_History'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now let’s try to find a way to fill the missing values in Loan_Amount_Term. We will \n",
    "#### look at the value count of the Loan amount term variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360.0    512\n",
       "180.0     44\n",
       "480.0     15\n",
       "300.0     13\n",
       "84.0       4\n",
       "240.0      4\n",
       "120.0      3\n",
       "36.0       2\n",
       "60.0       2\n",
       "12.0       1\n",
       "Name: Loan_Amount_Term, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Loan_Amount_Term'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### It can be seen that in loan amount term variable, the value of 360 is repeating the most. \n",
    "#### So we will replace the missing values in this variable using the mode of this variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Loan_Amount_Term'].fillna(train['Loan_Amount_Term'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now we will see the LoanAmount variable. As it is a numerical variable, we can use mean or median to impute the missing \n",
    "#### values. We will use median to fill the null values as earlier we saw that loan amount have outliers so the mean will not be\n",
    "####  the proper approach as it is highly affected by the presence of outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['LoanAmount'].fillna(train['LoanAmount'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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             614 non-null    object \n",
      " 2   Married            614 non-null    object \n",
      " 3   Dependents         614 non-null    object \n",
      " 4   Education          614 non-null    object \n",
      " 5   Self_Employed      614 non-null    object \n",
      " 6   ApplicantIncome    614 non-null    int64  \n",
      " 7   CoapplicantIncome  614 non-null    float64\n",
      " 8   LoanAmount         614 non-null    float64\n",
      " 9   Loan_Amount_Term   614 non-null    float64\n",
      " 10  Credit_History     614 non-null    float64\n",
      " 11  Property_Area      614 non-null    object \n",
      " 12  Loan_Status        614 non-null    int64  \n",
      "dtypes: float64(4), int64(2), object(7)\n",
      "memory usage: 62.5+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "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>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>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</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>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>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</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>1</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>1</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       128.0             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            1  \n",
       "1             1.0         Rural            0  \n",
       "2             1.0         Urban            1  \n",
       "3             1.0         Urban            1  \n",
       "4             1.0         Urban            1  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Gender'].fillna(train['Gender'].mode()[0], inplace=True) \n",
    "test['Dependents'].fillna(train['Dependents'].mode()[0], inplace=True) \n",
    "test['Self_Employed'].fillna(train['Self_Employed'].mode()[0], inplace=True) \n",
    "test['Credit_History'].fillna(train['Credit_History'].mode()[0], inplace=True) \n",
    "test['Loan_Amount_Term'].fillna(train['Loan_Amount_Term'].mode()[0], inplace=True)\n",
    "test['LoanAmount'].fillna(train['LoanAmount'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Next Section\n",
    "\n",
    "## Outlier Treatment\n",
    "As we saw earlier in univariate analysis, LoanAmount contains outliers so we have to treat them as the presence of outliers affects the distribution of the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kEU3jbRMMd62KtxCQNgbn3CWpQZ65b0At/mVf0nitWbgnuR54P3AJcHdV3blWx9LqOLUitW9Nwj3JJcAfAW8FzgD/mORIVT22Fseb1B8zpvGPKJIEazfnfg3wRFV9oaq+ARwGdq/RsSRJ50hVjX+nyc8B11fVL3bL7wF+pKp+eck2+4B93eLrgM8DVwL/NvaCNj77Mph9OZ89GazVvnxXVb1m0Iq1mnPPgLH/91Okqg4CB//fi5LjVTW3RjVtWPZlMPtyPnsy2MXYl7WaljkDXLVkeTvw9BodS5J0jrUK938EdiS5Osm3A3uAI2t0LEnSOdZkWqaqFpL8MvA3LF4K+cGqenQFLz24/CYXJfsymH05nz0Z7KLry5r8QVWSNFnefkCSGmS4S1KDJh7uSa5K8ndJTiV5NMmtk65pGiR5RZJPJ/mnri+/M+mapkmSS5J8JslfTrqWaZHkdJKTST6b5Pik65kWSS5Lcn+Sz3U582OTrmk9TMONwxaA/VX1cJLvAE4kObpWtyrYQF4EfrKq5pO8DPiHJH9dVQ9NurApcStwCnjVpAuZMruqqsUv64zi/cDHq+rnuqv3XjnpgtbDxM/cq+qZqnq4e/6fLH5gt022qsmrRfPd4su6f/71G0iyHbgBuHvStWi6JXkV8BbgHoCq+kZVPTfZqtbHxMN9qSSzwA8Dn5psJdOhm3r4LHAWOFpV9mXRHwK/AfzPpAuZMgV8IsmJ7vYegu8GvgL8STeNd3eSSydd1HqYmnBPsgX4CPCrVfUfk65nGlTVN6vqh1j8hu81Sd446ZomLcnbgbNVdWLStUyha6vqTcDPALckecukC5oCm4A3AX9cVT8MPA8cmGxJ62Mqwr2bU/4IcG9VfXTS9Uyb7tfIPnD9hEuZBtcC70hymsW7jf5kkj+dbEnToaqe7h7PAh9j8e6sF7szwJklv/Xez2LYN2/i4Z4kLM6Hnaqq3590PdMiyWuSXNY93wz8FPC5yVY1eVV1W1Vtr6pZFm9r8bdV9e4JlzVxSS7tLkigm3b4aeCRyVY1eVX1ZeBLSV7XDV0HXBQXa0zD1TLXAu8BTnbzywC/VVV/NcGapsFW4FD3H598G3BfVXnZny5kBvjY4rkSm4A/q6qPT7akqfE+4N7uSpkvAD8/4XrWhbcfkKQGTXxaRpI0foa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJatD/AjM7GSOQHsSCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['LoanAmount_log'] = np.log(train['LoanAmount'])\n",
