Example Concept: One common approach is to use resampling techniques such as oversampling the minority class (e.g., SMOTE - Synthetic Minority Over-sampling Technique) or undersampling the majority class to balance the class distribution. Additionally, algorithmic adjustments like using class weights in models (e.g., in sklearn's logistic regression or random forest) can help the model pay more attention to the minority class. It's also crucial to select appropriate evaluation metrics like precision-recall curves or F1-score, which are more informative than accuracy in imbalanced scenarios.