Please solve using R studio

We will use the Carseats dataset that is part of the ISLR package. We may also need the following packages: caret, dplyr and glmnet

For this assignment, we only need the following attributes: "Sales", "Price", "Advertising", "Population",

"Age", "Income" and "Education". The goal of the assignment is to build models to predict the sales of the

carseats $($Sales$$ attribute$)$ using the other attributes.

We can use the dplyr select function to select these attributes.

Carseats$\_$Filtered $<-$ Carseats $\%>\%$ select$("$Sales$",$ "Price",

"Advertising","Population","Age","Income","Education"$)$

$1)$ Build a Lasso regression model to predict Sales based on all other attributes $("$Price$",$ "Advertising", "Population", "Age", "Income" and "Education"$).$ What is the best value of lambda for such a lasso model? $($Hint$1$: Do not forget to scale your input attributes $$ you can use the caret preprocess$()$ function to scale and center the data. Hint $2$: glment library expect the input attributes to be in the matrix format. You can use the as$.$matrix$()$ function for converting

$2)$ What is the coefficient for the price $($normalized$)$ attribute in the best model $($i$.$e$.$ model with the optimal lambda$)?$

$3)$ How many attributes remain in the model if lambda is set to $0\mathrm{.}01?$ How that number changes if lambda is increased to $0\mathrm{.}1?$ Do you expect more variables to stay in the model $($i$.$e$.,$ to have non$-$zero coefficients$)$ as we increase lambda?

$4)$ Build an elastic$-$net model with alpha set to $0\mathrm{.}6.$ What is the best value of lambda for such

a model?