Loan_Prediction_Train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('loan_predic_train.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>600.00000</td>\n",
       "      <td>564.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5403.459283</td>\n",
       "      <td>1621.245798</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>342.00000</td>\n",
       "      <td>0.842199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6109.041673</td>\n",
       "      <td>2926.248369</td>\n",
       "      <td>85.587325</td>\n",
       "      <td>65.12041</td>\n",
       "      <td>0.364878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2877.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3812.500000</td>\n",
       "      <td>1188.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5795.000000</td>\n",
       "      <td>2297.250000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>480.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       614.000000         614.000000  592.000000         600.00000   \n",
       "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
       "std        6109.041673        2926.248369   85.587325          65.12041   \n",
       "min         150.000000           0.000000    9.000000          12.00000   \n",
       "25%        2877.500000           0.000000  100.000000         360.00000   \n",
       "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
       "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
       "max       81000.000000       41667.000000  700.000000         480.00000   \n",
       "\n",
       "       Credit_History  \n",
       "count      564.000000  \n",
       "mean         0.842199  \n",
       "std          0.364878  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
      "Loan_ID              614 non-null object\n",
      "Gender               601 non-null object\n",
      "Married              611 non-null object\n",
      "Dependents           599 non-null object\n",
      "Education            614 non-null object\n",
      "Self_Employed        582 non-null object\n",
      "ApplicantIncome      614 non-null int64\n",
      "CoapplicantIncome    614 non-null float64\n",
      "LoanAmount           592 non-null float64\n",
      "Loan_Amount_Term     600 non-null float64\n",
      "Credit_History       564 non-null float64\n",
      "Property_Area        614 non-null object\n",
