X=df['total_exp']
y=df['sales']
np.polyfit(X,y, deg=1)
Output
array([0.04868788, 4.24302822]) #B1x1 + B0
and another one is
X=df.drop('sales', axis=1) # included 4 float columns out of 5 column
fianl_model=LinearRegression().fit(X,y)
fianl_model.coef_
Ouput
array([-0.01254965, 0.13021572, -0.05935179, 0.05831429]) #B3X**3+B2**2+B1x+B0
and both outputs are identical in their own way
in the above case, we have only one column so the polynomial is B1x1 + B0
in another one, we have 4 columns so the polynomial is B3X**3 + B2**2 + B1x + B0
Q> they are the same thing? how