# Data Preprocessing

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

# Importing the dataset
dataset = pd.read_csv('Data.csv')

#splitting Data as required
X = dataset.iloc[:, :-1].values
Y = dataset.iloc[:, 3].values
                
#Filling missing Data
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values = "NaN" , strategy="mean" , axis = 0)
imputer = imputer.fit(X[:, 1:3])
X[:, 1:3] = imputer.transform(X[:, 1:3])

#Changing Catogitical Data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[: , 0] = labelencoder_X.fit_transform(X[: , 0])

#Dummy Encoding
onehotencoder = OneHotEncoder(categorical_features= [0])
X= onehotencoder.fit_transform(X).toarray()

#Encoding Pruchased(Y)
labelencoder_Y = LabelEncoder()
Y = labelencoder_Y.fit_transform(Y)


#Spliting the dataset into the Training 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)

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