For this exercise, your goal is to build a model to identify inputs or predictors that differentiate risky customers from others (based on patterns pertaining to previous customers) and then use those inputs to predict new risky customers.
The sample data to be used is attached. The data set has 425 cases and 15 variables pertaining to past and current customers who have borrowed from a bank for various reasons. The data set contains customer-related information such as financial standing, reason for the loan, employment, demographic information, and the outcome or dependent variable for credit standing, classifying each case as good or bad, based on the institution’s past experience.
Take 400 of the cases as training cases and set aside the other 25 for testing. Build a decision tree model to learn the characteristics of the problem. Test its performance on the other 25 cases. Report on your model’s learning and testing performance. Prepare a report that identifies the decision tree model and training parameters, as well as the resulting performance on the test set. Use any decision tree software. (This exercise is courtesy of StatSoft, Inc., based on a German data set from ftp.ics.uci.edu/pub/machine-learning-databases/statlog/german renamed CreditRisk and altered.You could use R for the decision tree as well.)