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.

Every method of machine learning is available on Weka. They may not be the ultimate solution, as R has many solutions since many university scholars use R to improve knowledge. 

In Weka, every method is at your fingertips. In a language, the method must first be installed.

We discovered that it could be used well with labs, thanks to the attributes and instances.

I like the fact that it doesn't require programming skills.

They have YouTube videos you can watch and learn and stuff. It’s pretty straightforward. I know that it's user-friendly.

It doesn’t cost anything to use the product.

There are some reviews a few people said they had used it, and they said it was good. With the little that I have done, I’ve noted that it has a good interface. There's data visualization. You can filter and create different scenarios and instances, add missing data, et cetera.

Weka eliminates the need for coding, allowing you to easily set parameters and complete the majority of the machine learning task with just a few clicks.

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.

The features that I found most valuable are the classification features. They have a lot of information and a lot of intel. With classification, there's always a chance to split the data into two datasets. You can split one metadata into 92 datasets during that train or test, and the performance can easily be identified after you've trained a model. 

With clustering, if it's a yes, it's a yes, if it's a no, it's a no. It gives you a 100% level of accuracy of a model that has been trained, and that is in most cases, usually misleading. Classification is highly valuable when done as opposed to clustering.

It is quicker than using languages like R and Python. Wizards make the job more "comfortable" than syntax in languages.

Performance is one of the most valuable features. I can plug in any machine learning algorithm and it works perfectly. It is very easy to use the filter. Weka has a good number of filter options. If I compare it with other platforms, I can just use a filter with Weka and apply it. If I want to just convert the numeric data into categorical data, is only a one minute job. We just use a filter and it works. In other platforms, I need to write at least four or five lines of code and I have to check the data.

I'm very comfortable with Weka, for these kinds of things, especially their filter and their Classification Algorithm. They have a good number of algorithms. If I want to do this kind of thing with Python, it will take 20 lines of code minimum.

The path of machine learning in classification and clustering is useful. The GUI can get you results. No programming is needed. There is no need to write down your script first or send to your model or input your data. 

Weka classification is very valuable, it gives results, like prediction results. Weka is a little bit better than other tools I have expertise on. Weka is just much better for the classification path and clustering path. 

If you are going with some predictions that a procedure recalls, it's better than any other tool like R Programming and Python. In machine learning, like deep learning, if the network works, I can run it with the console buttons. 

I like the machine algorithm for clustering systems. Weka has larger capabilities. There are multiple algorithms that can be used for clustering. It depends upon the user requirements. For clustering, I've used DBSCAN, whereas, for supervised learning, I've used AVM and RFT. 

Weka is useful for analyzing any data set you want to analyze or if you want to run algorithms of small data sets. When it comes to the enterprise solution, you can use Weka libraries or at least this algorithm that is very available in the Weka libraries. In Java, I can manipulate all these algorithms and the libraries of Weka to produce the desired result for a customer.

There are many options where you can fill all of the data pre-processing options that you can implement when you're importing the data. You can also normalize the data and standardize it in an easier way. You have to do it in Python and you have to write some lines but in this case, it's in data pre-processing. For the model evaluation, you can build models for classification. 

Weka's best features are its user-friendly graphic interface interpretation of data sets and the ease of analyzing data.

Weka is a very easy to use Data Mining solution. It is great for learning and for doing small experiments before exploring the data deeper. Another important feature is the number and diversity of algorithms that make Weka an excellent solution for rapid testing. The several interfaces that are provided also allow a diverse range of applications and uses. Another important aspect is the ability to easily integrate new algorithms to the solution, and also it’s integration in terms of Java code.