# -*- coding: utf-8 -*-
"""
Created on Thu Mar 26 01:59:13 2020

@author: vanda
"""

# Multiple Linear Regression

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

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

# Encoding categorical data
# Encoding the Independent Variable
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.compose import ColumnTransformer
labelencoder_X = LabelEncoder()
X[:, 3] = labelencoder_X.fit_transform(X[:, 3])
columtransformer = ColumnTransformer(
    [('one_hot_encoder', OneHotEncoder(categories='auto'), [3])],   
    remainder='passthrough' )                                       
X = columtransformer.fit_transform(X)

# Avoiding the dummy Variable Trap
X = X[:, 1:] #To remove the first column from the dataset

# 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)"""

# Fitting Multiple Linear Regression to the Training Set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)

# Predicting the Test Set results
y_pred = regressor.predict(X_test)

# Building the optimal model using Backward Elimination
# to get the statistical information of the model, in the form of value of p
import statsmodels.api as sm
X = np.append(arr = np.ones((50, 1)).astype(int), values = X, axis = 1)
X_opt = np.array(X[:, [0, 1, 2, 3, 4, 5]], dtype = float)
#OrdinaryLeastSquares
regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()
regressor_OLS.summary()

X_opt = np.array(X[:, [0, 1, 3, 4, 5]], dtype = float)
#OrdinaryLeastSquares
regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()
regressor_OLS.summary()

X_opt = np.array(X[:, [0, 3, 4, 5]], dtype = float)
#OrdinaryLeastSquares
regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()
regressor_OLS.summary()

X_opt = np.array(X[:, [0, 3, 5]], dtype = float)
#OrdinaryLeastSquares
regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()
regressor_OLS.summary()

X_opt = np.array(X[:, [0, 3]], dtype = float)
#OrdinaryLeastSquares
regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()
regressor_OLS.summary()
#R&D spend is the optimal choice












