RapidMiner, the Best Features for ML
Overall Satisfaction with RapidMiner Studio
DisperSurance is the radical disruptive substitute for
insurance. We don’t sell insurance, we sell “risk coverage”. We have been using
RapidMiner in traditional insurance company data to:
Identify optimization and automation opportunities
in all the insurance processes. Moreover, we had created special extensions for
the most important processes.
Determining the most profitable e-commerce
strategies for selling policies.
We have been able to design new Risk Coverage products that
are as low as 70% cheaper than traditional insurance.
- RapidMiner has a very large ML algorithms library and excellent tools for automated optimization of those algorithms.
- Is one of the best tools I know for text mining and analytics. It’s not only very powerful but also very intuitive and easy to use.
- Since it’s is very easy to pass from design to production, it’s an excellent tool for building and testing complete models.
- It should improve it friendliness with using multimedia (video, pictures, audio). For instance, is not easy to connect between raw audio and its related text data for analytics.
- It also should improve it interface design and intuitiveness. Its design isn’t very motivational and sometimes it’s hard to find some key operators.
- It should improve the capabilities to integrate RapidMiner to third party applications.
- Our team has learnt how to work very efficiently with RapidMiner, reducing our developing cots by 9,2 %.
- We have been able to discover cost reduction potentialities in the main Risk Coverage processes (from buying an insurance policy to paying a claim) between 5 % and 65 %.
- The ROI of using is exponential, far above 1,000 %.
- IBM SPSS Statistics: RapidMiner is much better at statistical
- Knime it’s much better than RapidMiner when the project
involves extensive use of java.
- Alteryx is the fast but RapidMiner is much
better for multipurpose projects.
- Tableau is one of the best tools for data visualization.
And also, is better than RapidMiner.
- For creating predictive models.
- Excellent for cleaning and preparing data for a
better modeling process.
- Most of the common ML algorithms can be
Is “The Tool” when you need rapid results and the data is
not extremely large or complex.
When you need cooperation between multiple developers in
separate geographical places.
There’re much better tools for Data visualization.
When a project uses lots of memory.