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Predicting the performance of a student is a great concern to the higher education managements. The scope of this paper is to investigate the accuracy of data mining techniques in such an environment. The first step of the study is to gather student's data. The authors collected records of 300 Under Graduate students of computer science course, from a private Educational Institution. The second step is to clean the data and choose the relevant attributes. In the third step, NaiveBayesSimple, MultiLayerPerception, SMO, J48, REPTree algorithms were constructed and their performances were evaluated. The paper revealed that the MultiLayerPerception is more accurate than the other algorithms.
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