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. 

The original dataset had data imbalance for 'good' and 'bad' credit, there were 700 'good' credits and only '300' bad credits. I have modified the dataset to be more balanced by increasing the number of 'bad' credits. The resampling technique was done by oversampling the minority class with random replications.

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