Weka is a collection of machine learning algorithms for solving real-world data mining problems. It is written in Java and runs on almost any platform. The algorithms can either be applied directly to a dataset or called from your own Java code.

Features

  • machine learning
  • data mining
  • preprocessing
  • classification
  • regression
  • clustering
  • association rules
  • attribute selection
  • experiments
  • workflow
  • visualization

Project Samples

Project Activity

See All Activity >

Categories

Machine Learning

License

GNU General Public License version 3.0 (GPLv3)

Follow Weka

Weka Web Site

Other Useful Business Software
Migrate to innovate with Red Hat Enterprise Linux on Azure Icon
Migrate to innovate with Red Hat Enterprise Linux on Azure

Streamline your IT modernization journey with a holistic environment running Red Hat Enterprise Linux on Azure.

With Red Hat Enterprise Linux on Azure, businesses can confidently modernize their IT environment, knowing they don’t have to compromise on security, scalability, reliability, and ease of management. Securely accelerate innovation and unlock a competitive edge with enterprise-grade modern cloud infrastructure.
Rate This Project
Login To Rate This Project

User Ratings

★★★★★
★★★★
★★★
★★
48
1
1
0
0
ease 1 of 5 2 of 5 3 of 5 4 of 5 5 of 5 4 / 5
features 1 of 5 2 of 5 3 of 5 4 of 5 5 of 5 4 / 5
design 1 of 5 2 of 5 3 of 5 4 of 5 5 of 5 4 / 5
support 1 of 5 2 of 5 3 of 5 4 of 5 5 of 5 4 / 5

User Reviews

  • no hablo espanol I don't speak english either
  • good
  • excellent tool. Sir, In earlier version we had artificial immune algorithms AIRS algorithms and Immunos algorithms and neural network algorithms , with Welaclassalgo do we have same algorithms in 3.8.4 version.
  • It is very efficient
  • Complete suite of ML algorithms
Read more reviews >

Additional Project Details

Intended Audience

Developers, End Users/Desktop

User Interface

Java Swing, Command-line

Programming Language

Java

Related Categories

Java Machine Learning Software

Registered

2000-04-27