We performed a comparison between SAS Analytics and Weka based on real PeerSpot user reviews.
Find out in this report how the two Data Mining solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."I use SAS daily to analyze data, produce reports, and other outputs."
"The team immediately resolves the issues."
"It has improved the level of efficacy and validity of our reports."
"The technical support is okay."
"It's very easy to use once you learn it."
"All of the data analytics features in SAS Analytics are valuable to us since we're using them daily across our entire analytics team."
"SAS Analytics plays a vital role in enhancing our decision-making processes, particularly in areas such as customer segmentation and operational efficiency."
"I use it to replicate our entire financial system to verify/duplicate calculations."
"I mainly use this solution for the regression tree, and for its association rules. I run these two methodologies for Weka."
"It doesn’t cost anything to use the product."
"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."
"In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low."
"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."
"The interface is very good, and the algorithms are the very best."
"The path of machine learning in classification and clustering is useful. The GUI can get you results. No programming is needed. No need to write down your script first or send to your model or input your data."
"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."
"Once a SAS figure is produced one would like to modify things, such as titles, legends, and incorporate risk sets as a footer on the plots."
"The graphing and visualization features could be enhanced, in my opinion. I would especially stress improving the visualization capabilities."
"The installation could also be easier, and the price could be better."
"One of the things that can be simplified is self-service analytics, especially for a citizen developer or a citizen data scientist."
"The training for SAS Business Intelligence is often difficult to arrange. It is often cancelled due to not enough people being enrolled."
"I would like to see their interface to R added to either Base SAS or SAS Analytics."
"There is potential for enhancement, particularly in the virtualized dashboard's capability to generate reports."
"The natural language querying and automated preparation of dashboards should be improved."
"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."
"While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results."
"The filter section lacks some specific transformation tools. If you want to change a variable from a numeric variable to a categorical variable, you don't have a feature that can enable you to change a variable from a numeric variable to a categorical variable."
"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."
"Weka could be more stable."
"I believe is there are a few newer algorithms that are not present in the Weka libraries. Whereas, for example, if I want to have a solution that involves deep learning, so I don't think that Weka has that capability. So in that case I have to use Python for ... predict any algorithms based on deep learning."
"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."
SAS Analytics is ranked 5th in Data Mining with 11 reviews while Weka is ranked 2nd in Data Mining with 14 reviews. SAS Analytics is rated 9.0, while Weka is rated 7.6. The top reviewer of SAS Analytics writes "Provides comprehensive data analysis tools and functionalities, but its higher pricing and potential stability issues may present drawbacks". On the other hand, the top reviewer of Weka writes "Open source, good for basic data mining use cases except for the visualization results". SAS Analytics is most compared with KNIME, IBM SPSS Statistics, SAS Enterprise Miner and IBM SPSS Modeler, whereas Weka is most compared with KNIME, IBM SPSS Statistics, IBM SPSS Modeler, Oracle Advanced Analytics and SAS Enterprise Miner. See our SAS Analytics vs. Weka report.
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