Prediction of Road Accidents Using Data Mining Techniques

• Python
• Machine Learning

Road accidents are the main cause of death as well as serious injuries in the world. As a human being, everyone wants to avoid traffic accidents and stay safe. In order to stay safe, careful analysis of roadway traffic accident data is important to find out factors that are related to fatal, grievous injury, minor injuries, and non-injuries. Road accident detection is considered to be the contemporary ever growing process focused primarily to reduce death. This project provides road accident detection techniques by analyzing the novel ideas. The analysis of these methods provides a better understanding of the steps involved in each process in a way of consequently increasing the scope for finding the efficient techniques to achieve maximum accurate performance. The comparison of the techniques used here that is Apriori, Naive-Bayes and K-Means is carried out in terms of precision and recall. Environmental factors like roadway surface, weather, and light conditions do not strongly affect the fatal rate, while the human factors like being drunk or not, and the collision type, have a stronger effect on the fatality rate. From the clustering result we can see the states/regions which have a higher fatality rate, while some others lower. We should pay more attention when driving within these risky states/regions. Current system is manual where government sector makes use of this data and analyze it manually. Based on the analysis, they will take precautionary measures to reduce the number of accidents.