RapidMiner, the Best Features for ML
Use Cases and Deployment Scope
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:
1. Identify optimization and automation opportunities in all the insurance processes. Moreover, we had created special extensions for the most important processes.
2. Fraud detection.
3. 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.
Pros
- 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.
Cons
- 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.
Likelihood to Recommend
- For creating predictive models.
- Excellent for cleaning and preparing data for a better modeling process.
- Most of the common ML algorithms can be integrated easily.
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.
