jmp needs to be downloaded to perform this exercise. The file UniversalBank.jmp contains data on 5000 customers. 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 (= 9.6%) accepted the personal loan that was offered to them in the earlier campaign. Data Mining Tasks:
Perform a k-NN classification with all predictors except ID and ZIP code using k = 10. (Hint: Cast the variable Personal Loan to Y, Response and Validation to Validation, all other variables (except for ID and ZIP) to X factors. (Note: This analysis may take a few minutes)
WHAT IS THE MISCLASSIFICATION RATE FOR K=1 ON VALIDATION SET?
[ Select ]
[“0.041”, “0.039”, “0.049”]
Now, you are supposed to classify a new customer with the following information: Age = 40, Experience = 10, Income = 84, Family = 2, CCAvg = 2, Education = 2, Mortgage = 0, Securities Account = 0, CD Account = 0, Online = 1, and Credit Card = 1. (Hint:
Under the red triangle in the K NN output, select Save Prediction Formula, for best K.
Then, add a row, and enter the values specified above (be sure to tab all the way through all fields in the row.)
WHAT IS THE PREDICTED VALUE OF YOUR THE NEW CUSTOMER? [ Select ]
[“Unpredictable”, “No”, “Yes”]
"96% of our customers have reported a 90% and above score. You might want to place an order with us."
