Student performance classification: A data mining approach

In this information era to maintain the highest academic performance is one of the challenges in front of educators. To maintain academic performance educators must know the strengths and weaknesses of their staff and students. The present paper focuses on Student Performance based on different factors. By using the classification technique educators predict student’s academic performance. Based on the result of classification, they decided their policies to maintain academic performance as highest. Different classification algorithms namely Random Forrest, Navie Bayse, Decision tree, bagging, etc. are used from the Weka tool. The present study shows that the J48 and Bagging algorithm are good algorithms for classifying student’s academic performance.