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Waikato Environment for Knowledge Analysis (Weka), created at the University of Waikato in New Zealand, is free software distributed under the GNU General Public License and serves as the companion programme to the book "Data Mining: Practical Machine Learning Tools and Techniques.

In other words, Weka is a library of machine learning methods that may be used to solve real-world data mining challenges. It is developed in Java and may operate on nearly any platform. The algorithms may be applied on a dataset directly or invoked from your own Java code.

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Description of Weka

Weka supports a number of common data mining tasks, including data preparation, clustering, classification, regression, visualisation, and feature selection. Weka expects input to be structured in accordance with the Attribute-Relational File Format, with the filename ending in.arff. All of Weka's solutions assume that the data is provided as a single flat file or relation, with each data item represented by a given number of characteristics.

Weka enables SQL database access through Java Database Connectivity and can handle the results of database queries. With Deeplearning4j, Weka gives access to deep learning. It does not support multi-relational data mining, however there is additional software available for turning a group of connected database tables into a single table appropriate for processing with Weka. Sequence modelling is another key field that is presently not addressed by the methods provided in the Weka package. This can seem to be a lot to process, but it does not end here, check out our Weka assignment help online, to know the rest.

Features of Weka

Weka includes a set of visualisation tools and algorithms for data analysis and predictive modelling, as well as graphical user interfaces for quick access to these features. Weka's first non-Java version featured a Tcl/Tk front-end to (mainly third-party) modelling algorithms written in other programming languages, as well as data pretreatment utilities written in C and a makefile-based mechanism for launching machine learning experiments.

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This first version was primarily intended as a tool for evaluating data from agricultural domains, but the more current entirely Java-based version (Weka 3), which began development in 1997, is now utilised in a wide range of applications, particularly for educational and scientific reasons. Weka has the following advantages:

  • The GNU General Public License allows for free distribution.
  • Because it is entirely built in the Java programming language, it is portable and can operate on practically any current computing platform.
  • A complete set of data preparation and modelling methods.
  • Because of its graphical user interfaces, it is simple to use.

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