# -*- coding: utf-8 -*-
"""
Created on Tue May 16 23:52:00 2017

@author: Afkar
"""

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

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

# Handling missing values
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values='NaN', 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
encoder_X = LabelEncoder()
X[:, 0] = encoder_X.fit_transform(X[:, 0])
hotencoder = OneHotEncoder(categorical_features = [0])
X = hotencoder.fit_transform(X).toarray()
encoder_y = LabelEncoder()
y = encoder_y.fit_transform(y)

# Split training and test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 9873)

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















