Feature Engineering: The Skill That Matters More Than Algorithm Choice — Encoding, Scaling, Creating Features, Handling Dates, Text Features, and Feature Selection
The complete feature engineering guide. Why features matter more than algorithms (XGBoost with bad features loses to Logistic Regression with great features). Encoding categoricals: label, one-hot, ordinal, target encoding with decision guide. Scaling: StandardScaler, MinMaxScaler, RobustScaler (and why trees do not need scaling). Creating features: date extraction with cyclical encoding, recency features, ratios, aggregations, interactions, binning, text features, TF-IDF. Handling missing values with indicator column strategy. Handling outliers. Feature selection: correlation, RFE, XGBoost importance. Four real-world scenarios (credit risk, churn, demand forecasting, fraud detection). Complete pipeline code.