Loan+Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train=pd.read_csv(r'C:\\Users\\naveen chauhan\\Desktop\\mldata\\loan prediction\\train.csv')\n",
    "train.Loan_Status=train.Loan_Status.map({'Y':1,'N':0})\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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  \n",
       "0             1.0         Urban  \n",
       "1             1.0         Rural  \n",
       "2             1.0         Urban  \n",
       "3             1.0         Urban  \n",
       "4             1.0         Urban  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Loan_status=train.Loan_Status\n",
    "train.drop('Loan_Status',axis=1,inplace=True)\n",
    "test=pd.read_csv(r'C:\\Users\\naveen chauhan\\Desktop\\mldata\\loan prediction\\test.csv')\n",
    "Loan_ID=test.Loan_ID\n",
    "data=train.append(test)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>981.000000</td>\n",
       "      <td>981.000000</td>\n",
       "      <td>954.000000</td>\n",
       "      <td>961.000000</td>\n",
       "      <td>902.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5179.795107</td>\n",
       "      <td>1601.916330</td>\n",
       "      <td>142.511530</td>\n",
       "      <td>342.201873</td>\n",
       "      <td>0.835920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5695.104533</td>\n",
       "      <td>2718.772806</td>\n",
       "      <td>77.421743</td>\n",
       "      <td>65.100602</td>\n",
       "      <td>0.370553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2875.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3800.000000</td>\n",
       "      <td>1110.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5516.000000</td>\n",
       "      <td>2365.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>360.000000</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>480.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       981.000000         981.000000  954.000000        961.000000   \n",
       "mean       5179.795107        1601.916330  142.511530        342.201873   \n",
       "std        5695.104533        2718.772806   77.421743         65.100602   \n",
       "min           0.000000           0.000000    9.000000          6.000000   \n",
       "25%        2875.000000           0.000000  100.000000        360.000000   \n",
       "50%        3800.000000        1110.000000  126.000000        360.000000   \n",
       "75%        5516.000000        2365.000000  162.000000        360.000000   \n",
       "max       81000.000000       41667.000000  700.000000        480.000000   \n",
       "\n",
       "       Credit_History  \n",
       "count      902.000000  \n",
       "mean         0.835920  \n",
       "std          0.370553  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               24\n",
       "Married               3\n",
       "Dependents           25\n",
       "Education             0\n",
       "Self_Employed        55\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           27\n",
       "Loan_Amount_Term     20\n",
       "Credit_History       79\n",
       "Property_Area         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Dependents.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23e532a3748>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UxsbdPfD8fub2GUx+z8ZM/z1Pt3/yecYGtscG4lgPeHxV3TlTQFW1GlgN8KANthvNOaWS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23e50e84208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline \n",
    "corrmat=data.corr()\n",
    "f,ax=plt.subplots(figsize=(9,9))\n",
    "sns.heatmap(corrmat,vmax=.8,square=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    775\n",
       "0.0    182\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Gender=data.Gender.map({'Male':1,'Female':0})\n",
