Nora Jackson owns a number of vacation homes on a beach. She works with a consortium of rental home owners to gather a data set to build a classification model to predict the likelihood of potential customers renting a beachfront home during holidays. A portion of the data set is shown in the accompanying table with the following variables: whether the potential customer owns a home (Own = 1 if yes, 0 otherwise), whether the customer has children (Children = 1 if yes, 0 otherwise), the customer’s age in years (Age), annual income (Income), and whether or not the customer has previously rented a beachfront house (Rental = 1 if yes , 0 otherwise). 

a. Bin the Age and Income variables as follows. For Analytic Solver, choose the Equal count option and two bins for each of the two variables. For R, bin Age into [22, 45) and [45, 85) and Income into [0, 85000) and [85000, 300000). What are the bin numbers for Age and Income of the first two observations? 

b. Partition the transformed data to develop a naïve Bayes classification model. Report the accuracy, sensitivity, specificity, and precision rates for the validation data set.

c. Generate the decile-wise lift chart. What is the lift value of the leftmost bar? 

d. Generate the ROC curve. What is the area under the ROC curve (or AUC value)? 

e. Interpret the performance measures and evaluate the effectiveness of the naïve Bayes model.

Transcribed Image Text:

Own 0 1 ⠀ 0 Children 1 0 ⠀ 0 Age 41 64 ⠀ 67 Income 64000 22000 A 146000 Rental 0 0 ⠀ 1