Machine Learning-Based Early Prediction of Employee Burnout using Behavioral and Work Pattern Analysis

  The Burnout Risk Prediction System successfully combines machine learning methods with a web-based application to offer accurate and timely prediction of employee burnout risk. By analyzing important personal and professional factors such as stress levels, work hours, job satisfaction, and lifestyle patterns, The system helps find people who are at high risk early on. Using Random Forest and other classification methods and Logistic Regression ensures reliable performance, with improved accuracy and stability. The web interface allows users to easily access predictions and mental wellness resources, while administrators can efficiently manage users and content. Overall, the system offers A workable, expandable, and efficient way to enhance employee well-being and enhancing organizational productivity.

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