> library(ISLR)

> library(glmnet)

> lasso.mod = glmnet(x[train,],y[train],alpha =1,lambda = grid)

> set.seed(100)

> cv.out2 = cv.glmnet(x[train,],y[train],alpha=1)

> plot(cv.out2)

> bestlam2 = cv.out2$lambda.min

> bestlam2

> lasso.pred = predict(lasso.mod,s=bestlam2,newx= x[test,])

> mean((lasso.pred-y.test)^2)

> out = glmnet(x,y,alpha = 1,lambda = grid)

> lasso.coef =predict(out,type="coefficients",s=bestlam2)[1:11,]

> lasso.coef