Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{175440,
        author = {Mrs.S.Sangeetha and Manojkumar K and Nandha Kumar A and Rohidh Danny R and ThiruMurthy C J and Balaji S},
        title = {Advanced Employee Attrition and Layoff  Prediction System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {3300-3305},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175440},
        abstract = {Employee attrition and layoffs are critical challenges organizations face, often leading to significant financial and operational setbacks. This project aims to develop a comprehensive Machine Learning-based Employee Attrition and Layoff Prediction System to address these issues proactively. Leveraging advanced data analytics and machine learning techniques, the system analyzes various employee-related factors, such as performance metrics, work history, job satisfaction, and organizational changes. The core objective is to predict potential employee attrition and layoff risks. Historical employee data is used for training, ensuring accuracy and reliability in the predictions. By identifying at-risk employees and understanding the underlying causes, organizations can reduce turnover rates, improve employee retention, and optimize workforce planning. Ultimately, the proposed system aims to enhance organizational stability and foster a supportive work environment through data-driven decision-making.},
        keywords = {Employee attrition, Layoff prediction, Machine learning, Predictive analytics, Workforce management, Employee retention, HR analytics, Data-driven decision-making, Supervised learning, Workforce planning.},
        month = {April},
        }