Time-based features are deceptively tricky — feeding raw hour or month values into your model treats December and January as maximally distant when they're actually neighbors. Cyclical encoding (sin/cos transforms) fixes this elegantly. A small preprocessing step that can meaningfully boost performance on anything from demand forecasting to user behavior prediction
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