      "Loan_Status          614 non-null object\n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 62.4+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122a27f28>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['ApplicantIncome'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1229cbdd8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['CoapplicantIncome'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122c97a90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting Boxplot to check outliers\n",
    "dataset.boxplot(column='ApplicantIncome', by= 'Education')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122daa080>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting Boxplot to check outliers\n",
    "dataset.boxplot(column='CoapplicantIncome', by= 'Education')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122e3d780>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAEBpJREFUeJzt3X+MZWV9x/H3t6wI7CjLD52SXeJAJFTTbYWdUCitmQXbKhjlD0wxxC4Gs0m1VOs2urRJTf9ogk391aSp3Yh2/7AsirYQpLUEd5q0qau7gC6wUlbc4MIK2rJrl5jYrd/+cZ+B22Vm75mZe+498/B+JZN7zpnnznzu5fLZZ557z72RmUiSVr6fG3cASdJwWOiSVAkLXZIqYaFLUiUsdEmqhIUuSZWw0CWpEha6JFXCQpekSqwa5S87++yzc2pqqtHY5557jtWrV7cbaIjM2y7ztsu87Vpu3j179vwoM181cGBmjuxrw4YN2dTOnTsbj+0C87bLvO0yb7uWmxfYnQ061iUXSaqEhS5JlbDQJakSFrokVcJCl6RKWOiSVAkLXZIqYaFLUiUsdEmqxEhP/X+pm9r6lXmPH7jl6hEnkVQjZ+iSVAkLXZIqYaFLUiUsdEmqhIUuSZWw0CWpEha6JFXCQpekSljoklQJC12SKmGhS1IlLHRJqoSFLkmVsNAlqRKNCj0i/iAiHo6IhyLitog4JSLOi4hdEfFYRNweESe3HVaStLCBhR4Ra4HfB6Yz8xeBk4DrgI8Cn8jMC4BngRvbDCpJOrGmSy6rgFMjYhVwGnAIuAK4o3x/O3DN8ONJkpoaWOiZ+STwF8AT9Ir8CLAHOJyZx8qwg8DatkJKkgaLzDzxgIgzgC8Bvw0cBr5Y9j+Sma8tY84F7snM9fNcfzOwGWBycnLDjh07GgU7evQoExMTzW/JmDXJu/fJI/MeX7/29DYinVCN92+XmLddL7W8Gzdu3JOZ04PGNflM0TcB38vMHwJExJeBXwXWRMSqMktfBzw135UzcxuwDWB6ejpnZmYa3YDZ2Vmaju2CJnlvWOgzRa8/8fXaUOP92yXmbZd559dkDf0J4NKIOC0iArgSeATYCVxbxmwC7mwnoiSpiSZr6LvoPfl5P7C3XGcb8GHggxGxHzgLuLXFnJKkAZosuZCZHwE+ctzhx4FLhp5IkrQknikqSZWw0CWpEha6JFXCQpekSljoklQJC12SKmGhS1IlLHRJqoSFLkmVaHSmqBZnaoE34ZKkNjlDl6RKWOiSVAkLXZIqYaFLUiUsdEmqhIUuSZWw0CWpEha6JFXCQpekSljoklQJC12SKmGhS1IlLHRJqoTvtrgM/e+quGX9MW7wXRYljZEzdEmqhIUuSZWw0CWpEha6JFXCQpekSljoklQJC12SKmGhS1IlLHRJqoSFLkmVsNAlqRIWuiRVwkKXpEpY6JJUiUaFHhFrIuKOiPhOROyLiMsi4syIuDciHiuXZ7QdVpK0sKYz9E8B/5SZvwD8MrAP2Arcl5kXAPeVfUnSmAws9Ih4JfBG4FaAzPxpZh4G3g5sL8O2A9e0FVKSNFiTGfr5wA+Bz0XEAxHxmYhYDUxm5iGAcvnqFnNKkgaIzDzxgIhp4OvA5Zm5KyI+BfwYuCkz1/SNezYzX7SOHhGbgc0Ak5OTG3bs2NEo2NGjR5mYmGh8Q8Zh75NHnt+ePBWe/snSfs76tacPKVFzK+H+7Wfedpm3XcvNu3Hjxj2ZOT1oXJNC/3ng65k5VfZ/nd56+WuBmcw8FBHnALOZeeGJftb09HTu3r270Q2YnZ1lZmam0dhxOf4zRT+2d2kf0XrglquHFamxlXD/9jNvu8zbruXmjYhGhT5wySUzfwB8PyLmyvpK4BHgLmBTObYJuHOJWSVJQ9B0SnkT8PmIOBl4HHg3vX8MvhARNwJPAO9oJ6IkqYlGhZ6ZDwLzTfevHG4cSdJSeaaoJFXCQpekSljoklQJC12SKmGhS1IlLHRJqoSFLkmVsNAlqRIWuiRVwkKXpEpY6JJUCQtdkiphoUtSJZb2iQwaif4P0Og3jg/EkNR9ztAlqRIWuiRVwkKXpEpY6JJUCQtdkiphoUtSJSx0SaqEhS5JlbDQJakSninaAQudESpJi+EMXZIqYaFLUiUsdEmqhIUuSZWw0CWpEha6JFXCQpekSljoklQJTyyqiB9ZJ720OUOXpEpY6JJUCQtdkiphoUtSJSx0SapE40KPiJMi4oGIuLvsnxcRuyLisYi4PSJObi+mJGmQxczQ3w/s69v/KPCJzLwAeBa4cZjBJEmL06jQI2IdcDXwmbIfwBXAHWXIduCaNgJKkpppOkP/JPAh4Gdl/yzgcGYeK/sHgbVDziZJWoTIzBMPiHgrcFVmvjciZoA/BN4N/HtmvraMORe4JzPXz3P9zcBmgMnJyQ07duxoFOzo0aNMTEws4qaM3t4njzy/PXkqPP2T0fze9WtPH5hn0PiVcP/2M2+7zNuu5ebduHHjnsycHjSuyan/lwNvi4irgFOAV9Kbsa+JiFVllr4OeGq+K2fmNmAbwPT0dM7MzDS6AbOzszQdOy439J1qv2X9MT62dzTvpHDg+pmBeQaNXwn3bz/ztsu87RpV3oFLLpl5c2auy8wp4Drga5l5PbATuLYM2wTc2VpKSdJAy3kd+oeBD0bEfnpr6rcOJ5IkaSkWtUaQmbPAbNl+HLhk+JEkSUvhmaKSVAkLXZIqYaFLUiUsdEmqhIUuSZWw0CWpEha6JFXCQpekSljoklQJC12SKmGhS1IlLHRJqoSFLkmVGM0nMqwAUwt8OIQkrRTO0CWpEha6JFXCQpekSljoklQJC12SKmGhS1IlLHRJqoSFLkmVsNAlqRIWuiRVwkKXpEpY6JJUCQtdkiphoUtSJSx0SaqEhS5JlbDQJakSFrokVcJCl6RKWOiSVAkLXZIqYaFLUiUsdEmqxKpxB9DiTW39yrgjSOqggTP0iDg3InZGxL6IeDgi3l+OnxkR90bEY+XyjPbjSpIW0mTJ5RiwJTNfB1wKvC8iXg9sBe7LzAuA+8q+JGlMBhZ6Zh7KzPvL9n8D+4C1wNuB7WXYduCatkJKkgZb1JOiETEFXATsAiYz8xD0Sh949bDDSZKai8xsNjBiAvgX4M8y88sRcTgz1/R9/9nMfNE6ekRsBjYDTE5ObtixY0ej33f06FEmJiYajR2GvU8eWdb1J0+Fp38ypDBDtn7t6S86Nur7d7nM2y7ztmu5eTdu3LgnM6cHjWtU6BHxMuBu4KuZ+fFy7FFgJjMPRcQ5wGxmXniinzM9PZ27d+9udANmZ2eZmZlpNHYYlvvKkS3rj/Gxvd180dCBW65+0bFR37/LZd52mbddy80bEY0KvcmrXAK4Fdg3V+bFXcCmsr0JuHMpQSVJw9FkSnk58C5gb0Q8WI79EXAL8IWIuBF4AnhHOxElSU0MLPTM/FcgFvj2lcONI0laKk/9l6RKWOiSVIluviyjRb4PiqRaOUOXpEpY6JJUCQtdkiphoUtSJSx0SaqEhS5Jlaj2ZYu+PPEF890XW9YfY2b0USS1yBm6JFXCQpekSljoklQJC12SKmGhS1IlLHRJqkS1L1vUYAu9tHO+zyCV1H3O0CWpEha6JFXCQpekSljoklQJnxRVq3ziVRodZ+iSVAkLXZIqYaFLUiVW/Bq673veDf53kMbPGbokVcJCl6RKrPglFw2fyyfSyuQMXZIq4QxdY7HYE472PnmEG+a5jicoSS9whi5JlXCGrk5ZaOa+Zf2Ig1TEt1946XCGLkmVsNAlqRIuuWhFa3s5oebliv7btmX9seefdK7htr1UOUOXpEqsmBm6J7toGEY1o++f8Q77d7wUHf/fbe7+9T79/5Y1Q4+IN0fEoxGxPyK2DiuUJGnxljxDj4iTgL8CfgM4CHwzIu7KzEeGFU5aqsX+RTfOvwBrXqdfSNdu87DyLPRz/vbNqxedaSmWM0O/BNifmY9n5k+BHcDbhxNLkrRYyyn0tcD3+/YPlmOSpDGIzFzaFSPeAfxWZr6n7L8LuCQzbzpu3GZgc9m9EHi04a84G/jRksKNh3nbZd52mbddy837msx81aBBy3mVy0Hg3L79dcBTxw/KzG3AtsX+8IjYnZnTS483WuZtl3nbZd52jSrvcpZcvglcEBHnRcTJwHXAXcOJJUlarCXP0DPzWET8HvBV4CTgs5n58NCSSZIWZVknFmXmPcA9Q8pyvEUv04yZedtl3naZt10jybvkJ0UlSd3ie7lIUiU6WehdfEuBiPhsRDwTEQ/1HTszIu6NiMfK5RnleETEX5b8346Ii0ec9dyI2BkR+yLi4Yh4f5fzlgynRMQ3IuJbJfOfluPnRcSukvn28gQ8EfHysr+/fH9qDJlPiogHIuLurmctOQ5ExN6IeDAidpdjXX5MrImIOyLiO+WxfFlX80bEheV+nfv6cUR8YOR5M7NTX/SeYP0ucD5wMvAt4PUdyPVG4GLgob5jfw5sLdtbgY+W7auAfwQCuBTYNeKs5wAXl+1XAP8BvL6reUuGACbK9suAXSXLF4DryvFPA79btt8LfLpsXwfcPobMHwT+Dri77Hc2a/ndB4CzjzvW5cfEduA9ZftkYE2X8/blPgn4AfCaUecdyw0ecGdcBny1b/9m4OZx5ypZpo4r9EeBc8r2OcCjZftvgHfON25Mue+k9547KyXvacD9wK/QOxlj1fGPDXqvrrqsbK8q42KEGdcB9wFXAHeX/zE7mbUv83yF3snHBPBK4HvH309dzXtcxt8E/m0cebu45LKS3lJgMjMPAZTLV5fjnbkN5c/7i+jNeDudtyxhPAg8A9xL7y+1w5l5bJ5cz2cu3z8CnDXCuJ8EPgT8rOyfRXezzkngnyNiT/TO4IbuPibOB34IfK4sa30mIlZ3OG+/64DbyvZI83ax0GOeYyvtpTiduA0RMQF8CfhAZv74REPnOTbyvJn5v5n5Bnqz30uA1803rFyOLXNEvBV4JjP39B8+QZ5O3L/A5Zl5MfAW4H0R8cYTjB135lX0ljj/OjMvAp6jt2SxkHHn7YXoPW/yNuCLg4bOc2zZebtY6I3eUqAjno6IcwDK5TPl+NhvQ0S8jF6Zfz4zv1wOdzZvv8w8DMzSW1tcExFz50v053o+c/n+6cB/jSji5cDbIuIAvXcZvYLejL2LWZ+XmU+Vy2eAv6f3j2ZXHxMHgYOZuavs30Gv4Luad85bgPsz8+myP9K8XSz0lfSWAncBm8r2Jnpr1XPHf6c8k30pcGTuz65RiIgAbgX2ZebHu54XICJeFRFryvapwJuAfcBO4NoFMs/dlmuBr2VZjGxbZt6cmesyc4re4/NrmXl9F7POiYjVEfGKuW1667wP0dHHRGb+APh+RFxYDl0JPNLVvH3eyQvLLXO5Rpd3HE8aNHhS4Sp6r8z4LvDH485TMt0GHAL+h96/rjfSWwe9D3isXJ5Zxga9D//4LrAXmB5x1l+j9+fbt4EHy9dVXc1bMvwS8EDJ/BDwJ+X4+cA3gP30/ox9eTl+StnfX75//pgeFzO88CqXzmYt2b5Vvh6e+/+q44+JNwC7y2PiH4AzOp73NOA/gdP7jo00r2eKSlIlurjkIklaAgtdkiphoUtSJSx0SaqEhS5JlbDQJakSFrokVcJCl6RK/B8R6oHPc7tTCgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['LoanAmount'].hist(bins = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x123148c18>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking for outliers\n",
    "dataset.boxplot(column = 'LoanAmount',  by = 'Education')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a25349a90>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# how loan_status is dependent on credit history\n",
    "sns.catplot(x = 'Loan_Status', y = 'Credit_History' , kind=\"bar\", data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a29e29c18>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 463.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# how loan_status is dependent on Education\n",
    "sns.catplot(x= 'Loan_Status', hue = 'Education', palette=\"ch:.25\", kind = \"count\", data = dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a29aac710>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 450.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# how loan_status is dependent on Property Area\n",
