International Journal of Scientific Research and Engineering Development

Road accidents are a major cause of injuries and fatalities worldwide. Early prediction of accident severity can help traffic authorities implement preventive measures and improve road safety. This study focuses on the analysis of historical road accident data and the development of machine learning models to predict accident severity levels.The dataset includes factors such as weather conditions, time of accident, road type, traffic density, and vehicle type. Data preprocessing techniques including data cleaning, encoding, and feature selection are applied to prepare the dataset for model training. Machine learning algorithms such as Decision Tree, RandomForest are used to classify accident severity into three categories: Low, Medium, and High.The performance of the models is evaluated using Accuracy, Precision, Recall, and F1- score. A comparative analysis is conducted to determine the best-performing algorithm. Experimental results demonstrate that the proposed approach effectively predicts accident severity and supports datadriven decision-making.This system can assist traffic management authorities in identifying high-risk conditions and taking proactive measures to reduce accident severity and enhance road safety.

M.Rajalakshmi, Mr.G.Ramkumar,"Analysis and Prediction of Road Accident Severity Using ML" International Journal of Scientific Research and Engineering Development, V9(2): Page(1102-1107) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.