Predicting Relationship Between Traffic Accidents with Related Injuries

Abstract

Abstract: Traffic collisions are one of the world's 14 most crucial issues right now since they cause countless fatalities, severe injuries, and financial losses every year. The difficulty of creating accurate forecasting models, Traffic accident severity is important for transportation networks. This study creates models to choose a number of important features and build a model for classifying injury severity. These models are produced utilizing various Machine Learning techniques. The data on traffic accidents, trained and methods for unsupervised machine learning are also used. The primary aim is to draw a connection between the many kinds of injuries and traffic accident types. The study's conclusions imply that unsupervised learning methods may be effective in predicting the severity of harm from traffic accidents.