ds-feature-engineering

What problem does it solve?

Feature engineering is the craft of transforming raw data into predictive signals that improve model performance. It's the bridge between EDA and modeling, enabling models to learn from meaningful, domain-aware features.

Core Features & Use Cases

• Extracts meaningful features from numeric, categorical, and datetime data (encodings, time-based features, ratios, and interactions).
• Builds aggregated and interaction features for per-entity modeling (RFM-like features and cohort-level signals).
• Applies best practices to avoid leakage and ensures reproducible feature pipelines.

Quick Start

Provide a feature engineering plan for a tabular dataset to improve model performance.