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Students opting for Engineering as their discipline is increasing rapidly.But due to various factors and inappropriate primary education in India,failure rates are high. Students are unable to excel in core engineeringbecause of complex and mathematical subjects. Hence, they fail in suchsubjects. With the help of data mining techniques, we can predict theperformance of students in terms of grades and failure in subjects. This paperperforms a comparative analysis of various classification techniques, such asNa\"ive Bayes, LibSVM, J48, Random Forest, and JRip and tries to choose bestamong these. Based on the results obtained, we found that Na\"ive Bayes is themost accurate method in terms of students failure prediction and JRip is mostaccurate in terms of students grade prediction. We also found that JRipmarginally differs from Na\"ive Bayes in terms of accuracy for students failureprediction and gives us a set of rules from which we derive the key factorsinfluencing students performance. Finally, we suggest various ways to mitigatethese factors. This study is limited to Indian Education system scenarios.However, the factors found can be helpful in other scenarios as well.

A Comparative Analysis of classification data mining techniques : Deriving key factors useful for predicting students performance