Loan_predictor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_train = pd.read_csv('train.csv')\n",
    "data_test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 13)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(367, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>361.000000</td>\n",
       "      <td>338.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4805.599455</td>\n",
       "      <td>1569.577657</td>\n",
       "      <td>136.132597</td>\n",
       "      <td>342.537396</td>\n",
       "      <td>0.825444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4910.685399</td>\n",
       "      <td>2334.232099</td>\n",
       "      <td>61.366652</td>\n",
       "      <td>65.156643</td>\n",
       "      <td>0.380150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2864.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.250000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3786.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5060.000000</td>\n",
       "      <td>2430.500000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>72529.000000</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>550.000000</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       367.000000         367.000000  362.000000        361.000000   \n",
       "mean       4805.599455        1569.577657  136.132597        342.537396   \n",
       "std        4910.685399        2334.232099   61.366652         65.156643   \n",
       "min           0.000000           0.000000   28.000000          6.000000   \n",
       "25%        2864.000000           0.000000  100.250000        360.000000   \n",
       "50%        3786.000000        1025.000000  125.000000        360.000000   \n",
       "75%        5060.000000        2430.500000  158.000000        360.000000   \n",
       "max       72529.000000       24000.000000  550.000000        480.000000   \n",
       "\n",
       "       Credit_History  \n",
       "count      338.000000  \n",
       "mean         0.825444  \n",
       "std          0.380150  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               11\n",
       "Married               0\n",
       "Dependents           10\n",
       "Education             0\n",
       "Self_Employed        23\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount            5\n",
       "Loan_Amount_Term      6\n",
       "Credit_History       29\n",
       "Property_Area         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      489\n",
       "Female    112\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      286\n",
       "Female     70\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_combined_data():\n",
    "    train = pd.read_csv('train.csv')\n",
    "    test = pd.read_csv('test.csv')\n",
    "    targets = train.Loan_Status\n",
    "    train.drop('Loan_Status', 1, inplace=True)\n",
    "    combined = train.append(test)\n",
    "    combined.reset_index(inplace=True)\n",
    "    combined.drop(['index', 'Loan_ID'], inplace=True, axis=1)\n",
    "    return combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined = get_combined_data()\n",
    "combined.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def impute_gender():\n",
    "    global combined\n",
    "    combined['Gender'].fillna('Male', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def impute_martial_status():\n",
    "    global combined\n",
    "    combined['Married'].fillna('Yes', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def impute_employment():\n",
    "    global combined\n",
    "    combined['Self_Employed'].fillna('No', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def impute_loan_amount():\n",
    "    global combined\n",
    "    combined['LoanAmount'].fillna(combined['LoanAmount'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def impute_credit_history():\n",
    "    global combined\n",
    "    combined['Credit_History'].fillna(2, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    754\n",
       "0.0    148\n",
       "Name: Credit_History, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined['Credit_History'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "impute_gender()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "impute_martial_status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "impute_employment()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "impute_loan_amount()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "impute_credit_history()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                0\n",
       "Married               0\n",
       "Dependents           25\n",
       "Education             0\n",
       "Self_Employed         0\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount            0\n",
       "Loan_Amount_Term     20\n",
       "Credit_History        0\n",
       "Property_Area         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_gender():\n",
    "    global combined\n",
    "    combined['Gender'] = combined['Gender'].map({'Male':1,'Female':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_martial_status():\n",
    "    global combined\n",
    "    combined['Married'] = combined['Married'].map({'Yes':1,'No':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def process_dependents():\n",
    "    global combined\n",
    "    combined['Singleton'] = combined['Dependents'].map(lambda d: 1 if d=='1' else 0)\n",
    "    combined['Small_Family'] = combined['Dependents'].map(lambda d: 1 if d=='2' else 0)\n",
    "    combined['Large_Family'] = combined['Dependents'].map(lambda d: 1 if d=='3+' else 0)\n",
