Can someone help with this using R? I am trying to teach myself and could really use some help. Thank you.

Load and read the documentation for the College data set from the ISLR package. We want to build a model to predict the number of applications received using the other variables. (a) Split the data set into a training set and a validation/test set, approximately 70%, 30%, respectively. (b) Fit a linear least-squares regression model on the training set. Compute the test MSE and test R2. (c) Fit a ridge regression model on the training set. Use cross-validation to choose the tuning parameter A. Give the test MSE and test R2 (d) Fit a lasso regression model on the training set. Use cross-validation to choose the tuning parameter 1. Give the test MSE and test R2. (e) Fit a principle components regression model on the training set, and use cross-validation to choose the number of principle com- ponents. Give the test MSE and test R2, and the number of principle components. (f Fit a partial least squares regression model on the training set, and use cross-validation to choose the number of new model features. NNNNAT Give the test MSE and test Ro, and the number of new features used in the model. (g) Compare the five models. Which ones seem better? Is there much /20 difference between the test R2 and test MSE values? How well /20 do these models predict the number of college applications? 20: /20