  Road accidents have significant economic and societal costs, with a small
number of severe accidents accounting for a large portion of these costs.
Predicting accident severity can help in the proactive approach to road safety
by identifying potential unsafe road conditions and taking well-informed
actions to reduce the number of severe accidents. This study investigates the
effectiveness of the Random Forest machine learning algorithm for predicting
the severity of an accident. The model is trained on a dataset of accident
records from a large metropolitan area and evaluated using various metrics.
Hyperparameters and feature selection are optimized to improve the model's
performance. The results show that the Random Forest model is an effective tool
for predicting accident severity with an accuracy of over 80%. The study also
identifies the top six most important variables in the model, which include
wind speed, pressure, humidity, visibility, clear conditions, and cloud cover.
The fitted model has an Area Under the Curve of 80%, a recall of 79.2%, a
precision of 97.1%, and an F1 score of 87.3%. These results suggest that the
proposed model has higher performance in explaining the target variable, which
is the accident severity class. Overall, the study provides evidence that the
Random Forest model is a viable and reliable tool for predicting accident
severity and can be used to help reduce the number of fatalities and injuries
due to road accidents in the United States
