
# 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
labelencoder_X = LabelEncoder()
X[:, 3] = labelencoder_X.fit_transform(X[:, 3])  # we need to use labelencoder first, because we have to have numbers instead string values, than use onehotencoder
onehotencoder = OneHotEncoder(categorical_features = [3]) # 3 index in categorical_features means which column to group
X = onehotencoder.fit_transform(X).toarray()

#Avoidingthe Dummy Varibale Trap
# we don't need to remove it, because Linear Regression takes care of it. But there are some libraries which don't do that
X = X[:, 1:] #remove one first column

# Splitting the dataset into the 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)

#We don't need to apply Feature Scalling because library will apply it for us

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

# Predict the 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)
X_opt = X[:, [0, 1, 2, 3, 4, 5]]       #X_opt will contain only variables which have significant impact on model prediction. 
regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()
regressor_OLS.summary()

X_opt = X[:, [0, 1, 3, 4, 5]]       #X_opt will contain only variables which have significant impact on model prediction. 
regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()
regressor_OLS.summary()

X_opt = X[:, [0, 3, 4, 5]]       #X_opt will contain only variables which have significant impact on model prediction. 
regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()
regressor_OLS.summary()

X_opt = X[:, [0, 3, 5]]       #X_opt will contain only variables which have significant impact on model prediction. 
regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()
regressor_OLS.summary()

X_opt = X[:, [0, 3]]       #X_opt will contain only variables which have significant impact on model prediction. 
regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()
regressor_OLS.summary()



from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X_opt, y, test_size = 0.2, random_state = 0)

regressor = LinearRegression()
regressor.fit(X_train, y_train)

# Predict the Test set results
y_pred = regressor.predict(X_test)