Feature Engineering: Make Data ML-Ready

⏱️ 8 sec read 🤖 AI & Machine Learning

Raw data doesn't work in ML. Timestamps, addresses, IDs mean nothing to algorithms.

Feature engineering transforms data into formats models can learn from. Difference between 70% and 90% accuracy.

What Works

Extract from dates. Day of week, month, quarter, is_weekend, days_since_event. Not the raw timestamp.

Encode categories right. One-hot for nominal (colors). Ordinal for ordered (small/medium/large). Label encoding is usually wrong.

Create interactions. Relationships between variables matter. Price alone doesn't predict sales. Price vs. competitor price does.

Normalize numbers. Features on different scales (age: 0-100, salary: 0-200k) confuse models. Standardize.

Ratios. Differences. Aggregates. Counts. Percentages.

Transaction amount? Add: avg per day, max single, count last 30 days. Suddenly fraud detection works.

More features ≠ better. More features = more noise, overfitting, slow training. Add what has predictive power. Remove the rest.

Domain knowledge beats algorithms. One good feature from business understanding improves models more than days of hyperparameter tuning.