Feature engineering boosts model R

Short summary

Feature engineering—creating new features from raw data—often matters more than algorithm selection in practice. The post covers essential techniques: categorical encoding (label, one-hot, ordinal, target), transformations (log scaling for skewed data), and creating interaction features. Code examples demonstrate how three engineered features improved model R² from 0.789 to 0.806 without algorithm changes.

• •Feature engineering drives accuracy more than algorithm choice
• •Core techniques: categorical encoding, scaling, and interaction features
• •Includes runnable Python/sklearn examples with real-world results

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