# biolab/orange3

🍊 :bar_chart: :bulb: Orange: Interactive data analysis

Repository: https://github.com/biolab/orange3
Canonical: https://ross.abutalabs.com/products/orange3
Homepage: https://orangedatamining.com
Language: Python
License: NOASSERTION
License Family: other
Topics: data-mining, data-science, machine-learning, data-visualization, orange, python, scikit-learn, numpy, scipy, pandas, decision-trees, clustering, visualization, orange3, random-forest, plotting, classification, regression, visual-programming
Last push: 2026-08-01T05:08:57+00:00

## Health v2 (maintenance only)
Score: 76/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 95, release rhythm 38, longevity 100
- inputs: {"age_days": 4940, "days_push": 32, "days_rel": 256, "gap_med": 172, "n_releases_24m": 4}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5677, forks 1113 (observed 2026-08-28T04:09:27.766654+00:00)

## What it is
Orange is an open-source visual programming toolbox for interactive data mining, machine learning, and data visualization. Users build analysis workflows by connecting widgets on a canvas, requiring no programming knowledge, and it can also be used as a Python library.

## Use cases
- analyze datasets without writing code
- build machine learning workflows visually
- visualize clustering and dimensionality reduction like t-SNE and MDS
- teach data mining concepts to students interactively
- explore statistical distributions with box plots and scatter plots
- classify and regress with decision trees and random forests
- mine text, time series, or genomics data via add-ons

## When to choose
- you want no-code, interactive exploratory data analysis
- you are teaching data science and want visual demonstrations
- you need rapid prototyping of ML pipelines with visual feedback
- you want a GUI on top of scikit-learn-style algorithms

## When to avoid
- you need fully automated, scriptable production ML pipelines
- you require headless execution in CI or servers
- your workflow depends on highly customized algorithms not covered by widgets

## Facets
- artifact type: application
- maturity: stable
- function: data-visualization, machine-learning, gui, data-science
- domain: data-science, data-visualization, machine-learning, education
- platform: cross-platform, python
- tags: visual-programming, data-mining, no-code, widgets, workflow-based, teaching, exploratory-data-analysis, desktop

## Member repositories
- biolab/orange3 (main) score 76

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:27.766654+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 2026-08-29T17:54:16.235947+00:00, confidence not recorded.
  - readme: https://github.com/biolab/orange3 (fetched 2026-08-28T04:09:27.766654+00:00, sha 8602a7b52756)
  - homepage: https://orangedatamining.com (fetched 2026-08-29T08:49:26.900872+00:00, sha a8d0a5474284)
  - site_page: https://orangedatamining.com/docs (fetched 2026-08-29T08:49:26.910073+00:00, sha b3e50b7f11c6)
  - site_page: https://orangedatamining.com/getting-started (fetched 2026-08-29T08:49:26.913790+00:00, sha 37ec12b0b4b4)
  - site_page: https://orangedatamining.com/faq (fetched 2026-08-29T08:49:26.911948+00:00, sha 5bb2409a958d)
- Data as of 2026-08-30T08:39:29.467469+00:00.