I use the free version of Weka, and I do not know the cost of the professional version or the lite version. I can recommend Weka to other users. I give this review a rating of 10.
Treasury Management in Finance Department at National University of Pakistan
Real User
Dec 27, 2023
I recommend it. It's been around in the industry for a while, and I consider it one of the best, except for the visualization issue. It does not have good user-friendly graphics. It does not offer good visualization or presentation. Otherwise, it's very viable. Overall, I would rate the solution a seven out of ten.
In a new machine learning job, if the method is a bit foreign to me, if I have to do it in R, it could be a tedious task. First, I need to identify the libraries required for the new methodology. This can involve identifying two, three, or even four libraries. Then, I need to read their manuals thoroughly. This is time-consuming. In Weka, as all machine learning tools are on my desktop, I easily find out the method. As a freelancer, people send me datasets, and I work on the statistics at home before providing the solution. When a solution needs to be implemented on a server, server programmers install it on the server. This is similar to Power BI, where I prepare files on my desktop, and someone else uploads them to the server for others to access. I think I cannot send a Weka solution to a server programmer. In Weka, anyone can run the program without being a programmer, which is a good feature since the entry cost is very low.
Student at a university with 1,001-5,000 employees
Real User
Jul 19, 2022
Users are often students and data scientists mostly. Most enterprises and maybe small business owners might use this product. People should check it out. Depending on what they want to achieve with it and what they need, it might be a solution to look into. I’d rate the solution a seven out of ten. It's free, and it's easy to use. You don't need to teach yourself, and video guides help you. There are books. There are literally online e-books on machine learning using Weka. It’s pretty straightforward.
Weka is good to start in data mining. The base had to be clear with base concepts about the models or algorithms you are going to use. You want to test or do some research first. But for production, it's not the best option. It would be a good tool for prototyping. Knime is the best tool for data mining. Weka is good for structured table data. You can use many supervised or unsupervised algorithms, but it's very difficult to get interpretable results about the multilayer option it has. It's not so easy to understand the neural networks if you work with Weka. It would be better to work with unsupervised algorithms like tree-based or clustering algorithms, but not for neural networks. There are other tools that can be more useful. I would rate Weka a seven out of ten.
The basic configuration is very easy. Compared to writing code in Jupyter Notebook, it's really easy to handle and work with very complicated algorithms in Weka. There are some steps that are not very simple, but overall, it's very easy. It's easy to load data and implement different algorithms with Weka. From my experiences so far, that's the basic advantage with Weka — it's easy to use, easy to handle, and once you learn it, it's not that hard to work with. Working with complicated algorithms in huge datasets is really easy in Weka. Training datasets is equally easy and it's quite speedy as well — the same goes implementation-wise. Without writing immeasurable amounts of code, we can quickly perform machine learning using Weka. That's the main advantage of Weka. Overall, on a scale from one to ten, I would give Weka a rating of six. If they improved the visualization issues, the documentation issues, and the implementation capabilities, I would give them a higher rating. According to my knowledge, there are not any boundaries when it comes to machine learning. The possibilities are endless, it's really big. It would be really helpful if pre-process data sets were used in machine learning as well — If more data visualization options and pre-processing options were supported. That's something very basic that we need when doing machine learning. If that could be improved, that would be really great. And if more documentation was available, again, that would be great. You can find specific knowledge on YouTube, but you can't go much further than that because the resources are just not available. These are the reasons why I am giving it a six. With Python and R, you can do anything — you have that confidence, but with Weka, I don't have that confidence.
My main recommendation is that if you want artificial intelligence, or machine learning, go for an easy and quick tool like Weka, otherwise, any language will have a more expensive entry cost. I would rate this solution an eight out of 10.
The solution is a desktop application. I did not deploy it on the cloud, actually. It's an application that is on my desktop, on my laptop. If they want their task done faster, and they do not have enough coding expertise, this is definitely an excellent solution to choose from. If they want additional experience because Python and R might be a good option. With Weka, it looks like you're using maybe something like a Microsoft power BI. With Python or R you're actually giving a data scientist a run for his money as things change every day and things evolve and you have to dig deeper, you have to provide new stuff. Overall, I'd rate the solution nine out of ten. It's tied with R in terms of how I would rate it. However, I find Python the best.
Weka is a very simple tool and it has built-in algorithms, which we need do not to implement. It gives concise results that we can display to our clients. Weka is also a very useful tool for filtering. There are a set of built-in filters that we can use to filter our data. If you want to take a sample set of data, suppose a specific percentage of data, or if we want to convert a specific data type to another data type, Weka has good filtering features. We can also use cluster and association rules in Weka. These are the advantages of Weka. If I compared this with R and Python, both can do things better than Weka. There is no doubt. But it is not easy to implement algorithms in R and Python, you need at least 20 lines of code and you need a specific setup. You need a specific setup. You need to import the data set. You need to use a different kind of package. With respect to Weka, those are a bit complex. Weka cannot use its visualization power. I would rate Weka a six out of ten. The visualization and statistical analysis need improvement.
Solution Architect / Data Scientist (upwork) at Freelancer
Real User
Nov 10, 2020
Weka is pretty comprehensive and easy to use. This is the first time that I used machine learning. I have a master's in technology. I analyze small data to get insights into algorithms. I learned a lot from all the files, then I implemented those into a Dell program. It has many features that are not available and there is not much development since it is open source. It should be developed faster. I would rate Weka a six out of ten for these reasons.
