construct-data

What problem does it solve?

Machine learning pipelines often struggle with consistent feature engineering and proper documentation. This skill standardizes the process of deriving, transforming, and recording features to support reproducible CRISP-DM workflows.

Core Features & Use Cases

• Feature engineering: derive new attributes, create derived measurements, and generate synthetic records when appropriate.
• Documentation: produce a structured feature engineering report and a reproducible notebook.
• Outputs: two artifacts — a working notebook at notebooks/3.3-construct-data.ipynb and a summary document at docs/crisp-dm/3-data-preparation/3.3-construct-data.md.

Quick Start

Load your cleaned dataset, run the feature construction pipeline, and generate both a Jupyter notebook and a CRISP-DM summary document.