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."IBM SPSS Statistics depends on AI."
"SPSS is quite robust and quicker in terms of providing you the output."
"You can find a complete algorithm in the solution and use it. You don't need to write your own algorithms for predictive analytics. That's the most valuable feature and the main one we use."
"Most of the product features are good but I particularly like the linear regression analysis."
"in terms of the simplicity, I think the SPSS basic can handle it."
"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."
"The most valuable features mainly include factor analysis, correlation analysis, and geographic analysis."
"The best part is that they have an algorithm handbook, so you can open it up and understand how it works, and if it is useful, this is very important."
"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."
"Weka's best features are its user-friendly graphic interface interpretation of data sets and the ease of analyzing data."
"In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low."
"It is a stable product."
"Working with complicated algorithms in huge datasets is really easy in Weka."
"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."
"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."
"I'd like to see them use more artificial intelligence. It should be smart enough to do predictions and everything based on what you input."
"The statistics should be more self-explanatory with detailed automated reports."
"The solution could improve by providing a visual network for predictions and a self-organizing map for clustering."
"In developing countries, it would be beneficial to provide certain features to users at no cost initially, while also customizing pricing options."
"The solution needs to improve forecasting using time series analysis."
"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."
"The solution needs more planning tools and capabilities."
"It could provide even more in the way of automation as there are many opportunities."
"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."
"In terms of scalability, I think Weka is not prepared to handle a large number of users."
"Not particularly user friendly."
"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."
"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."
"While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results."
"If there are a lot more lines of code, then we should use another language."
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, SAS Analytics and Splunk User Behavior Analytics. See our IBM SPSS Statistics vs. Weka report.
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