PREDICTING EMPLOYEES UNDER STRESS USING EMPTIVE REMEDIATION

Abstract

The increasing demands of modern workplaces, coupled with tight deadlines, high workloads, and evolving organizational expectations, have significantly contributed to rising levels of employee stress, which can negatively impact productivity, job satisfaction, and overall well-being. Traditional approaches to identifying workplace stress are often reactive, relying on selfreporting or periodic assessments, which may fail to detect early signs of stress. To address this limitation, this project proposes a system for Predicting Employees Under Stress Using Emptive Remediation, which leverages advanced machine learning techniques to proactively identify stress patterns and provide timely interventions. The proposed system collects and analyzes multi-dimensional employee data, including behavioral patterns, work performance metrics, communication frequency, and physiological indicators where available. Through data preprocessing and feature engineering, relevant attributes are extracted and transformed into a format suitable for model training. Machine learning algorithms such as Random Forest, Support Vector Machines, and Neural Networks are employed to classify employees based on stress levels. The system is designed to detect subtle changes in behavior and performance that may indicate early stress conditions, enabling proactive identification rather than reactive response. A key component of the system is the emptive remediation module, which provides personalized recommendations such as workload adjustments, break suggestions, wellness programs, or managerial interventions based on the predicted stress level. This ensures not only detection but also actionable solutions to mitigate stress effectively. Experimental results demonstrate improved prediction accuracy and timely intervention, leading to enhanced employee wellbeing and organizational efficiency. Overall, the proposed approach offers a data-driven, scalable, and proactive solution for stress management in workplaces. By integrating predictive analytics with intelligent remediation strategies, the system contributes to building healthier work environments and improving employee performance, making it highly relevant for modern organizations