1.Try out the regression methods such as Best subset selection, PCR, PLS, Ridge regression and the Lasso regression. Present and discuss results for the approaches that you consider. 2.Propose a model (or set of models) that seem to perform best on this data set, and justify your answer. Make sure that you are evaluating model performance using validation set error, cross-validation (CV), or some other reasonable alternatives such as AIC, BIC, R^2 (or Adj.R^2), MSE, etc., as opposed to using training error. 3.Does your chosen model involve all the features in the data set? Why or Why not? use the following rcode, https://files.fm/u/w4bkp4us