Accidents are currently regarded as the most disturbing cause in many countries. Several deaths have been recorded for generating massive deaths throughout numerous countries just as a result of road accidents that predominantly occur during traffic. Vehicle accidents are the leading cause of deception, distress, and fatality. The majority of accidents occur only over a long period of time from various countries, and are referred to be unsafe or dangerous conditions that are associated with large volumes of traffic, particularly vehicle traffic. Exploring the causes of these incidents can help identify the features that are most important in determining the severity of the accident. Almost all of the repercussions, such as light conditions, speed zones, part of injury, climate, and so on, are also participating in and closely linked to the cause of traffic accidents, of which only a few are emphasized and addressed in accident criticality rules. The overall goal of this study is to measure the severity of traffic accidents that occur. The key directing vectors are the accident attributes, which include the part of the slight, car allocation on the highway, and ecologically responsible properties, all of which help to the output results in relation to the strong levels of the accident criticality classes.

severity prediction, Machine Learning, Accident Prediction