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Weka Reviews

Vendor: Weka
3.9 out of 5
Badge Ranked 1
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What is Weka?

Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.

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Weka is the #1 ranked solution in top Anomaly Detection Tools and #4 ranked solution in top Data Mining solutions. PeerSpot users give Weka an average rating of 7.8 out of 10. Weka is most commonly compared to KNIME Business Hub: Weka vs KNIME Business Hub. Weka is popular among the large enterprise segment, accounting for 43% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a educational organization, accounting for 17% of all views.
Buyer's Guide
Weka
February 2026
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Weka mindshare

Product category:
Data Mining
As of March 2026, the mindshare of Weka in the Data Mining category stands at 8.8%, down from 21.1% compared to the previous year, according to calculations based on PeerSpot user engagement data.
Data Mining Mindshare Distribution
ProductMindshare (%)
Weka8.8%
IBM SPSS Modeler18.9%
IBM SPSS Statistics18.3%
Other54.0%
Data Mining

PeerResearch reports based on Weka reviews

TypeTitleDate
CategoryData MiningMar 28, 2026Download
ProductReviews, tips, and advice from real usersMar 28, 2026Download
ComparisonWeka vs KNIME Business HubMar 28, 2026Download
ComparisonWeka vs IBM SPSS StatisticsMar 28, 2026Download
ComparisonWeka vs IBM SPSS ModelerMar 28, 2026Download
Suggested products
TitleRatingMindshareRecommending
KNIME Business Hub4.112.8%94%60 interviewsAdd to research
IBM SPSS Statistics4.118.3%89%40 interviewsAdd to research
 
 
Key learnings from peers
Last updated Mar 27, 2026

Valuable Features

Weka provides ease of use with its intuitive interface and extensive algorithm library for data mining, classification, and clustering. It supports integration with new algorithms and facilitates rapid testing and data preprocessing. Users appreciate its ability to handle machine learning tasks without coding. The numerous filters and transformation tools streamline data manipulation. Weka is considered quicker than Python or R, offering accessible machine learning tools and comprehensive documentation suitable for education and quick analysis.
  • "Weka's best features are its user-friendly graphic interface interpretation of data sets and the ease of analyzing data."
  • "It’s pretty straightforward, it's user-friendly, it’s free, and with YouTube videos, online guides, and e-books on machine learning using Weka, you can quickly learn to use its good interface for data visualization, filtering, and creating different scenarios and instances."
  • "But Weka helped me with this analysis in an easy way."

Room for Improvement

Weka faces challenges in scalability and performance due to Java limitations. Its pre-processing is complicated and lacks deep learning algorithms. Issues arise with handling missing values and visualizations are limited. Users find documentation inconsistent and crave more user-friendly resources. The software struggles with big data integration and lacks support for automation. A more intuitive user interface and enhanced machine learning capabilities are desired. Users indicate stability concerns and difficulty in server deployment.
  • "Weka is a little complicated and not necessarily suited for users who aren't skilled and experienced in data science."
  • "The solution doesn’t really have very good technical support."
  • "For production, it's not the best option."

Pricing

Weka users report that the software is primarily free and open-source, making it an accessible choice for enterprises with varying needs. While the open-source version is widely used, some organizations opt for the paid version to access more advanced features and customization options. Pricing details for the paid version are not widely known among users, as many prioritize the tool's capabilities over cost, utilizing open-source for budget-friendly solutions.
  • "We use the free version now. My faculty is very small."
  • "As far as I know, Weka is a freeware tool, and I am not aware if they have an online solution or if it is a commercial product."
  • "The solution is free and open-source."

Popular Use Cases

Weka is primarily utilized for machine learning, handling tasks such as classification, clustering, and anomaly detection. Users apply Weka for data exploration, deploying algorithms like regression trees, association rules, and support vector machines. It facilitates quick analysis, even for text data classification, by transforming data types. Popular among academic researchers and businesses, it supports cloud and on-premise deployments. Despite competition from R and Python, it's preferred for rapid approaches by non-programmers.

Service and Support

Weka's customer service relies on a user community and documentation. Users report mixed experiences with support. Some find the technical support responsive and interactive, while others rate it as average, with slow response times. Many rely on forums or platforms like Stack Overflow for assistance. Some users express uncertainty about accessing official support channels. Generally, users can resolve 80% of issues through forums, but specific information may be lacking.

