The original dataset contains 1000 entries with 20 categorial/symbolic attributes prepared by Prof. Hofmann. In this dataset, each entry represents a person who takes credit by a bank. Each person is classified as good or bad credit risks according to the set of attributes. 

Perform classification on the credit risk dataset to decide whether a particular person would be deemed to have a good or bad credit rating.