• Dhanashri Ramrao Patil
• Dr Tushar phadtare

Stress in the Information Technology (IT) industry has emerged as a significant issue due to fast-paced work environments, long hours, and high demands. Unmanaged stress can result in mental health challenges, burnout, and reduced productivity, highlighting the need for effective detection and management solutions. Traditional methods, such as surveys and interviews, are subjective and often lack real-time insights. Machine learning (ML) offers a powerful tool to address these challenges by analyzing data from various sources, including communication logs, emails, and biometric data, to identify early stress indicators. This research explores the application of machine learning for stress detection and management in the IT industry. The study uses algorithms, such as sentiment analysis, classification models, and natural language processing (NLP), to detect stress patterns in communication and physiological data. Moreover, ML models are evaluated for their ability to predict stress and recommend personalized interventions for employees. The findings reveal that factors such as work interference, supervisor support, and employee benefits significantly impact mental health. Transgender individuals, in particular, experience higher stress levels, especially when there is a family history of mental health issues. The research demonstrates the efficacy of ML in providing real-time insights for stress management, allowing organizations to proactively support employees and improve overall well-being. By integrating machine learning into workplace wellness strategies, organizations can create healthier, more productive environments.

"STRESS DETECTION AND MAMGEMENT USING MACHINE LEARNING IN IT INDUSTRY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.m262-m272, April-2025, Available :http://www.jetir.org/papers/JETIR2504C35.pdf

"STRESS DETECTION AND MAMGEMENT USING MACHINE LEARNING IN IT INDUSTRY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppm262-m272, April-2025, Available at : http://www.jetir.org/papers/JETIR2504C35.pdf