International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Interpretation of Students Placement Prediction using ML |
|---|---|
| ABSTRACT | The rapid growth of educational data has opened new avenues for applying machine learning methods to predict the employability of students better. Traditional placement evaluation methodologies are heavily based on subjective judgment and manual screenings, which are often inconsistent and incorrect. This paper proposes a machine learning-based approach using academic and technical features to forecast the student placement status and predicted income. In the proposed solution, a Flask-based web application is used to implement a two-stage Random Forest model. While the second model calculates the projected income based on the outcome of the placement, the first model predicts the placement status (Placed/Not Placed). Experimental evaluation demonstrates that the approach provides rapid, fair, and accurate predictions, hence yielding significant career counseling support to the institutions and students. |
| AUTHOR | PROF. SONY V HOVALE, ABHISHEK, GAGAN M, K M SUMITKUMAR, SHASHANK S Assistant Professor, Department of CS&DS, Bapuji Institute of Engineering and Technology, Davanagere, Karnataka, India U.G. Student, Department of CS&DS, Bapuji Institute of Engineering and Technology, Davanagere, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312088 |
| pdf/88_Interpretation of Students Placement Prediction using ML.pdf | |
| KEYWORDS | |
| References | [1] M. Pothuganti and N. Swaroopa, “Campus placement prediction using supervised machine learning techniques,” International Journal of Applied Engineering Research, vol. 14, no. 9, pp. 2188–2191, 2019. Available: https://www.ripublication.com/ijaer19/ijaerv14n9_33.pdf [2] S. K. Thangavel, P. D. Bharatki, and A. Sankar, “Student placement analyzer – A recommendation system using machine learning,” in Proc. 2017 Int. Conf. on Computing, Communication and Automation, 2017. Available: https://ieeexplore.ieee.org/document/8256898 [3] J. R. Quinlan, C4.5: Programs for Machine Learning. San Mateo, CA, USA: Morgan Kaufmann, 1993. Available: https://www.sciencedirect.com/book/9781558602380/c45 [4] S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 2nd ed. Upper Saddle River, NJ, USA: Prentice Hall, 2003. Available: https://aima.cs.berkeley.edu/ [5] Manvitha et al., “Factors Affecting Campus Placement,” Kaggle Dataset, 2019. Available: https://www.kaggle.com/code/ajeetchaudhary/factors-affecting-campus-placement [6] L. S. Maurya, M. S. Hussain, and S. 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. Available: https://www.tandfonline.com/doi/abs/10.1080/08839514.2020.1846648 |
