Association rule mining (ARM) is used to advance decisions making in the application. ARM became important in an information and decision-overloaded world. They changed the way user make decisions, and helped their creators to increase revenue at the same time. Bringing ARM is essential in order to popularize them beyond the limits of scientific research and high technology entrepreneurship. This paper extracts attractive correlations frequent patterns and association among set of items in the transaction database. This paper describes the show analysis of Naïve Bayes and J48 classification algorithm based on the correct and incorrect instances of data classification. Naive Bayes is probability based and j48 algorithm is decision tree based. In this paper Comparison weka and orange by using tool to 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 and orange tool. The experiments results reveals the 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 than Orange tools. The efficiency and accuracy of J48 -than Naive Bayes is good

Data mining, Weka Tool, Orange tool J48,Navie Bayes

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