IBM Smart Analytics vs Weka comparison

"Log Analytics (LA) allows a user to see patterns of behavior and isolate issues quickly, without the need to manually access individual systems and parse logs manually."

"If they want their task done faster, and they do not have enough coding expertise, this is definitely an excellent solution to choose from."

"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."

"Working with complicated algorithms in huge datasets is really easy in Weka."

"It’s pretty straightforward, it's user-friendly, it’s free, and with YouTube videos, online guides, and e-books on machine learning using Weka, you can quickly learn to use its good interface for data visualization, filtering, and creating different scenarios and instances."

"Weka is a very nice tool, it needs very small requirements. If I want to implement something in Python, I need a lot of memory and space but Weka is very lightweight. Anyone can implement any kind of algorithm, and we can show the results immediately to the client using the one-page feature. The client always wants to know the story. They want the result."

"Weka is very easy to use, is very complete, and provides many benefits to the end user."

"Weka is a very nice tool and it helped me to solve any machine learning problem in one minute."

"It doesn’t cost anything to use the product."

"The indexing engine (proprietary build of LogStash) is well... very LogStash'ish... It requires more work to normalize the log feeds than competing products."

"The visualization of Weka is subpar and could improve. Machine learning and visualization do not work well together. For example, we want to know how we can we delete empty cells or how can we fill in the empty cells without cleaning the data system and putting it together."

"If you have one missing value in your dataset and this missing value belongs to a specific attribute and the attribute is a numeric attribute and there is only one missing data, whenever you import this data, the problem is that Weka cannot understand that this is a numeric field; it converts everything into a string, and there is no way to convert the string into numerical math."

"The product is good, but I would like it to work with big data. I know it has a Spark integration they could use to do analysis in clusters, but it's not so clear how to use it."

"If there are a lot more lines of code, then we should use another language."

"A few people said it became slow after a while."

"Scalability and performance are the main aspect of improvement in Weka, since it has the main Java limitations, regarding the JVM."

"With Python and R, you can do anything — you have that confidence, but with Weka, I don't have that confidence."

"While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results."

"Currently, I am using an open-source version so I don't know much about the price of this solution."

"As far as I know, Weka is a freeware tool, and I am not aware if they have an online solution or if it is a commercial product."

"The solution is free and open-source."

"We use the free version now. My faculty is very small."