    "data.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23e535633c8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XIQyM+x6+v+sQBsIv772fNRet0XUYo8UxUpIkDQeTKM0VK1KSJKl1wzpGykRKkiS1z649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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23e50dead68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat=data.corr()\n",
    "f,ax=plt.subplots(figsize=(9,9))\n",
    "sns.heatmap(corrmat,vmax=.8,square=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Married=data.Married.map({'Yes':1,'No':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    631\n",
       "0.0    347\n",
       "Name: Married, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Married.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.Dependents=data.Dependents.map({'0':0,'1':1,'2':2,'3+':3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    545\n",
       "2.0    160\n",
       "1.0    160\n",
       "3.0     91\n",
       "Name: Dependents, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Dependents.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23e53aecb00>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SJEmaxIqUJElqnbP2JEmSNIkVKUmS1DorUpIkSZrEipQkSWpdOWtPkiRJ/axI\nSZKk1o3rGCkTKUmS1LpxTaTs2pMkSRqQFSlJktQ6H1osSZKkSaxISZKk1vnQYkmSJE1iRUqSJLXO\nWXuSJEmaxIqUJElqnRWpeSLJzSvhGnsnqSRbtX2tOeI4NMlaXcYgSZJmNnKJ1EqyP3AqsF/HcRwK\nmEhJkkZerYTXXJI8L8klSS5Lcvg0x9+Q5KIkFyQ5Mckmc7U5FolUkk2aG5648Y2b/S9I8pMk5yb5\nXpL1m/1HJPl0kpOTXJ7kkL621gF2BV5BXyKVZPckP0jylSSXJnlXkgOSnJFkSZLN54jls0n26Wvv\n5r52T07y1SQXJzkqPYcAjwZOSnJS699ESZLGWJIFwEeA5wNbA/sn2XrKaecCO1XVtsBXgXfP1e5Y\nJFLAh4H/bm78KOCDzf5TgSdX1fbAl4A39n1mK+BPgV2AtyZZrdn/QuB/q+pS4PokO/R95onA64An\nAC8FtqyqXYBPAQfPEctstqdXfdoa2AzYtao+CPwG2KOq9pj6gSQHJTkryVmX3XzlMlxCkqTuLE37\nrznsAlxWVZdX1Z308oK9+k+oqpOq6tZm88fAhnM1Oi6J1FOALzTvjwR2a95vCByfZAnwD8Dj+z7z\n7aq6o6quA64F1m/270/vm0vzdf++z5xZVddU1R3AL4ATmv1LgEVzxDKbM6rq6qpaCpzX19aMqmpx\nVe1UVTs9Zp05T5ckaez1Fxma10F9hzcArurbvrrZN5NXAN+Z65rjOmtvoqv0Q8B7q+rYJLsDR/Sd\nc0ff+3uAVZM8FHgmsE2SAhYAleSN03xmad/2Umb+Xk7EcjdN4pokwMLZYpnt5iRJGjUrY9ZeVS0G\nFs9weLqa1bRDq5K8BNgJeMZc1xyXitRp3Dee6QB6XXoA6wG/bt6/bBna2Ydet9wmVbWoqjYCrmDZ\nqkpzxXIlsGPzfi9gNeZ2E7DuclxbkiRN72pgo77tDekNoZkkybOBNwN7Nj1QsxrFRGqtJFf3vd4A\nHAK8PMkF9MYuva459wjg6CQ/BK5bhrb3B46Zsu9rwIuXI76ZYvkk8IwkZwBPAm5ZhrYWA99xsLkk\nadTNg1l7ZwJbJNk0yUJ6RY9j+09Isj3wCXpJ1LXLcl8j14VUVTMlf8+c5txvAN+YZv8RU7a3ad7u\nPs25/YPFT+7bv3vf+5MnjlXVlTPE8lvgyX273jT1s832a/vef4he96QkSVoBVXV3ktcCx9MbuvPp\nqrowyduBs6rqWOA9wDr0ijAAv6qqPWdrd+QSKUmSNHqWLtNKT+2qquOA46bse0vf+2cvb5uj2LUn\nSZI0L1iRkiRJrfNZe5IkSZqNDHq8AAAgAElEQVTEipQkSWpd9yOk2mEiJUmSWmfXniRJkiaxIiVJ\nklq3DA8VHklWpCRJkgZkRUqSJLVuPizI2QYrUpIkSQOyIiVJklo3nvUoK1KSJEkDsyIlSZJa5zpS\nkiRJmsSKlCRJap2z9iRJkjSJFSlJktS68axHWZGSJEkamBWpMXDCHy7sOoShWGPVhV2HMBQ33nFr\n1yEMxbc/9pCuQxia2++5o+sQhuL07V/TdQhD8a1zP9J1CEPxhK337TqEkeKsPUmSJE1iRUqSJLXO\nWXuSJEmaxIqUJElq3XjWo6xISZIkDcyKlCRJat24ztozkZIkSa2rMe3cs2tPkiRpQFakJElS68a1\na8+KlCRJ0oCsSEmSpNa5IKckSZImsSIlSZJaN571KCtSkiRJA7MiJUmSWucYKUmSJE1iRUqSJLXO\ndaQkSZI0iRUpSZLUOp+1J0mSpEmsSEmSpNY5RkqSJEmTzJlIJbl5ZQQyzXX3TlJJturi+n1xHJpk\nrVmO/yTJeUl+leR3zfvzkixaeVFKkjS/1Ur4rwvzuSK1P3AqsF/HcRwKzJhIVdWTqmo74C3Al6tq\nu+Z15bI0nsTuVUmSRtRAiVSSTZKcmOSC5uvGzf4XNBWac5N8L8n6zf4jknw6yclJLk9yyBztrwPs\nCryCvkQqye5JfpDkK0kuTfKuJAckOSPJkiSbzxHfZ5Ps09fezX3tnpzkq0kuTnJUeg4BHg2clOSk\nAb5Pz09yepJzknw5ydrN/quT/EuSHwF7Jzk1yXuT/DDJRUl2SnJMkp8nOWKGtg9KclaSs26/84bl\nDU2SpJVq6Up4dWHQitSHgf+uqm2Bo4APNvtPBZ5cVdsDXwLe2PeZrYA/BXYB3ppktVnafyHwv1V1\nKXB9kh36jj0ReB3wBOClwJZVtQvwKeDgOeKbzfb0qk9bA5sBu1bVB4HfAHtU1R7L0Ma9kjwCOBx4\nVlXtAFzQxD3hlqrataqObrZvq6qnAf8FfB14dXOPByV58NT2q2pxVe1UVTutsXC95QlNkiQNyaDd\nSk8BXtS8PxJ4d/N+Q+DLSR4FLASu6PvMt6vqDuCOJNcC6wNXz9D+/sD7m/dfarbPabbPrKprAJL8\nAjih2b8EmEh2ZopvNmdU1dVNu+cBi+glhoN6Kr2k7LQk0Pt+9Lf35SnnH9t8XQIsqarfNrFcSe/7\n+scViEWSpE4trfFcR2pY43MmvjsfAt5bVccm2R04ou+cO/re3zPTtZM8FHgmsE2SAhYAlWSiutXf\nztK+7aUztdkX3900Vbj0spuFyxvfcgi9qtpLZzh+y5Tt/vuYeo+Oo5IkjbTxTKMG79o7jfvGLh3A\nfZWW9YBfN+9fNmDb+9DrltukqhZV1Ub0Klu7DSG+K4Edm/d7AbN1L064CVh3Oa7dH8MzkmwGkGTt\nJFsM0I4kSZqnliWRWqsZHD3xegNwCPDyJBfQG6c0MfbnCODoJD8Erhswpv2BY6bs+xrw4uVoY6b4\nPkkvuTkDeBL3rwpNZzHwneUdbN50zb2CXlfn+fQSqy2Xpw1JksbFUqr1VxdSY9pn+UDysAdtORZ/\niGusunDuk0bAjXfc2nUIQ/HItR7SdQhDc/s9d8x90gh43Nobdh3CUHzr3I90HcJQPGHrfbsOYWgu\nvvbMtH2NF2+yd+s/q77wy2Nav4+pHHsjSZJaN64PLe4skWoGlZ84zaFnVdXvV3Y8yyLJT4DVp+x+\naVUt6SIeSZLUrc4SqSZZ2q6r6w+iqp7UdQySJI0iH1osSZKkSRwjJUmSWtfVrLq2WZGSJEkakBUp\nSZLUunGdtWdFSpIkaUBWpCRJUuuctSdJkqRJrEhJkqTWjesj6axISZIkDciKlCRJap3rSEmSJGkS\nK1KSJKl1ztqTJEnSJFakJElS68Z1ZXMTKUmS1LpxHWxuIjUGtlh3g65DUJ9f1bVdhzAUD124btch\nDM11d47HP+C/vvMPXYcwFE/Yet+uQxiKJRd9uesQNA+YSEmSpNa5IKckSZImsSIlSZJa5/IHkiRJ\nmsSKlCRJat24Ln9gRUqSJGlAVqQkSVLrxnUdKStSkiRJA7IiJUmSWuc6UpIkSZrEipQkSWqdY6Qk\nSZI0iRUpSZLUOteRkiRJ0iRWpCRJUuuWOmtPkiRJ/axISZKk1o1nPcqKlCRJ0sCsSEmSpNa5jpQk\nSZImsSIlSZJaN64VKRMpSZLUOh9avBIleWSSLyX5RZKLkhyXZMsB2zowyYeb969O8td9+x89x2dP\nTrJT3/aiJD9t3u+U5IOzfHZRkhcPErMkSRoN864ilSTAMcDnqmq/Zt92wPrApc32gqq6Z3nbrqqP\n920eCPwU+M0gcVbVWcBZs5yyCHgx8IVlbTPJqlV19yDxSJI0n41r1958rEjtAdzVn/RU1XnAgiQn\nJfkCsAQgyUuSnJHkvCSfSLKg2f/yJJcm+QGw60Q7SY5IcliSfYCdgKOaz665vEEm2T3Jt5r3z2ja\nOS/JuUnWBd4FPK3Z9/okayT5TJIlzTl7NJ89MMnRSb4JnJDkyCR79V3nqCR7Lvd3UZIktW7eVaSA\nbYCzZzi2C7BNVV2R5HHAvsCuVXVXko8CByT5LvA2YEfgBuAk4Nz+Rqrqq0leCxzWVJZmc1SS25r3\nC4Gl05xzGPCaqvpRknWA24HDm/b/HCDJ3zfXfkKSreglTRPdlU8Btq2q65M8A3g98I0k6wFPBV42\n9YJJDgIOAth0vS1Zf+1ZeyklSeqUDy2eH86oqiua98+ilyydmeS8Znsz4EnAyVX1u6q6E/jyCl7z\ngKrarqq2A/5shnN+BLw3ySHAg2fontsNOBKgqi4GfglMJFLfrarrm2M/AB6T5BHA/sDXpmuvqhZX\n1U5VtZNJlCRJ3ZiPFakLgX1mOHZL3/vQG0f1pv4TkryQlbwSfVW9K8m36SVaP07y7GlOyyxN3DJl\n+0jgAGA/4G+GE6UkSd1x1t7K831g9SSvnNiRZGfgGVPOOxHYp6nckOQhSTYBfgLsnuShSVYD/nKG\n69wErDuMgJNsXlVLqurf6Q1A32qa9k+hlxzRdOltDFwyQ5OfBQ4FqKoLhxGjJEkavnmXSFUvZd0b\neE6z/MGFwBFMmV1XVRcB/0xvrNEFwHeBR1XVNc35pwPfA86Z4VKfBT4+6GDzKQ5N8tMk5wO3Ad8B\nLgDuTnJ+ktcDH6U3YH4Jve7GA6vqjukaq6rfAj8DPrOCcUmSNC8spVp/zSXJ85JckuSyJIdPc3z1\nJF9ujv8kyaI52xzXUtsoS7IWvZmJO1TVDXOd/5QN9vAPcR751a3Xdh3CUGy81iO6DmForrvzxq5D\nGIrVV1mt6xCG4u7lX71mXlpy0YoOwZ0/VnvYZrMNPxmKHR61W+s/q8655tQZ76OZ2X8p8BzgauBM\nYP+mMDNxzt/Rm/z16iT7AXtX1b6zXXPeVaQe6JrxVRcDH1qWJEqSpFFQVa2/5rALcFlVXd5MRvsS\nsNeUc/YCPte8/yrwrGZ9yxnNx8HmK12SY4BNp+z+x6o6fmXHUlXfozd+SpIkLYf+pYEai6tqcfN+\nA+CqvmNX05vp3+/ec6rq7iQ3AA8FrpvpmiZSQFXt3XUMkiSNs5WxsnmTNC2e4fB0laWpQS3LOZPY\ntSdJkh4IrgY26tvekPs/Ju7ec5KsCqwHXD9boyZSkiSpdbUS/pvDmcAWSTZNspDeWo3HTjnnWO57\nmsg+wPdrjsFXdu1JkqSx14x5ei1wPLAA+HRVXZjk7cBZVXUs8F/AkUkuo1eJ2m+udk2kJElS65bO\ng+WWquo44Lgp+97S9/52Zl7Ie1p27UmSJA3IipQkSWrdMoxhGklWpCRJkgZkRUqSJLVuPoyRaoOJ\nlCRJap1de5IkSZrEipQkSWrduHbtWZGSJEkakBUpSZLUOsdISZIkaRIrUpIkqXWOkZIkSdIkVqTG\nwOm/PiltXyPJQVW1uO3rrAzjci/ex/zifcw/43Iv43IfjpHSA91BXQcwRONyL97H/OJ9zD/jci/j\nch9jyYqUJElqXdXSrkNohRUpSZKkAVmR0rIa+f75PuNyL97H/OJ9zD/jci9jcR9Lx3SMVGpMpyNK\nkqT5Y5OHbtt6wvHL31/Q+uSrqaxISZKk1o1r4cYxUpIkSQOyIiVJklo3rmOkrEhJIyrJKkke1HUc\ngiRrdx2DxkuSP0/iz+gRYEVK00qyADi+qp7ddSwrIskbZjteVe9dWbEMQ5IvAK8G7gHOBtZL8t6q\nek+3kS27JFsCHwPWr6ptkmwL7FlV/3/HoS23JE8FPgWsA2yc5InAq6rq77qNbPkkObGqnjXXvlHR\n/IKxIX0/46rqgu4iGsh+wAeSfA34TFX9rOuAVpRjpPSAUlX3ALcmWa/rWFbQus1rJ+BvgQ2a16uB\nrTuMa1BbV9WNwAuB44CNgZd2G9Jy+yTwJuAuuPcH3H6dRjS49wF/CvweoKrOB57eaUTLIckaSR4C\nPCzJnyR5SPNaBDy62+gGk+StwM/oLRnwkeb14U6DGkBVvQTYHvgF8Jkkpyc5KMm6HYemKaxIaTa3\nA0uSfBe4ZWJnVR3SXUjLp6reBpDkBGCHqrqp2T4COLrD0Aa1WpLV6CVSH66qu5KVPtt3Ra1VVWdM\nifvuroJZUVV11ZR7uaerWAbwKuBQeknT2cDEjdxILwEZRS8GNquqO7oOZEVV1Y1NRWpNen9OewP/\nkOSDVfWhbqNbfkvHtCJlIqXZfLt5jYONgTv7tu8EFnUTygr5BHAlcD5wSpJNgBs6jWj5XZdkc+iN\nPE2yD3BNtyEN7Kqme6+SLAQOoVcNGQlV9QF63UcHj+IP5hlcSK8KPdKJVJI9gZcDmwNHArtU1bVJ\n1qL3d2zk/rzG9aHFLsipWSVZE9i4qi7pOpYVkeTNwF8Bx9D7Ab438JWq+tdOA1tOSTatqiv6tgM8\npqp+3mFYyyXJZvS6XZ4K/AG4AnhJVV3ZZVyDSPIw4APAs+lVc04AXldVv+80sAE0CeEiJo8r+u/O\nAhpQkh2BrwMX0JdMVdWLOgtqAEk+B/xXVZ0yzbFnVdWJHYS1Qh754Me1nnD83z/+bKWX6E2kNKMk\nLwD+A1hYVZsm2Q54e1Xt2XFoA0myA/C0ZvOUqjq3y3gGkeScqtphyr6zq2rHrmIaVDPTbZWJ7lZ1\nJ8mR9Cof53Ff12SNUjf+hCQ/BT4NLAHufUruKCUe4zLZZ6r119uq9YTjtzdc7MrmmleOAHYBTgao\nqvOSbNplQCtoLeDGqvpMkodPre7MZ0m2Ah5Pb5Ze/2/WDwLW6CaqwSR5MPDXNNWPifFFI/pDe1Pg\nYO5fyRm1XzZ2ojeRYRx