Most gains in a supervised learning project don't come from switching algorithms; they come from what you feed the algorithm. Feature engineering is the process of transforming raw data into representations that make patterns easier for a model to learn. Done well, it routinely closes more of the accuracy gap than hyperparameter tuning or trying a fancier model.This article walks through the feature engineering techniques that consistently move the needle in real projects, with working Python code (pandas + scikit-learn) and a visual look at where the gains actually come from.