RapidMiner is an open-source, data science software platform that provides environment for data mining, predictive analytics, clustering and machine learning. To make the data mining process smooth, it has a set of predefined operators. It consists an array of tools that provides centralized solutions for advanced data analytics. It ensures smooth transformation from modelling to implementation. RapidMiner(RM) was initially known as YALE (Yet Another Learning Environment).
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Products Offered by RM
The data science platform of RapidMiner contains different products like:
- Studio – This is a cross-platform product that can run on Microsoft Windows, macOS 10.8 or later, and Linux. It is a workflow designer which helps in prototyping and model validation. It provides a GUI client that enables users to design code-free data analysis.
- Server – It is an optimized server which allows periodic scheduling and execution of analytical processes within the server’s GUI.
- Radoop – As the name suggests, this enables building and executing predictive models in Hadoop without coding in Spark.
- Turbo Prep – This product helps in data preparation; eliminating useless columns, creating dummies, mutating new columns, creating pivot tables to explore and visualize data.
- Auto Model helps in preparing predictive models using automated machine learning and data science practices.
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Benefits of using RapidMiner
RM can integrate data from different sources. They include: Excel, Access, Oracle, IBM DB2, Microsoft SQL, Sybase, Ingres, MySQL, Postgres, IBM SPSS, dBASE, text files and many other structured and unstructured data formats. It is a complete tool for ETL process. It contains more than 400 analytic functions. Its main benefits being: user-friendliness, robust features and maximized data usage. The visual workflow designer, as mentioned above is where analytics processes can be created, designed and then deployed. With some available integrations, users can also analyze sentiment, extract entities, translate names, and tokenize the extracted multilingual input and more— all within the workflow.
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Comparison between RapidMiner and Python
Though one is a platform, the latter being a language, a comparison can be made based on the following aspects.
Visualization
RM: Many built-in visualization tools, like decision trees
Python: Different packages available, or coding
Data Preparation
RM: Chain of operators is available
Python: Scikit-learn pipelines are used for data wrangling
Programmability
RM: Fixed operators with ability to code new operators
Python: Fully programmable
Workflow Style
RM: Visual GUI-based
Python: Via coding, text and comments
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