Example Concept: One common approach 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. Alternatively, algorithmic adjustments like using class weights in models (e.g., sklearn's `class_weight` parameter) can help balance the influence of each class during training. Additionally, choosing evaluation metrics like F1-score, precision-recall curves, or AUC-ROC that are sensitive to class imbalance can provide a more accurate assessment of model performance.