Enhancing Stress Assessment with Machine Learning

Machine Learning

The project titled "Enhancing Stress Assessment with Machine Learning" aims to address the pervasive issue of stress among employees in corporate sectors through the utilization of two machine-learning techniques: Support Vector Machine (SVM) and Random Forest. Despite the availability of mental health programs, stress disorders persist, necessitating a more targeted approach. Through meticulous data preprocessing and cleaning, we ensure the accuracy of our analysis prior to applying the machine learning algorithms. Our findings highlight family background and the accessibility of health benefits in the workplace as significant factors contributing to stress levels. Armed with this insight, corporate sectors can implement tailored strategies to alleviate stress and foster a more supportive work environment, thereby promoting employee well-being. This research provides valuable insights into addressing stress-related challenges in the modern workplace and underscores the potential of machine learning in enhancing stress assessment and intervention. By leveraging these insights, organizations can prioritize employee well-being and productivity, thereby contributing to the overall success and sustainability of the corporate sector. Given the dynamic nature of work and the increasing demands placed on employees, understanding and addressing stress is crucial for maintaining a healthy and thriving workforce. Through our project, we aim to offer a comprehensive framework for identifying and mitigating stress factors, ultimately fostering a more resilient and productive corporate environment.

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