Abstract:
Classification is an important data mining technique which predicts the class of a given data sample.Classification allocates new object to one out of a finite set of previously defined classes pm the basis of observations on several characteristics of the objects called attributes(or)features.The accuracy of the performance of classifier can be enhanced by using some techniques.One of these techniques is Bagging.The proposed system intends to implement a bagged classifier based on naïve Bayesian classifications to predict the class label of an unknown sample.The implemented classifier can be used as a supporting tool for decision making problems.The system will use german credit data approval at the bank as a case study.In this system,it will train based on credit data set and show how to get credit information with high accuracy for each customer.
