# -*- coding: utf-8 -*-
"""
Created on Wed Nov 28 22:40:50 2018

@author: LENOVO
"""

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

dataset=pd.read_csv('Startup_Kar.csv')

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

turkce_kolon=['Ar&Ge','yonetici','pazarlama','Şehir','kazanç']
dataset.columns=turkce_kolon

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()

X=X[:,1:]


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)

from sklearn.linear_model import LinearRegression
regressor=LinearRegression()

regressor.fit(X_train,y_train)

y_pred=regressor.predict(X_test)


import statsmodels.formula.api as sm

X=np.append(arr=np.ones((50,1)),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()

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()
