Mitigating Imbalance: Cost-Sensitive Learning for Enhanced Weather Prediction in Imbalanced Datasets

Weather prediction plays a pivotal role in numerous sectors, yet traditional machine learning models encounter challenges when confronted with imbalanced datasets, particularly in forecasting rare weather events. This study employs cost sensitive learning techniques to overcome the imbalance in datasets which includes algorithms such as Decision Tree, Logistic Regression, Ridge Classifier, and Support Vector Machine (SVM). The algorithms are implemented with and without weight balancing to evaluate potential improvements in their performance metrics. The primary problem to be addressed is the need for a more robust and accurate weather prediction model that can effectively handle imbalanced datasets and offer improved accuracy, thereby enhancing the overall reliability of weather forecasts.