Harnessing Machine Learning for Precise Rainfall Forecasting: A Comparative Study

This work looks into machine learning as a means of enhancing rainfall prediction. A logistic regression model is trained using meteorological data, including meteorological parameters such as temperature, wind speed, and humidity, from different locations. Preprocessing of the data addresses missing values and uses feature engineering to extract useful features. Using performance evaluation criteria such as accuracy, precision, and recall, the model achieves excellent accuracy in differentiating between wet and non-rainy days. This shows how machine learning can be used for weather forecasting. The study highlights the significance of feature generation and data preparation for precise forecasts, which can improve decision-making and be useful for applications in emergency response, agriculture, and water resource management.