👉 Attribute engineering is the process of transforming raw data into meaningful features or attributes that can be used to train machine learning models more effectively. It involves selecting, modifying, and creating new attributes from existing data to enhance model performance, interpretability, and efficiency. This can include techniques like feature scaling, encoding categorical variables, generating interaction terms, or extracting domain-specific features. The goal is to capture the underlying patterns and relationships in the data that are most relevant for the predictive task at hand, ultimately leading to better model accuracy and generalization.
