Identifying Patterns and Trends in Campus Placement Data Using Machine Learning

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Author(s)

Raghavendra C K 1,* Smaran N. G. 1 Spandana A. P. 1 Vijay D. 1 Vishruth M. V. 1

1. Department of Computer Science and Engineering, B N M Institute of Technology, Bangalore, 560070, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijeme.2025.01.02

Received: 20 Apr. 2024 / Revised: 23 May 2024 / Accepted: 11 Jul. 2024 / Published: 8 Feb. 2025

Index Terms

Machine learning, In-demand skills, Ensemble Technique, Trends in Placement

Abstract

This research delves into the utilization of machine learning algorithms to address the urgent challenge of assisting students in navigating a highly competitive job market. Recognizing the limitations of conventional methods in delivering effective guidance for securing job opportunities, there is a growing imperative to integrate advanced technology. Our model using Machine Learning (ML) algorithms offers customized solutions and emphasizes the algorithms that exhibit the highest effectiveness within this context. In the contemporary employment, achieving success extends beyond mere academic credentials, necessitating a holistic grasp of industry trends and in-demand skills. Through the application of machine learning, a fresh approach is presented, encompassing the gathering, and preprocessing of diverse data that encompasses skill proficiencies. This data forms the bedrock upon which ML algorithms operate, predicting and enhancing students’ likelihood of securing favorable job placements. The proposed work focuses on the careful selection of suitable machine learning algorithms, with special attention given to classification techniques such as Linear Regression, Random Forest, Decision Tree Classifier, K-nearest neighbors Classifier, and ensembled models. By meticulous evaluation and Ensemble Technique, these algorithms unearth intricate patterns within the data, deciphering the multifaceted factors influencing job placement outcomes. By deconstructing the performance of each algorithm, the report provides valuable insights into their strengths and potential synergies.

Cite This Paper

Raghavendra C. K., Smaran N. G., Spandana A. P., Vijay D., Vishruth M. V., " Identifying Patterns and Trends in Campus Placement Data Using Machine Learning", International Journal of Education and Management Engineering (IJEME), Vol.15, No.1, pp. 10-24, 2025. DOI:10.5815/ijeme.2025.01.02

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]Kumar, N., Singh, A. S., T. K., & Rajesh, E. (2020). ”Campus Placement Predictive Analysis using Machine Learning,” In 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), Greater Noida, India, pp. 214-216, doi: 10.1109/ICACCCN51052.2020.9362836.
[19]Bhoite, Sachin et al. “A Data-Driven Probabilistic Machine Learning Study for Placement Prediction.” In 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), 2023, pp. 402-408.
[20]Basha, Md. Shaik Amzad et al. “Unraveling Campus Placement Success Integrating Exploratory Insights with Predictive Machine Learning Models.” In 2023 7th International Conference for Computation System and Information Technology for Sustainable Solutions (CSITSS), 2023, pp. 1-6.