pytholabsbot1

def ent(df,attribute):
    target_variables = df.play.unique()  #This gives all 'Yes' and 'No'
    variables = df[attribute].unique()    #This gives different features in that attribute (like 'Sweet')


    entropy_attribute = 0
    for variable in variables:
        entropy_each_feature = 0
        for target_variable in target_variables:
            num = len(df[attribute][df[attribute]==variable][df.play ==target_variable]) #numerator

pytholabsbot1

##create a nodel to estimate the relationship b/w x and y
##optimization model = OLS estimator

def ols(x,y):

    y_ = y.mean()
    x_ = x.mean()


    b1 = np.sum((y-y_)*(x-x_))/np.sum((x-x_)**2)