Telecommunications companies providing cell-phone service are interested in customer retention. In particular, identifying customers who are about to churn (cancel their service) is potentially worth millions of dollars if the company can proactively address the reason that customer is considering cancellation and retain the customer. The file Cellphone contains customer data to be used to classify a customer as a churner or not. Classify the data using k-nearest neighbors with k=1, . . ., 10. Use Churn as the output variable and all the other variables as input variables. Standardize the input variables to adjust for the different magnitudes of the variables.
a. What is the proportion of churners in the training set? What is the proportion of churners in the validation and test sets? Explain why this discrepancy is appropriate.
b. For the cutoff probability value of 0.5, what value of k minimizes the overall error rate on the validation data?
c. What is the overall error rate, the Class 1 error rate, and the Class 0 error rate on the test set?
d. Compute and interpret the sensitivity and specificity for the test set.
e. How many false positives and false negatives did the model commit on the test set? What percentage of predicted churners were false positives? What percentage of predicted non-churners were false negatives?
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