# Multiple Linear Regression

#%% Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os
os.chdir('/Users/denismariano/pcloud/udemy/Machine Learning A-Z Template Folder/Part 2 - Regression/Section 5 - Multiple Linear Regression')

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

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

# 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)

plt.scatter(y_pred,y_test)

#%% building the optimal model using backward elimination
import statsmodels.formula.api as sm
#adding a column of ones to the begining of the dataset
X = np.append(arr=np.ones((50,1)).astype(int),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()

#%%remove x4
X_opt = X[:,[0,1,2,3,5]]
regressor_OLS = sm.OLS(endog=y,exog=X_opt).fit()
regressor_OLS.summary()

#%%remove x3
X_opt = X[:,[0,1,2,5]]
regressor_OLS = sm.OLS(endog=y,exog=X_opt).fit()
regressor_OLS.summary()