    "    combined.drop(['Dependents'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_education():\n",
    "    global combined\n",
    "    combined['Education'] = combined['Education'].map({'Graduate':1,'Not Graduate':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def process_employment():\n",
    "    global combined\n",
    "    combined['Self_Employed'] = combined['Self_Employed'].map({'Yes':1,'No':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_income():\n",
    "    global combined\n",
    "    combined['Total_Income'] = combined['ApplicantIncome'] + combined['CoapplicantIncome']\n",
    "    combined.drop(['ApplicantIncome','CoapplicantIncome'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_loan_amount():\n",
    "    global combined\n",
    "    combined['Debt_Income_Ratio'] = combined['Total_Income'] / combined['LoanAmount']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360.0    823\n",
       "180.0     66\n",
       "480.0     23\n",
       "300.0     20\n",
       "240.0      8\n",
       "84.0       7\n",
       "120.0      4\n",
       "36.0       3\n",
       "60.0       3\n",
       "12.0       2\n",
       "350.0      1\n",
       "6.0        1\n",
       "Name: Loan_Amount_Term, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined['Loan_Amount_Term'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdadc69240>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S09OTJOnp6cmdd945JdmVOAAAgJ/ymc98Ju985zuTJMPDw5k/f/7hzzo7OzM8PDxuzt69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\nmjgAAIAJ0cQBAABMiCYOAABgQv4D9TCU7cqXoMAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdadc8dd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "approved_term = data_train[data_train['Loan_Status']=='Y']['Loan_Amount_Term'].value_counts()\n",
    "unapproved_term = data_train[data_train['Loan_Status']=='N']['Loan_Amount_Term'].value_counts()\n",
    "df = pd.DataFrame([approved_term,unapproved_term])\n",
    "df.index = ['Approved','Unapproved']\n",
    "df.plot(kind='bar', stacked=True, figsize=(15,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def process_loan_term():\n",
    "    global combined\n",
    "    combined['Very_Short_Term'] = combined['Loan_Amount_Term'].map(lambda t: 1 if t<=60 else 0)\n",
    "    combined['Short_Term'] = combined['Loan_Amount_Term'].map(lambda t: 1 if t>60 and t<180 else 0)\n",
    "    combined['Long_Term'] = combined['Loan_Amount_Term'].map(lambda t: 1 if t>=180 and t<=300  else 0)\n",
    "    combined['Very_Long_Term'] = combined['Loan_Amount_Term'].map(lambda t: 1 if t>300 else 0)\n",
    "    combined.drop('Loan_Amount_Term', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_credit_history():\n",
    "    global combined\n",
    "    combined['Credit_History_Bad'] = combined['Credit_History'].map(lambda c: 1 if c==0 else 0)\n",
    "    combined['Credit_History_Good'] = combined['Credit_History'].map(lambda c: 1 if c==1 else 0)\n",
    "    combined['Credit_History_Unknown'] = combined['Credit_History'].map(lambda c: 1 if c==2 else 0)\n",
    "    combined.drop('Credit_History', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_property():\n",
    "    global combined\n",
    "    property_dummies = pd.get_dummies(combined['Property_Area'], prefix='Property')\n",
    "    combined = pd.concat([combined, property_dummies], axis=1)\n",
    "    combined.drop('Property_Area', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "process_gender()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "process_martial_status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "process_dependents()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "process_education()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "process_employment()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "process_income()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "process_loan_amount()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "process_loan_term()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "process_credit_history()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "process_property()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
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      "text/plain": [
       "    Gender  Married  Education  Self_Employed  LoanAmount  Singleton  \\\n",
       "60       1        1          1              0       120.0          0   \n",
       "61       1        1          1              0        99.0          0   \n",
       "62       1        1          0              1       165.0          0   \n",
       "63       1        1          1              0       126.0          1   \n",
       "64       0        0          1              0       116.0          0   \n",
       "65       1        1          1              0       258.0          0   \n",
       "66       1        0          0              0       126.0          0   \n",
       "67       1        1          1              0       312.0          1   \n",
       "68       1        1          0              1       125.0          0   \n",
       "69       0        0          1              0       136.0          0   \n",
       "\n",
       "    Small_Family  Large_Family  Total_Income  Debt_Income_Ratio  \\\n",
       "60             0             0        6296.0          52.466667   \n",
       "61             0             1        3029.0          30.595960   \n",
       "62             0             0        6058.0          36.715152   \n",
       "63             0             0        4945.0          39.246032   \n",
       "64             0             0        4166.0          35.913793   \n",
       "65             0             0       10321.0          40.003876   \n",
