Avoid the most common Machine Learning pitfalls with this practical guide. Learn how to detect and prevent data leakage, handle imbalanced datasets, choose the right evaluation metrics, apply proper feature scaling, and ensure reproducible results. This tutorial highlights essential best practices to build reliable, accurate, and production-ready ML models.

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