In my university, we used Weka. Weka was used in marketing by my professor, I was preparing a presentation for PhD proposals specifically on energy consumption and renewable resource utilization. So, I needed data mining tools.
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Best MLOps provider
Open source, good for basic data mining use cases except for the visualization results
What is our primary use case?
What is most valuable?
Weka's is fine for basic results, no big issues there.
But if you need visualizations, it's not the best choice. For someone who just needs analysis, Weka serves the purpose.
But nowadays, good visuals are crucial for presenting findings and telling a story. In my practical experience at the university, when people need quick results with strong visualizations, I don't recommend Weka.
What needs improvement?
I haven't found it particularly useful. It lacks state-of-the-art algorithms and impressive outcomes. While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results.
Moreover, a new user interface would be great, especially for beginners. Something that guides them through the available tools and helps them achieve their goals. I haven't seen anything like that myself, though maybe it's there and I missed it.
For how long have I used the solution?
I worked with Weka for about a year and a half.
What do I think about the stability of the solution?
It is a stable product.
What do I think about the scalability of the solution?
It is very easy to scale. I would rate the scalability a seven out of ten.
Which solution did I use previously and why did I switch?
My primary interest lies in energy analytics, optimization, and related fields.
Unfortunately, I couldn't complete the project due to work commitments, but I realized KNIME would have been far better suited for such analysis. For data mining, I had two main options: Python libraries and KNIME. KNIME is a bit expensive, requiring membership, so I opted for Python's ease of use.
Python libraries offer free access and flexibility for various data mining tasks. Python is better than some of the state-of-the-art software.
How was the initial setup?
Weka's ease of use is around seven out of ten. Anyone can pick it up. But the results could be improved, especially visualizations.
We used it offline, directly on the software.
The deployment is in between. Not super fast, but I haven't used other software for comparison.
Python libraries seem quicker at giving results, but Weka's not terribly slow for backups. It took a good three hours, but not excessively long.
What's my experience with pricing, setup cost, and licensing?
Weka is free and open-source software. That is why I used it over KNIME.
What other advice do I have?
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.
Has a great interface, and good documentation and algorithms
What is most valuable?
The interface is very good, and the algorithms are the very best. Weka has a lot of algorithms and beautiful documentation for beginner learning. It is very good for my students. My students use Weka to learn data mining.
What needs improvement?
Weka could be more stable.
For how long have I used the solution?
I have been using Weka for about five years.
What do I think about the scalability of the solution?
Weka is scalable. About 50 people use Weka in my organization.
How was the initial setup?
The initial setup is very easy.
The solution is deployed on the cloud for the local computer.
What's my experience with pricing, setup cost, and licensing?
We use the free version now. My faculty is very small.
What other advice do I have?
Weka is a good product.
Overall, I would rate Weka a nine out of ten.