In ML training, data scientists spend 70–80% of their time not on flashy algorithm tuning but on feature engineering. Raw data has missing values, mixed units, and strings like "Seoul/Busan/Daegu." Models can't read that raw. Feature engineering is the entire process of transforming raw data into numerical representations that algorithms can learn patterns from.
The MLA-C01 exam constantly asks scenarios: "What transformation should be applied to this data?" The key is when and which transformation to choose, not algorithm names. Today we cover four major transformations: scaling, encoding, missing values, and outliers.