Overview
What is RapidMiner?
RapidMiner is a data science and data mining platform, from Altair since the late 2022 acquisition. RapidMiner offers full automation for non-coding domain experts, an integrated JupyterLab environment for seasoned data scientists, and a visual drag-and-drop designer. RapidMiner’s project-based…
RapidMiner Studio: Great Training Tool and Data Compiler
Very user-friendly ETL with mining capabilities. Will save you a lot of time with your data.
Data Analytics for Marketing—yes, it can be done
RapidMiner as Educational Data Mining Tool
Easy To Use and powerful
RapidMiner, the Best Features for ML
DisperSurance is the radical disruptive substitute for insurance. We don’t sell insurance, we sell “risk coverage”. We have been using
RapidMiner is rapid, easy and fun
RapidMiner, education and research short review
An already very consistent tool with potential for more
Data admirer's playroom - RapidMiner Studio
Predictive Analytics with RapidMiner
Shallow learning curve but deep capabilities and support
Fast and easy to use but hogs memory
RapidMiner makes data science a lot simpler.
RapidMiner Studio - An excellent educational tool
My introduction to RapidMiner Studio began in 2014 when I decided to write a second edition of my data mining textbook. Although I was not …
Awards
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Pricing
Professional
$7,500.00
Enterprise
$15,000.00
AI Hub
$54,000.00
Entry-level set up fee?
- No setup fee
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
Product Demos
RapidMiner Platform Demo: Part 1 - Getting Started with RapidMiner 7.3
Product Details
- About
- Tech Details
What is RapidMiner?
RapidMiner Videos
RapidMiner Technical Details
Deployment Types | Software as a Service (SaaS), Cloud, or Web-Based |
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Operating Systems | Unspecified |
Mobile Application | No |
Comparisons
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Reviews and Ratings
(56)Community Insights
- Business Problems Solved
- Pros
- Cons
- Recommendations
RapidMiner Studio has been widely utilized across various industries for a range of use cases. Users have found success in using RapidMiner Studio to create ETL processes for loading BI datamarts with data from operational databases. Additionally, the software has been instrumental in performing data mining tasks such as text processing, image processing, and algorithm data analysis.
Marketing teams have leveraged RapidMiner Studio for predictive analytics in direct mail programs and text mining for call transcripts. The software has also served as a central data science platform for teaching data analytics and machine learning, providing an essential tool for educational purposes. Furthermore, RapidMiner Studio has demonstrated its versatility by being used for real-world analyses in healthcare, retail, manufacturing, and BFSI sectors.
Companies like Aptus Data Labs rely heavily on RapidMiner Studio to deliver their solutions efficiently. The software has proven valuable for fraud analysis in banking and financial industries, claim and travel analytics in manufacturing, and text mining in pharmaceutical firms. Individual analysts have also found success using RapidMiner Studio for analysis and predictive modeling of student-related data.
In addition to these use cases, RapidMiner Studio has been applied to build and test energy usage models for buildings. Risk coverage providers like DisperSurance have utilized the software for optimization, automation, fraud detection, and determining profitable e-commerce strategies. It has also been instrumental in processing data from clients in telecom and banking industries while assisting in modeling machine learning structures.
Moreover, RapidMiner Studio has served as a powerful data organizational tool by enabling users to sort through massive amounts of data and run statistical algorithms efficiently. It has been effectively employed for client churning analysis, client and banker clustering, and market basket analysis. Sales and marketing teams have benefited from its predictive analytics capabilities using CRM data.
The software's versatility extends even further with applications in pattern finding in epilepsy clinical data, protein interaction networks analysis, and gaining insights about various datasets. RapidMiner Studio offers a wide range of machine learning algorithms, making it suitable for text mining, data analysis, and machine learning tasks. Its diverse applications and user-friendly interface have made it a valuable tool for users across industries.
Intuitive User Interface: Several users have praised RapidMiner Studio for its intuitive user interface, which has made it easy for them to learn and navigate the software. The intuitive workflow paradigm has allowed users to quickly grasp the functionality of the software and perform tasks with ease.
Versatile Operators: Many reviewers have highlighted the versatility and power of RapidMiner Studio's operators. These operators are complete and powerful, especially in handling tasks such as data preprocessing, data visualization, and data mining analytics. Users have found these operators to be valuable tools in various areas of analysis.
