KEEL
KEEL: A free desktop workbench for comparing data-mining algorithms on local datasets. Ranked #30 of 31 in Data Science Platforms by our editors (6.2/10); pricing: Free plan; best for free educational algorithm experiments.
At a glance
- Editor score6.2 / 10
- PricingFree plan
- Best forFree educational algorithm experiments
- Free planYes
- Paid fromFree
- Hosted notebooksNo
- Facts checked24 Sep 2026
Where it wins
- Design experiments through a graphical data-flow interface.
- Includes classification, regression, clustering, and pattern mining.
- Compares experimental results with statistical methods.
Where it doesn't
- Desktop-only; it does not offer hosted notebooks.
- Experiments run locally rather than through a deployment workflow.
- Requires Java 7 and is launched as a JAR.
Our verdict on KEEL
KEEL is an open-source Java application for data discovery and data-mining research. Its graphical, data-flow interface lets researchers and practitioners design experiments with datasets and computational-intelligence algorithms, then compare algorithm behavior across datasets. The scope suits educational work and exploratory evaluation, including solo and small teams that want a free way to study classification, regression, clustering, or pattern mining without adopting a hosted notebook service.
The experiment workflow is broader than a collection of learning algorithms. KEEL includes preprocessing such as feature selection, discretization, and missing-value imputation, as well as computational-intelligence learning algorithms, evolutionary algorithms, and hybrid models. Dataset import and conversion cover CSV, ARFF, and plain text, which gives users several common routes for bringing data into local experiments. Statistical methods for comparing results help make algorithm comparisons part of the workflow rather than leaving them entirely to separate analysis tools.
KEEL is desktop software: the project documentation describes a Java application launched as a JAR and requires Java 7. It is not positioned for hosted notebooks or deployment, so teams seeking a cloud workspace or a path from experiment to production should choose a platform built for those needs. Its local, research-oriented focus is a fit for learners and practitioners evaluating algorithms; it is less suitable as a shared, hosted environment or an operational data science platform.
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KEEL pricing
KEEL fact sheet
| Free plan | Yes |
|---|---|
| Paid from | Free |
| Hosted notebooks | No |
| Deployment options | Self_hosted |
| Workflow automation | Not verified |
| Model deployment | Not verified |
| Version control | Not verified |
| Supported languages | Not verified |
| Deployment | Desktop |
| Built for | Solo, Small business, Mid-market (editorial estimate) |
| Pricing | Free plan |
| Website | sci2s.ugr.es |
| Facts checked | 24 Sep 2026 |
Alternatives to KEEL
- SAS ViyaA broad analytics and model-operations platform with flexible deployment and sales-led pricing.9.3
- Amazon SageMaker AIA broad AWS-hosted ML workflow for teams developing and operating models.9.2
- Azure Machine LearningA broad Azure ML lifecycle platform, with compute costs billed separately.9.1
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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