Feature Engineering Techniques That Actually Improve Model Accuracy

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.