+s75+1GbjTlVV9yS5Ncl6VTVqXfcPOCZSms3dVXXDlIG0I/kPbTOTZyfg\nscBngNWAzwO7dhnXcngs8OfAg4EX9O2/CXhlJxEN7jjgx0ypGIyorwP/BXyT0b6XnwKPZHTHqvU7\nM8k7gGOZ3LU3assfjPxkn6nGdUFOEynN5qdJXgwsSLIFvYG0p3Uc06D2pjeV+ByAqvrNKE0jrqpv\nAN9I8pSqOr3reFbQGlU16/peI+T2qvpg10EMwcOAi5KcweTkY9Qqa9CrogPs3revGKFlKRrjNNln\nrJlIaTYHA2+m9w/rF4HjgXd0GtHg7qyqSjIxU2xUV6K+LMk/cf+upL/pLKLld2SSVwLfYvIP7eu7\nC2lgH2iqnScw+V7O6S6kgRzRdQDD0Iwten9Vfa3rWFZUVX2umQm6ZbPrkqq6q8uYVtR49Bzfn4PN\n9YCQ5DBgC+A5wL8BfwN8YdSmfCc5DfghvTV/7l2vaJR+cCR5DfBO4I/c11VcVbVZd1ENJsm/0VsQ\n9Rfc17VXVfXM7qJ6YEvyw6p62txnzm9Jdgc+R2+5kwAbAS+bbhbfqHjYg7ZsPeG47sZLnbWn7iX5\nJrOMhRrRcj9JngM8l94/SsdX1Xc7Dmm5JTmvqrbrOo4VkeQXwJOq6rquY1lRSS4Gtq2qO+c8eR5L\nchP3/T+/kN4YwluqauSe5Zjkn4GbgS8zeWzRjZ0FNYAkZwMvnlh6pnm00hdHcYbuhIesu0XrCcf1\nN/3cWXuaF/6j+foiegNQP99s70/vt6OR1CROI5c8TfGtJH9WVcd1HcgKuBC4tesghuR8ehMAru06\nkBVRVZPGCyZ5IfeNNRo1r2q+/j295DDN1407i2gwq/Wv31dVlzZPNdA8Y0VKM0pySlU9fa5981mS\nU6tqtym/cUPzj+uo/cbd3Mfa9FZmv5MRvI8kx9BbyuEkJo8rGrnZSElOBrYFzmT0B2lPkuTHVfXk\nruN4oEryaXr/Zh3Z7DoAWLWqXt5dVCvmT9Z5TOsJxx9uvsyKlOaVhyfZrKouh3vXzHl4xzEtl6ra\nrfk6MjP0ZjMm9/H15jUO3tp1AMMwZW2yVegtFTKSv2U3T2N4HbBJVf1tkscAW1TVdzoObXn9LfAa\nerOlA5zC6D7/cKyZSGk2rwdOTnJ5s72I+8rmIyPJKsAFVbVN17GsqOaRMAcAm1bVO5JsBDyqqs7o\nOLRlNk6zkarqB0nWB3Zudp1RVaPYzde/Ntnd9Lrw9+omlBU2sar5xIDz39B7QPmoJVKvbhYWvXdx\n0SSvo/dIopE0rutI2bWnWSVZHdiq2bx4VJ+onuQo4E1V9auuY1kRST5Gb3bYM6vqcUn+BDihqnae\n46PzxjjNRkryV8B76K3+H3o/vP+hqr7aZVwPZEnOqqqdkpxbVds3+0ZuksYMj4O6955G0XrrbN56\nwnHDzb+wa0/zzo7ct2bRE5OM5INMgUcBFzYLDvbP5Bm1sSxPqqodkpwLUFV/aKo7o+Q/gedOnY1E\n7+/aqHkzsPNEFSrJw4HvASOVSCXZEPgQvZX+CziV3sOXr+40sMHcmWQNmq7JZkjCyMyqTLI/8GJg\n0yTH9h16EDByD8PuN66FGxMpzWimB5kCo5hIva3rAIbkrmbRwYkfEg9n9B5NMk6zkVaZ0pX3e3pj\njEbNZ4AvAH/ZbL+k2fecziIa3NuB/wU2TPI54BnAK7oNabmcRu9RPQ+j90vHhJuAUXvMzQOCXXua\nUZKfMQYPMh2nJ6knOQDYF9iBXvfYPsA/V9XRnQa2HMZpNlKS99CbtffFZte+wJKqemN3US2/6bq+\nRq07LMnGE133zS8YT6XX3XraKI5ba56+cFtVLW2qtlsB3xnV8YQA66y1aes/S26+9QoX5NT8keRo\n4JCqGvkHmTYl8peOw5PUk2wFPIveD4kTq+pnHYe0XJpxd68BduO+2UgfHeHxdy+i716q6piOQ1pu\nSb4HfJb7EsL9gZdX1bM6C2o5TTemaJQ1C3I+DfgTeg/5Pgu4taoO6DSwFWAipQecJCcB2wEj/yDT\nJF8BnkxvQc6Re5J6kofMdnyUnlPX/KZ9e1Xd02wvAFavqpFbpLMZf3NNVd3ebK8JrF9VV3Ya2HJK\nsjHwYeAp9KqFp9EbI/XLTgNbDqM+EHuqicQwycHAmlX17lG/x7XXWtR6wnHLrVc62FzzyhFdBzBE\no/4k9bO5b5XmjYE/NO8fDPwK2LS70JbbicCz6T3GA2BNeg/9fWpnEQ3uaCbHfU+zb2RmUQI0XWIj\n9wvSFBsk+eBMB0fll6Y+SfIUel3fE2O8/Jk9D/mHohk1a+RsQm8xu+8lWQtY0HVcg6iqz3Udw4qo\nqk0BknwcOHbiETFJnk8vKRkla1TVRBJFVd3c/N0aRav2P2evqu4cwVmUE5W1g7lvhi4wctXn2+j9\nwjEuDgXeBBxTVRcm2Yze0wBG1tIx7QEzkdKMkrwSOAh4CL3ZexsAH6c3PmekJNkC+Ddga2CNif1V\ntVlnQQ1m56p69cRGVX0nyTu6DGgAtyTZoarOAUiyI70fgqPod0n2rKpjAZLsBYziw5i/DvwX8E1G\nbxbohN+P+i9M/arqB8AP+rYvp7fK+cga16FEJlKazWvoPbj0JwBV9fMkj+g2pIF9ht7jPN4H7AG8\nnF7X2Ki5rnm6/efpdfW9hNFbW+ZQ4Ogkv2m2H0VvttsoejVwVJIP0/v7dBXw192GNJDbq2rGbrER\nsUxrRSV5fFVd2HYwg0ry/qo6NMk3meYxPSNWJXxAcLC5ZpTkJ1X1pIkBjklWBc6pqm27jm15JTm7\nqnZMsqSqntDs+2FVPW2uz84nzaDztwITD44+BXjbKA02B2jWjXosveTj4lGe0g2QZB16/57e1HUs\ng0jyYmALemPV+ieWnNNZUC2Z77P7kuxYVWcnecZ0x5tK1UhafY2NWk847rj9Kgeba175QZJ/AtZM\n8hzg7+iV/kfR7c0z936e5LXAr4GRq641CdPruo5jCHbmvvE424/qivnNUg5/QXMvvUchQlW9vcOw\nBvEE4KXAM7mva6+a7XEzryvRVXV283VkE6YHGhMpzeZwerNFltAbK/XtqvpUtyEN7FBgLXpjDN5B\n7wfEyzqNaADNwnyHcf9BwSPzA2/MVsz/BnADvUHOI7kOVmNvYLP+gfNjbF53wyRZwiwxjmKPwIRx\n7QEzkdL9NANmN6yqjwCfbAadPxzYMckfR/GBrFV1ZvP2Znrjo0bV0fQG/H+K+5KQUbMTY7BifmPD\nqnpe10EMwfn0ltIYuRXAx9CfN19Db8mWP+swFi0DEylN543Afn3bC+k9UHYdeoO2RyaRmvLQz/sZ\nwYGbd1fVx7oOYgX9FHgkveeJjbrTkjyhqpZ0HcgKWh+4OMmZjPjiu8tgXlfd+hdBTXLHKC2KOpfx\n+N3p/kykNJ2FVXVV3/apzdic65tVqUfJ/2vv3mMtrco7jn9/MwWHIHdGBHEAi2LLRJBLRTGdQqVN\ngxouKg6iVSnSiFTAtlHbCFYSW41oRdFE7UBwCkhgEKRF5CL1glyttAgU5TJWZQbG2xREufz6x3o3\nZ8/hzBnOPmf2Omuf3yfZmXnfNyd5duayn73Ws57n5ZSTVOdRTh/O6vqIZ+AySe8EVrDuB15Lxebb\nA9+X1HzHfMpomLdKupfyXgS4we2XU2sHMFMkXT1+tE3/PdsH1IksRlUSqZjINv0Xtt/Vd7lwyLFM\n13MpE+yXAkdTlsrPm83HnzegV9f1N333DLTUD+u02gHMoD+rHcBMGF/YLOlAyr+XZgqeJS2g1EFu\nL2kbxr40bQnsVC2wKZLUf6JwM0kvpe8LYMsnKUdzPSqJVEzsBknH2f5c/01Jx1Pm7jWjm+d2BXBF\nd8JqKfB1Sf9g+8y60U1dr8N5y0bhNFLf7MMm2x1MRNLelOTpDcC9wEV1I5qy4ymHSnaiFP/3ko9f\nAZ+uFdQAPtb3+weAM/quR/UkZdPSRyqepmu6eQllq6L37Wdf4FnAYbZX1YptEF0CdSglidoVuBT4\nF9s/rhnXILpRKqcAi2y/o+vYvoftr1QObYMkrWVsXmD/fzy97bAtqwQ2gG4rr/dexnMrHfO7U6Bv\npPzbWANcAPy17V2qBjYNkk5s8UvSVEk6xPbXascRSaRiEpIOBvbsLm+3fU3NeAYh6RxgMfDvwPm2\n/7tySNMi6QLKt+232F4saTPgett7Vw4tGiTpSeAbwLG2f9Ddu6eVRHB9JL2Cp7cIabG9xnrN9sai\nc0kSqRhp3QfFw91l06sgAJJutr1fr9t8d+97tveqHdszJelc22/e0L0WbKiwebaTdDhlReoVlC3w\n84HPt7yFvL4+ZbabnlM3Xv//AVFXaqRipNmeVzuGGfbbbhXKAJJ+l/YaQe7Zf9GNHtq3UiwD6Qqb\nN6fxwmbbK4AV3Wncw4CTgR0kfQZYYfvKqgEOZpT6lE1m1N9fM0btQyZi1J1KWTl4vqTlwNWUvl+z\nnqT3dXVSL5H0q+61FlhF6RDekuOBm4EXU7Zae68v01ZhMwC2H7a93PargZ0pqznvrRzWoHp9yiKG\nIlt7EY2RtB1wAGUV5Du2H6oc0pRI+rDt99WOYyaMUmGzpPmUxpz9dUUr60U0GEnXAntTThg326dM\n0rNs/2Z99yRdbPuIOtFFvyRSEY2RdASlEaQpzVJXVA5pyiQ9D9iFdT+0/6NeRIMbhcJmSSdSVjtX\n0Te0uMHGokhaMtH91tpuTFRMngLz2Sk1UhENkXQWsDulUzvA8ZJeZfuEimFNiaR/pBQ4f591hxY3\nl0iN0ADmd1PaaKypHch0tZYwjSfpucDzeHozzi0pDUdjlkkiFdGWJcDiXiFt196htTlvh1M+tFsr\nkp/IqBQ2/wj4Ze0gZkJfvzIoc0I3AR5u6ITunwJvpdSq9TfjXAu8v0ZAMbkkUhFtuQtYBPQGmT4f\nuK1eOAO5h/LhNgqJ1KgMYL6H0vH/ctatKzpj/T8yO9neov9a0mHAH1QKZ8psnwOcI+lI2611l5+T\nkkhFtGU74I5u4C/A/sD1ki6FZgpqHwH+U9LVrPuh3WKfn1EZwLyye23avUaG7UskNXMCUdIxtr8I\n7CrplPHPW0xuR10SqYi2fKB2ADPg0u41Ck6rHcBMsP3B2jHMlO4wRs88yvZrS1uvm3e/PrtqFPGM\n5dReRGMk7QK80PZVXXPO37E9MsNzY/gkLaT0I9sTWNC7b7u5AbmSlvVdPg7cB3zO9uo6EcWoy4pU\nREMkHQe8A9iWclpsZ+CzQBMjSQC6QcsfBn6fdT+0m5vvJukA4Ezg9yhbYvNpq7C5ZzllYPGrgb8E\n/hx4sGpEA7L9ttoxTIekT072vNEt8JGWzuYRbTkBOBD4FYDtu4HnVI1o6pYBn6GsFhxEaRVwbtWI\nBvcpYClwN7AZ8BfdvdZsZ/sLwGO2r7P9dkrT1+ZI2lnSCkmrJa2SdJGknWvHNQW9LvkLgH0of7fu\npjQZfWKSn4tKkkhFtOU3tn/bu+jm1LW2P7+Z7asppQX32z4NaG4Lqcf2D4D5tp+wvQz4o8ohDeKx\n7tefSjq061/UUvLRbxmlBm8nSj+my7p7TbB9Tndy74XAQbbP7Lrn/zElmYpZJlt7EW25TtL7Kc36\nDgHeSfmgaMmjkuYBd+DZ5XcAAAlNSURBVEt6F/Bj2ltV63lE0qaUU4gfobRB2HwDPzMbnS5pK+A9\nlK3KLSkDjFu0sEtoe86WdFK1aAa3E7AF8LPu+tk0NBB7LkmxeURDugTkWOBPKB2Pvwp8vqWGkJL2\nB+4AtgY+RPnQ/qjt71QNbABd4f8qSn3UycBWwFndKlVUIOkq4GzGuv8vBd5mu5k6QgBJb6OcCr22\nu7UEOK1brYpZJIlURGO6E1bYbrIYeJRI2hz4te0nu+v5wLNsP1I3sqnpaojOpMxwfBL4JvBu2/9b\nNbABSFpEqVN7OWXb+9uU93L/pD84C3XjYl7WXd5g+4Ga8cTEUiMV0QAVp0l6CLgTuEvSg5Ka6ysl\n6WuStu673kbSV2vGNA1Xs+78s82AqyrFMh29uqIdabCuqJ/tlbZfa3uh7efYPqzRJErAq4C9bH8Z\n2FRSMx3a55IkUhFtOIlyWm9/29vZ3pbyTfVASa3Vsmxv+xe9C9s/p90aqQW2/6930f2+xcGyC20v\ns/149zobWFg7qEFI2k3SGZIulnRp71U7rgGcRVlVW9pdrwU+XS+cWJ8Um0e04S3AIbYf6t2wfY+k\nY4ArgY9Xi2zqnpS0yPZKeKrOqNUag4cl7WP7VgBJ+wK/rhzTIB7q/i711xWtqRjPdFwCfIGyqvZk\n5Vim42W295H0XShfOLqDDTHLJJGKaMMm/UlUj+0HJW1SI6Bp+Dvgm5Ku667/kNJktEUnARdK+kl3\nvSNwVMV4BvV2Sl3RxxmrK2q1seWjtidtatmIx7qaO8NTtZEtJ4YjK8XmEQ2QdKvtfab6bLaStD2l\n4aOA6ydKElvRJbJ7UN7LnbYf28CPNEHSSbY/UTuOqZJ0NKUH05WsO0j61mpBDUDSmyhJ+T7AOcDr\ngL+3fWHVwOJpkkhFNEDSE8DDEz2i1OnM+lUpSS+2faekCZO+lj7oJB1s+5pxA3KfYvviYcc00ySt\ntL2odhxTJenDwJuBHzK2guNG5wa+mNKIU8DVtu+oHFJMIFt7EQ2wPb92DDPgPcBxwMcmeGba6m6+\nBLgGeM0Ezww0n0hRPrxbdDjwgv4JAK3p+sXdZnsx5ZRuzGJZkYqIiKdpeEXqAuBE26trxzIdkpYD\n7+sdyojZKytSETEU69sG62lpO0zSKZM9t33GsGKZDklrmfjEpCg9sVq0A3CnpJtYt0bqtfVCGsiO\nwO2SbqRvW7/B9zHykkhFxLBMtA3W09p22Ba1A5gJtkfifYxzau0ApkPS7pRk8IPjHi2hzKWMWSZb\nexERMbIkHQgcbfuE2rE8E5K+Arzf9m3j7u8HnGp7si8kUUE6m0fEUEnaTtInJd0q6RZJ/yxpu9px\nDULSCyRd1o3rWS3py5JeUDuuuU7S3pI+Iuk+4HTKkOxW7Do+iQKwfTOw6/DDiQ1JIhURw3Y+8CBw\nJKU3zoPABVUjGty/Al+i1LPsBFzIWHfwGCJJL5L0AUl3UJqL/oiy63KQ7U9VDm8qFkzyrNW6tZGW\nRCoihm1b2x+yfW/3Oh3YeoM/NTvJ9rl9M+q+SLvjblp3J6Xn0mtsv9L2mcATlWMaxE2Sjht/U9Kx\nwC0V4okNSLF5RAzbtZLeSFnJgbIqdXnFeKbjWknvpayymdKJ+nJJ2wLY/lnN4OaYI4E3Uv5MrqD8\nmbTYC+skYEXX2byXOO0HbErpkRWzTIrNI2KouiP3mzPWdXoeY8e7bXvLKoENQNK9kzy27dRLDZmk\nzYHDKIOXD6aMV1lh+8qqgU2RpIOAxd3l7bavqRlPrF8SqYiIGEndyuDrgaN6I2IkbWP753Uji1GS\nRCoihq5rzvlKynbYN2xfUjmkgUhaALyTvvcCfNb2o1UDi/Vqcch3zG5JpCJiqCSdBezO2Om2o4Af\nttLnp5+kLwFrgS92t5YC29h+fb2oYjKSvmv7pbXjiNGRYvOIGLYlwGJ33+IknQP8V92QBraH7b36\nrq+V9L1q0cQzkdWDmFFpfxARw3YX0D8M9/nA0xoQNuK7kg7oXUh6GfCtivFExJBlay8ihkrSdcD+\nwI3drf2B64FHoK2hrF3zxz2Ald2tRZQu2k9STu29pFZsMbFs7cVMSyIVEUMlaUn/JaVQeymlaBvb\n19WIaxCSdpnsue37hxVLjJE0nzL496nyFdsru2fbpr9XzKQkUhExdJL2Bo4G3gDcC1zcdaJukqTn\n0Dfao/ehHcMn6UTgVGAVY73KsjoYG02KzSNiKCS9iNJ5eimwhjJfT7YPqhrYNEh6LfAxypy91cAu\nlK29PWvGNce9m3IIYE3tQGJuSLF5RAzLqMxC6/ch4ADgf2zvRnl/KTav60fAL2sHEXNHVqQiYlhG\nZRZav8dsr5E0T9I829dK+qfaQc1x9wBfl3Q58JveTdtn1AspRlkSqYgYCtsrKMNYe7PQTgZ2kPQZ\nGpyF1vmFpGdTOpovl7QaeLxyTHPdyu61afeK2KhSbB4R1Uw0C60lXVL4KGVl7U3AVsDy1OdEzB1J\npCIipkHSDpReWAA32l5dM565TtJC4G8pBf/9JymbS9SjDSk2j4gYkKQ3UBqLvp7SyuEGSa+rG9Wc\nt5xysGE34IPAfcBNNQOK0ZYVqYiIAXVz9Q7prUJ1qyFXjZu/F0Mk6Rbb+0q6rdc7StJ1tpds6Gcj\nBpFi84iIwc0bt5W3hqz01/ZY9+tPJR0K/ATYuWI8MeKSSEVEDO4KSV8FzuuujwL+rWI8AadL2gp4\nD3AmsCXlhGjERpGtvYiIKZK0O7CD7W9JOoIyL1DAzymn9n5YNcCIGJosQUdETN0ngLUAti+2fYrt\nkymrUZ+oGtkcJ2lnSSskPShplaSLJGVrLzaaJFIREVO3q+3bxt+0fTOw6/DDiT7LgEuBHYHnAZd1\n9yI2iiRSERFTt2CSZ5sNLYqYyELby2w/3r3OBhbWDipGVxKpiIipu0nSceNvSjoWuKVCPDHmIUnH\nSJrfvY6hnKaM2ChSbB4RMUVdN/MVwG8ZS5z2o8x2O9z2A7Vim+skLQI+BbwcMPBt4K9sr6waWIys\nJFIREQOSdBCwuLu83fY1NeOJiUk6yXYOAcRGkUQqIiJGmqSVthfVjiNGU2qkIiJi1Kl2ADG6kkhF\nRMSoy9ZLbDQZERMREc2TtJaJEyaRlhSxEaVGKiIiImJA2dqLiIiIGFASqYiIiIgBJZGKiIiIGFAS\nqYiIiIgB/T9oF2zHewhy/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23e537bf588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat=data.corr()\n",
    "f,ax=plt.subplots(figsize=(9,9))\n",
    "sns.heatmap(corrmat,vmax=.8,square=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.Education=data.Education.map({'Graduate':1,'Not Graduate':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    763\n",
       "0    218\n",
       "Name: Education, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Education.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Self_Employed=data.Self_Employed.map({'Yes':1,'No':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    807\n",
       "1.0    119\n",
       "Name: Self_Employed, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Self_Employed.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Semiurban    349\n",
       "Urban        342\n",
       "Rural        290\n",
       "Name: Property_Area, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Property_Area.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.Property_Area=data.Property_Area.map({'Urban':2,'Rural':0,'Semiurban':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    349\n",
       "2    342\n",
       "0    290\n",
       "Name: Property_Area, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Property_Area.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23e5389b4a8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pwl3BSN8hjPH+pW/rO4QJNt3uyX2HMMZv3zBc8QDs/YEf9x3CGFvPH67/9gFW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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23e538d7128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat=data.corr()\n",
    "f,ax=plt.subplots(figsize=(9,9))\n",
    "sns.heatmap(corrmat,vmax=.8,square=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID  Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "0  LP001002     1.0      0.0         0.0          1            0.0   \n",
       "1  LP001003     1.0      1.0         1.0          1            0.0   \n",
       "2  LP001005     1.0      1.0         0.0          1            1.0   \n",
       "3  LP001006     1.0      1.0         0.0          0            0.0   \n",
       "4  LP001008     1.0      0.0         0.0          1            0.0   \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  \n",
       "0             1.0              2  \n",
       "1             1.0              0  \n",
       "2             1.0              2  \n",
       "3             1.0              2  \n",
       "4             1.0              2  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "981"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Credit_History.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Credit_History.fillna(np.random.randint(0,2),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               24\n",
