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Comparative Study of Data Mining Classifiers for Students’ Academic Performance

Tremendous growth of educational institutions forced educational institutes to adopt data mining techniques to bring out important and yet unknown facts from educational data to have a competitive edge over their counterparts. In this paper, student performance dataset comprises of
 131 records is taken from UCI repository and data mining tool Orange is used to study the comparative analyses of accuracy for classifying the performance of student in graduation using four classifiers namely random forest, k nearest neighbor (KNN), decision tree and naïve bayes.
 The result shows that decision tree accuracy is highest among all other classifier