Implementation of Credit Classification Using Competitive Neural Tree

Credit classification is a system thatdetermines credit applicants, either “good credit”one that is likely to repay financial obligation or“bad credit” one who has high possibility ofdefaulting on financial obligation, by analyzingcustomer’s data.In a credit classification system,an applicant’s data are assessed and evaluated,like financial status, preceding past payments andcompany background to distinguish between a“good” and a “bad” applicant. This is usuallydone by taking a sample of past customers.Manymodels and algorithms have been applied tosupport credit classification, including statistical,genetic algorithm and neural networks. Neuralnetwork and decision trees are widely used invarious classification task that is required noknowledge on the data. The advantages of neuralnetwork and decision trees are combined inCompetitive Neural Trees(CNeT).This system isimplemented Credit Classification usingCompetitive Neural Tree. There are 1000 recordsto implement this system. In each record includes20 attributes. This system displays one of twoclasses of Credit (good or bad credit)