       "Married               3\n",
       "Dependents           25\n",
       "Education             0\n",
       "Self_Employed        55\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           27\n",
       "Loan_Amount_Term     20\n",
       "Credit_History        0\n",
       "Property_Area         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.Married.fillna(np.random.randint(0,2),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               24\n",
       "Married               0\n",
       "Dependents           25\n",
       "Education             0\n",
       "Self_Employed        55\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           27\n",
       "Loan_Amount_Term     20\n",
       "Credit_History        0\n",
       "Property_Area         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.LoanAmount.fillna(data.LoanAmount.median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.Loan_Amount_Term.fillna(data.Loan_Amount_Term.mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               24\n",
       "Married               0\n",
       "Dependents           25\n",
       "Education             0\n",
       "Self_Employed        55\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount            0\n",
       "Loan_Amount_Term      0\n",
       "Credit_History        0\n",
       "Property_Area         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    775\n",
       "0.0    182\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import randint \n",
    "data.Gender.fillna(np.random.randint(0,2),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    775\n",
       "0.0    206\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.Dependents.fillna(data.Dependents.median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender                0\n",
       "Married               0\n",
       "Dependents            0\n",
       "Education             0\n",
       "Self_Employed        55\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount            0\n",
       "Loan_Amount_Term      0\n",
       "Credit_History        0\n",
       "Property_Area         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23e53c90320>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ez8JdyUjfIYzx/mVv6zuECTZ56JP6DmGMW197YN8hTLD3kmv7DmGMbRds1XcI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\n3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23e538d7668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat=data.corr()\n",
    "f,ax=plt.subplots(figsize=(9,9))\n",
    "sns.heatmap(corrmat,vmax=.8,square=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Self_Employed.fillna(np.random.randint(0,2),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              0\n",
       "Gender               0\n",
       "Married              0\n",
       "Dependents           0\n",
       "Education            0\n",
       "Self_Employed        0\n",
       "ApplicantIncome      0\n",
       "CoapplicantIncome    0\n",
       "LoanAmount           0\n",
       "Loan_Amount_Term     0\n",
       "Credit_History       0\n",
       "Property_Area        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID  Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "0  LP001002     1.0      0.0         0.0          1            0.0   \n",
       "1  LP001003     1.0      1.0         1.0          1            0.0   \n",
       "2  LP001005     1.0      1.0         0.0          1            1.0   \n",
       "3  LP001006     1.0      1.0         0.0          0            0.0   \n",
       "4  LP001008     1.0      0.0         0.0          1            0.0   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0       126.0             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  \n",
       "0             1.0              2  \n",
       "1             1.0              0  \n",