Weka provides a user-friendly platform for data processing and classification with a no-code interface, visual tools, and diverse algorithms. Its robust GUI supports seamless enterprise data integration and efficient performance on large datasets.Weka is known for its simplicity and comprehensive algorithm selection, making it a popular choice for data exploration, processing, clustering, and mining. The platform is user-friendly and caters to both beginners and advanced users, supporting...
I use the free version of Weka, and I do not know the cost of the professional version or the lite version. I can recommend Weka to other users. I give this review a rating of 10.
I recommend it. It's been around in the industry for a while, and I consider it one of the best, except for the visualization issue. It does not have good user-friendly graphics. It does not offer good visualization or presentation. Otherwise, it's very viable. Overall, I would rate the solution a seven out of ten.
Weka is a good product. Overall, I would rate Weka a nine out of ten.
In a new machine learning job, if the method is a bit foreign to me, if I have to do it in R, it could be a tedious task. First, I need to identify the libraries required for the new methodology. This can involve identifying two, three, or even four libraries. Then, I need to read their manuals thoroughly. This is time-consuming. In Weka, as all machine learning tools are on my desktop, I easily find out the method. As a freelancer, people send me datasets, and I work on the statistics at home before providing the solution. When a solution needs to be implemented on a server, server programmers install it on the server. This is similar to Power BI, where I prepare files on my desktop, and someone else uploads them to the server for others to access. I think I cannot send a Weka solution to a server programmer. In Weka, anyone can run the program without being a programmer, which is a good feature since the entry cost is very low.
I would recommend this solution to those who do not understand coding very well. I rate Weka a nine out of ten.
I would rate Weka eight out of ten.
Users are often students and data scientists mostly. Most enterprises and maybe small business owners might use this product. People should check it out. Depending on what they want to achieve with it and what they need, it might be a solution to look into. I’d rate the solution a seven out of ten. It's free, and it's easy to use. You don't need to teach yourself, and video guides help you. There are books. There are literally online e-books on machine learning using Weka. It’s pretty straightforward.
Weka is good to start in data mining. The base had to be clear with base concepts about the models or algorithms you are going to use. You want to test or do some research first. But for production, it's not the best option. It would be a good tool for prototyping. Knime is the best tool for data mining. Weka is good for structured table data. You can use many supervised or unsupervised algorithms, but it's very difficult to get interpretable results about the multilayer option it has. It's not so easy to understand the neural networks if you work with Weka. It would be better to work with unsupervised algorithms like tree-based or clustering algorithms, but not for neural networks. There are other tools that can be more useful. I would rate Weka a seven out of ten.
I would give Weka a nine out of ten.
The basic configuration is very easy. Compared to writing code in Jupyter Notebook, it's really easy to handle and work with very complicated algorithms in Weka. There are some steps that are not very simple, but overall, it's very easy. It's easy to load data and implement different algorithms with Weka. From my experiences so far, that's the basic advantage with Weka — it's easy to use, easy to handle, and once you learn it, it's not that hard to work with. Working with complicated algorithms in huge datasets is really easy in Weka. Training datasets is equally easy and it's quite speedy as well — the same goes implementation-wise. Without writing immeasurable amounts of code, we can quickly perform machine learning using Weka. That's the main advantage of Weka. Overall, on a scale from one to ten, I would give Weka a rating of six. If they improved the visualization issues, the documentation issues, and the implementation capabilities, I would give them a higher rating. According to my knowledge, there are not any boundaries when it comes to machine learning. The possibilities are endless, it's really big. It would be really helpful if pre-process data sets were used in machine learning as well — If more data visualization options and pre-processing options were supported. That's something very basic that we need when doing machine learning. If that could be improved, that would be really great. And if more documentation was available, again, that would be great. You can find specific knowledge on YouTube, but you can't go much further than that because the resources are just not available. These are the reasons why I am giving it a six. With Python and R, you can do anything — you have that confidence, but with Weka, I don't have that confidence.
My main recommendation is that if you want artificial intelligence, or machine learning, go for an easy and quick tool like Weka, otherwise, any language will have a more expensive entry cost. I would rate this solution an eight out of 10.
The solution is a desktop application. I did not deploy it on the cloud, actually. It's an application that is on my desktop, on my laptop. If they want their task done faster, and they do not have enough coding expertise, this is definitely an excellent solution to choose from. If they want additional experience because Python and R might be a good option. With Weka, it looks like you're using maybe something like a Microsoft power BI. With Python or R you're actually giving a data scientist a run for his money as things change every day and things evolve and you have to dig deeper, you have to provide new stuff. Overall, I'd rate the solution nine out of ten. It's tied with R in terms of how I would rate it. However, I find Python the best.
Weka is a very simple tool and it has built-in algorithms, which we need do not to implement. It gives concise results that we can display to our clients. Weka is also a very useful tool for filtering. There are a set of built-in filters that we can use to filter our data. If you want to take a sample set of data, suppose a specific percentage of data, or if we want to convert a specific data type to another data type, Weka has good filtering features. We can also use cluster and association rules in Weka. These are the advantages of Weka. If I compared this with R and Python, both can do things better than Weka. There is no doubt. But it is not easy to implement algorithms in R and Python, you need at least 20 lines of code and you need a specific setup. You need a specific setup. You need to import the data set. You need to use a different kind of package. With respect to Weka, those are a bit complex. Weka cannot use its visualization power. I would rate Weka a six out of ten. The visualization and statistical analysis need improvement.
Weka is pretty comprehensive and easy to use. This is the first time that I used machine learning. I have a master's in technology. I analyze small data to get insights into algorithms. I learned a lot from all the files, then I implemented those into a Dell program. It has many features that are not available and there is not much development since it is open source. It should be developed faster. I would rate Weka a six out of ten for these reasons.