IJBTT

Enhanced Student Placement Prediction Using Machine Learning: A Comparative Evaluation of Algorithms

Enhanced Student Placement Prediction Using Machine Learning: A Comparative Evaluation of Algorithms
	© 2025 by IJETT Journal
	Volume-73 Issue-1
	Year of Publication : 2025
	Author : Milind Ruparel, Priya Swaminarayan
	DOI : 10.14445/22315381/IJETT-V73I1P119

How to Cite?
Milind Ruparel, Priya Swaminarayan, "Enhanced Student Placement Prediction Using Machine Learning: A Comparative Evaluation of Algorithms," International Journal of Engineering Trends and Technology, vol. 73, no. 1, pp. 225-236, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I1P119

Abstract
Predicting the placement of students is a prime aspect of determining career outcomes and optimizing educationally strategic decisions. For that purpose, in this piece of research, an analysis of how to predict student placement outcomes via machine learning algorithms, according to the College Placement Predictor Dataset, has been presented. This presents how Logistic Regression, Random Forest, Decision Tree, Naïve Bayes, SVM, KNN, Gradient Boosting, and LDA algorithms performed. The performances of these models have been compared using important metrics like precision, recall, F1-score, and accuracy. The results depict that KNN, Logistic Regression, and SVM have been performing quite well against other models, with an accuracy of around 94%. Naïve Bayes and Decision Trees, however, performed much worse and proved the difference model selection and optimization make. The study calls for preprocessing data, specifically feature scaling and handling outliers, to enhance the model's performance. Results have underlined the potential for machine learning to transform student placement processes into ones that offer personalized interventions and efficient resource allocation. Further work will include adding more features and overcoming datasets' limitations to improve model robustness and applicability to real-world settings.

Keywords
Student placement prediction, Machine Learning, Ensemble methods, Educational data, Optimization.

