You are working for the ABC Telecom and are given some customer records for data mining. You are asked to discover, from the data, patterns that characterize low-, medium- and high-usage customers. He would like to make sure that newly recruited salespersons be able to recommend the right service plans (500-free-mins (low-usage), 2500-free-mins (medium-usage), and 5000-free-mins (high-usage)) to the right customers.
a) Show how you can make use of the ID3 algorithm to discover in a sample of customer records (shown in Table 2) what best plan to make to which kind of customers. (Please show clearly your calculations and steps.)
b) You are given a testing data set (shown in Table 3) as follows, how much should you trust the recommendations made according to the rules discovered by ID3 algorithm?
c) Use the Naïve Bayesian Approach to check the recommendations against the testing data set. How many recommendation(s) is/are trustful?
d) Given a choice between the Naïve Bayesian Approach and the ID3 algorithm for this task, which one would you choose? Why?