# Multiple Linear Regression

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

# Importing the dataset
org_dataset = pd.read_csv('50_Startups.csv')
dataset=org_dataset.copy()


#X = dataset.iloc[:, :-1].values
#y = dataset.iloc[:, 4].values

y=dataset["Profit"].values
del dataset["Profit"]
X = dataset.values


# Encoding categorical data
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()

# 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.reshape(-1,1))"""

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

regressor.intercept_
regressor.coef_  # we can compare the values in stats model 

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


import statsmodels.api as sm
from sklearn.metrics import r2_score

X_train = sm.add_constant(X_train)
X_test = sm.add_constant(X_test)

model = sm.OLS(y_train, X_train).fit()
predictions = model.predict(X_test) # make the predictions by the model
r2_OHC=r2_score(y_test,predictions)

model.summary()