       "66             0             0        5454.0          43.285714   \n",
       "67             0             0       10750.0          34.455128   \n",
       "68             0             1        7100.0          56.800000   \n",
       "69             0             0        4300.0          31.617647   \n",
       "\n",
       "    Very_Short_Term  Short_Term  Long_Term  Very_Long_Term  \\\n",
       "60                0           0          0               1   \n",
       "61                0           0          0               1   \n",
       "62                0           0          1               0   \n",
       "63                0           0          0               1   \n",
       "64                0           0          0               1   \n",
       "65                0           0          0               1   \n",
       "66                0           0          1               0   \n",
       "67                0           0          0               1   \n",
       "68                1           0          0               0   \n",
       "69                0           0          0               1   \n",
       "\n",
       "    Credit_History_Bad  Credit_History_Good  Credit_History_Unknown  \\\n",
       "60                   0                    1                       0   \n",
       "61                   0                    1                       0   \n",
       "62                   1                    0                       0   \n",
       "63                   1                    0                       0   \n",
       "64                   1                    0                       0   \n",
       "65                   0                    1                       0   \n",
       "66                   1                    0                       0   \n",
       "67                   0                    1                       0   \n",
       "68                   0                    1                       0   \n",
       "69                   1                    0                       0   \n",
       "\n",
       "    Property_Rural  Property_Semiurban  Property_Urban  \n",
       "60               0                   0               1  \n",
       "61               0                   0               1  \n",
       "62               1                   0               0  \n",
       "63               1                   0               0  \n",
       "64               0                   1               0  \n",
       "65               0                   1               0  \n",
       "66               0                   0               1  \n",
       "67               0                   0               1  \n",
       "68               0                   0               1  \n",
       "69               0                   1               0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined[60:70]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def feature_scaling(df):\n",
    "    df -= df.min()\n",
    "    df /= df.max()\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "combined['LoanAmount'] = feature_scaling(combined['LoanAmount'])\n",
    "combined['Total_Income'] = feature_scaling(combined['Total_Income'])\n",
    "combined['Debt_Income_Ratio'] = feature_scaling(combined['Debt_Income_Ratio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.066570</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.063652</td>\n",
       "      <td>0.210614</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.072359</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013035</td>\n",
       "      <td>0.062573</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.170767</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.024837</td>\n",
       "      <td>0.033259</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Married  Education  Self_Employed  LoanAmount  Singleton  \\\n",
       "200       1        1          0              0    0.117221          1   \n",
       "201       1        0          1              0    0.227207          0   \n",
       "202       1        1          0              0    0.169320          0   \n",
       "203       1        1          0              0    0.182344          1   \n",
       "204       1        1          0              0    0.166425          0   \n",
       "205       0        0          0              0    0.160637          0   \n",
       "206       0        0          1              0    0.102750          0   \n",
       "207       1        0          0              0    0.066570          0   \n",
       "208       1        0          1              0    0.072359          0   \n",
       "209       1        0          1              0    0.170767          0   \n",
       "\n",
       "     Small_Family  Large_Family  Total_Income  Debt_Income_Ratio  \\\n",
       "200             0             0      0.045979           0.091007   \n",
       "201             1             0      0.043754           0.038583   \n",
       "202             0             1      0.032052           0.042515   \n",
       "203             0             0      0.039481           0.046913   \n",
       "204             1             0      0.031109           0.042333   \n",
       "205             0             0      0.037281           0.052318   \n",
       "206             0             0      0.022650           0.059726   \n",
       "207             0             0      0.063652           0.210614   \n",
       "208             0             0      0.013035           0.062573   \n",
       "209             0             0      0.024837           0.033259   \n",
       "\n",
