Weka (Waikato Environment for Knowledge Analysis) is an open-source machine learning and data mining workbench developed at the University of Waikato. It provides a comprehensive suite of tools for data preprocessing, classification, regression, clustering, association rule mining, and visualization, all within a user-friendly graphical interface or command-line environment. Weka is widely used in academia, research, and industry for exploring datasets, building predictive models, and teaching machine learning concepts.

Key Features of Weka:

• Wide range of machine learning algorithms, including decision trees, Naive Bayes, support vector machines, neural networks, and ensemble methods.
• Support for data preprocessing, cleaning, feature selection, and transformation before model building.
• Clustering and association rule mining tools, including k-means, EM, and Apriori.
• Visualization of datasets, predictions, and evaluation metrics with interactive charts and plots.
• Extensible through additional packages, allowing integration with new algorithms and tools.
• Runs on any platform with Java, making it highly portable and cross-platform.
• Command-line interface for scripting and batch processing of large-scale experiments.

To check your Weka version:

snap list weka

Disclaimer: Weka is intended for educational, research, and professional use in approved environments. Users must ensure that data processing complies with organizational, legal, and ethical guidelines. The University of Waikato and contributors are not responsible for misuse or unauthorized application of the software.