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Road accidents are a major problem, the consequences of which have negative impacts on the economy, health and development of a country in general. The recrudescence of road accidents in Senegal during these last years has aroused a great debate in which certain responsibilities have been pointed out. A serious road traffic accident constitutes in most cases an emotional and economic disaster, hence the need to find sustainable solutions for the improvement of road safety. The objective of this work is to identify the major risk factors of road accidents in Senegal in order to set up a training corpus for the predictive study of the gravity of these accidents. According to our research on the bibliography, there is no study on the implementation of a corpus adapted to the data of accidents in Senegal. In this research, we used an exploratory study on the collected data to determine the factors most likely to cause accidents and supervised learning for predictive modeling of accident severity. The results showed that modelling with supervised learning can be used as an accident severity prediction system with promising accuracy.