Abstract
This paper aims to study, compare and analyse the performance of six major machine learning techniques to better understand the occurrence of traffic accidents. The methods considered are Decision Trees, Support Vector Machines, Naïve Bayes, Random Forest, K-Nearest Neighbour and Logistic Regression. For the most realistic and conceivable accident reduction effects with budgetary constraints, the study must be based on objective and scientific surveys to detect and further prevent accidents, understand the causes and the acuteness of injuries.
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Singhal, S., Priyamvada, B., Jain, R., Chawla, M. (2021). Machine Learning Approach Towards Road Accident Analysis in India. In: Goyal, D., Chaturvedi, P., Nagar, A.K., Purohit, S. (eds) Proceedings of Second International Conference on Smart Energy and Communication. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-6707-0_29
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DOI: https://doi.org/10.1007/978-981-15-6707-0_29
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