Revisiting Vacations at Vegas Villas

Reconsider Case $2-1,$ Vacations at Vegas Villas. As in the final part of that case study, Silver State Development $($SSD$)$ would like to

classify each of the following potential buyers as either likely to purchase $($at least a $25$ percent chance$)$ or not $($less than a $25$ percent

chance$).$ They would also like to predict how many vacation credits they each might purchase.

For each model, use Income and Visits as the predictor variables.

a$.$ Using the entire dataset on the Clean Data worksheet tab, generate a classification tree to classify potential buyers as likely to

purchase vacation credits $($or not$).$ Allow a maximum of four splits as the stopping rule. Use the resulting classification tree to classify

each of the three potential buyers.

b$.$ Generate a regression tree to predict the number of vacation credits a potential buyer might purchase. Allow a maximum of four splits

as the stopping rule. Use the resulting regression tree to predict how many vacation credits each of the three potential buyers will

purchase.

c$.$ The historical data for Income and Visits have each now been binned into five categories $($Low$,$ Medium Low, Medium, Medium High,

and High$)$ on the Binned Data worksheet tab. Apply naive Bayes to the binned dataset to classify each of the three potential buyers as

likely to purchase $($or not$).$

d$.$ Apply multiple linear regression to determine a prediction equation for the number of vacation credits purchased as a function of

Income and Visits. Use the resulting multiple linear regression model to predict the number of vacation credits purchased for each of

the three potential buyers.