In version 3.7.2, a package manager was added to allow the easier installation of extension packages. Another important area that is currently not covered by the algorithms included in the Weka distribution is sequence modeling. It is not capable of multi-relational data mining, but there is separate software for converting a collection of linked database tables into a single table that is suitable for processing using Weka. Weka provides access to deep learning with Deeplearning4j. Weka provides access to SQL databases using Java Database Connectivity and can process the result returned by a database query. All of Weka's techniques are predicated on the assumption that the data is available as one flat file or relation, where each data point is described by a fixed number of attributes (normally, numeric or nominal attributes, but some other attribute types are also supported). Input to Weka is expected to be formatted according the Attribute-Relational File Format and with the filename bearing the. Weka supports several standard data mining tasks, more specifically, data preprocessing, clustering, classification, regression, visualization, and feature selection. Ease of use due to its graphical user interfaces.A comprehensive collection of data preprocessing and modeling techniques.Portability, since it is fully implemented in the Java programming language and thus runs on almost any modern computing platform.Free availability under the GNU General Public License.This original version was primarily designed as a tool for analyzing data from agricultural domains, but the more recent fully Java-based version (Weka 3), for which development started in 1997, is now used in many different application areas, in particular for educational purposes and research. The original non-Java version of Weka was a Tcl/ Tk front-end to (mostly third-party) modeling algorithms implemented in other programming languages, plus data preprocessing utilities in C, and a makefile-based system for running machine learning experiments. Weka contains a collection of visualization tools and algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to these functions. It was developed at the University of Waikato, New Zealand and is the companion software to the book 'Data Mining: Practical Machine Learning Tools and Techniques'. Waikato Environment for Knowledge Analysis ( Weka) is a collection of machine learning and data analysis free software licensed under the GNU General Public License.

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