profit_spyder.py

import pandas as pd
import os
os.chdir("c:\\users\\lappy\\desktop")
dataset=pd.read_csv("50_startups.csv")
X = dataset.iloc[:, :-1].values
Y = dataset.iloc[:, 4].values
from sklearn.preprocessing import LabelEncoder
labelencoder_X=LabelEncoder()
X[:,3]=labelencoder_X.fit_transform(X[:,3])
from sklearn.preprocessing import OneHotEncoder
onehotencoder=OneHotEncoder(categorical_features=[3])
X=onehotencoder.fit_transform(X).toarray()
import statsmodels.formula.api as sm
import numpy as np
X=X[:,1:]
X=np.append(arr=np.ones((50,1)).astype(int),values=X,axis=1)
X_opt=X[:,[3,4]]
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()