       "     Very_Short_Term  Short_Term  Long_Term  Very_Long_Term  \\\n",
       "200                0           0          0               1   \n",
       "201                0           0          0               1   \n",
       "202                0           0          1               0   \n",
       "203                0           0          0               1   \n",
       "204                0           0          0               1   \n",
       "205                0           0          0               1   \n",
       "206                0           0          0               1   \n",
       "207                0           0          0               1   \n",
       "208                0           0          0               1   \n",
       "209                0           0          0               1   \n",
       "\n",
       "     Credit_History_Bad  Credit_History_Good  Credit_History_Unknown  \\\n",
       "200                   0                    1                       0   \n",
       "201                   1                    0                       0   \n",
       "202                   0                    1                       0   \n",
       "203                   0                    1                       0   \n",
       "204                   0                    1                       0   \n",
       "205                   0                    1                       0   \n",
       "206                   0                    1                       0   \n",
       "207                   0                    1                       0   \n",
       "208                   0                    1                       0   \n",
       "209                   0                    1                       0   \n",
       "\n",
       "     Property_Rural  Property_Semiurban  Property_Urban  \n",
       "200               0                   1               0  \n",
       "201               0                   1               0  \n",
       "202               0                   0               1  \n",
       "203               0                   0               1  \n",
       "204               0                   1               0  \n",
       "205               0                   1               0  \n",
       "206               0                   0               1  \n",
       "207               1                   0               0  \n",
       "208               0                   0               1  \n",
       "209               0                   1               0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined[200:210]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import SelectFromModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def compute_score(clf, X, y, scoring='accuracy'):\n",
    "    xval = cross_val_score(clf, X, y, cv = 5, scoring=scoring)\n",
    "    return np.mean(xval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def recover_train_test_target():\n",
    "    global combined, data_train\n",
    "    targets = data_train['Loan_Status'].map({'Y':1,'N':0})\n",
    "    train = combined.head(614)\n",
    "    test = combined.iloc[614:]\n",
    "    return train, test, targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train, test, targets = recover_train_test_target()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(n_estimators=50, max_features='sqrt')\n",
    "clf = clf.fit(train, targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "features = pd.DataFrame()\n",
    "features['Feature'] = train.columns\n",
    "features['Importance'] = clf.feature_importances_\n",
    "features.sort_values(by=['Importance'], ascending=False, inplace=True)\n",
    "features.set_index('Feature', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdb2085860>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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iEgAAAABDIhIAAAAAQyISAAAAAEMiEgAAAABDIhIAAAAAQyISAAAAAEMiEgAA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdb10a85c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features.plot(kind='bar', figsize=(20, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 5)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SelectFromModel(clf, prefit=True)\n",
    "train_reduced = model.transform(train)\n",
    "train_reduced.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(367, 5)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_reduced = model.transform(test)\n",
    "test_reduced.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
       "            max_depth=6, max_features='sqrt', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=3, min_samples_split=10,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=50, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = {'bootstrap': False,\n",
    "              'min_samples_leaf': 3,\n",
    "              'n_estimators': 50,\n",
    "              'min_samples_split': 10,\n",
    "              'max_features': 'sqrt',\n",
    "              'max_depth': 6}\n",
    "\n",
    "model = RandomForestClassifier(**parameters)\n",
    "model.fit(train, targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80304071919619235"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_score(model, train, targets, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "output = model.predict(test).astype(int)\n",
    "df_output = pd.DataFrame()\n",
    "aux = pd.read_csv('test.csv')\n",
    "df_output['Loan_ID'] = aux['Loan_ID']\n",
    "df_output['Loan_Status'] = np.vectorize(lambda s: 'Y' if s==1 else 'N')(output)\n",
    "df_output[['Loan_ID','Loan_Status']].to_csv('output.csv',index=False)"
   ]
  }
 ],
 "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}