Classification is an important data mining technique based on machine learning with broad applications. It classifies various kinds of data and used in nearly every field of our life. Classification is used to classify every item in a set of data into one of predefined set of classes or groups. This paper describes the performance analysis of Naïve Bayes and J48 classification algorithm based on the correct and incorrect instances of data classification. Naive Bayes algorithm is based on probability and j48 algorithm is based on decision tree. In this paper we compare and perform evaluation of classifiers NAIVE BAYES and J48 in the context of mushroom dataset in UCI repository to maximize true positive rate and minimize false positive rate of defaulters rather than achieving only higher classification accuracy using WEKA. The experiments results shown in this paper are about true positive rate, false positive rate, classification accuracy and cost analysis. The results in the paper on mushroom dataset in UCI repository performance best in WEKA tools also show that the efficiency and accuracy of J48 than Naive Bayes is good.

Data mining, Weka Tool, J48,Navie Bayes

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