Features make or break models

Feature engineering is turning raw data into inputs a model can learn from — and it usually beats fancier algorithms.

Common techniques

• Encoding categories (one-hot, target encoding).
• Scaling numeric features.
• Dates → day-of-week, month, is-weekend, recency.
• Aggregates → "average spend per customer".
• Handling skew and interactions.

The full modeling workflow

Clean → engineer features → split → train → evaluate → tune → interpret. You've seen each piece — now you run the whole loop on real data.

Takeaway: spend your time on features and validation, not on chasing the newest algorithm.