# johnmyleswhite/ML_for_Hackers

Code accompanying the book "Machine Learning for Hackers"

Repository: https://github.com/johnmyleswhite/ML_for_Hackers
Canonical: https://ross.abutalabs.com/products/ml_for_hackers
Homepage: http://shop.oreilly.com/product/0636920018483.do
Language: R
License Family: other
Last push: 2019-05-26T16:52:29+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 5318, "days_push": 2656, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3729, forks 2177 (observed 2026-08-28T04:08:15.714182+00:00)

## What it is
Companion code repository for the O'Reilly book 'Machine Learning for Hackers' (2012), containing R code examples for each chapter. It serves as a hands-on learning resource for practical machine learning in R.

## Use cases
- learn machine learning with R through worked examples
- follow along with the Machine Learning for Hackers book
- find example R code for classification and regression
- study practical data analysis case studies in R
- get started with R packages for ML

## When to choose
- you are reading the book and want its runnable code
- you prefer learning ML through R examples
- you want classic introductory ML case studies

## When to avoid
- you need modern, maintained ML tooling or current best practices
- you work in Python rather than R
- you need a supported library with an active community

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, data-visualization
- domain: machine-learning, data-science, tutorials
- platform: python, cross-platform
- tags: r-language, book-code, oreilly, code-examples

## Member repositories
- johnmyleswhite/ML_for_Hackers (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:15.714182+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 2026-08-29T18:29:14.142077+00:00, confidence not recorded.
  - readme: https://github.com/johnmyleswhite/ML_for_Hackers (fetched 2026-08-28T04:08:15.714182+00:00, sha eace4e2735a6)
- Data as of 2026-08-30T08:39:29.467469+00:00.