# -*- coding: utf-8 -*-
"""
Created on Sun Feb 12 19:19:48 2017

@author: marcos
"""

# Pre-processamento de dados

# Importando as bibliotecas

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

# Importando o dataset

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

# Arrumando dados não-presentes

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])

#Encodando dados categóricos (ex. países, purchased (yes, no))

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)

# Dividindo o dataset em training dataset e test dataset

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.transform(X_test)


