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, algorithmic approaches like cost-sensitive learning, where misclassification costs are adjusted, can be employed. Evaluation metrics like precision-recall curves or the F1-score are preferred over accuracy to better reflect model performance on imbalanced data.