    "sns.catplot(x= 'Loan_Status', hue = 'Property_Area', palette=\"ch:.25\", kind = \"count\", data = dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a2a325160>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 413.359x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# how loan_status is dependent on marrital status\n",
    "sns.catplot(x= 'Loan_Status', hue = 'Married', palette=\"ch:.25\", kind = \"count\", data = dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a2a360588>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 441.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# how loan_status is dependent on slef employment\n",
    "sns.catplot(x= 'Loan_Status', hue = 'Self_Employed', palette=\"ch:.25\", kind = \"count\", data = dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a2a31ceb8>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbIAAAFgCAYAAADehfw4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAFxZJREFUeJzt3X2wZVV95vHvQ4svvEQgNAShEcbgDDgxYG6QDKkphMQASQQdIVjRoDI2TEFQo85gcAaoFFXWGGCMMCgGAlhGxSiKSlRsUYoYXi4tItBh0iEIDRTd7RuQF7Tb3/xxdssJ3u4+NHefc9fp76fq1N1nnb3P+t0L1U+ttfdeO1WFJEmt2mbSBUiS9EwYZJKkphlkkqSmGWSSpKYZZJKkphlkkqSm9RZkSZYkuT7JiiR3JXlr1352kgeT3N69jh465t1JVia5J8lv9VWbJGl6pK/7yJLsAexRVcuT7AjcBhwLHA88XlV/+pT9DwA+BhwMvAD4CvDiqlrfS4GSpKnQ24isqh6uquXd9mPACmDPTRxyDPDxqnqiqv4RWMkg1CRJ2qixnCNLsg9wEHBz13RakjuSXJZk565tT+CBocNWMUfwJVmaZLZ7Le2xbElSA57VdwdJdgA+Bbytqh5NcjHwJ0B1P88D3gxkjsN/Zt6zqi4BLgE48sgjC/hQT6VL0iTN9W+i5tDriCzJtgxC7KNV9WmAqnqkqtZX1U+AD/Pk9OEqYMnQ4XsBD23q+9euXTv/RUuSmtLnVYsBLgVWVNX5Q+17DO32auDObvsa4IQkz0myL7AfcEtf9UmSpkOfU4uHAm8Avp3k9q7tj4HXJTmQwbThfcDJAFV1V5KrgLuBdcCpXrEoSdqc3i6/H4eZmZmanZ2ddBmS1AfPkY3IlT0kSU0zyCRJTTPIJElNM8gkSU0zyCRJTTPIJElNM8gkSU0zyCRJTTPIJElN6331+4Xs/hU3Tazvvfc/ZGJ9S9I0cUQmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqWm9BlmRJkuuTrEhyV5K3du27JLkuyd93P3fu2pPkz5KsTHJHkpf1VZskaXr0OSJbB7yjqvYHDgFOTXIAcAawrKr2A5Z17wGOAvbrXkuBi3usTZI0JXoLsqp6uKqWd9uPASuAPYFjgCu63a4Aju22jwGurIGbgJ2S7NFXfZKk6TCWc2RJ9gEOAm4Gdq+qh2EQdsBu3W57Ag8MHbaqa3vqdy1NMptkds2aNX2WLUlqQO9BlmQH4FPA26rq0U3tOkdb/UxD1SVVNVNVM4sXL56vMiVJjeo1yJJsyyDEPlpVn+6aH9kwZdj9XN21rwKWDB2+F/BQn/VJktrX51WLAS4FVlTV+UMfXQOc2G2fCHx2qP0PuqsXDwF+uGEKUpKkjXlWj999KPAG4NtJbu/a/hh4L3BVkpOA+4Hjus+uBY4GVgL/DLypx9okSVOityCrqhuZ+7wXwBFz7F/AqX3VI0maTq7sIUlqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJappBJklqmkEmSWqaQSZJalpvQZbksiSrk9w51HZ2kgeT3N69jh767N1JVia5J8lv9VWXJGm69Dkiuxw4co72C6rqwO51LUCSA4ATgJd0x/zfJIt6rE2SNCV6C7KqugH43oi7HwN8vKqeqKp/BFYCB/dVmyRpekziHNlpSe7oph537tr2BB4Y2mdV1/YzkixNMptkds2aNX3XKkla4MYdZBcDLwIOBB4GzuvaM8e+NdcXVNUlVTVTVTOLFy/up0pJUjPGGmRV9UhVra+qnwAf5snpw1XAkqFd9wIeGmdtkqQ2jTXIkuwx9PbVwIYrGq8BTkjynCT7AvsBt4yzNklSm57V1xcn+RhwGLBrklXAWcBhSQ5kMG14H3AyQFXdleQq4G5gHXBqVa3vqzZJ0vToLciq6nVzNF+6if3PBc7tqx5J0nRyZQ9JUtMMMklS0wwySVLTDDJJUtMMMklqTJL13cLrdyX5VpI/SjKxf8+T3Jdk1y089thuvd0tZpBJUnv+pVt4/SXAbwJHM7jFqUXHAgaZJG2tqmo1sJTBOrZJsijJ+5Lc2q1rezJAksOS3JDk6iR3J/nghlFcklcm+dsky5N8MskOXft9Sc7p2r+d5D907T+f5MtJvpnkQwwtM5jk9Ulu6UaMH9rwJJMkjyc5txtB3pRk9yT/CXgV8L5u/xclOb2r744kHx/lb2CQSVLjqupeBv+e7wacBPywqn4V+FXgLd2KSTBYFvAdwC8xWPf2Nd2U4HuA36iqlwGzwB8Nff3arv1i4J1d21nAjVV1EIOVmfYGSLI/8HvAoVV1ILAe+P3umO2Bm6rql4EbgLdU1Te649/VjTD/ATgDOKiqXgqcMsrv39sN0ZKksdowKnol8NIkr+3eP5/Bsn8/Am7pQm/D6ku/Dvwrg6m9v0kC8Gzgb4e+99Pdz9uA13Tb/3nDdlV9Icn3u/YjgF8Bbu2+63nA6u6zHwGfH/qu39zI73EH8NEknwE+M8ovbpBJUuOS/DsGo5/VDALtD6vqS0/Z5zB+9qki1e1/3UZWYwJ4ovu5nn+bGXM9oSTAFVX17jk++3FVbTjmqd817LcZBOWrgP+Z5CVVtW4j+wJOLUpS05IsBj4IXNgFxZeA/5Zk2+7zFyfZvtv94CT7dufGfg+4EbgJODTJL3b7b5fkxZvp9ga6KcMkRwEbni25DHhtkt26z3ZJ8sLNfNdjwI7d/tsAS6rqeuC/AzsBO2zub+CITJLa87wktwPbMlho/SPA+d1nfw7sAyzPYH5vDYMrA2EwZfheBufIbgCurqqfJHkj8LEkz+n2ew/w/zbR/znd/suBrwP3A1TV3UneA3y5C6UfA6cC39nEd30c+HCS04ETgEuTPJ/B6O6CqvrB5v4YeXKk156ZmZmanZ3d4uPvX3HTPFbz9Oy9/yET61tSE+Z64PCWf9lgavGdVfU78/m9C4FTi5Kkpo0UZEmWjdImSVqYqupr0zgag82cI0vyXGA7Bg/H3Jknh7o/B7yg59okSdqszV3scTLwNgahdRtPBtmjwEU91iVJ0kg2GWRV9X7g/Un+sKo+MKaaJEka2UiX31fVB7o1sfYZPqaqruypLkmSRjJSkCX5CIN1uW5ncEc2DO7qNsgkSSQ5Eng/sAj486p677j6HvWG6BnggGr5pjNJ2krcv+Kmef23eu/9D9nkPW3dCvcXMVg/cRWDtRavqaq757OOjRn1PrI7gV/osxBJUrMOBlZW1b1V9SMGq3UcM67ORx2R7QrcneQWnlxAkqp6VS9VSZJasifwwND7VcDLx9X5qEF2dp9FSJKaNtfU49hORY161eLX+y5EktSsVcCSofd7AQ+Nq/NRl6h6LMmj3etfk6xP8mjfxUmSmnArsF/3iJhnM1jF/ppxdT7qiGzH4fdJjmVwck+StJWrqnVJTmPwLLRFwGVVdde4+t+i55FV1WeSnDHfxUiSnrnNXS7fh6q6Frh23P3C6DdEv2bo7TYM7ivznjJJ0sSNOiL73aHtdcB9jPEeAUmSNmbUc2Rv6rsQSZK2xKhXLe6V5Ookq5M8kuRTSfbquzhJkjZn1CWq/oLBpZQvYHAH9+e6NkmSJmrUIFtcVX9RVeu61+XA4h7rkiRpJKMG2dokr0+yqHu9Hvhun4VJktqR5LLu9NOd4+571KsW3wxcCFzA4LL7bwBeACJJC9BXL7xgXm+POvy0t49yX9rlDHJi7M+pHDXI/gQ4saq+D5BkF+BPGQScJGkrV1U3JNlnEn2POrX40g0hBlBV3wMO6qckSZJGN2qQbZNk5w1vuhHZFi1vJUnSfBo1jM4DvpHkrxicIzseOLe3qiRJGtGoK3tcmWQWOJzBA9ReU1V391qZJEkjGHl6sAsuw0uS9DOSfAw4DNg1ySrgrKq6dBx9e55LkqbMiJfLz6uqet24+9xg1Is9JElakAwySVLTDDJJUtM8RyaNyf0rbppY33vvf8jE+pb65ohMktQ0g0yS1DSDTJL0jCRZkuT6JCuS3JXkrePs33NkkjRlzj3ulHl9jMuZn/zg5u5LWwe8o6qWJ9kRuC3JdeNaAcoRmSTpGamqh6tqebf9GLAC2HNc/fcWZHM9LTTJLkmuS/L33c+du/Yk+bMkK5PckeRlfdUlSepP90yyg4Cbx9VnnyOyy4Ejn9J2BrCsqvYDlnXvAY4C9uteS4GLe6xLktSDJDsAnwLeVlWPjqvf3oKsqm4AvveU5mOAK7rtK4Bjh9qvrIGbgJ2S7NFXbZKk+ZVkWwYh9tGq+vQ4+x73ObLdq+phGMypArt17XsCDwztt4qNzK8mWZpkNsnsmjVrei1WkrR5SQJcCqyoqvPH3f9Cudhjriti5rzqpqouqaqZqppZvHhxz2VJkkZwKPAG4PAkt3evo8fV+bgvv38kyR5V9XA3dbi6a18FLBnaby/goTHXJklTYYTL5edVVd3I3AOSsRj3iOwa4MRu+0Tgs0Ptf9BdvXgI8MMNU5CSJG1KbyOyuZ4WCrwXuCrJScD9wHHd7tcCRwMrgX8G3tRXXZKk6dJbkG3iaaFHzLFvAaf2VYskaXotlIs9JEnaIgaZJKlpBpkkqWkGmSTpGUny3CS3JPlW9xiXc8bZv49xkaQpM/PCmXl9jMvsd2Y3d4/YE8DhVfV4t1TVjUn+ultyEIAk91XVPvNZ1wYGmSTpGemuPH+8e7tt95rXMN0UpxYlSc9YkkVJbmewYtN1VTUVj3GRJG0lqmp9VR3IYInBg5P8xyQXbVh7EXjB0DqMZ85n304tSpLmTVX9IMnXgCOr6qcLXXTnyA7so09HZJKkZyTJ4iQ7ddvPA34D+Ltx9e+ITJL0TO0BXJFkEYMB0lVV9flxdW6QSdKUGeFy+XlVVXcAB21mn3366t+pRUlS0wwySVLTDDJJUtMMMklS0wwySVLTDDJJUtMMMklS0wwySVLTDDJJUtMMMklS0wwySVLTXGtxK3TucadMrO8zP/nBifUtaTo5IpMkNc0gkyQ1zSCTJDXNIJMkNc0gkyQ1zSCTJDXNIJMkNc0gkyQ1zSCTJDXNIJMkNc0gkyQ1zbUWJ+SrF14w6RIkaSo4IpMkNc0gkyQ1zSCTJDXNIJMkNc0gkyQ1zSCTJDXNIJMkNc0gkyQ1zSCTJDXNIJMkNc0gkyQ1zSCTJDXNIJMkNc0gkyQ1zSCTJDVtIs8jS3If8BiwHlhXVTNJdgE+AewD3AccX1Xfn0R9kqR2THJE9oqqOrCqZrr3ZwDLqmo/YFn3XpKkTVpIU4vHAFd021cAx06wFklSIyYVZAV8OcltSZZ2bbtX1cMA3c/d5jowydIks0lm16xZM6ZyJUkL1UTOkQGHVtVDSXYDrkvyd6MeWFWXAJcAzMzMVF8FSpLaMJERWVU91P1cDVwNHAw8kmQPgO7n6knUJklqy9iDLMn2SXbcsA28ErgTuAY4sdvtROCz465NktSeSUwt7g5cnWRD/39ZVV9McitwVZKTgPuB4yZQmySpMWMPsqq6F/jlOdq/Cxwx7nokSW1bSJffS5L0tBlkkqSmGWSSpKYZZJKkphlkkqSmGWSSpKYZZJKkphlkkqSmGWSSpKYZZJKkphlkkqSmTep5ZJLG6KsXXjCxvg8/7e0T61tbB0dkkqSmGWSSpKYZZJKkpnmOTGM188KZifY/+53ZifYvaf45IpMkNc0gkyQ1zSCTJDXNIJMkNc0gkyQ1zSCTJDXNIJMkNc0gkyQ1zSCTJDXNlT0k9erc406ZWN9nfvKDE+tb4+OITJLUNINMktQ0g0yS1DSDTJLUNINMktQ0g0yS1DSDTJLUNO8jkzS1fCL51sERmSSpaQaZJKlpBpkkqWkGmSSpaQaZJKlpBpkkqWkGmSSpaQaZJKlpBpkkqWkGmSSpaQaZJKlpBpkkqWkGmSSpaQaZJKlpBpkkqWkGmSSpaQaZJKlpBpkkqWkLLsiSHJnkniQrk5wx6XokSQvbggqyJIuAi4CjgAOA1yU5YLJVSZIWsgUVZMDBwMqqureqfgR8HDhmwjVJkhawZ026gKfYE3hg6P0q4OXDOyRZCizt3j6e5J4x1TZNdgXWTrqISUgy6RK2Rv7/tmW+WFVHzlct02yhBdlc/9Xr37ypugS4ZDzlTKcks1U1M+k6tHXw/zf1baFNLa4Clgy93wt4aEK1SJIasNCC7FZgvyT7Jnk2cAJwzYRrkiQtYAtqarGq1iU5DfgSsAi4rKrumnBZ08ipWY2T/7+pV6mqze8lSdICtdCmFiVJeloMMklS0wyyrUQGbkxy1FDb8Um+OMm6NJ2SVJLzht6/M8nZEyxJU8wg20rU4GToKcD5SZ6bZHvgXODUyVamKfUE8Joku066EE0/g2wrUlV3Ap8D/gdwFnBlVf3DZKvSlFrH4GrFt0+6EE2/BXX5vcbiHGA58CPA1RbUp4uAO5L870kXoulmkG1lquqfknwCeLyqnph0PZpeVfVokiuB04F/mXQ9ml5OLW6dftK9pL79H+AkYPtJF6LpZZBJ6k1VfQ+4ikGYSb0wyCT17TwGj3KReuESVZKkpjkikyQ1zSCTJDXNIJMkNc0gkyQ1zSCTJDXNIJMkNc0gU5OSPD6BPs9McleSO5LcnuTlXfvbkmw3wvEj7Sfp6fE+MjUpyeNVtcMY+/s14HzgsKp6ons8ybOr6qEk9wEzVbV2M98x0n6Snh5HZJoaSV6YZFk3YlqWZO+u/XeT3Jzkm0m+kmT3rv3sJJcl+VqSe5Ocvomv3wNYu2Gh5apa24XY6cALgOuTXN9978VJZrvR2zld21z7/XRUmeS1SS7vto9LcmeSbyW5YZ7/TNLUcUSmJs01IkvyOeCvquqKJG8GXlVVxybZGfhBVVWS/wrsX1Xv6J5Y/ErgFcCOwD3AL1TVj+fobwfgRmA74CvAJ6rq691n9zE00kqyS1V9L8kiYBlwelXdMcd+P/0dkrwW+J2qemOSbwNHVtWDSXaqqh/M599OmjaOyDRNfg34y277I8Cvd9t7AV/qAuJdwEuGjvlCVT3RhctqYPe5vriqHgd+BVgKrAE+keSNG6nj+CTLgW92fR3wNH+PvwEuT/IWYNHTPFba6hhkmmYbphs+AFxYVb8EnAw8d2if4WeyrWcTz+irqvVV9bWqOgs4DfgvT90nyb7AO4EjquqlwBee0t9c9TG8T1WdArwHWALcnuTnN1aTJINM0+UbwAnd9u8zmAoEeD7wYLd94pZ8cZJ/n2S/oaYDge90248xmJoE+Dngn4Afdufijho6Zng/gEeS7J9kG+DVQ329qKpurqr/BaxlEGiSNsInRKtV2yVZNfT+fAZPIr4sybsYTP+9qfvsbOCTSR4EbgL23YL+dgA+kGQnYB2wksE0I8AlwF8nebiqXpHkm8BdwL0MpgmZaz/gDODzwAPAnV0fAO/rQjMMzrF9awvqlbYaXuwhSWqaU4uSpKY5tSgN6S6sWDbHR0dU1XfHXY+kzXNqUZLUNKcWJUlNM8gkSU0zyCRJTTPIJElN+//cha9X2l6M4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 428.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# how loan_status is dependent on number of dependents\n",
    "sns.catplot(x= 'Loan_Status', hue = 'Dependents', palette=\"ch:.25\", kind = \"count\", data = dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for missing values for each variables\n",
    "dataset.apply(lambda x: sum(x.isnull()),axis=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2aa17390>"
      ]
     },
     "execution_count": 80,
     "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": [
    "#Imputing mean value to the Loan Amount\n",
    "\n",
    "dataset['LoanAmount'].fillna(dataset['LoanAmount'].mean(), inplace=True)\n",
    "dataset['LoanAmount'].hist(bins = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2ab07f60>"
      ]
     },
     "execution_count": 81,
     "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": [
    "# Treating the outlier in the loan amount with log transformation\n",
    "dataset['LoanAmount'] = np.log(dataset['LoanAmount'])\n",
    "dataset['LoanAmount'].hist(bins = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2ad0ab70>"
      ]
     },
     "execution_count": 82,
     "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": [
    "#Similarly for the Income, Adding all the income and \n",
    "dataset['TotalIncome'] = dataset['ApplicantIncome']+ dataset['CoapplicantIncome']\n",
    "dataset['TotalIncome'].hist(bins = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2a93fe80>"
      ]
     },
     "execution_count": 83,
     "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": [
    "#Applying Log Transformation\n",
    "dataset['TotalIncome'] = np.log(dataset['LoanAmount'])\n",
    "dataset['TotalIncome'].hist(bins = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}