Data classification of student perception analysis based onnaive bayes and j48 algorithm

Classification is an important data mining technique with broad applications to classify the various kinds of data used in nearly every field of our life. Classification is used to classify the item according to the features of the item with respect to the predefined set of classes. A student survey and open-response questions was developed to measure attitudes and beliefs about the course, skills and confidence. Results indicate that the group of students communication skills, and confidence in writing and explanation techniques.Naive Bayes algorithm is based on probability and j48 algorithm is based on decision tree. The paper sets out to make comparative evaluation of classifiers NAIVE BAYES AND J48 in the context of bank dataset to maximize true positive rate and minimize false positive rate of defaulters rather than achieving only higher classification accuracy usingWEKA tool. Research findings suggest that students’ perceptions about assessment, have considerable influences on students’ approaches to learning and recruitment.