# pbharrin/machinelearninginaction

Source Code for the book: Machine Learning in Action published by Manning

Repository: https://github.com/pbharrin/machinelearninginaction
Canonical: https://ross.abutalabs.com/products/machinelearninginaction
Language: HTML
License Family: other
Last push: 2020-02-01T14:21:27+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": 5600, "days_push": 2405, "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 2402, forks 1779 (observed 2026-08-28T04:06:43.835882+00:00)

## What it is
Source code repository accompanying the Manning book 'Machine Learning in Action' by Peter Harrington. It contains Python example code (written for Python 2.6/2.7 with NumPy) demonstrating classic machine learning algorithms.

## Use cases
- learn machine learning algorithms from a book with runnable code
- study classic ML examples like kNN, decision trees, SVM, and k-means
- follow along with the Machine Learning in Action textbook chapter by chapter
- find reference implementations of fundamental ML algorithms in Python
- teach an intro machine learning course with existing example code

## When to choose
- you are reading the Machine Learning in Action book and want its official code
- you want simple, educational implementations of classic ML algorithms
- you work in Python 2.7 or are willing to port small examples to Python 3

## When to avoid
- you need production-ready or maintained machine learning libraries
- you require Python 3 support out of the box
- you want modern ML techniques like deep learning or transformers
- you need a project with an active community or license

## Facets
- artifact type: learning-resource
- maturity: abandoned
- function: machine-learning, data-science
- domain: machine-learning, tutorials, data-science
- platform: python, cross-platform
- tags: book-code, python2, numpy, textbook-examples, manning

## Member repositories
- pbharrin/machinelearninginaction (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:43.835882+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 2026-08-30T02:34:13.715869+00:00, confidence not recorded.
  - readme: https://github.com/pbharrin/machinelearninginaction (fetched 2026-08-28T04:06:43.835882+00:00, sha ece8725445ed)
- Data as of 2026-08-30T08:39:29.467469+00:00.