#Data Preprocessing

#Importing 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

#Taking care of Categorical variable

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder = LabelEncoder()
X[:,3] = labelencoder.fit_transform(X[:,3])
onehotencoder = OneHotEncoder(categorical_features=[3])
X = onehotencoder.fit_transform(X).toarray()

#Taking care of Dummy variable trap

X = X[:, 1:6]

#Feature scaling
"""
from sklearn.preprocessing import StandardScaler
standardscaler = StandardScaler()
X_train = standardscaler.fit_transform(X_train)
X_Test = standardscaler.transform(X_Test)"""

# Splitting the dataset into training set ad 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)

#Fitting multiple linear model

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

#Prediction

y_pred = regressor.predict(X_test)

#Backward Elimination

import statsmodels.formula.api as sm
X = np.append(arr= np.ones((50,1)), 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()