Example Concept: One effective method to handle imbalanced data is to use resampling techniques. This includes oversampling the minority class, undersampling the majority class, or a combination of both. Additionally, using algorithms that are robust to class imbalance, such as ensemble methods (e.g., Random Forest) or cost-sensitive learning, can help improve model performance. Implementing evaluation metrics like precision, recall, and F1-score instead of accuracy is also crucial to better assess the model's performance on imbalanced datasets.