# -*- coding: utf-8 -*-
"""
Created on Tue May 29 01:25:56 2018

@author: Kunal Gupta
"""

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

# import dataset
dataset = pd.read_csv('50_Startups.csv')
X = dataset.iloc[:,:-1].values
y = dataset.iloc[:,4].values

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[:, 3] = labelencoder_X.fit_transform(X[:, 3])
onehotencoder = OneHotEncoder(categorical_features = [3])
X = onehotencoder.fit_transform(X).toarray()

#Avoid dummy variable trap
X = X[:,1:]

# Splitting the dataset into 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 = 0.2, 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)

# Fitting multiple Linear Regression model to the training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train,y_train)

# Predicting the Test Results
y_pred = regressor.predict(X_test);

#Building the optimal model using Backward Elimination
import statsmodels.formula.api as sm
X = np.append(arr = np.ones((50,1)).astype(int), values = X, axis=1)

X_opt = X[:, [0,1,2,3,4,5]]
regressor_OLS = sm.OLS(endog=y, exog= X_opt).fit()
regressor_OLS.summary()

X_opt = X[:, [0,3]]
regressor_OLS = sm.OLS(endog=y, exog= X_opt).fit()
regressor_OLS.summary() 
