road car accident prediction by using machine learning techniques

Traffic accidents globally pose a significant public health crisis, resulting in loss of life, injuries, and substantial economic burdens. predictive analytics, combining data science and health policy, offers a solution by identifying accident factors and creating proactive safety measures to save lives. this study uses machine learning to analyze traffic accident data from self-report questionnaires, revealing the importance of socio-economic status, psychosocial factors, and long driving hours in predicting accident occurrence. the research suggests interventions targeting socio-economic factors, psychological assessments and improved road safety to effectively prevent accidents.