We performed a comparison between IBM SPSS Statistics 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 offers very good visualization."
"in terms of the simplicity, I think the SPSS basic can handle it."
"The features that I have found most valuable are the Bayesian statistics and descriptive statistics."
"The SPSS interface is very accessible and user-friendly. It's really easy to get information in it. I've shared it with experts and beginners, and everyone can navigate it."
"Custom tables and macros: They allow us to create useful reports quickly for a broad audience."
"The software offers consistency across multiple research projects helping us with predictive analytics capabilities."
"The learning curve to using this product is not steep. The program is appropriate for those who do not have a lot of background in programming, yet have to perform basic statistical analysis."
"Some of the most valuable features that we are using with some business models are machine learning algorithms, statistical models given to us by the business, and getting data from the database or text files."
"It is a stable product."
"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."
"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."
"The interface is very good, and the algorithms are the very best."
"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."
"In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low."
"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."
"I mainly use this solution for the regression tree, and for its association rules. I run these two methodologies for Weka."
"The solution needs to improve forecasting using time series analysis."
"The solution needs more planning tools and capabilities."
"The solution could improve by providing a visual network for predictions and a self-organizing map for clustering."
"Improvements are needed in the user interface, particularly in terms of user-friendliness."
"If there is any self-generation data collection plan (DCP), it would be helpful in gathering data. It would also be useful if there is a function to scale it up to, let's say, UiPath and have it consolidate and integrate into a UiPath solution."
"Most of the package will give you the fixed value, or the p-value, without an explanation as to whether it it significant or not. Some beginners might need not just the results, but also some explanation for them."
"It could allow adding color to data models to make them easier to interpret."
"Better documentation on how to use macros."
"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."
"A few people said it became slow after a while."
"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."
"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."
"Within the basic Weka tool, I don't see many tools that are available where we can analyze and visualize the data that well."
IBM SPSS Statistics is ranked 3rd in Data Mining with 36 reviews while Weka is ranked 2nd in Data Mining with 14 reviews. IBM SPSS Statistics is rated 8.0, while Weka is rated 7.6. The top reviewer of IBM SPSS Statistics writes "Enhancing survey analysis that provides valued insightfulness". On the other hand, the top reviewer of Weka writes "Open source, good for basic data mining use cases except for the visualization results". IBM SPSS Statistics is most compared with Alteryx, TIBCO Statistica, Microsoft Azure Machine Learning Studio, IBM SPSS Modeler and Oracle Advanced Analytics, whereas Weka is most compared with KNIME, IBM SPSS Modeler, Oracle Advanced Analytics, Splunk User Behavior Analytics and SAS Analytics. See our IBM SPSS Statistics vs. Weka report.
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