ML-Based: Placement Prediction Application
Sahil Gupta1, Sourabh2, Rounak Kumar3, Sourav Raj4, Vishal Shrivastava5, Devesh Kumar Bandil6
1Sahil Gupta, Scholar, Department of Computer Science & Engineering, Arya College of Engineering & IT, Jaipur (Rajasthan), India.
2Sourabh, Scholar, Department of Computer Science & Engineering, Arya College of Engineering & IT, Jaipur (Rajasthan), India.
3Rounak Kumar, Scholar, Department of Computer Science & Engineering, Arya College of Engineering & IT, Jaipur (Rajasthan), India.
4Sourav Raj, Scholar Department of Computer Science & Engineering, Arya College of Engineering & IT, Jaipur (Rajasthan), India.
5Dr. Vishal Shrivastava, Professor, Department of Computer Science & Engineering, Arya College of Engineering & IT, Jaipur (Rajasthan), India.
6Dr. Devesh Kumar Bandil, Professor, Department of Computer Science & Engineering, Arya College of Engineering & IT, Jaipur (Rajasthan), India.
Manuscript received on 29 April 2025 | First Revised Manuscript received on 03 May 2025 | Second Revised Manuscript received on 06 May 2025 | Manuscript Accepted on 15 May 2025 | Manuscript published on 30 May 2025 | PP: 20-25 | Volume-13 Issue-6, May 2025 | Retrieval Number: 100.1/ijese.F260313060525 | DOI: 10.35940/ijese.F2603.13060525
Open Access | Editorial and Publishing Policies | Cite | Zenodo | OJS | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This research paper examines machine learning models in predicting student placement outcomes in technical education. Given the increasing focus on employability in higher education, institutions require robust predictive models to enhance placement readiness. We perform a stringent comparison of four sophisticated machine learning methods—Random Forest, XGBoost, Logistic Regression with Regularisation, and Support Vector Machines with an RBF Kernel—on a comprehensive dataset that includes academic, technical, and behavioural metrics. Our approach requires feature engineering methods and advanced hyperparameter tuning to achieve the best predictive performance. Results show that the ensemble techniques consistently outperform traditional algorithms, with XGBoost achieving 92.3% accuracy and more favourable recall measures. We further introduce a feature importance analysis tool that identifies the dominant determinants of placement success. The study proposes actionable suggestions to academic administrators and placement cells to help them develop intervention mechanisms tailored to their specific needs, thereby addressing the gap in industry-academia collaboration.
Keywords: Machine Learning, Placement Prediction, Resume Analyser, Mock Interview, Flask, Flutter, NLP, XGBoost, Career Guidance.
Scope of the Article: Computer Science and Applications