| Summary: | Rainfall prediction is a crucial aspect of weather forecasting and plays a significant role in various fields, including agriculture, water resource management, and disaster preparedness. In this chapter, the authors explore the application of two machine learning algorithms, random forest and cat boost, for predicting rainfall events. They utilize historical weather data from a specific location to train and evaluate the performance of both models. The evaluation metrics employed include accuracy, precision, recall, and F1-score. The findings suggest that incorporating additional features, such as humidity, can enhance the predictive capabilities of both random forest and cat boost. Overall, this project demonstrates the effectiveness of random forest and cat boost in predicting rainfall events. © 2024 by IGI Global. All rights reserved.
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