Data_Camp_Exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Who is eligible for loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import library pandas\n",
    "import pandas as pd\n",
    "\n",
    "# Import training data as train\n",
    "train = pd.read_csv(\"https://s3-ap-southeast-1.amazonaws.com/av-datahack-datacamp/train.csv\")\n",
    "\n",
    "# Import testing data as test\n",
    "test = pd.read_csv(\"https://s3-ap-southeast-1.amazonaws.com/av-datahack-datacamp/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    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  \n"
     ]
    }
   ],
   "source": [
    "# Print top 5 observation of training dataset\n",
    "print (train.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Store total number of observation in training dataset\n",
    "train_length =len(train)\n",
    "\n",
    "# Store total number of columns in testing data set\n",
    "test_col = len(test.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       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  \n"
     ]
    }
   ],
   "source": [
    "# Look at the summary of numerical variables for train data set\n",
    "df= train.describe()\n",
    "print (df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Semiurban    233\n",
      "Urban        202\n",
      "Rural        179\n",
      "Name: Property_Area, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Print the unique values and their frequency of variable Property_Area\n",
    "df1=train['Property_Area'].value_counts()\n",
    "print (df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding distribution of numerical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x88843c8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7c61ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# Plot histogram for variable LoanAmount\n",
    "train['LoanAmount'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8a67eb8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WdWxby/we2tqQn1O1l6Ykf0k7AZ8G/gJ4GfB2SS9txrVsW+vXry86BLNtTPac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\nIZAuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x894c198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a box plot for variable LoanAmount by variable Gender of training data set\n",
    "train.boxplot(column='LoanAmount', by = 'Gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding distribution of categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "422 number of loans were approved.\n"
     ]
    }
   ],
   "source": [
    "# Loan approval rates in absolute numbers\n",
    "loan_approval = train['Loan_Status'].value_counts()['Y']\n",
    "print \"%d number of loans were approved.\" %loan_approval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Loan_Status</th>\n",
       "      <th>N</th>\n",
       "      <th>Y</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Credit_History</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>82</td>\n",
       "      <td>7</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>97</td>\n",
       "      <td>378</td>\n",
       "      <td>475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>192</td>\n",
       "      <td>422</td>\n",
       "      <td>614</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Loan_Status       N    Y  All\n",
       "Credit_History               \n",
       "0.0              82    7   89\n",
       "1.0              97  378  475\n",
       "All             192  422  614"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Two-way comparison: Credit History and Loan Status\n",
    "pd.crosstab(train [\"Credit_History\"], train [\"Loan_Status\"], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79.578947 percent of the applicants whose loans were approved have Credit_History equals to 1.\n"
     ]
    }
   ],
   "source": [
    "#Function to output percentage row wise in a cross table\n",
    "def percentageConvert(ser):\n",
    " return ser/float(ser[-1])\n",
    "\n",
    "# Two-way comparison: Loan approval rate for customers having Credit_History (1)\n",
    "df=pd.crosstab(train [\"Credit_History\"], train [\"Loan_Status\"], margins=True).apply(percentageConvert, axis=1)\n",
    "loan_approval_with_Credit_1 = df['Y'][1]\n",
    "print \"%f percent of the applicants whose loans were approved have Credit_History equals to 1.\" %(loan_approval_with_Credit_1*100) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Credit_History\n",
       "0.0    0.078652\n",
       "1.0    0.795789\n",
       "All    0.687296\n",
       "Name: Y, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Y']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dealing with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of variables with missing values\n",
    "variables_missing_value = train.isnull().sum()\n",
    "variables_missing_value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Impute missing value of Loan_Amount_Term with median\n",
    "train['Loan_Amount_Term'].fillna(train['Loan_Amount_Term'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Impute missing value of Self_Employed with more frequent category\n",
    "train['Self_Employed'].fillna('No',inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Treat Outliers of LoanAmount and Applicant Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8ba7ac8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x8ac69b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add both ApplicantIncome and CoapplicantIncome to TotalIncome\n",
    "train['TotalIncome'] = train['ApplicantIncome'] + train['CoapplicantIncome']\n",
    "\n",
    "# Looking at the distribtion of TotalIncome\n",
    "train['LoanAmount'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9874da0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Oo7lngDyxwlzUFakLyAbrojmaOwC0EJk7OofMHZOMzB0AOozmngHyxApzUVekLiAbrIvm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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8bf5358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "# Perform log transformation of TotalIncome to make it closer to normal\n",
    "train['LoanAmount_log'] = np.log(train['LoanAmount'])\n",
    "\n",
    "# Looking at the distribtion of TotalIncome_log\n",
    "train['LoanAmount_log'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preparation for Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "# Impute missing values for Gender\n",
    "train['Gender'].fillna(train['Gender'].mode()[0],inplace=True)\n",
    "\n",
    "# Impute missing values for Married\n",
    "train['Married'].fillna(train['Married'].mode()[0],inplace=True)\n",
    "\n",
    "# Impute missing values for Dependents\n",
    "train['Dependents'].fillna(train['Dependents'].mode()[0],inplace=True)\n",
    "\n",
    "# Impute missing values for Credit_History\n",
    "train['Credit_History'].fillna(train['Credit_History'].mode()[0],inplace=True)\n",
    "\n",
    "# Convert all non-numeric values to number\n",
    "cat_col=['Gender','Married','Dependents','Education','Self_Employed','Credit_History','Property_Area']\n",
    "for var in cat_col:\n",
    "    le = preprocessing.LabelEncoder()\n",
    "    train[var]=le.fit_transform(train[var].astype('str'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Credit_History'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "LoanAmount_have_missing_value = train['LoanAmount'].isnull().sum() > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LoanAmount_have_missing_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender                0\n",
       "Married               3\n",
       "Dependents           15\n",
       "Education             0\n",
       "Self_Employed        32\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount            0\n",
       "Loan_Amount_Term     14\n",
       "Credit_History        0\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>600.00000</td>\n",
       "      <td>564.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5403.459283</td>\n",
       "      <td>1621.245798</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>342.00000</td>\n",
       "      <td>0.842199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6109.041673</td>\n",
       "      <td>2926.248369</td>\n",
       "      <td>85.587325</td>\n",
       "      <td>65.12041</td>\n",
       "      <td>0.364878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2877.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3812.500000</td>\n",
       "      <td>1188.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5795.000000</td>\n",
       "      <td>2297.250000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>480.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       614.000000         614.000000  592.000000         600.00000   \n",
       "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
       "std        6109.041673        2926.248369   85.587325          65.12041   \n",
       "min         150.000000           0.000000    9.000000          12.00000   \n",
       "25%        2877.500000           0.000000  100.000000         360.00000   \n",
       "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
       "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
       "max       81000.000000       41667.000000  700.000000         480.00000   \n",
       "\n",
       "       Credit_History  \n",
       "count      564.000000  \n",
       "mean         0.842199  \n",
       "std          0.364878  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Impute missing value of LoanAmount with 168\n",
    "train['LoanAmount'].fillna(168, inplace=True)\n",
    "\n",
    "# Impute missing value of LoanAmount with median\n",
    "#train['LoanAmount'].fillna(train['LoanAmount'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "129.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['LoanAmount'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train['Gender'].fillna('Male',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    475\n",
       "0.0     89\n",
       "Name: Credit_History, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Credit_History'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train['Credit_History'].fillna(1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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