# Data Preprocessing Template

# 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 independent variable state
#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()


from sklearn.preprocessing import LabelEncoder,OneHotEncoder
from sklearn.compose import ColumnTransformer
ct = ColumnTransformer([("State", OneHotEncoder(), [3])], remainder = 'passthrough')
X= ct.fit_transform(X)

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

# 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
import statsmodels.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 = np.array(X[:, [0, 1, 2, 3, 4, 5]], dtype=float)
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)
regressor_OLS=sm.OLS(endog=y,exog=X_opt).fit()
regressor_OLS.summary()

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


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


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
















