Integrated Machine Learning Framework for Layoff Risk Assessment and Job Recommendation

Nilam Sahebrao Honmane; Shukti Khanorkar; Karanveer Singh; Prajwal Kavhar; Hridayansh Kaware

doi:10.2991/978-94-6239-723-1_26

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Integrated Machine Learning Framework for Layoff Risk Assessment and Job Recommendation

Authors

Nilam Sahebrao Honmane¹^{, *}, Shukti Khanorkar¹^{, *}, Karanveer Singh¹^{, *}, Prajwal Kavhar¹^{, *}, Hridayansh Kaware¹^{, *}

¹Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, India

^*Corresponding author. Email: nilam.honmane@vit.edu

^*Corresponding author. Email: shukti.khanorkar24@vit.edu

^*Corresponding author. Email: karanveer.singh24@vit.edu

^*Corresponding author. Email: prajwal.kavhar24@vit.edu

^*Corresponding author. Email: hridayansh.kaware24@vit.edu

Corresponding Authors

Nilam Sahebrao Honmane, Shukti Khanorkar, Karanveer Singh, Prajwal Kavhar, Hridayansh Kaware

Available Online 14 July 2026.

DOI: 10.2991/978-94-6239-723-1_26 How to use a DOI?
Keywords: Job Risk Prediction; Machine Learning; Career Recommendation; Layoff Detection; Skill Gap Analysis; Rural-to-Urban Employment; XGBoost; Random Forest; Workforce Transition; Career Guidance
Abstract: The contemporary labor market is quickly changing because of cogs going digital and moving the industrial demands. This paper introduces a com binned machine learning model integrating job risk forecasting and individualized career guidance. The system evaluates a user’s risk of job loss by analyzing their employment history, skills, and industry trends. It offers customized job on recognition of potential risk. It provides support and recommendations on skill development resources and career transitions. The framework specifically addresses the needs of rural individuals seeking jobs while transitioning to urban labor markets. It uses ensemble machine learning model which includes Random Forest, XGBoost, and Logistic Regression in order to obtain risk classification. Experimental results confirm that the system reliably classifies users into low, medium, and high-risk categories and delivers relevant job matches. The proposed system functions as a proactive career support tool helping individuals make informed decisions and confidently adapt to a changing workforce.
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the International Conference on Responsible, Risk-aware, and Regulated AI (RRRAI 2026)
Series: Advances in Intelligent Systems Research
Publication Date: 14 July 2026
ISBN: 978-94-6239-723-1
ISSN: 1951-6851
DOI: 10.2991/978-94-6239-723-1_26 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Nilam Sahebrao Honmane
AU  - Shukti Khanorkar
AU  - Karanveer Singh
AU  - Prajwal Kavhar
AU  - Hridayansh Kaware
PY  - 2026
DA  - 2026/07/14
TI  - Integrated Machine Learning Framework for Layoff Risk Assessment and Job Recommendation
BT  - Proceedings of the International Conference on Responsible, Risk-aware, and Regulated AI (RRRAI 2026)
PB  - Atlantis Press
SP  - 279
EP  - 290
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-723-1_26
DO  - 10.2991/978-94-6239-723-1_26
ID  - Honmane2026
ER  -

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