What is feature engineering and why does it matter?

Feature engineering is transforming raw data into inputs that help a model learn — scaling numbers, encoding categories, creating ratios or date parts, handling missing values. For classic ML on tabular data it's often the biggest lever on performance: good features can make a simple model beat a complex one on poor features. Deep learning automates some of this by learning representations, but thoughtful features and clean data still matter enormously.