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."It has improved the level of efficacy and validity of our reports."
"It has also been around for an extremely long time, has a strong history, and good market penetration."
"The technical support is okay."
"They have provided virtually everything we have needed to accomplish our task, as well as continuously improving our accuracy."
"It's very easy to use once you learn it."
"Modeling ones and figures, such as PROC LIFETEST, PROC LOGISTICS, PROC GPLOT. PROC FREQ and PROC MEANS, are also among the valuable features."
"It has facilitated timely analysis results with quality work and meaningful output."
"The team immediately resolves the issues."
"I mainly use this solution for the regression tree, and for its association rules. I run these two methodologies for Weka."
"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."
"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."
"The interface is very good, and the algorithms are the very best."
"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."
"In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low."
"Weka's best features are its user-friendly graphic interface interpretation of data sets and the ease of analyzing data."
"It is a stable product."
"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 training for SAS Business Intelligence is often difficult to arrange. It is often cancelled due to not enough people being enrolled."
"There is potential for enhancement, particularly in the virtualized dashboard's capability to generate reports."
"One of the things that can be simplified is self-service analytics, especially for a citizen developer or a citizen data scientist."
"I would like to see their interface to R added to either Base SAS or SAS Analytics."
"The natural language querying and automated preparation of dashboards should be improved."
"They could enhance the AI capabilities of the product."
"If there are a lot more lines of code, then we should use another language."
"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."
"Within the basic Weka tool, I don't see many tools that are available where we can analyze and visualize the data that well."
"While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results."
"Not particularly user friendly."
"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."
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 Splunk User Behavior Analytics. See our SAS Analytics vs. Weka report.
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