Example Concept: One common approach to handle imbalanced datasets is to use resampling techniques such as oversampling the minority class (e.g., SMOTE - Synthetic Minority Over-sampling Technique) or undersampling the majority class. Additionally, employing evaluation metrics like precision-recall curves or the F1-score, which are more informative than accuracy in imbalanced scenarios, can provide better insights into model performance. Algorithmic techniques like cost-sensitive learning, where different misclassification costs are assigned to different classes, can also be effective.