The file universalbank.csv contains data on $5000$ customers of Universal Bank. The data include customer demographic information $($age$,$ income, etc.$),$ the customer$$s relationship with the bank $($mortgage$,$ securities account, etc.$),$ and the customer response to the last personal loan campaign $($Personal Loan$).$ Among these $5000$ customers, only $480(=9\mathrm{.}6\%)$ accepted the personal loan that was offered to them in the earlier campaign. In this exercise, we focus on three predictors: age, income, experience, and the outcome Personal Loan. Partition the data into training $(60\%)$ and validation $(40\%)$ sets. # Load the data into dataframe bank$\_$df $.$ Only keep the columns we need: ncome, Experience, Age, Personal Loan.Drop the rest. Use critical functions to explore the dat using R code