Rattle is Free (as in Libre) Open Source Software and the source code is available from the Bitbucket repository.
Data Mining Software Free
Small (<50 employees), Medium (50 to 1000 Enterprise (>1001 employees)
• Learn and develop skills in R
• Provides ease of use
• Build your own models
• View performance graphically
• Get freedom to review, use or extend code
• Get regular updates
Rattle is Free Open Source Software and the source code is available from the Bitbucket repository. Rattle gives the user the freedom to review the code, use it for whatever purpose the user likes, and to extend it however they like, without restriction. Rattle is a popular GUI for data mining using R.
It presents statistical and visual summaries of data, transforms data that can be readily modelled, builds both unsupervised and supervised models from the data, presents the performance of models graphically, and scores new datasets. One of the most important features is that all of the user’s interactions through the graphical user interface are captured as an R script that can be readily executed in R independently of the Rattle interface.
Rattle clocks between 10,000 and 20,000 downloads per month from the RStudio CRAN node. Rattle is open source and freely available from Togaware. You can download Rattle and get familiar with its functionality without any obligation, except for the obligation to freely share! Organisations are also welcome to purchase Rattle, including support for installation and initial training, and ongoing data mining support.
Through a simple and logical graphical user interface based on Gnome, Rattle can be used by itself to deliver data mining projects. Rattle also provides an entry into sophisticated data mining using the open source and free statistical language R. Rattle runs under GNU/Linux, Macintosh OS/X, and MS/Windows.
The aim is to provide an intuitive interface that takes you through the basic steps of data mining, as well as illustrating the R code that is used to achieve this. Whilst the tool itself may be sufficient for all of a user's needs, it also provides a stepping stone to more sophisticated processing and modelling in R itself, for sophisticated and unconstrained data mining.