Example Concept: One common approach to handle imbalanced datasets is to use resampling techniques. Oversampling the minority class (e.g., SMOTE - Synthetic Minority Over-sampling Technique) or undersampling the majority class can help balance the class distribution. Additionally, using evaluation metrics like precision, recall, and F1-score, instead of accuracy, provides a better understanding of model performance on imbalanced data. Algorithms like Random Forest or Gradient Boosting can be tuned with class weights to give more importance to the minority class.