# nidhaloff/igel

a delightful machine learning tool that allows you to train, test, and use models without writing code

Repository: https://github.com/nidhaloff/igel
Canonical: https://ross.abutalabs.com/products/igel
Homepage: https://igel.readthedocs.io/en/latest/
Language: Python
License: MIT
License Family: permissive
Topics: machine-learning, machinelearning, machine-learning-algorithms, machine-learning-library, artificial-intelligence, neural-network, neural-networks, sklearn, scikit-learn, scikitlearn-machine-learning, data-science, data-analysis, preprocessing, automation, automl, automl-experiments, hacktoberfest, hacktoberfest2021
Last push: 2025-12-07T18:25:00+00:00

## Health v2 (maintenance only)
Score: 48/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 56, release rhythm 8, longevity 100
- inputs: {"age_days": 2197, "days_push": 269, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_readme
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3138, forks 210 (observed 2026-08-28T04:07:45.759403+00:00)

## Summary
No AI-extracted summary yet.

## Member repositories
- nidhaloff/igel (main) score 48

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:45.759403+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Data as of 2026-08-30T08:39:29.467469+00:00.