RapidMiner supported my data preparation tasks by helping me identify and treat noise, handle duplicates, and analyze variables efficiently. These steps are crucial for any data preparation process, regardless of the tool used. The most impactful features for data modeling tasks in RapidMiner include its intuitive interface for creating relationships between datasets, similar to Power BI's capabilities but with a more intuitive approach. This allows for easier and more efficient data modeling, enhancing the overall workflow experience. Overall, I would rate RapidMiner as an eight out of ten.
Data Analyst at a tech services company with 1,001-5,000 employees
Real User
Top 20
2023-08-30T08:10:16Z
Aug 30, 2023
As long as RapinMiner helps users interpret data and use it, there is no problem using it. Overall, I would rate the solution an eight out of ten. If the solution could improve its user-interface, that would make it a ten.
Student at a university with 1,001-5,000 employees
Real User
Top 20
2023-05-15T15:46:17Z
May 15, 2023
I'm a student and use the solution on-premises for education purposes. I'd advise other users to look at other options and compare the findings. It's very easy-to-use, intuitive data mining software. I'd rate it nine out of ten.
Senior Manager at a consultancy with 201-500 employees
Real User
2021-12-13T16:19:00Z
Dec 13, 2021
I rate RapidMiner nine out of 10. If you're planning to get into machine learning projects, my advice is to start as fast as you can. Many of our clients waste too much time thinking about use cases. We advise our clients to start doing some modeling and not wait for the perfect data because the clients usually have some bottlenecks. They don't have complete and accurate data, so they think they can't have predictive modeling projects in place. Our advice is to hit the ground running and start retrieving some results.
Executive Director at a philanthropy with 201-500 employees
Real User
2021-10-20T09:11:43Z
Oct 20, 2021
I rate RapidMiner 10 out of 10. It's helpful if you want to make informed decisions using data. We can take the information, tease out the attributes, and label everything. It's suitable for profiling and forecasting in any industry.
I have worked with RapidMiner, but I have not yet explored all of the functionality of the software. As an example, the relation with big data and the relation with the Cloud. I have used the utilities quite a bit. In the last model, they added automation cleaning for data preparation. It is very interesting. I am a computer scientist and I received my Ph.D. 23 years ago. I am a researcher, and when I have a problem, I use it to research and to find a solution to much more difficult problems. I would rate this solution an eight out of ten.
Senior Manager, Digital Solutions at a tech services company with 1,001-5,000 employees
Real User
2020-01-16T08:44:00Z
Jan 16, 2020
On a scale from one to ten where one is the worst and ten is the best, I would rate RapidMiner as around a seven. I choose seven because of the UI things and other parts of the product that might be improved. RapidMiner is more of an enterprise product. Here, in this region, most people like a packaged solution like Alteryx which covers more. Alteryx is also more attractive to many users because it is cheaper and easier to use from the perspective of the user interface. With Alteryx or Tableau, for example, you can just pick up data sources and then start EDL (enterprise data lake). It takes more effort to bring the data on to the data mart for RapidMiner and other enterprise products in the traffic mining category. These enterprise solutions have an additional level of complexity and flexibility but not everyone even needs it.
