Feature Engineering is the critical preprocessing step in machine learning and statistical modeling that transforms raw data into meaningful features suitable for model consumption. It involves feature creation (constructing new variables via domain knowledge, mathematical transformations, or aggregations), feature transformation (scaling, normalization, encoding categorical variables through one-hot, embeddings), and feature selection (identifying the most predictive attributes via filter, wrapper, or embedded methods). Well-engineered features can drastically improve model accuracy and interpretability, whereas poor features often limit any algorithm’s performance. Feature Engineering also encompasses handling missing values, outlier treatment, discretization, and dimensionality reduction techniques like PCA. Enterprise-scale pipelines may leverage feature stores to ensure consistency between training and serving environments.