How can I effectively handle imbalanced datasets in classification problems?

Example Concept: One common approach to handle imbalanced datasets is to use resampling techniques such as oversampling the minority class or undersampling the majority class. Oversampling can be done using methods like SMOTE (Synthetic Minority Over-sampling Technique), which generates synthetic samples for the minority class. Additionally, using evaluation metrics like F1-score, precision-recall curves, or area under the precision-recall curve (AUC-PR) instead of accuracy can provide a better assessment of model performance on imbalanced data.