Title: Student placement prediction using optimised ensemble machine learning approach

Authors: Ankur Arun Kulkarni; Shweta Agrawal; Ritu Tandon

Addresses: Department of Civil Engineering, SAGE University, Indore, MP, India ' Department of AI/ML, Indore institute of Science and Technology, Indore, MP, India ' Institute of Advance Computing, SAGE University, Indore, MP, India

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.

DOI: 10.1504/IJSEM.2025.148474

International Journal of Services, Economics and Management, 2025 Vol.16 No.4/5, pp.366 - 379

Received: 24 Apr 2023
Accepted: 14 Sep 2023
Published online: 08 Sep 2025 *

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