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 risk customers. This sample case is typical for this domain.

The data to be used in this exercise are in Online File W4.1_CreditRisk.xls

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 dataset contains customer-related information such as financial standing, the reason for the loan, employment, demographic data, 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. Make a report that identifies the decision tree model and training parameters, as well as the resulting performance on the test set.

Using Tableau to predict with step-by-step guides.

here is the link to the file

https://drive.google.com/file/d/1Mm9gdn1xmosJ4U86iZeRScracnT7zoHB/view?usp=sharing