# -*- coding: utf-8 -*-
"""
Created on Wed Nov  8 21:56:11 2017

@author: Krunal
"""

# Importing the libraries
import numpy as np
import matplotlib.pyplot as pt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv("Data.csv")
X = dataset.iloc[:,:-1].values
y = dataset.iloc[:,3].values

# Taking care of missing data
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)
imputer.fit(X[:, 1:3])
X[:, 1:3] = imputer.transform(X[:, 1:3])

# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[:, 0] = labelencoder_X.fit_transform(X[:, 0])
onehotencoder = OneHotEncoder(categorical_features = [0])
X = onehotencoder.fit_transform(X).toarray()
# Encoding dependent variables
labelencoder_y = LabelEncoder()
y = labelencoder_y.fit_transform(y)

# Splitting dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state= 0)

# Feature Scalling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)