# -*- coding: utf-8 -*-
"""
Created on Sun Feb  9 10:35:13 2020

@author: Mavrik
"""

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

# Importing the dataset

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

# Encoding the Independent Variable

from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
ct = ColumnTransformer([('encoder', OneHotEncoder(), [0])], remainder='passthrough')
X = np.array(ct.fit_transform(X), dtype=np.float)

# Encoding Y data

from sklearn.preprocessing import LabelEncoder
y = LabelEncoder().fit_transform(y)

# Splitting the dataset into the Training set 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 = 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)
sc_y = StandardScaler()
y_train = sc_y.fit_transform(y_train.reshape(-1,1))





