#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 27 11:19:56 2018

@author: giacomolini
"""

#Multiple Linear Regression
#importing libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#Import a dataset
dataset = pd.read_csv('50_Startups.csv') #name of the dataset
X = dataset.iloc[:, :-1].values 
Y = dataset.iloc[:, 4].values #modify extrems if necessary

# Encoding categorical data
# Encoding the Independent Variable
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()

#Avoiding the Dummy Variable Trap
X = X[:, 1:] #eliminate 1st coloumn to avoid DVa Trap 

#splitting into training and test datasets
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)
#possibly different test_size


#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)
"""

#Fitting MLR Model to training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, Y_train)

#Predicting on Test set 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)
#adding a coloumn of all 1s to the matrix on the leftside in order to be able to 
#compute the p value

#Bacward elimination
X_opt = X[:, [0,1,2,3,4,5]] #will contain only significant features at the end
regressor_OLS = sm.OLS(endog = Y, exog = X_opt).fit()
regressor_OLS.summary()
X_opt = X[:, [0,1,3,4,5]] 
regressor_OLS = sm.OLS(endog = Y, exog = X_opt).fit()
regressor_OLS.summary()
X_opt = X[:, [0,3,4,5]] 
regressor_OLS = sm.OLS(endog = Y, exog = X_opt).fit()
regressor_OLS.summary()
X_opt = X[:, [0,3,4,5]] 
regressor_OLS = sm.OLS(endog = Y, exog = X_opt).fit()
regressor_OLS.summary()
X_opt = X[:, [0,3,5]] 
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()





