Leveraging Educational Data Mining Techniques to Predict Students’ Performance in Campus Placement

Abstract

One of the most significant problems in Educational Data Mining (EDM) is predicting students' performance. The objective being identifying students who are at a risk of failure at an early stage so that preventive actions such as continuous mentoring, assisted or personalized learning, continuous assessments can be taken by the educators. In this paper, we have analyzed students’ academic and placement data of past 4 years of our Institute to understand the extent to which academic results affect placement outcomes. We have used Linear Regression, ID3(J48) and Naïve Bayesian data mining models.