Deployment

Weka's initial setup is straightforward and easy to install, with many finding it intuitive and suitable for those familiar with Java. Installation times vary, ranging from a few minutes to several hours depending on data complexity. Some report challenges with large datasets and documentation, while others note that training and model testing become simpler with experience. Though easy for non-programmers, some find visualizations lacking compared to other tools like Python.

Scalability

Weka is often praised for its machine learning algorithms, yet opinions differ on its scalability. Some users report satisfactory scaling for individual use, while others note challenges with larger datasets and multi-user environments. Horizontal scalability is not natively supported, but vertical expansion is possible. Users highlight the challenge of integrating with external tools for improved scalability. Weka's stability and low maintenance needs are commonly appreciated.

Stability

Users find Weka stable and reliable for their needs. It has performed well over two years according to many, with stability confirmed by colleagues. Stability is contingent on selecting accurate predictor variables for better outcomes. Most users rate its stability highly, with some suggesting better documentation and output for dynamic results. Despite occasional crashes, it's considered stable with resource management. Users suggest improvement areas while acknowledging its stable performance.
These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.
Download our Weka Buyer's Guide for additional reliable information.

Review data by company size

By reviewers
Company SizeCount
Small Business5
Midsize Enterprise1
Large Enterprise2
By reviewers
By visitors reading reviews
Company SizeCount
Small Business70
Midsize Enterprise34
Large Enterprise78
By visitors reading reviews

Top industries

By visitors reading reviews
Educational Organization
17%
University
12%
Computer Software Company
8%
Comms Service Provider
7%
Government
5%
Construction Company
5%
Non Tech Company
4%
Recreational Facilities/Services Company
4%
Financial Services Firm
4%
Healthcare Company
4%
Performing Arts
4%
Manufacturing Company
3%
Real Estate/Law Firm
3%
Retailer
3%
Non Profit
3%
Energy/Utilities Company
2%
Transportation Company
2%
Media Company
2%
Wholesaler/Distributor
1%
Hospitality Company
1%
Insurance Company
1%
Logistics Company
1%
Marketing Services Firm
1%
Pharma/Biotech Company
1%
Outsourcing Company
1%
Mining And Metals Company
1%
Aerospace/Defense Firm
1%
Consumer Goods Company
1%
Agriculture
1%

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Product Categories

Product Categories
Data Mining
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Weka Reviews Summary
Author infoRatingReview Summary
Phd at a educational organization with 51-200 employees5.0I find Weka extremely useful for research, easy to use, and very complete with excellent stability, scalability, and support. I highly recommend it, though more analysis options would be beneficial. I consider it first place.
Treasury Management in Finance Department at National University of Pakistan3.5In my university experience, Weka was adequate for basic data analysis but lacked visualizations and advanced features. I preferred Python libraries due to their flexibility and free access, while KNIME would have been better for energy analytics.
Manager at XS AMSAFIS DATASETS, S.L.3.5I run methods like Association rules and RandomForest in Weka, mainly using menu options as I don't know Java. While Weka is accessible for non-programmers, I prefer R and Python for most tasks. However, Weka's simplicity is useful for unfamiliar methods.
Weka Specialist at freelancer4.5I use Weka for machine learning tasks because it requires no coding and simplifies setting parameters. While it offers ROI, its visualization needs improvement, particularly regarding data cleaning. Previously, I used Python and R for similar tasks.
Student at a university with 1,001-5,000 employees3.5I find Weka a free, user-friendly tool with good data visualization and easy setup, suitable for labs. My main concerns are the lack of strong technical support and reports of slowness. I'd rate it 7/10.
Weka Specialist at freelancer4.5Having used Weka for seven years, I appreciate its classification capabilities and the confidence it builds. While its data transformation tools and visualization could improve, its ease of use for non-coders and responsive support are strong positives.
Consultant for the implementation of maintenance management and repair of equipment at IT-Enterprise4.0I use Weka for anomaly detection. It offers a user-friendly GUI and easy data analysis, but it's complicated for less experienced users and lacks scalability. Setup was easy, and it is stable. I rate it 8/10.
Freelance Data Scientist at Freelancer4.0I use Weka for quick, lightweight machine learning, appreciating its ease, filters, and client-ready results. However, I find its handling of missing numeric data, lack of pipelines, and limited visualization significant drawbacks.