# -*- coding: utf-8 -*-


# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd



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()
X = X[: , 1:]
                


from sklearn.cross_validation import train_test_split 
X_train , X_test , y_train , y_test = train_test_split(X , y , test_size = 1/3 , random_state = 0)

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train , y_train)

y_pred = regressor.predict(X_test)


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, 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, 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()

"""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)"""