All the institutions strive to find a student who is the best possible fit for their institute. They look forward to recruiting students who have the highest potential to succeed. Most of them are looking into their previous scores for making the recruiting decision. That does not always work out well for the institute, because past performance does not always prove future success. A machine learning model could solve this problem. Machine learning algorithms aim to discover hidden knowledge and patterns about student’s performance. Support Vector Clustering is a relatively new learning algorithm that has the desirable characteristics like controlling the decision function, kernel method and sparsity of the solution. In this paper, we present a theoretical and empirical framework to apply the Support Vector Machines for predicting the students future performance in an educational institution. There are many factors like personality, curiosity, past academic performance, etc that are taken into account for predicting the students performance. Our results suggest that support vector clustering is a powerful tool for selecting students in the educational institution.
Educational institute, student performance prediction, predictive models, predictive algorithms and training data
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