# jbrownlee/Datasets

Machine learning datasets used in tutorials on MachineLearningMastery.com

Repository: https://github.com/jbrownlee/Datasets
Canonical: https://ross.abutalabs.com/products/jbrownlee-datasets
Homepage: http://MachineLearningMastery.com
License Family: other
Topics: machine-learning, datasets
Last push: 2023-08-15T10:33:21+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3099, "days_push": 1114, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- 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 1246, forks 1489 (observed 2026-08-28T04:04:07.167891+00:00)

## What it is
A repository of machine learning datasets (CSV and zipped files) used in tutorials on MachineLearningMastery.com, covering classification, regression, time series, and NLP problems. It exists to keep tutorial datasets reliably available with a consistent format (no header, target in last column, '?' for missing values).

## Use cases
- download standard datasets for machine learning tutorials
- find small CSV datasets for practicing classification
- get time series datasets for forecasting practice
- load iris or pima diabetes datasets for teaching
- practice regression with housing and wine quality data
- get datasets with a consistent simple format

## When to choose
- you are following MachineLearningMastery tutorials and need the exact datasets
- you want small, clean, beginner-friendly datasets for learning or teaching ML
- you need reliably hosted copies of classic datasets like iris, sonar, or airline passengers

## When to avoid
- you need large-scale, production-grade, or well-documented datasets with licenses
- you need datasets with headers, metadata, or rich documentation
- you need actively maintained or frequently updated benchmark datasets

## Facets
- artifact type: dataset
- maturity: maintenance
- function: machine-learning, data-science
- domain: machine-learning, data-science, tutorials
- platform: cross-platform
- tags: csv-datasets, classification, regression, time-series, tutorial-datasets, machine-learning-mastery

## Member repositories
- jbrownlee/Datasets (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:07.167891+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 2026-08-30T05:08:03.691710+00:00, confidence not recorded.
  - readme: https://github.com/jbrownlee/Datasets (fetched 2026-08-28T04:04:07.167891+00:00, sha acc1ff3a764c)
  - homepage: http://MachineLearningMastery.com (fetched 2026-08-29T12:20:00.260520+00:00, sha 96d4bc83bbde)
  - site_page: https://machinelearningmastery.com/about (fetched 2026-08-29T12:20:00.268894+00:00, sha 7aa37054a575)
  - site_page: https://machinelearningmastery.com/faq (fetched 2026-08-29T12:20:00.263633+00:00, sha 0cfa49a6ed79)
  - site_page: https://machinelearningmastery.com/setup-python-environment-machine-learning-deep-learning-anaconda (fetched 2026-08-29T12:20:00.270963+00:00, sha 312b075b02dd)
- Data as of 2026-08-30T08:39:29.467469+00:00.