A Data-Driven Study of Mental Health Trends in the Tech Industry: Statistical and Machine Learning Perspectives

Abstract

Mental wellbeing is critical for people to survive in the fast-paced workplace of today in order to succeed both personally and professionally. The computer sector poses particular difficulties for mental health because of its intensive work ethic, long hours, and high levels of stress. Research by Open Sourcing Mental Illness (OSMI) has shown that mental health disorders are especially prevalent in the technology sector. This study aims to identify important elements and facilitate early detection through a comprehensive analysis of mental health trends in the tech sector. This paper looks at the frequency of mental health problems among technical professionals in comparison to their non-technical counterparts using the Mental Health in Tech survey dataset, collected from people all around the world. Using rigorous statistical analysis and predictive modelling, the study explores variations in the frequency of mental disorders across different geographic locations and examines workplace attitudes towards mental health. The study combines a Python-based approach comprising feature engineering, exploratory data analysis, data preparation, and machine learning model building to forecast mental health diagnoses. The findings of this study highlight important mental illness and treatment-seeking attitude determinants as well as the prevalence of mental health problems in tech-related companies. This study aims to help create work environments that give mental health a top priority by elucidating effective strategies for promoting worker well-being and encouraging help-seeking behavior.