Employee Burnout Prediction Using Machine Learning
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Employee Burnout Prediction Using Machine Learning
Pratiksha Vaijanath Lagad
Department of Computer Application, R.J.S.P.M’s Institute of Computer & Management Research
ABSTRACT
Employee burnout has become a significant challenge in today’s dynamic workplace environment. This paper presents a theoretical and design-based approach for predicting employee burnout using machine learning techniques. The study emphasizes data-driven decision- making in human resource management by analyzing parameters such as workload, job satisfaction, working hours, and organizational support. Various algorithms like Logistic Regression, Random Forest, and Support Vector Machines are proposed for building predictive models. The objective is to design a system that can help organizations identify burnout-prone employees early, thereby reducing turnover rates and improving overall productivity. The paper highlights the potential of artificial intelligence in supporting employee mental health and well-being initiatives.
Key Words: employee burnout, machine learning, HR analytics, prediction, stress detection, workforce well- being.
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