Road Accident Analysis and Hotspot Prediction using Clustering

Road accidents are a major cause of fatalities in India and other nations too. Fatality rate in developing nations is very high due to various aspects. In the past, it was assumed that road accidents and fatalities cannot be avoided, but now with this tech era, everything is almost becoming possible. Machine learning (ML) is used to analyze various algorithms through experience and improve results. It includes three major types of learning techniques, namely supervised, unsupervised, and reinforcement learning. Our study focuses on reducing mortality rate by setting up a prediction model by means of an unsupervised learning technique, i.e., k-means clustering, which analyzes road accidents by taking into consideration various aspects like potholes on roads, sharp turns, and weather conditions and then provides suitable and precautionary measures to avoid mishaps by representing it on map and creating an intelligible model for everyone. The predicted model achieved an accuracy of 81%.