Absolutely agree real-world ML rarely plays out like the clean lab setups we see in papers.
In one project, I faced a similar challenge where mislabeled “active” users distorted churn predictions. The model looked great on paper but failed in production.

The biggest takeaway? Always validate what your data means, not just how it performs. Strong data understanding often matters more than tuning the perfect model.

How do others here ensure their datasets reflect real-world behavior before training?