(g) In this part, you will perform a quadratic regression analysis. To do this, proceed as follows: i. Rename the features. Call the features x1,x2, ii. Define a python function that receives a data-frame and automatically constructs the quadratic monomials and adds them as new columns to the input data-frame. The new columns should be of the form x1x1,x1x2, If you have N features, how many quadratic monomials should your function construct? Caution: Revisit Q2 (Collinearity)! iii. Apply the usual regression algorithm in the presence of both linear and quadratic monomials. Now you have performed the quadratic regression! iv. Record the accuracy metrics for both the train and test datasets. v. Present your assessment of the performance of the quadratic regression. (g) In this part, you will perform a quadratic regression analysis. To do this, proceed as follows: i. Rename the features. Call the features x1,x2, ii. Define a python function that receives a data-frame and automatically constructs the quadratic monomials and adds them as new columns to the input data-frame. The new columns should be of the form x1x1,x1x2, If you have N features, how many quadratic monomials should your function construct? Caution: Revisit Q2 (Collinearity)! iii. Apply the usual regression algorithm in the presence of both linear and quadratic monomials. Now you have performed the quadratic regression! iv. Record the accuracy metrics for both the train and test datasets. v. Present your assessment of the performance of the quadratic regression