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Classifier Comparisons On Credit Approval Prediction

Inspired by the paper of Simplifying Decision Trees by J.R. Quinlan and the book C4.5: programs for machine learning by J.R. Quinlan and Morgan Kaufmann where they test the very traditional pruned decision tree models on credit approval data set, we want to re-exam the data set used in Simplifying Decision Trees and build advanced models to increase the accuracy. No surprise that all of the models we built beat the bench mark (12.9%, the best result from Quinlans paper), among which SVM with polynomial kernel and random forest achieve the best result (9.18%).