       "2             1.0              2  \n",
       "3             1.0              2  \n",
       "4             1.0              2  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('Loan_ID',inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender               0\n",
       "Married              0\n",
       "Dependents           0\n",
       "Education            0\n",
       "Self_Employed        0\n",
       "ApplicantIncome      0\n",
       "CoapplicantIncome    0\n",
       "LoanAmount           0\n",
       "Loan_Amount_Term     0\n",
       "Credit_History       0\n",
       "Property_Area        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X=data.iloc[:614,]\n",
    "train_y=Loan_status\n",
    "X_test=data.iloc[614:,]\n",
    "seed=7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_X,test_X,train_y,test_y=train_test_split(train_X,train_y,random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "models=[]\n",
    "models.append((\"logreg\",LogisticRegression()))\n",
    "models.append((\"tree\",DecisionTreeClassifier()))\n",
    "models.append((\"lda\",LinearDiscriminantAnalysis()))\n",
    "models.append((\"svc\",SVC()))\n",
    "models.append((\"knn\",KNeighborsClassifier()))\n",
    "models.append((\"nb\",GaussianNB()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "seed=7\n",
    "scoring='accuracy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold \n",
    "from sklearn.model_selection import cross_val_score\n",
    "result=[]\n",
    "names=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logreg 0.745652 0.041304\n",
      "tree 0.678261 0.072623\n",
      "lda 0.763043 0.040612\n",
      "svc 0.691304 0.065795\n",
      "knn 0.636957 0.068089\n",
      "nb 0.715217 0.040612\n"
     ]
    }
   ],
   "source": [
    "for name,model in models:\n",
    "    #print(model)\n",
    "    kfold=KFold(n_splits=10,random_state=seed)\n",
    "    cv_result=cross_val_score(model,train_X,train_y,cv=kfold,scoring=scoring)\n",
    "    result.append(cv_result)\n",
    "    names.append(name)\n",
    "    print(\"%s %f %f\" % (name,cv_result.mean(),cv_result.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.792207792208\n",
      "[[25 23]\n",
      " [ 9 97]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.74      0.52      0.61        48\n",
      "          1       0.81      0.92      0.86       106\n",
      "\n",
      "avg / total       0.79      0.79      0.78       154\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "svc=LogisticRegression()\n",
    "svc.fit(train_X,train_y)\n",
    "pred=svc.predict(test_X)\n",
    "print(accuracy_score(test_y,pred))\n",
    "print(confusion_matrix(test_y,pred))\n",
    "print(classification_report(test_y,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_output=pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1,\n",
       "       1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outp=svc.predict(X_test).astype(int)\n",
    "outp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_output['Loan_ID']=Loan_ID\n",
    "df_output['Loan_Status']=outp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001022</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001031</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001035</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001051</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID  Loan_Status\n",
       "0  LP001015            1\n",
       "1  LP001022            1\n",
       "2  LP001031            1\n",
       "3  LP001035            0\n",
       "4  LP001051            1"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_output.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_output[['Loan_ID','Loan_Status']].to_csv(r'C:\\Users\\naveen chauhan\\Desktop\\mldata\\loan prediction\\output.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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