Ans. Yes, classification models can handle imbalanced datasets, but it requires careful consideration. Some techniques to address imbalanced datasets include:- Resampling: Over-sampling the minority class or under-sampling the majority class to balance the dataset.- Changing the Performance Metric: Instead of accuracy, metrics like precision, recall, or F1 score can be used to evaluate model performance on imbalanced datasets.- Using Ensemble Methods: Techniques like boosting and bagging can improve the performance of classification models on imbalanced datasets by combining multiple models.