TrustRadius Insights for RapidMiner are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.
Pros
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.
We use RapidMinerStudio as a data organizational tool. We feed our data into RapidMiner Studio and it helps us create different data trees to sort through the massive amounts of data that we work on in a daily basis. It is very useful as a training toll to familiarize our new hires with the data that we are working with.
Pros
Uploading data.
Has the code we need.
User friendly.
Cons
User interface could be a little more fluid.
Interfacing with other programs.
Processing speeds.
Likelihood to Recommend
RapidMiner Studio was very helpful when we were training our new hires as it allowed them to play around with the data and help us pull the information that we need and it gives them a better understanding of what we really do. Some areas it is less appropriate are when we are trying to work on different data sets and other data rapid miner takes up a lot of processing power and you are unable to do multitasking unless you have a really expensive computer.
We use RapidMiner to create ETL (Extract, Transform, Load) processes to load our BI datamarts with data from operational databases. We've created complex load processes, and we prepare that data to be fed, and later on, create Business Intelligence dashboards. We also use RapidMiner to perform some data mining with different techniques such as text processing, image processing, and algorithm data analysis (clustering, neuralnet, correlation, etc...)
Pros
RapidMiner is really fast at reading all kinds of databases. We read and merge databases like SQL Server, Informix, MySQL, and Oracle. Configuring access is easy, some drivers are inbuilt, but it's not difficult to find new java drivers to allow RapidMiner to connect to other databases.
Performing all kinds of transformations, calculations (date, percentages...), joins, and filters without coding. We have several different databases and this makes my life a lot easier. Knowing that this part is 80% of analyst work, you know that you can work more on the analyses itself and not on cleaning and preparing data.
You can clone transformations to reuse on new analyses, so you save a lot of time. There's a lot of add-ons to make different things (text, image analysis, recommender systems, etc).
Training is easy, the tool is intuitive and there's a lot of videos on the internet. The community is very active.
Cons
Sharing RapidMiner Studio analysis is not easy. You may think that the RapidMiner Server does that work but no. It's more automated job oriented or useful to run models on a web site. If you need to use it for Business Analytics dashboards, this is not the tool. It's more a backstage tool for analyst. Some charts are good but other not so much.
The free edition allows you to work with 10,000 rows, but if you need more, it's not cheap (100,000 rows - 2,500 USD/year, 1,000,000 rows - 5,000 USD/year).
The commercial team is not very reactive. I've asked for a RapidMiner Education Program and Rapidminer Server quotation with no answer. I guess that's because they were changing from an opensource company model to a more commercial one.
Likelihood to Recommend
RapidMiner is really fantastic to perform fast ETL processes and work on your data as you want, no matter what is the source. You will really save a lot of time when you learn how to use it. You can create mining analysis with several algorithms, and thanks to add-ons, you can apply a lot of techniques. It will not replace a business intelligence dashboard but it allows to create great datamarts for your BI tools.
One negative thing is that It's no easy to share your outputs.
We in marketing currently use RapidMiner studio to help with predictive analytics for our direct mail program as well as text mining for our call transcripts.
Pros
I am very impressed at how easily you can work within RapidMiner without much data analytics training. Plus with the help of the crowd, you can see what steps others have taken with their data analytics projects.
Text mining was simple and clean. We used this for our call transcription problem where we didn't have the resources to listen to each call. We needed to qualify each call based on some key phrases.
Our direct mail program was large and not very targeted. Using RapidMiner, we were able to isolate a predictive level we felt comfortable with and decided not to send to anyone below that level. We saved quite a bit of money.
Cons
Basic data cleaning is always a problem that RapidMiner might solve, but I am not aware of it.
Likelihood to Recommend
Our marketing department has benefited greatly from the use of RapidMiner. It is well suited to many marketing tracking issues.
We use RapidMiner as the central data science platform to teach data analytics and machine learning. We are using RapidMiner for real-world analyses. Currently, to the best of my knowledge, only my department is using RapidMiner for teaching while others are using R, SPSS or WEKA for educating data mining. Our business is in education, and therefore, our primary concern is finding the most effective way of learning analytics – both data handling and algorithms.
Pros
The RapidMiner provides a rich set of Machine Learning algorithms for Data Mining tasks, along with a comprehensive set of operators (functions) for data pre-processing. RapidMiner has a repository containing hundreds of machine learning algorithms and functions.
RapidMiner is easy to use because RapidMiner is a user-friendly visual workflow designer software. Visualization of the process really helps users with data preparation and modelling. It makes my job easier in teaching machine learning and predictive analytics because I can show them the role of each operator and which one is vital in getting the right model. Students can directly see and understand the effect of using specific algorithms and functions after a few clicks, drags and drops. RapidMiner is something quick and easy to master.
It is FREE! RapidMiner is available for free for educational use. I have been using RapidMiner for about three years, and I have never encountered any problem in renewing my license. The Educational Program License lasts for a year. My students have never complained about RapidMiner as the customer support is very efficient.
RapidMiner Marketplace: If there are 'missing algorithm' from the RapidMiner library, we always can install extensions from the RapidMiner Marketplace. For example, I can access an extra about 100 additional modelling schemes after installing the WEKA extension. What ever the tasks are, if the required algorithm or functions are not available in the RapidMiner repository of ML algorithms, I can always find it in the marketplace.
