Example Concept: Resampling techniques such as oversampling the minority class (e.g., using SMOTE - Synthetic Minority Over-sampling Technique) or undersampling the majority class can help balance the dataset. Additionally, algorithmic adjustments like using class weights in models such as logistic regression or decision trees can make the model more sensitive to the minority class. Evaluation metrics like precision-recall curves or the F1 score are more informative than accuracy for imbalanced datasets, as they focus on the performance related to the minority class.