Dealing With Missing Data in Real-World ML Projects
Missing data is the silent saboteur of machine learning projects. While academic datasets come pristine and complete, real-world data is messy—filled with gaps, nulls, and inconsistencies that can derail even the most sophisticated models. I’ve seen projects fail not because of poor algorithm choices or insufficient computing power, but because missing data was handled carelessly … Read more