Conduct regularization method for models to describe relationships among variables and make useful predictions. Use the College dataset (https://rdrr.io/cran/ISLR/man/College.html) from the ISLR library to build regularization models by using Ridge and Lasso (least absolute shrinkage and selection operator).

Predict Grad.Rate for all models.

1. Split the data into a train and test set

2. Ridge Regression

3. Use the cv.glmnet function to estimate the lambda.min and lambda.1se values.

4. Compare and discuss the values. Plot the results from the cv.glmnet function provide an interpretation. What does this plot tell us?

5. Fit a Ridge regression model against the training set and report on the coefficients.

6. Is there anything interesting?

7. Determine the performance of the fit model against the training set by calculating the root mean square error (RMSE). sqrt(mean((actual - predicted)^2))

8. Determine the performance of the fit model against the test set by calculating the root mean square error (RMSE).

9. Is your model overfit? LASSO Use the cv.glmnet function to estimate the lambda.min and lambda.1se values.

10. Compare and discuss the values. Plot the results from the cv.glmnet function provide an interpretation.

11. What does this plot tell us?