# -*- coding: utf-8 -*-
"""
Created on Thu Aug 29 09:57:22 2019

@author: BrysDom
"""
#Importing libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#Importing 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_X = LabelEncoder()
X[:,3]=labelencoder_X.fit_transform(X[:,3])
onehotencoder=OneHotEncoder(categorical_features=[3])
X = onehotencoder.fit_transform(X).toarray()

#Avoinding Dummy Variable Trap
X=X[:,1:]

#Splitting database into Training 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)"""

#Fitting Linear Regression to Set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train,y_train)

#Predicting Test result
y_pred = regressor.predict(X_test)

#Building 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)

#Option 1 - automatic - with function
def backwardElimination(x, sl):
    numVars = len(x[0])
    for i in range(0, numVars):
        regressor_OLS = sm.OLS(y, x).fit()
        maxVar = max(regressor_OLS.pvalues).astype(float)
        if maxVar > sl:
            for j in range(0, numVars - i):
                if (regressor_OLS.pvalues[j].astype(float) == maxVar):
                    x = np.delete(x, j, 1)
    regressor_OLS.summary()
    return x
 
SL = 0.05
X_opt = X[:, [0, 1, 2, 3, 4, 5]]
X_Modeled = backwardElimination(X_opt, SL)

#Option 2 - manual
'''
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()

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