Student placement prediction using optimised ensemble machine learning approach

Ankur Arun Kulkarni, Shweta Agrawal and Ritu Tandon

International Journal of Services, Economics and Management, 2025, vol. 16, issue 4/5, 366-379

Abstract: This study presents an optimised machine learning model to predict student placements. The methodology used here is to use optimised ensemble machine learning technique. The results of optimised ensemble model are compared with other machine learning methods like naive Bayes, KNN, support vector machines and decision trees. The key aspects of this work are the examination of historical student placement data and the prediction of current students' placement. The attributes used for prediction are age, gender, stream, internship, CGPA, living facility, academic performance, etc. The performance of the implemented model for the placement prediction will be measured by various performance matrices like accuracy, sensitivity, specificity and AUC. Results show that maximum accuracy of prediction is 89% which is achieved through ensemble learning approach.

Keywords: machine learning; student performance; placement; naive Bayes; ensemble learning. (search for similar items in EconPapers)
Date: 2025
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