Feature Engineering

Feature engineering involves transforming raw data into meaningful features that can enhance the performance of machine learning models. This process may include selecting relevant attributes, creating new features through mathematical transformations, or encoding categorical variables. Effective feature engineering can significantly impact model accuracy and is often a critical step in the data preprocessing phase. It requires domain knowledge and creativity to uncover useful patterns and interactions.