🛠️ 01 — Feature Engineering Overview¶
Feature engineering is the process of turning raw data into model-ready input.
Good features often matter more than fancy models.
Examples¶
| Raw Data | Engineered Feature |
|---|---|
| order_date | month, day_of_week, is_weekend |
| signup_date | customer_tenure_days |
| transaction_amount | log_amount |
| city | one-hot encoded city |
| review_text | TF-IDF vectors |
Feature Engineering Workflow¶
- Understand the problem.
- Identify feature types.
- Clean invalid values.
- Create useful transformations.
- Encode categorical variables.
- Scale when needed.
- Build pipeline.
- Validate no leakage.
Guiding Rule¶
Only use information available at prediction time.
If a feature would not exist when the model is used, it is leakage.