Performance Comparison of Machine Learning Algorithms that Predicts Students’ Employability
14 Pages Posted: 9 Mar 2018
Date Written: November 15, 2017
Educational data mining is a field related to developing processes or methods by exploring large scale data to understand students’ performance and devise methods to improve. As in other fields, the availability of data in electronic form helps us to leverage it to get insights. Those insights can be used further to improve the academic system. In this paper, we explore the probability of students’ placement during campus drives through six different Machine Learning (ML) algorithms. Our objective is to narrow down on the better performing ML algorithms and further identify the causal parameters. The students’ demographical and performance data are collected and analyzed. The empirical results show that logistic regression algorithm and support vector machine gives better results.
Keywords: Educational Data Mining, Machine Learning, Predictive Analysis, Classification
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