Example Concept: Imbalanced datasets occur when one class significantly outnumbers others, leading to biased models. To handle this, you can use techniques such as oversampling the minority class, undersampling the majority class, or applying synthetic data generation methods like SMOTE (Synthetic Minority Over-sampling Technique). Additionally, choosing evaluation metrics like precision, recall, or F1-score instead of accuracy can provide a better understanding of model performance on imbalanced data.