#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 14 08:26:32 2017

@author: vinay
"""

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


# np.set_printoptions(threshold=np.inf)
# create dependent and independent variables matrix

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

# solve problem for 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_Y.fit_transform(Y)

# Split the dataset for training and testing
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 Scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.fit_transform(X_test)