Cons
I hope RapidMiner would be the first data science platform that allows data scientists to change the behaviour of a machine learning algorithm that already exists in the repository. For example, I want to be able to change the way a genetic algorithm mutates.
Automatic programming: One day, I hope RapidMiner can automatically generate codes in any 4th generation programming language based on the developed model.
More tutorials/samples needed: Why doesn't RapidMiner becomes the next 'UC Irvine Machine Learning Repository'? Provide real examples and real cases for users to study and understand the best practices in modelling. RapidMiner already has some datasets for a tutorial. Besides the existing samples, I hope RapidMiner can provide more sample data and examples.
Likelihood to Recommend
RapidMiner has been used as a teaching aid in my classes, and I found it effective in educating data mining and analytics students. RapidMiner is well suited for any descriptive and predictive analytics because it has a pervasive set of machine learning algorithms, i.e. classification, regression, clustering, association rule mining, and other applications such as text mining. However, RapidMiner Studio is not yet designed for prescriptive analytics (i.e. solving optimization problem) such as solving scheduling, logistic route planning or bin packing problems. It has a great facility to find an optimal parameter for an algorithm but not yet for solving automatic scheduling and planning problems.
We use RapidMiner to run statistical algorithms on the data and answer questions which are not answered previously. It is currently used only in the business intelligence team in the company. We used RapidMiner Studio to analyze the client churning, client clustering, banker clustering and market basket analysis of the company products.
Pros
No need to script anything, and still doing modeling is amazing
Easy to use by just dragging and dropping operators
Can use the tool for data cleaning, data analysis, data modeling
Cons
Wish the tool was more efficient in terms of processing power. The tool takes a lot of CPU processing power, even for a small process on a small data set
Wish there were more options on charts and graphs to visualize the data
Likelihood to Recommend
RapidMiner Studio is a great starting point if you want your organization to try and start using data science or statistics. It is easy to use and self explanatory. It also has some great built in algorithms. The Recommendations feature using crowd sourcing is amazing, and will guide with next steps in the process. It is more suitable for smaller data sets. We haven't tried with larger data sets yet, so try it beforehand if you want to use large data on it.
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
WELL SUITED FOR:
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.
LESS APROPIATE FOR:
When you need cooperation between multiple developers in
separate geographical places.
There’re much better tools for Data visualization.
I use RapidMiner it to process data from our clients mainly Telecom and Banking and I also use a version of RapidMiner purchased by our business partner at their site for their clients. I use it in my company and no on else and I advise my clients to purchase RapidMiner. I currently don't have employees that use RapidMiner.
Pros
Build a model
Validate a model
See how accurate our predictions are
We prefer to use our own coding to clean the data since we use huge databases using MySQL, Oracle or MS-SQL
We use the visualization tools of RapidMiner to analyze the data
Cons
The graphics of the charts needs some work. Sometimes it is hard to read on high resolution screens
Sometimes it is hard to find the operators
The interface is far from the standards of Office or Microsoft in general.
Likelihood to Recommend
It is easy to use. It provides exceptional performance. We tried different tools and decided to use RapidMiner.
I currently use RapidMinder to do two things. First, to find patterns in Epilepsy Clinical Data. Second, to teach students machine learning tools.
So, in general, I'd say the business problem is research and education. I used it before to find patterns in protein interaction networks analysis. And I've taught several machine learning workshops using RapidMiner as a tool.
Pros
Easy to use. The Graphic User Interface allows users to build their models very fast and very intuitively
Fast to learn. There are plenty online resources (official and unofficial) to learn how to use RapidMiner
Multiple Tools. RapidMiner has several tools to help with the machine learning activity that a person is doing, different models, importing, etc.
Cons
Export. It would be great to be able to export the resulting data, graphs and models in an easier way. Currently I find that not intuitive enough.
It would be wonderful (not sure if it fits the company business model) to have an API access, so people would be able to integrate some of RapidMiner functionalities inside their applications
Likelihood to Recommend
Well suited: for data mining and general machine learning tasks, teaching machine learning, creating machine learning models.
Not well suited: if the user is beginning to learn machine learning, it would be advisable to get some general understanding before using the tool.
We are only 5 consultants. We are all using RapidMiner Studio in order to model machine learning structures for our clients. The problem we had before, for instance with Python code, is that it was really difficult for our clients to understand what we were doing exactly. Even when it is actually possible to build up visualization capacities in Python and R, RapidMiner gave us the option to do it so fast and accurately. I would love to see updates which can improve the graphical quality and precision, or prevent some graphical mistakes. For instance, it is very difficult to guess how much is too much when we have so many categories to diagram, sometimes saturates the space and reduces the explainability of the graphs. But still, AutoModel, Feature Engineering and the drag and drop method is wonderful to share our thoughts and discoveries about our clients reality and plans.
Aptus Data Labs is based out of Bangalore, India. We are a Big Data and Advanced Analytics company, providing consulting and project delivery services & solutions, catering to enterprises of all sizes, across different industries such as Healthcare, Retail & Consumer Industries, BFSI, Manufacturing & Supply Chain.
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
Pros
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
Cons
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
Likelihood to Recommend
RapidMiner is the best tool to build models on textual data. It is rich in ML algorithms and reduces the need to manually tune the parameters. It automatically optimizes them, thus providing a better solution. RapidMiner again extends great capability for data preparation, its insane connections to almost every data source pulls in the data easily into one environment. And it can comfortably perform data cleaning and process tasks over that.
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.)
