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Student placement prediction is a crucial task for educational institutions and recruiters to optimize campus hiring processes. This study employs a data-driven machine learning approach to predict student placement outcomes based on academic performance, extracurricular activities, certifications, aptitude scores, and soft skills ratings. A dataset comprising 10,000 student records was analyzed, and multiple machine learning models were trained, including LR, SVM, DT, RF, KNN, and GNB. To address data imbalance, the SMOTE was used to ensure robust model performance. Feature selection identified CGPA, aptitude test scores, and placement training as the most influential factors. Among all models, RF achieved the highest accuracy of 79.72%, outperforming traditional statistical methods. Model performance was evaluated metrics and execution time analysis. The findings provide valuable insights for students, helping them understand the key factors influencing placement success, and assist institutions in refining training programs. This study demonstrates the effectiveness of machine learning in enhancing placement prediction accuracy and suggests potential future improvements using deep learning techniques for more precise outcomes.
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Copyright © 2025, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: Abuthahir, syedabuthahirs@mits.ac.in
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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