Example Concept: One effective method to handle imbalanced data is to use techniques such as resampling, which includes oversampling the minority class or undersampling the majority class. Additionally, you can apply techniques like using class weights to penalize the model more for errors on the minority class, or employing advanced algorithms like SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic examples of the minority class.