Extensive Support System: Numerous users have commended RapidMiner Studio for its excellent documentation, countless worked examples, and large user community that provides training support. This extensive support system has been highly valued by users as it offers valuable resources for learning and troubleshooting, ensuring effective utilization of the software's capabilities.
Outdated User Interface: Several users have expressed dissatisfaction with RapidMiner's user interface, stating that it is outdated and not up to the standards of other software like Office or Microsoft.
Difficulties in Finding Key Operators: Some users have reported difficulties in finding key operators within RapidMiner's interface, which has caused frustration and hindered their workflow.
Lack of Documentation for Operators: Many reviewers have mentioned that there is a lack of documentation for several operators in RapidMiner. This makes it challenging for users to understand the impact of these operators on their analysis, leading to confusion and inefficiency.
Users of RapidMiner have provided several recommendations based on their experiences with the tool. The three most common recommendations are as follows:
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Provide more support for finding mining algorithms and add Indonesian language support: Many users suggest that RapidMiner should make it easier to locate mining algorithms within the application. Additionally, they recommend adding support for the Indonesian language to cater to a wider audience.
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Utilize RapidMiner for various data analysis purposes: Users highly recommend using RapidMiner for different data mining and analysis tasks, including predictive analysis and marketing purposes. They find the tool user-friendly and effective in developing prediction models.
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Take advantage of RapidMiner's features and resources: Users recommend exploring RapidMiner's Auto-ML feature, which they consider a significant advancement. They also suggest accessing the extensive training material provided by RapidMiner, taking online classes, and working on hands-on tutorials to gain a competitive edge. Additionally, they emphasize the importance of engaging with sales representatives and utilizing the continuous updates from the pro-active team.
Overall, users find RapidMiner to be an excellent tool for data mining and analysis, particularly for implementing complex machine learning algorithms and improving dataset analysis. They appreciate its wide range of machine learning algorithms, powerful ETL operations, interactive visualizations, and great online support. However, some users suggest improvements such as adding Korean language support and expanding popularity in business settings.
Attribute Ratings
Reviews
(1-1 of 1)Data admirer's playroom - RapidMiner Studio
Since we are into advanced analytics, most of our solutions are delivered using RapidMiner. As a result of which, most of the employees in the organization use RapidMiner. We have dedicated developers for building extensions for RapidMiner as well. Some of the business problems built using RapidMiner are:
- Fraud analysis for Banking and Financial industries
- Claim and travel analytics for a manufacturing firm
- Text mining and text analytics for a pharmaceutical firm and many other organizations
- Optimization for e-commerce and manufacturing firm
- Supply chain management for manufacturing
- Supply chain planning and scheduling for oil and gas companies
- A great tool to start exploring data science and machine learning. Its intuitive GUI, tutorials, help window, sample processes, and recommendations make it the best place to learn and expand your knowledge horizon.
- RapidMiner is an expert in building end to end solutions. Creating a process in the studio and then running it in production using the server is easy and fast. And also using web services, we can integrate the solution into an organization's in-house application or create a new web application in RapidMiner server. This makes solution delivery faster compared to R and Python.
- Text mining and analytics capability in RapidMiner. I think text processing is very easy here. Using Rosette and deep learning extensions, I have delivered such great solutions.
- Smart Automations like automatically identifying parameter values, auto model and turbo prep etc. saves a lot of time and provide better results
- RapidMiner Server- It is very basic in terms of appearance. Web Apps can be improved by providing default themes and it needs a lot more features to be added.
- Multi-process window in RapidMiner Studio. Multiple design view can be added for switching between processes and model building can be made easier.
- Git Integration for version control. We have something called MyExperiment in RapidMiner but it is far from Git. But if we could have git integration, multiple users can work on the same process and this version control can help to refer previous solutions as well.
- Graphs in RapidMiner Studio are a bit old fashioned
RapidMiner is not so good with image, audio or video data. These data points cannot be used directly in their raw form. They must be transformed into some intermediate form for performing analytics over it. Moreover, there are no connectors to directly pull data from their varied sources. For example, we don't have a connector to read audio data directly from a switch and then convert it to text (although Google speech API is available for audio to text conversion.)
- From an organization's point of view, we have been able to deliver better and quicker solutions. Thus saving a lot of time and investing in other projects.