Example Concept: One effective technique is to use cost-sensitive learning, where the model is penalized more for misclassifying the minority class. This can be implemented by adjusting the class weights in algorithms like logistic regression, decision trees, or SVMs. Another approach is to use ensemble methods such as Random Forests or Gradient Boosting, which are inherently more robust to class imbalance. Additionally, using evaluation metrics like the F1 score, precision-recall curve, or area under the precision-recall curve (AUC-PR) can provide better insights into model performance on imbalanced datasets.