We performed a comparison between SAS Analytics and Weka based on real PeerSpot user reviews.
Find out what your peers are saying about Knime, Weka, IBM and others in Data Mining."It has also been around for an extremely long time, has a strong history, and good market penetration."
"The most valuable feature is the ability to handle large data sets."
"Modeling ones and figures, such as PROC LIFETEST, PROC LOGISTICS, PROC GPLOT. PROC FREQ and PROC MEANS, are also among the valuable features."
"SAS Analytics plays a vital role in enhancing our decision-making processes, particularly in areas such as customer segmentation and operational efficiency."
"I use SAS daily to analyze data, produce reports, and other outputs."
"It's very easy to use once you learn it."
"It is able to connect to all major platforms, and all the smaller platforms that I have come across."
"I like that it is quickly embedding interactive reports and dashboards into a website, Outlook Mail, or even a mobile app."
"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."
"It doesn’t cost anything to use the product."
"It is a stable product."
"Weka's best features are its user-friendly graphic interface interpretation of data sets and the ease of analyzing data."
"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."
"Working with complicated algorithms in huge datasets is really easy in Weka."
"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."
"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."
"The training for SAS Business Intelligence is often difficult to arrange. It is often cancelled due to not enough people being enrolled."
"They could enhance the AI capabilities of the 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."
"This solution should be made more user-friendly."
"The graphing and visualization features could be enhanced, in my opinion. I would especially stress improving the visualization capabilities."
"Support at universities used to be limited, but I hear this is changing."
"While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results."
"Within the basic Weka tool, I don't see many tools that are available where we can analyze and visualize the data that well."
"In terms of scalability, I think Weka is not prepared to handle a large number of users."
"Not particularly user friendly."
"Weka is a little complicated and not necessarily suited for users who aren't skilled and experienced in data science."
"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."
"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. It's really very complicated."
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.
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