With Road Accidents happening every minute , Our Country is in distress with severe losses of life and demolition of property. Being one of the most life threatening incidents in the world, mitigating this severity and minimizing the occurrence of accidents is to be put into action.

Road Accidents does not occur by chance: It has patterns and can be examined, analyzed, predicted and avoided. So, mitigating this severity and minimizing the occurrence of accidents is to be put into action.

Data that is collected ranging from accident details, road features to the traffic details of past few years, we build a predictive Model, which gives a pattern on accident black spots, peak hour of a day and dependency on weather conditions.

We work on a machine learning approach for road accident classification t improve the effectiveness of prediction accuracy. Hence getting of best paradigm helps to identify the most determinant road accident factors. The study used hybrid clustering and classification algorithms to predict road accident severity prediction. The approach includes removing disturbing noise and filling missing data , splitting the dataset into training and test dataset, clustering using DBSCAN, finally predicting the target specific road accident severity using Random Forest algorithm.

This model can be mainly used in fields like building of a system for the prevention of traffic accidents and violations of law, the design of procedures for selecting a professional driver, to alert the road users about the accident black spots, rehabilitation of drivers who have been deprived of the driving license.

Accident could happen not only based on the expected parameters, but also under unanticipated circumstances. Predicting an occurrence leads to taking precautionary measures and hence reducing the risk of a casualty. The ML Model proves to be a reliable technique which can be used to analyze the data and to identify the major reasons which cause road accidents.

This could be a major step for enhancing the road safety in India.