Exploring Efficient Techniques for Handling Imbalanced Datasets in ML-For-Beginners

[Shaikhasna]

Hello everyone,

While working through the classification notebooks, I noticed that most examples assume relatively balanced datasets. In real-world scenarios, imbalanced datasets are common, and standard metrics like accuracy can be misleading.

I’m curious about:

The most effective strategies to integrate resampling methods (SMOTE, ADASYN, undersampling) within beginner-focused pipelines.

How to guide learners in choosing appropriate evaluation metrics like F1-score, ROC-AUC, or Matthews correlation coefficient without overwhelming them.

Best ways to demonstrate the impact of imbalance visually and practically in small-scale notebooks.

I’d love to hear thoughts from the community on balancing educational clarity with realistic ML practices. Sharing examples, tips, or alternative approaches would be highly appreciated.