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

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

#encoding the catgorical values into discreet 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()

#avoiding the dummy variable trap
X = X[:, 1:]

#splitting the data into training and test sets
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)

#fitting multiple linear rehression to the training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)

#predicting the test set result
y_pred = regressor.predict(X_test)

#building the optimal model uusing backward elemination
import statsmodels.formula.api as sm
#X = np.append(arr =X , values = np.ones((50,1)).astype(int), axis =1)


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()

























