What is feature engineering and why is it important?

Feature engineering transforms raw data into meaningful representations that improve model performance and interpretability.\n\n• Techniques: Scaling, encoding categorical variables, polynomial features, binning\n• Domain knowledge: Creating interaction terms, temporal features, aggregations\n• Automated: Feature selection (RFE, LASSO), dimensionality reduction (PCA)\n• Validation: Use cross-validation to avoid data leakage\n\nExample: For time-series sales data, create lag features, moving averages, seasonal indicators. Transform skewed distributions with log transformation. Use domain expertise to create meaningful ratios and interactions.