Company Selection Predictor for Graduates Using Machine Learning

International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 3, pp. 33-41, March 2026.

https://doi.org/10.58482/ijersem.v2i3.5

This work is licensed under a Creative Commons Attribution 4.0 International License.

Company Selection Predictor for Graduates Using Machine Learning

Full Text PDF

*Dara sudhakara

Purandla Sai Srinivas

Nandhikaluva Sudeepthi

Kaku Yogitha

Sejal Gupta

Putta Duggireddy Sunil Kumar Reddy

 

*Department of Mechanical Engineering, Siddartha Institute of Science and Technology, Puttur, India.

Department of CSE, Siddartha Institute of Science and Technology, Puttur, India.

Abstract: Engineering graduates have experienced greater difficulty in finding companies that match their qualifications and experience, as well as their career goals, in recent years. Placement guidance has traditionally relied on manual counselling or generally accepted criteria, but these methods may not always be inclusive of individual capabilities or current industry needs. A selection predictor for companies using machine learning is presented to aid engineering graduates in making informed career decisions, addressing the challenge. The system considers a variety of variables, including academic performance, technical skills, internship experience, certifications, aptitude scores, and personal interests. Machine learning algorithms are trained on historical placement and recruitment data to identify patterns that impact company selection and hiring. Each graduate is given a list of suitable companies, organised by domain and role in accordance with the trained model, along with specific eligibility criteria, for potential acquisition opportunities. This approach is more personalised and accurate than traditional rule-based systems, as it allows for greater personalisation of predictions. The model’s experimental outcomes demonstrate that it can assist students in comprehending their placement readiness and improve institution placement strategies and career guidance. Ultimately, the system is data-driven, scalable and intelligent for career selection and engineering education, respectively.

Keywords: Company Selection Prediction, Engineering Graduates, Career Guidance System, Student Performance Analysis, Data-Driven Decision Making.

References: 

  1. C. Mariani and M. Mancini, “Project portfolio selection and prioritization through supervised machine learning in continuous improvement projects,” International Journal of Lean Six Sigma, vol. 17, no. 8, pp. 27–55, Dec. 2025, doi: 10.1108/ijlss-03-2025-0047.
  2. H. Jeong, S.-C. Yang, S.-G. Park, I. Hong, and H. D. Kim, “Predicting the risk of burnout syndrome using Korean Occupational Stress Scale (KOSS): a machine learning approach,” Safety and Health at Work, vol. 16, no. 4, pp. 462–469, Sep. 2025, doi: 10.1016/j.shaw.2025.08.006.
  3. S. Deng et al., “Financial distress warning and risk path analysis for Chinese listed companies: An interpretable machine learning approach,” Economic Modelling, vol. 152, p. 107288, Aug. 2025, doi: 10.1016/j.econmod.2025.107288.
  4. A. Göncü, T. U. Kuzubaş, and B. Saltoğlu, “Machine learning for risk profiling: An analysis of pension fund participants,” Borsa Istanbul Review, p. 100800, Jan. 2026, doi: 10.1016/j.bir.2026.100800.
  5. F. Daghero et al., “Machine learning for predicting digital block layout feasibility in Analog-On-Top designs,” Microprocessors and Microsystems, vol. 119, p. 105221, Nov. 2025, doi: 10.1016/j.micpro.2025.105221.
  6. F. Mozaffari, M. Rahimi, H. Yazdani, and B. Sohrabi, “Employee attrition prediction in a pharmaceutical company using both machine learning approach and qualitative data,” Benchmarking an International Journal, vol. 30, no. 10, pp. 4140–4173, Dec. 2022, doi: 10.1108/bij-11-2021-0664.
  7. O. Iparraguirre-Villanueva and M. Cabanillas-Carbonell, “Predicting business bankruptcy: A comparative analysis with machine learning models,” Journal of Open Innovation Technology Market and Complexity, vol. 10, no. 3, p. 100375, Aug. 2024, doi: 10.1016/j.joitmc.2024.100375.
  8. Y. Choi, C. Lee, and S. Han, “Predicting wearable IoT Adoption: Identifying core consumers through Machine learning algorithms,” Telematics and Informatics, vol. 93, p. 102176, Aug. 2024, doi: 10.1016/j.tele.2024.102176.
  9. J. Gibbs et al., “Immersive Innovation: The role of Virtual Reality in Orthopedic Surgery graduate Education training in 2025,” Journal of Orthopaedic Reports, p. 100886, Dec. 2025, doi: 10.1016/j.jorep.2025.100886.
  10. N. Bari, T. Saleem, M. Shah, A. Algarni, A. Patel, and I. Ullah, “A Filter-Based feature selection framework to detect phishing URLs using stacking ensemble machine learning,” Computer Modeling in Engineering & Sciences, vol. 145, no. 1, pp. 1167–1187, Jan. 2025, doi: 10.32604/cmes.2025.070311.
  11. M. Lokanan and S. Sharma, “Reprint of: The use of machine learning algorithms to predict financial statement fraud,” The British Accounting Review, vol. 57, no. 1, p. 101560, Jan. 2025, doi: 10.1016/j.bar.2025.101560.
  12. R. Raman and D. Pramod, “The role of predictive analytics to explain the employability of management graduates,” Benchmarking an International Journal, vol. 29, no. 8, pp. 2378–2396, Oct. 2021, doi: 10.1108/bij-08-2021-0444.
Download PDF

This article is part of Volume 2, Issue 3 (March 2026)