Example Concept: One common approach to handle imbalanced classes is to use resampling techniques like oversampling the minority class (e.g., SMOTE) or undersampling the majority class. Additionally, you can use evaluation metrics such as precision, recall, and F1-score, which are more informative than accuracy for imbalanced datasets. Algorithmic adjustments, such as using class weights in models like logistic regression or decision trees, can also help balance the influence of each class during training.