Machine Learning-Based Classification of Road Accident Severity in India: A Comparative Analysis of Algorithms

In India, road accidents pose a serious threat to public health and safety, thus it's crucial to accurately categorize their severity so that first responders and policymakers can address the problem. In this research, a dataset of past accident records in India is used to test several machine-learning algorithms for accident severity classification. The purpose of this study is to evaluate existing categorization algorithms and determine which one performed the best. Several different machine learning methods like k-nearest neighbors, support vector machines, neural networks, etc. have been tried out. In addition, models are optimized by selecting relevant features to use. The findings demonstrate that the neural network approach beat the other techniques, with 90% accuracy in properly categorizing the severity of traffic accidents. Using machine learning algorithms to reliably categorize the severity of traffic accidents in India is a promising area of research, as shown by the results of this study. When it comes to enhancing emergency response and creating evidence-based policy for road safety in India, the neural network technique has shown to be very accurate.