What is RapidMiner?
It is a software platform for data science teams that unites data prep, machine learning, and predictive model deployment.
RapidMiner is a tool in the Languages category of a tech stack.
Who uses RapidMiner?
Companies
4 companies reportedly use RapidMiner in their tech stacks, including Rumble, SCMC-CMU, and Red Hat BIDS.
Developers
30 developers on StackShare have stated that they use RapidMiner.
RapidMiner Integrations
Python, HTML5, Java, MongoDB, and Groovy are some of the popular tools that integrate with RapidMiner. Here's a list of all 8 tools that integrate with RapidMiner.
RapidMiner's Features
- Graphical user interface
- Analysis processes design
- Multiple data management methods
- Data from file, database, web, and cloud services
- In-memory, in-database and in-Hadoop analytics
- Application templates
- -D graphs, scatter matrices, self-organizing map
- GUI or batch processing
RapidMiner Alternatives & Comparisons
What are some alternatives to RapidMiner?
Python
Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best.
R Language
R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.
DataRobot
It is an enterprise-grade predictive analysis software for business analysts, data scientists, executives, and IT professionals. It analyzes numerous innovative machine learning algorithms to establish, implement, and build bespoke predictive models for each situation.
Power BI
It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards.
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
