Mukesh Kumar; Nidhi Walia; Sushil Bansal; Girish Kumar; Korhan Cengiz

Predicting College Students’ Placements Based on Academic Performance Using Machine Learning Approaches

Full Text (PDF, 981KB), PP.1-13

Views: 0 Downloads: 0

Author(s)

Mukesh Kumar ^1,* Nidhi Walia ² Sushil Bansal ² Girish Kumar ³ Korhan Cengiz ⁴

1. School of Computer Application, Lovely Professional University, Phagwara, Punjab, India

2. Maharaja Agrasen University, Solan, Himachal Pradesh, India

3. School of Computer Application, Lovely Professional University-Phagwara, Punjab, 144001, India

4. Department of Information Technologies, Faculty of Informatics and Management, University of Hradec Kralove, Kralove, 50003, Czech Republic

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2023.06.01

Received: 17 Apr. 2023 / Revised: 15 May 2023 / Accepted: 20 Jun. 2023 / Published: 8 Dec. 2023

Index Terms

Machine Learning Techniques, Logistic Regression, Gaussian Naïve Bayes, Random Forest, Support Vector Machine, and K-Nearest Neighbor

Abstract

Predicting College placements based on academic performance is critical to supporting educational institutions and students in making informed decisions about future career paths. The present research investigates the use of Machine Learning (ML) algorithms to predict college students' placements using academic performance data. The study makes use of a dataset that includes a variety of academic markers, such as grades, test scores, and extracurricular activities, obtained from a varied sample of college students. To create predictive models, the study analyses numerous ML algorithms, including Logistic Regression, Gaussian Naive Bayes, Random Forest, Support Vector Machine, and K-Nearest Neighbour. The predictive models are evaluated using performance criteria such as accuracy, precision, recall, and F1-score. The most effective machine learning method for forecasting students' placements based on academic achievement is identified through a comparative study. The findings show that Random Forest approaches have the potential to effectively forecast college student placements. The findings show that academic factors such as grades and test scores have a considerable impact on prediction accuracy. The findings of this study could be beneficial to educational institutions, students, and career counsellors.

Cite This Paper

Mukesh Kumar, Nidhi Walia, Sushil Bansal, Girish Kumar, Korhan Cengiz, "Predicting College Students’ Placements Based on Academic Performance Using Machine Learning Approaches", International Journal of Modern Education and Computer Science(IJMECS), Vol.15, No.6, pp. 1-13, 2023. DOI:10.5815/ijmecs.2023.06.01

Reference

[1]Jeevalatha, T., Ananthi, N., & Kumar, D. S. (2014). Performance analysis of undergraduate student’s placement selection using decision tree algorithms. International Journal of Computer Applications, 108(15).
[2]Maurya, L. S., Hussain, M. S., & Singh, S. (2021). Developing classifiers through machine learning algorithms for Student placement prediction based on academic performance. Applied Artificial Intelligence, 35(6), 403-420.
[3]Sheetal, M., & Bakare, S. (2016). Prediction of campus placement using data mining algorithm-fuzzy logic and k nearest neighbor. IJARCCE, 5(6), 309-312.
[4]Ahmed, S., Zade, A., Gore, S., Gaikwad, P., & Kolhal, M. (2018). Performance Based Placement Prediction System. IJARIIE-ISSN (O), 4(3), 2395-4396.
[5]Ishizue, R., Sakamoto, K., Washizaki, H., & Fukazawa, Y. (2018). Student placement and skill ranking predictors for programming classes using class attitude, psychological scales, and code metrics. Research and Practice in Technology Enhanced Learning, 13, 1-20.
[6]Manikandan, K., Sivakumar, S., & Ashokvel, M. (2018). A Classification Model for Predicting Campus Placement performance Class using Data Mining Technique. International Journal of Advance Research in Science and Engineering, 7(6).
[7]Rathore, R. K., & Jayanthi, J. (2017). Student prediction system for placement training using fuzzy inference system. ICTACT Journal on Soft Computing, 7(3), 1443-1446.
[8]Patel, T., & Tamrakar, A. (2017). A data mining technique for campus placement prediction in higher education. Indian J. Sci. Res, 14(2).
[9]Goyal, J., & Sharma, S. (2017). Placement Prediction Decision Support System using Data Mining. International Journal of Engineering and Techniques, 4(2).
[10]Surya, M. S., Kumar, M. S., & Gandhi Mathi, D. (2022). Student Placement Prediction Using Supervised Machine Learning. In 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 1352-1355). IEEE.
[11]Nagamani, S., Reddy, K. M., Bhargavi, U., & Kumar, S. R. (2020). Student placement analysis and prediction for improving the education standards by using supervised machine learning algorithms. J. Crit. Rev, 7(14), 854-864.
[12]Thakar, P., & Mehta, A. (2017). A unified model of clustering and classification to improve students’ employability prediction. International Journal of Intelligent Systems and Applications, 9(9), 10.
[13]Casuat, C. D., & Festijo, E. D. (2019). Predicting students' employability using machine learning approach. In 2019 IEEE 6th international conference on engineering technologies and applied sciences (ICETAS) (pp. 1-5). IEEE.
[14]Bai, A., & Hira, S. (2021). An intelligent hybrid deep belief network model for predicting students’ employability. Soft Computing, 25(14), 9241-9254.
[15]Saidani, O., Menzli, L. J., Ksibi, A., Alturki, N., & Alluhaidan, A. S. (2022). Predicting student employability through the internship context using gradient boosting models. IEEE Access, 10, 46472-46489.
[16]Hariharan, V. J., Abdullah, S., Rithish, R., Prabakar, V., Suguna, M., Ramakrishnan, M., & Selvakumar, S. (2022). Predicting student’s placement prospects using Machine learning Techniques. Available at SSRN 4140544.
[17]Manvitha, P., & Swaroopa, N. (2019). Campus placement prediction using supervised machine learning techniques. International Journal of Applied Engineering Research, 14(9), 2188-2191.
[18]Online-Link of Placement predictions Dataset: https://www.kaggle.com/code/arindambaruah/placement-predictions-using-log-reg-knn-rfc-xgb/input, Access on Date: 15/05/2023
[19]Harihar, V. K., & Bhalke, D. G. (2020). Student Placement Prediction System using Machine Learning. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 12(SUP 2), 85-91.
[20]Shejwal, P. N., Patil, N., Bobade, A., Kothawade, A., & Sangale, S. (2019). A Survey on Student Placement Prediction using Supervised Learning Algorithms. International Journal of Research in Engineering, Science and Management, 2(11), 2581-5792.
[21]Shukla, M., & Malviya, A. K. (2019). Modified classification and prediction model for improving accuracy of student placement prediction. In Proceedings of 2nd International Conference on Advanced Computing and Software Engineering (ICACSE).
[22]Hariharan, V. J., Abdullah, S., Rithish, R., Prabakar, V., Suguna, M., Ramakrishnan, M., & Selvakumar, S. (2022). Predicting student’s placement prospects using Machine learning Techniques. Available at SSRN 4140544.

International Journal of Modern Education and Computer Science (IJMECS)