Using RapidMiner is a two-stage process. At first, it's something simple whereby you can get quick results. This is done by clicking to get the mean standard deviations or the numerical variables, for example. You can get a bar chart and a frequency count of all of the categorical variables. I would suggest that you get somebody to do that, just to get used to it, but then stop them and make sure that they do a course on machine learning. Otherwise, they may be missing something important like cleaning up the data. For example, I did one project many years ago whereby I was asked by the Department of Education in the UK to look at the survey data on primary school children. It had been done by a market research company and they were a bit uneasy about the results. They didn't know what was wrong with the results, but they felt they weren't right, so they asked me to look at it. The first thing I did was to take a simple look at the values of all the variables and the first thing that became clear was that on the bar chart of variables, the right-hand end shot up. This was the value 99, which was clearly a missing value. Now it was a missing value, but in SPSS, which is what they use to finalize it, they had not declared it to be a missing value, so it found a child whose age is apparently 99 and they treated that child as being age 99. I found that out very easily by working out the arithmetic mean age of these primary school children, who should be under the age of 10, and their average age was 34.4. That came up merely because they hadn't specified a missing value. Now that's a very simple example, but it's the sort of thing that can go wrong when people just use a package and they don't know what the underlying assumptions are. Or people produce a linear regression when the relationship is nowhere linear. I recently refused to referee an article, for example, from China, because they did linear regression on data which clearly were not linear. They were exponential in format. All this to say that this is the two-stage process. You can get started very quickly, but you must then make sure that your staff is properly trained not to make these kinds of mistakes. The beginning learning curve is very shallow, but when you want to go on and do really advanced things then it takes more time. Companies know this, so they try to find cheap solutions such as employing sociology graduates to use the software. They don't understand the issues the same way a computer science or mathematics graduate would. With respect to functionality, at the moment it has more features than I need and can handle. I would rate this solution as nine out of ten.
We're in the banking and finance space, so mostly our clients use the on-premises deployment model. As part of compliance, it's required that data should not go out of the bank's boundaries or firewall. This solution is a great tool for users that are experimenting and is an alternative to doing the coding and everything themselves. It's perfect for those who want to focus more on data analysis rather than spending days coding everything. Users can go pretty far because of the solution's Auto ML capability which cuts down on coding. It allows for great productivity. I'd rate the solution eight out of ten.
The tools have a complete function for doing data. I'm not quite sure about the speed of RapidMiner but I think it's the fastest solution that I use. I don't think the product consumes a lot of RAM, which is good. There is something confusing in the product but it's possible that the error is mine and maybe I'm not yet familiar enough with the product. I would therefore rate this product a nine out of 10.
I have not worked with all of the features in RapidMiner. For example, I have not worked with all of the features for Big Data, and I have not used it with the cloud. I would rate this solution an eight out of ten.
RapidMiner's unified data science platform accelerates the building of complete analytical workflows - from data prep to machine learning to model validation to deployment - in a single environment, improving efficiency and shortening the time to value for data science projects.
RapidMiner supported my data preparation tasks by helping me identify and treat noise, handle duplicates, and analyze variables efficiently. These steps are crucial for any data preparation process, regardless of the tool used. The most impactful features for data modeling tasks in RapidMiner include its intuitive interface for creating relationships between datasets, similar to Power BI's capabilities but with a more intuitive approach. This allows for easier and more efficient data modeling, enhancing the overall workflow experience. Overall, I would rate RapidMiner as an eight out of ten.
Overall, I would rate the solution a seven out of ten. I would like to see more integration capabilities.
As long as RapinMiner helps users interpret data and use it, there is no problem using it. Overall, I would rate the solution an eight out of ten. If the solution could improve its user-interface, that would make it a ten.
I'm a student and use the solution on-premises for education purposes. I'd advise other users to look at other options and compare the findings. It's very easy-to-use, intuitive data mining software. I'd rate it nine out of ten.
For someone who does not want to code this is the best solution and I would recommend it. I rate RapidMiner a nine out of ten.
I rate RapidMiner nine out of 10. If you're planning to get into machine learning projects, my advice is to start as fast as you can. Many of our clients waste too much time thinking about use cases. We advise our clients to start doing some modeling and not wait for the perfect data because the clients usually have some bottlenecks. They don't have complete and accurate data, so they think they can't have predictive modeling projects in place. Our advice is to hit the ground running and start retrieving some results.
I rate RapidMiner 10 out of 10. It's helpful if you want to make informed decisions using data. We can take the information, tease out the attributes, and label everything. It's suitable for profiling and forecasting in any industry.
We are using the latest version of the solution right now. In general, we've been happy with the solution. I'd rate it at a nine out of ten.