    "train['LoanAmount_log'].hist(bins=20)\n",
    "test['LoanAmount_log'] = np.log(test['LoanAmount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets drop the Loan_ID variable as it do not have any effect on the loan status. \n",
    "#We will do the same changes to the test dataset which we did for the training dataset.\n",
    "\n",
    "train=train.drop(['Loan_ID'],axis=1) \n",
    "test=test.drop(['Loan_ID'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Next Section\n",
    "\n",
    "save csv  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>LoanAmount_log</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Married_No</th>\n",
       "      <th>...</th>\n",
       "      <th>Dependents_0</th>\n",
       "      <th>Dependents_1</th>\n",
       "      <th>Dependents_2</th>\n",
       "      <th>Education_Graduate</th>\n",
       "      <th>Education_Not Graduate</th>\n",
       "      <th>Self_Employed_No</th>\n",
       "      <th>Self_Employed_Yes</th>\n",
       "      <th>Property_Area_Rural</th>\n",
       "      <th>Property_Area_Semiurban</th>\n",
       "      <th>Property_Area_Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.85203</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>4.85203</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0       128.0             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "\n",
       "   Credit_History  Loan_Status  LoanAmount_log  Gender_Female  Gender_Male  \\\n",
       "0             1.0            1         4.85203              0            1   \n",
       "1             1.0            0         4.85203              0            1   \n",
       "\n",
       "   Married_No  ...  Dependents_0  Dependents_1  Dependents_2  \\\n",
       "0           1  ...             1             0             0   \n",
       "1           0  ...             0             1             0   \n",
       "\n",
       "   Education_Graduate  Education_Not Graduate  Self_Employed_No  \\\n",
       "0                   1                       0                 1   \n",
       "1                   1                       0                 1   \n",
       "\n",
       "   Self_Employed_Yes  Property_Area_Rural  Property_Area_Semiurban  \\\n",
       "0                  0                    0                        0   \n",
       "1                  0                    1                        0   \n",
       "\n",
       "   Property_Area_Urban  \n",
       "0                    1  \n",
       "1                    0  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train=pd.get_dummies(train) \n",
    "train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</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",
       "      <th>LoanAmount_log</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Married_No</th>\n",
       "      <th>Married_Yes</th>\n",
       "      <th>...</th>\n",
       "      <th>Dependents_0</th>\n",
       "      <th>Dependents_1</th>\n",
       "      <th>Dependents_2</th>\n",
       "      <th>Education_Graduate</th>\n",
       "      <th>Education_Not Graduate</th>\n",
       "      <th>Self_Employed_No</th>\n",
       "      <th>Self_Employed_Yes</th>\n",
       "      <th>Property_Area_Rural</th>\n",
       "      <th>Property_Area_Semiurban</th>\n",
       "      <th>Property_Area_Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>4.700480</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3076</td>\n",
       "      <td>1500</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5720                  0       110.0             360.0   \n",
       "1             3076               1500       126.0             360.0   \n",
       "\n",
       "   Credit_History  LoanAmount_log  Gender_Female  Gender_Male  Married_No  \\\n",
       "0             1.0        4.700480              0            1           0   \n",
       "1             1.0        4.836282              0            1           0   \n",
       "\n",
       "   Married_Yes  ...  Dependents_0  Dependents_1  Dependents_2  \\\n",
       "0            1  ...             1             0             0   \n",
       "1            1  ...             0             1             0   \n",