References

[1] P.S. Ambili, and Biku Abraham, “A Comprehensive Evaluation of Employability Prediction Using Ensemble Learning Techniques,” EPRA International Journal of Multidisciplinary Research, vol. 10, no. 1, pp. 362-366, 2024.
[Publisher Link]
[2] Hicham El Mrabet, and Abdelaziz Ait Moussa, “A Framework for Predicting Academic Orientation Using Supervised Machine Learning,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, pp. 16539-16549, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Gerard J. van den Berg et al., “IZA DP No. 16426: Predicting Re-Employment: Machine Learning versus Assessments by Unemployed Workers and by Their Caseworkers,” IZA-Institute of Labor Economics, pp. 1-59, 2023.
[Publisher Link]
[4] Muhammad Hadiza Baffa, Muhammad Abubakar Miyim, and Abdullahi Sani Dauda, “Machine Learning for Predicting Students’ Employability,” UMYU Scientifica, vol. 2, no. 1, pp. 1–9, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Pranali chhagan Shinde et al., “Placement Prediction Using Machine,” International Journal of Advance Research and Innovative Ideas in Education, vol. 9, no. 2, pp. 646-650, 2023.
[Publisher Link]
[6] Neha Kunjan Shah, “Job Position Detection: A Data Science Approach,” International Journal of Research Publication and Reviews, vol. 4, no. 7, pp. 3229-3235, 2023.
[Publisher Link]
[7] P. Archana et al., “Student Placement Prediction Using Machine Learning,” Journal of Survey in Fisheries Sciences, vol. 10, no. 1, pp. 2734-2741, 2023.
[CrossRef] [Publisher Link]
[8] Binny Parida, Prashanta KumarPatra, and Sthitapragyan Mohanty, “Prediction of Recommendations for Employment Utilizing Machine Learning Procedures and Geo-Area-Based Recommender Framework,” Sustainable Operations and Computers, vol. 3, pp. 83-92, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Umesh Kumar Sah, and Awantika Singh, “Student Career Prediction Using Machine Learning,” International Journal of Scientific Development and Research, vol. 7, no. 5, pp. 343-347, 2022.
[Google Scholar] [Publisher Link]
[10] Matti Tedre et al., “Teaching Machine Learning In K–12 Classroom: Pedagogical and Technological Trajectories for Artificial Intelligence Education,” IEEE Access, vol. 9, pp. 110558-110572, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Akanksha Pandey, and L.S. Maurya, “Predicting Students’ Career by Using Machine Learning Algorithms,” International Journal of Innovations in Engineering and Science, vol. 7, no. 7, pp. 20-24, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] K.M. Naresh Patel et al., “Placement Prediction and Analysis Using Machine Learning,” International Journal of Engineering Research and Technology, vol. 10, no. 11, pp. 224-227, 2022.
[Google Scholar] [Publisher Link]
[13] Mayur Valte et al., “Placement Prediction,” International Journal of Advanced Research in Science, Communication and Technology, vol. 2, no. 5, pp. 512-520, 2022.
[Publisher Link]
[14] Akanksha Pandey, and L.S. Maurya, “Career Prediction Classifiers Based on Academic Performance and Skills Using Machine Learning,” SSRG International Journal of Computer Science and Engineering, vol. 9, no. 3, pp. 5-20, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Laxmi Shanker Maurya, Shadab Hussain, and Sarita Singh, “Developing Classifiers through Machine Learning Algorithms for Student Placement Prediction Based on Academic Performance,” Applied Artificial Intelligence, vol. 35, no. 6, pp. 403-420, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Rajiv Suresh Kumar et al., “Student Placement Prediction Using Support Vector Machine Algorithm,” International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, vol. 9, no. 5, pp. 40-43, 2021.
[Publisher Link]
[17] Neethu C. Sekhar et al., “WHAT’S NEXT? Prediction Model for Students Future Development,” International Journal of Innovative Research in Technology, vol. 8, no. 7, pp. 7-11, 2022.
[Publisher Link]
[18] N. Vidyashreeram, and A. Muthukumaravel, “Student Career Prediction Using Machine Learning Approaches,” Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC, Chennai, India, pp. 1-8, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Aniket Surve, Amit Singh, and Shivam Tiwari, “Student Career Guidance System Using Machine Learning,” International Research Journal of Engineering and Technology, vol. 8, no. 8, pp. 3543-3546, 2021.
[Publisher Link]
[20] V.J. Hariharan et al., “Predicting Student Placement Prospects Using Machine Learning Techniques,” Proceedings of the International Conference on Innovative Computing and Communication (ICICC), pp. 1-4, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Kotha Amitabh et al., “Campus Placement Prediction System Using Bagging Approach,” Journal of Emerging Technologies and Innovative Research, vol. 8, no. 8, pp. 306-311, 2021.
[Publisher Link]
[22] Vandana Mulye, and Atul Newase, “A Review: Recruitment Prediction Analysis of Undergraduate Engineering Students Using Data Mining Techniques,” SSRG International Journal of Computer Science and Engineering, vol. 8, no. 3, pp. 1-6, 2021.
[CrossRef] [Publisher Link]
[23] Jinguo Zhu et al., “Complementary Relation Contrastive Distillation,” Arxiv, pp. 1-10, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Yogesh Bharambe et al., “Assessing Employability of Students Using Data Mining Techniques Assessing Employability of Student Using Data Mining Techniques,” 2017 International Conference on Advances in Computing, Communications and Informatics, Udupi, India, pp. 2110-2114, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Prathamesh Gavhane et al., “Career Path Prediction Using Machine Learning,” International Journal of Scientific Research in Science and Technology, vol. 5, no. 8, pp. 300-304, 2020.
[Publisher Link]
[26] H. Al-Dossari et al., “CareerRec: A Machine Learning Approach to Career Path Choice for Information Technology Graduates,” Engineering, Technology and Applied Science Research, vol. 10, no. 6, pp. 6589-6596, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Viram Vishnukant Rathi et al., “Placement Prediction System Using Machine Learning,” International Journal of Creative Research Thoughts, vol. 8, no. 4, pp. 1507-1515, 2020.
[Google Scholar] [Publisher Link]
[28] Irene Treesa Jose et al., “Placement Prediction Using Various Machine Learning Models and their Efficiency Comparison,” International Journal of Innovative Science and Research Technology, vol. 5, no. 5, pp. 1005-1009, 2020.
[Google Scholar] [Publisher Link]
[29] J. Samatha et al., “Student Placement Chance,” Journal of Emerging Technologies and Innovative Research, vol. 7, no. 5, pp. 1011-1015, 2020.
[Publisher Link]
[30] Mohana Bangale et al., “A Survey on Placement Prediction System Using Machine Learning,” International Journal for Science and Advance Research in Technology, vol. 5, no. 2, pp. 1-4, 2019.
[Google Scholar] [Publisher Link]
[31] Kachi Anvesh et al., “Automatic Student Analysis and Placement Prediction Using Advanced Machine Learning Algorithms,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 12, pp. 4178-4183, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Shreyas Harinath et al., “Student Placement Prediction Using Machine Learning,” International Research Journal of Engineering and Technology, vol. 6, no. 4, pp. 4577-4579, 2019.
[Publisher Link]
[33] Geoffrey Hinton, Oriol Vinyals, and Jeff Dean, “Distilling the Knowledge in a Neural Network,” Arxiv, pp. 1-9, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Anita Bai, and Swati Hira, “An Intelligent Hybrid Deep Belief Network Model for Predicting Students Employability,” Soft Computing, vol. 25, pp. 9241-9254, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Oumaima Saidani et al., “Predicting Student Employability Through the Internship Context Using Gradient Boosting Models,” IEEE Access, vol. 10, pp. 46472-46489, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Sameer kumar, “College Placement Predictor Dataset,” Kaggle, 2023.
[CrossRef] [Publisher Link]
[37] Mukesh Kumar et al., “Predicting College Students’ Placements Based on Academic Performance Using Machine Learning Approaches,” International Journal of Modern Education and Computer Science, vol. 15, no. 6, pp. 1-13, 2023.
[CrossRef] [Google Scholar] [Publisher Link]