I have worked with RapidMiner, but I have not yet explored all of the functionality of the software. As an example, the relation with big data and the relation with the Cloud. I have used the utilities quite a bit. In the last model, they added automation cleaning for data preparation. It is very interesting. I am a computer scientist and I received my Ph.D. 23 years ago. I am a researcher, and when I have a problem, I use it to research and to find a solution to much more difficult problems. I would rate this solution an eight out of ten.
On a scale from one to ten where one is the worst and ten is the best, I would rate RapidMiner as around a seven. I choose seven because of the UI things and other parts of the product that might be improved. RapidMiner is more of an enterprise product. Here, in this region, most people like a packaged solution like Alteryx which covers more. Alteryx is also more attractive to many users because it is cheaper and easier to use from the perspective of the user interface. With Alteryx or Tableau, for example, you can just pick up data sources and then start EDL (enterprise data lake). It takes more effort to bring the data on to the data mart for RapidMiner and other enterprise products in the traffic mining category. These enterprise solutions have an additional level of complexity and flexibility but not everyone even needs it.
Using RapidMiner is a two-stage process. At first, it's something simple whereby you can get quick results. This is done by clicking to get the mean standard deviations or the numerical variables, for example. You can get a bar chart and a frequency count of all of the categorical variables. I would suggest that you get somebody to do that, just to get used to it, but then stop them and make sure that they do a course on machine learning. Otherwise, they may be missing something important like cleaning up the data. For example, I did one project many years ago whereby I was asked by the Department of Education in the UK to look at the survey data on primary school children. It had been done by a market research company and they were a bit uneasy about the results. They didn't know what was wrong with the results, but they felt they weren't right, so they asked me to look at it. The first thing I did was to take a simple look at the values of all the variables and the first thing that became clear was that on the bar chart of variables, the right-hand end shot up. This was the value 99, which was clearly a missing value. Now it was a missing value, but in SPSS, which is what they use to finalize it, they had not declared it to be a missing value, so it found a child whose age is apparently 99 and they treated that child as being age 99. I found that out very easily by working out the arithmetic mean age of these primary school children, who should be under the age of 10, and their average age was 34.4. That came up merely because they hadn't specified a missing value. Now that's a very simple example, but it's the sort of thing that can go wrong when people just use a package and they don't know what the underlying assumptions are. Or people produce a linear regression when the relationship is nowhere linear. I recently refused to referee an article, for example, from China, because they did linear regression on data which clearly were not linear. They were exponential in format. All this to say that this is the two-stage process. You can get started very quickly, but you must then make sure that your staff is properly trained not to make these kinds of mistakes. The beginning learning curve is very shallow, but when you want to go on and do really advanced things then it takes more time. Companies know this, so they try to find cheap solutions such as employing sociology graduates to use the software. They don't understand the issues the same way a computer science or mathematics graduate would. With respect to functionality, at the moment it has more features than I need and can handle. I would rate this solution as nine out of ten.
We're in the banking and finance space, so mostly our clients use the on-premises deployment model. As part of compliance, it's required that data should not go out of the bank's boundaries or firewall. This solution is a great tool for users that are experimenting and is an alternative to doing the coding and everything themselves. It's perfect for those who want to focus more on data analysis rather than spending days coding everything. Users can go pretty far because of the solution's Auto ML capability which cuts down on coding. It allows for great productivity. I'd rate the solution eight out of ten.
The tools have a complete function for doing data. I'm not quite sure about the speed of RapidMiner but I think it's the fastest solution that I use. I don't think the product consumes a lot of RAM, which is good. There is something confusing in the product but it's possible that the error is mine and maybe I'm not yet familiar enough with the product. I would therefore rate this product a nine out of 10.
I would rate this solution a nine out of ten.
I have not worked with all of the features in RapidMiner. For example, I have not worked with all of the features for Big Data, and I have not used it with the cloud. I would rate this solution an eight out of ten.
This is a solution that I recommend. I would rate this solution an eight out of ten.