Example Concept: One common approach to address imbalanced data is to use resampling techniques such as oversampling the minority class or undersampling the majority class. Oversampling can be done by duplicating samples or using methods like SMOTE (Synthetic Minority Over-sampling Technique), which generates synthetic examples. Alternatively, algorithm-level methods like adjusting class weights or using ensemble methods such as Random Forests or Gradient Boosting can help the model focus more on the minority class.