Application of Machine Learning Algorithms to Predict Students Performance
Student’s performance is a major problem for the society. Rapid growth of technologies and the application of differentmachine learning methodsin present years, the development of good models increase the progress of student’s performance progress have become more and more accurate. Therefore, development of machine learning techniques, which can effectivelypredict student’s performance, is of vastimportance.In this research paper, we apply five different data mining techniques Passive Aggressive Classifier (PAC), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Radius Neighbour Classifier (RNC) and Extra Tree (ET) and then compare the results of five machine learning algorithms to choose the best performing algorithm. We use educational data toanalysis differentmachine learning techniques to evaluate the performance of student. The results obtained by different machine learning algorithms are discussed in this paper and we get the highest accuracy in the case of Support Vector Machine (SVM).Various metrics are also evaluated to verify the results of accuracy like sensitivity, specificity and precision. These results can be applied on the new coming students to check whether they perform well or not and by knowing the non-performing students, higher educational institutions can pay attaint ion for improving student’s performance.