#import libraries 

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

#import datasets

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 = 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()

labelencoder_Y = LabelEncoder()
Y = labelencoder_X.fit_transform(Y)

#Splitting the data 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 = 0.3, random_state = 0)

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



