Loans-Predictions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column names:\n",
      "['Gender', 'Married', 'Dependents', 'Education', 'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term', 'Credit_History', 'Property_Area', 'Loan_Status']\n",
      "\n",
      "Sample data:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LP001002</th>\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>LP001003</th>\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>LP001005</th>\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>LP001006</th>\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>LP001008</th>\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": [
       "         Gender Married Dependents     Education Self_Employed  \\\n",
       "Loan_ID                                                          \n",
       "LP001002   Male      No          0      Graduate            No   \n",
       "LP001003   Male     Yes          1      Graduate            No   \n",
       "LP001005   Male     Yes          0      Graduate           Yes   \n",
       "LP001006   Male     Yes          0  Not Graduate            No   \n",
       "LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "          ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "Loan_ID                                                                      \n",
       "LP001002             5849                0.0         NaN             360.0   \n",
       "LP001003             4583             1508.0       128.0             360.0   \n",
       "LP001005             3000                0.0        66.0             360.0   \n",
       "LP001006             2583             2358.0       120.0             360.0   \n",
       "LP001008             6000                0.0       141.0             360.0   \n",
       "\n",
       "          Credit_History Property_Area Loan_Status  \n",
       "Loan_ID                                             \n",
       "LP001002             1.0         Urban           Y  \n",
       "LP001003             1.0         Rural           N  \n",
       "LP001005             1.0         Urban           Y  \n",
       "LP001006             1.0         Urban           Y  \n",
       "LP001008             1.0         Urban           Y  "
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv('train.csv',index_col=0)\n",
    "test_df = pd.read_csv('test.csv',index_col=0)\n",
    "col_names = train_df.columns.tolist()\n",
    "\n",
    "print \"Column names:\"\n",
    "print col_names\n",
    "\n",
    "print \"\\nSample data:\"\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "encode_features = ['Gender','Married','Education','Self_Employed','Dependents','Loan_Status']\n",
    "\n",
    "fillna_withmean = ['LoanAmount','Loan_Amount_Term']\n",
    "fillna_withmostcommon = ['Dependents','Gender','Credit_History','Married','Self_Employed']\n",
    "\n",
    "\n",
    "def transform_df(data):\n",
    "    \n",
    "    #Removing Loans_ID\n",
    "    df = data #.drop('Loan_ID',axis=1) \n",
    "    \n",
    "    # Filling NaN values \n",
    "    for feature in fillna_withmean:\n",
    "        if feature in data.columns.values:\n",
    "            df[feature] = df[feature].fillna(df[feature].mean()) \n",
    "        \n",
    "    for feature in fillna_withmostcommon:\n",
    "        if feature in data.columns.values:\n",
    "            df[feature] = df[feature].fillna(df[feature].value_counts().index[0])\n",
    "    \n",
    "    # Encoding Features\n",
    "    for feature in encode_features:\n",
    "        if feature in data.columns.values:\n",
    "            le = LabelEncoder()\n",
    "            df[feature] = le.fit_transform(df[feature])\n",
    "    \n",
    "    # Adding Applicant and Coapplicant Incomes as Household\n",
    "    df['Household_Income'] = df['ApplicantIncome'] + df['CoapplicantIncome']\n",
    "    df = df.drop(['ApplicantIncome','CoapplicantIncome'],axis=1)\n",
    "     \n",
    "    # Transforming some other values   \n",
    "    dummies = pd.get_dummies(df.Property_Area)\n",
    "    df = pd.concat([df,dummies],axis=1)\n",
    "    df = df.drop('Property_Area',axis=1)\n",
    "\n",
    "    \n",
    "    return df\n",
    "\n",
    "train_df = transform_df(train_df)\n",
    "test_df = transform_df(test_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_df.insert(len(train_df.columns)-1,'Loan_Status',train_df.pop('Loan_Status'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scale_features = ['LoanAmount','Loan_Amount_Term','Household_Income']\n",
    "\n",
    "train_df[scale_features] = train_df[scale_features].apply(lambda x:(x.astype(int) - min(x))/(max(x)-min(x)), axis = 0)\n",
    "test_df[scale_features] = test_df[scale_features].apply(lambda x:(x.astype(int) - min(x))/(max(x)-min(x)), axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = train_df.iloc[:, :-1]\n",
    "Y_train = train_df.iloc[:,-1]\n",
    "X_test = test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID\n",
       "LP001002    1\n",
       "LP001003    0\n",
       "LP001005    1\n",
       "LP001006    1\n",
       "LP001008    1\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "clf = LogisticRegression()\n",
    "clf = clf.fit(X_train, Y_train)\n",
    "\n",
    "Y_test = clf.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Y_test = ['Y' if x==1 else 'N' for x in Y_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_test['Loan_Status']=Y_test\n",
    "X_test = X_test['Loan_Status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_test.to_csv('loans_submission.csv',sep=',',header=True)"
   ]
  }
 ],
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