ROAD ACCIDENT SEVERITY PREDICTION USING MACHINE LEARNING

Road accidents are a major public safety concern and cause thousands of injuries and fatalities every year in India. The increasing accident rate highlights the need for analytical systems that can understand accident patterns and predict severity levels. Traditional accident analysis methods mainly provide historical information and fail to identify risk factors or predict accident severity in advance. To address these challenges, this research proposes a machine learning-based accident severity prediction system. The proposed model uses accident-related factors such as weather conditions, road type, vehicle type, time of accident, location, and human behavior to classify accident severity into categories such as Minor, Serious, and Fatal. The dataset is preprocessed and cleaned, followed by feature selection to identify the most influential variables. Machine Learning algorithms like Random Forest, Logistic Regression, XGBoost, and Neural Networks are trained and evaluated. Among them, XGBoost achieves the best performance with high accuracy in predicting serious accident cases. The system effectively identifies accident-prone conditions and patterns, helping authorities and decision-makers implement preventive measures. The results demonstrate that machine learning can significantly improve accident risk prediction and contribute to better road safety planning.