Academic performance prediction based on voting technique

Student's grade has always been critical issues that occur quite often in universities providing high learning education. Currently there are many techniques to predict student's grade. In this paper we compare the accuracy of data mining methods to classifying students in order to predicting student's class grade. These predictions are more useful for identifying weak students and assisting management to take remedial measures at early stages to produce excellent graduate that will graduate at least with second class upper. Firstly we examine single classifiers accuracy on our data set and choose the best one and then ensembles it with a weak classifier to produce simple voting method. We present results show that combining different classifiers outperformed other single classifiers for predicting student performance.

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