When working with machine learning models, I’ve noticed that data integrity often gets overlooked. Recently, while analyzing a dataset for a project, I found that minor discrepancies led to significant deviations in our model’s predictions. It really highlights how essential it’s to ensure accuracy and consistency in our data sources before feeding them into our algorithms. Anyone else run into similar issues?