Compare SAS Analytics vs. Weka

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1,768 views|1,419 comparisons
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Read 7 Weka reviews.
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Most Helpful Review
Find out what your peers are saying about SAS Analytics vs. Weka and other solutions. Updated: May 2021.
510,204 professionals have used our research since 2012.
Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros
"The most valuable feature is the ability to handle large data sets.""The technical support is okay.""It's very easy to use once you learn it."

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"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.""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.""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.""Working with complicated algorithms in huge datasets is really easy in 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.""I mainly use this solution for the regression tree, and for its association rules. I run these two methodologies for Weka.""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."

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Cons
"This solution should be made more user-friendly.""One of the things that can be simplified is self-service analytics, especially for a citizen developer or a citizen data scientist.""The installation could also be easier, and the price could be better."

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"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 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.""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.""Within the basic Weka tool, I don't see many tools that are available where we can analyze and visualize the data that well.""If there are a lot more lines of code, then we should use another language.""Not particularly user friendly.""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."

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Pricing and Cost Advice
"I think that the cost-benefit ratio is okay.""SAS is very expensive."

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

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Questions from the Community
Top Answer: It's very easy to use once you learn it.
Top Answer: They should provide technical support quickly and for free. I would also like them to offer the integration of various Base SAS modules into one like SAS Studio to make it more cost effective for… more »
Top Answer: We use SAS Analytics for all data analysis, quick exploratory data analysis, statistical analysis, predictive modeling, and rapid data management in risk analytics. Predominantly, we're using risk… more »
Top Answer: 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… more »
Top Answer: I like how the classification and prediction work. We should use Weka because the path is very big and much better. If there are a lot more lines of code, then we should use another language.
Top Answer: 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. In this case, it would… more »
Ranking
6th
out of 16 in Data Mining
Views
1,768
Comparisons
1,419
Reviews
3
Average Words per Review
354
Rating
9.0
5th
out of 16 in Data Mining
Views
2,970
Comparisons
2,489
Reviews
7
Average Words per Review
1,001
Rating
7.3
Popular Comparisons
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Weka
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Overview
SAS was founded in 1976 and actually began as a project at North Carolina State University to analyze agriculture research. It has since become a global company that is recognized for its innovation in data analytics and business intelligence. SAS is redefining what's possible with data analytics through greater efficiency, strong information value chains, effective collaboration tools, and state-of-the-art visualization software. SAS Analytics is designed for use in a variety of industries including government, manufacturing, higher education, defense & security, banking, automotive, communications, and much more. SAS Analytics is a business intelligence (BI) solution that has the ability to reveal patterns and anomalies in data, identify relationships and different variables, and predict future outcomes. Users of SAS Analytics will benefit from making more sound, better informed business decisions based on company data and market trends. Data mining, data visualization, text analytics, forecasting, statistical analysis, and more are all available through SAS Analytics. Staples, which boasts $27 billion in sales across the globe, has a business philosophy that prioritizes customer loyalty and satisfaction. In order to better engage their customers, Staples utilizes SAS Analytics to plan finely tuned marketing campaigns. Through forecasting and advanced analytics, Staples has been able to rely on fewer contractors, and cut their marketing budget, while improving their customer retention rate.
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
Offer
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Sample Customers
Aegon, Alberta Parks, Amway China, Axel Springer, Bank of America, Belgium Special Tax, CAP Index, CareSource, CBE Group, Cemig, Center for Responsible Lending, CESCE, Ceska sporitelna, Chantecler, Chico's, Chubb Group of Insurance Companies, CIGNA Thailand, City of Wiesbaden, Germany, Confused.com, Creditreform, Des Moines Area Community College, Deutsche Lufthansa, Directorate of Economics and Statistics, DIRECTV, Dow Chemical Company, Dow Chemical Company, Dun & Bradstreet, EDF Energy, Electrabel GDF SUEZ, ERGO Insurance Group, Erste Bank Croatia, Farmers Mutual Group, Finnair, Florida Department of Corrections, Geneia, Generali Hellas, Genting Malaysia Berhad, Grameenphone, Grandi Salumifici Italiani, HealthPartners, Highmark, Hong Kong Efficiency Unit, HP, Hyundai Securities, Illinois Department of Healthcare and Family ServicesInc Research, ING-DiBa, Institut Pertanian Bogor, InterContinental Hotels Group (IHG), IOM, Kelley Blue Book, Lenovo, Lillebaelt Hospital, Los Angeles County, Maspex Wadowice Group, National Bank of Greece, New Zealand Ministry of Health, New Zealand Ministry of Social Development, Nippon Paper, NMIMS, North Carolina Department of Transportation, North Carolina Office of Information Technology Services, Northern Virginia Electric Cooperative (NOVEC), Oberweis Dairy, ODEC, Ohio Mutual Insurance Group, Oklahoma State University, OneBeacon, Orange Business Services, Orange County Child Support Services, Organic, Orlando Magic, OTP Bank, Plano Independent School District, Project Odyssey, Royal Society for the Protection of Birds, RSA Canada, SCAD, Scotiabank, Singapore National Library Board, Sobeys Inc., SRA International, Staples, Statistics Estonia, Swisscom, SymphonyIRI Group, Telecom Italia, Telef‹nica O2, Town of Cary, Transitions Optical, TrueCar, Turkcell Superonline, UniCredit Bank Serbia, University of Alabama, University of Missouri, USDA National Agricultural Statistics Service
Information Not Available
Top Industries
REVIEWERS
Healthcare Company29%
Financial Services Firm29%
Insurance Company14%
Retailer14%
VISITORS READING REVIEWS
Comms Service Provider19%
Computer Software Company18%
Financial Services Firm10%
Educational Organization9%
VISITORS READING REVIEWS
Comms Service Provider34%
Educational Organization13%
Computer Software Company11%
Financial Services Firm7%
Company Size
REVIEWERS
Small Business10%
Midsize Enterprise10%
Large Enterprise80%
No Data Available
Find out what your peers are saying about SAS Analytics vs. Weka and other solutions. Updated: May 2021.
510,204 professionals have used our research since 2012.

SAS Analytics is ranked 6th in Data Mining with 3 reviews while Weka is ranked 5th in Data Mining with 7 reviews. SAS Analytics is rated 9.0, while Weka is rated 7.2. The top reviewer of SAS Analytics writes "A user-friendly, easy coding analytics solution that is good for typical predictive analytics". On the other hand, the top reviewer of Weka writes "Relatively stable with excellent accuracy and there's no need to know coding". SAS Analytics is most compared with KNIME, IBM SPSS Modeler, Oracle Advanced Analytics, IBM Watson Explorer and SAS Enterprise Miner, whereas Weka is most compared with KNIME, IBM SPSS Statistics, IBM SPSS Modeler, SAS Enterprise Miner and ELK Elasticsearch. See our SAS Analytics vs. Weka report.

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We monitor all Data Mining reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.