# 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

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)"""

# 5 Methods of building model:
# 1. All-in
# 2. Backward Elimination -------- Stepwise regression
# 3. Foward Selection ------------ Stepwise regression
# 4. Bidirectional Elimination --- Stepwise regression
# 5. Score Comparison

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

#Predicting the Testt 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]]
regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()
regressor_OLS.summary()

X_opt = X[:, [1, 2, 3, 4]]
regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()
regressor_OLS.summary()

X_opt = X[:, [2, 3, 4]]
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()

from sklearn.model_selection 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)

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

y_pred2 = regressor.predict(X_test)

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

