# alicezheng/feature-engineering-book

Code repo for the book "Feature Engineering for Machine Learning," by Alice Zheng and Amanda Casari, O'Reilly 2018

Repository: https://github.com/alicezheng/feature-engineering-book
Canonical: https://ross.abutalabs.com/products/feature-engineering-book
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2020-08-11T13:39:04+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3329, "days_push": 2213, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1493, forks 672 (observed 2026-08-28T04:04:52.780697+00:00)

## What it is
Companion code repository for the O'Reilly book 'Feature Engineering for Machine Learning' by Alice Zheng and Amanda Casari (2018). It contains Jupyter Notebook examples demonstrating feature engineering techniques, though the datasets must be downloaded separately via URLs in the book.

## Use cases
- learn feature engineering for machine learning
- find example notebooks on feature extraction and representation
- study numeric and categorical feature encoding techniques
- supplement reading the Feature Engineering for ML book with runnable code
- explore text and NLP feature engineering examples

## When to choose
- you are reading the book and want the accompanying code
- you want hands-on Jupyter examples of feature engineering concepts

## When to avoid
- you need a production feature engineering library or pipeline tool
- you expect bundled datasets - data must be sourced separately
- you want actively updated content - the repo mirrors a 2018 book

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science
- domain: machine-learning, data-science, tutorials
- platform: python
- tags: feature-engineering, jupyter-notebooks, book-companion, oreilly

## Member repositories
- alicezheng/feature-engineering-book (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:52.780697+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:33:26.225552+00:00, confidence not recorded.
  - readme: https://github.com/alicezheng/feature-engineering-book (fetched 2026-08-28T04:04:52.780697+00:00, sha 59365873df48)
- Data as of 2026-08-30T08:39:29.467469+00:00.