       "\n",
       "   Education_Graduate  Education_Not Graduate  Self_Employed_No  \\\n",
       "0                   1                       0                 1   \n",
       "1                   1                       0                 1   \n",
       "\n",
       "   Self_Employed_Yes  Property_Area_Rural  Property_Area_Semiurban  \\\n",
       "0                  0                    0                        0   \n",
       "1                  0                    0                        0   \n",
       "\n",
       "   Property_Area_Urban  \n",
       "0                    1  \n",
       "1                    1  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test=pd.get_dummies(test)\n",
    "test.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=train.drop(['LoanAmount'],axis=1) \n",
    "y=test.drop(['LoanAmount'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "x.to_csv('train_1.csv',index=False)\n",
    "y.to_csv('test_1.csv',index=False)\n",
    "\n",
    "del x,y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Next Section\n",
    "\n",
    "## Feature Engineering\n",
    "\n",
    "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",
    "1. 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",
    "2. 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",
    "3. 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": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Total_Income']=train['ApplicantIncome']+train['CoapplicantIncome'] \n",
    "test['Total_Income']=test['ApplicantIncome']+test['CoapplicantIncome']\n",
    "\n",
    "#Let’s check the distribution of Total Income.\n",
    "\n",
    "sns.distplot(train['Total_Income']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can see it is shifted towards left, i.e., the distribution is right skewed. So, let’s take the log transformation to make the distribution normal.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Total_Income_log'] = np.log(train['Total_Income']) \n",
    "sns.distplot(train['Total_Income_log']); \n",
    "test['Total_Income_log'] = np.log(test['Total_Income'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now the distribution looks much closer to normal and effect of extreme values has been significantly subsided. Let’s create the EMI feature now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['EMI']=train['LoanAmount']/train['Loan_Amount_Term']\n",
    "test['EMI']=test['LoanAmount']/test['Loan_Amount_Term']\n",
    "\n",
    "#Let’s check the distribution of EMI variable.\n",
    "\n",
    "sns.distplot(train['EMI']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": [
    "#Let us create Balance Income feature now and check its distribution.\n",
    "\n",
    "train['Balance Income']=train['Total_Income']-(train['EMI']*1000)\n",
    "# Multiply with 1000 to make the units equal \n",
    "\n",
    "test['Balance Income']=test['Total_Income']-(test['EMI']*1000)\n",
    "sns.distplot(train['Balance Income']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term', 'Credit_History', 'Loan_Status', 'LoanAmount_log',\n",
       "       'Gender_Female', 'Gender_Male', 'Married_No', 'Married_Yes',\n",
       "       'Dependents_3', 'Dependents_0', 'Dependents_1', 'Dependents_2',\n",
       "       'Education_Graduate', 'Education_Not Graduate', 'Self_Employed_No',\n",
       "       'Self_Employed_Yes', 'Property_Area_Rural', 'Property_Area_Semiurban',\n",
       "       'Property_Area_Urban', 'Total_Income', 'Total_Income_log', 'EMI',\n",
       "       'Balance Income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term', 'Credit_History', 'LoanAmount_log', 'Gender_Female',\n",
       "       'Gender_Male', 'Married_No', 'Married_Yes', 'Dependents_3',\n",
       "       'Dependents_0', 'Dependents_1', 'Dependents_2', 'Education_Graduate',\n",
       "       'Education_Not Graduate', 'Self_Employed_No', 'Self_Employed_Yes',\n",
       "       'Property_Area_Rural', 'Property_Area_Semiurban', 'Property_Area_Urban',\n",
       "       'Total_Income', 'Total_Income_log', 'EMI', 'Balance Income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.columns"
   ]
  },
  {
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=train.drop(['ApplicantIncome','LoanAmount','Total_Income', 'CoapplicantIncome',  'Loan_Amount_Term'], axis=1) \n",
    "test=test.drop(['ApplicantIncome','LoanAmount', 'Total_Income', 'CoapplicantIncome',  'Loan_Amount_Term'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Next Section\n",
    "\n",
    "save csv  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.to_csv('train_final.csv',index=False)\n",
    "test.to_csv('test_final.csv',index=False)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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