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."All of the data analytics features in SAS Analytics are valuable to us since we're using them daily across our entire analytics team."
"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."
"I use SAS daily to analyze data, produce reports, and other outputs."
"They have provided virtually everything we have needed to accomplish our task, as well as continuously improving our accuracy."
"SAS Business Intelligence is well-suited for our large corporation. We have demand for scalable and reliable insights into information which is housed in our large systems."
"It's very easy to use once you learn it."
"The most valuable feature is the ability to handle large data sets."
"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 a stable product."
"In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low."
"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."
"It doesn’t cost anything to use the product."
"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 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."
"The installation could also be easier, and the price could be better."
"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."
"There is potential for enhancement, particularly in the virtualized dashboard's capability to generate reports."
"I would like to see their interface to R added to either Base SAS or SAS Analytics."
"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."
"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."
"Weka could be more stable."
"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 is a little complicated and not necessarily suited for users who aren't skilled and experienced in data science."
"Not particularly user friendly."
"While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results."
"In terms of scalability, I think Weka is not prepared to handle a large number of users."
"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."
"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 SAS Enterprise Miner. See our SAS Analytics vs. Weka report.
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