Stress Detection in IT Professionals Using Machine Learning

Abstract: In today's fast-paced technology landscape, stress management. The work environment in the IT field is often characterized by long hrs, and high expectations, which can leading to elevated stress levels. Unchecked stress not only impacts the health and well-being of professionals but also affects in job satisfaction. This study aims to predicting the stress levels of IT professionals using machine learning techniques, thereby aiding in proactive stress management. We utilize a range of features indicative of work stress, including Heart Rate, Skin Conductivity, Hours Worked, Number of Emails Sent, and Meetings Attended. These features provide a physiological and work-related factors that contribute to stress. The application of ML in this context serves as an innovative approach to an increasingly pertinent issue. By leveraging the power of data analytics, this model aims to organizations. Individuals we can use for self-monitoring and early intervention, while organizations can utilize them to identify high-stress environments or roles, thereby allocating resources or interventions more effectively. Our preliminary results indicate a strong correlation between the chosen features and stress levels, demonstrating the viability of using ml for stress prediction in IT professionals. This study stands as a crucial step towards a more data-driven approach to mental health condition