Feature engineering, the process of selecting, transforming, and creating variables from raw data, stands as a cornerstone of high-performing machine learning (ML) models. While the choice of algorithm often gets the spotlight, it’s the quality of the features fed into the model that ultimately dictates its success. In essence, even the most sophisticated algorithm cannot extract meaningful insights from poorly prepared data.