Compare IBM SPSS Statistics vs. Weka

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IBM SPSS Statistics Logo
4,039 views|3,128 comparisons
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Read 7 Weka reviews.
2,970 views|2,489 comparisons
Most Helpful Review
Find out what your peers are saying about IBM SPSS Statistics vs. Weka and other solutions. Updated: May 2021.
511,607 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
"Most of the product features are good but I particularly like the linear regression 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.""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.""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.""They have many existing algorithms that we can use and use effectively to analyze and understand how to put our data to work to improve what we do.""It has the ability to easily change any variable in our research.""The most valuable feature is the user interface because you don't need to write code.""In terms of the features I've found most valuable, I'd say the duration, the correlation, and of course the nonparametric statistics. I use it for reliability and survival analysis, time series, regression models in different solutions, and different types of solutions."

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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
"I think the visualization and charting should be changed and made easier and more effective.""Technical support needs some improvement, as they do not respond as quickly as we would like.""The statistics should be more self-explanatory with detailed automated reports.""Each algorithm could be more adaptable to some industry-specific areas, or, in some cases, adapted for maintenance.""The product should provide more ways to import data and export results that are user-friendly for high-level executives.""The design of the experience can be improved.""This solution is not suitable for use with Big Data.""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."

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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
"We think that IBM SPSS is expensive for this function.""The price of this solution is a little bit high, which was a problem for my company.""The pricing of the modeler is high and can reduce the utility of the product for those who can not afford to adopt it."

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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: You can quickly build models because it does the work for you.
Top Answer: In comparing the price of other products, SPSS Statistics is too expensive. Even when most of the universities in the Middle East have licenses for SPSS Statistics, they do not have licenses for the… more »
Top Answer: The technical support should be improved.
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
2nd
out of 16 in Data Mining
Views
4,039
Comparisons
3,128
Reviews
15
Average Words per Review
720
Rating
7.9
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
Also Known As
SPSS Statistics
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Overview
Your organization has more data than ever, but spreadsheets and basic statistical analysis tools limit its usefulness. IBM SPSS Statistics software can help you find new relationships in the data and predict what will likely happen next. Virtually eliminate time-consuming data prep; and quickly create, manipulate and distribute insights for decision making.
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.
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Sample Customers
LDB Group, RightShip, Tennessee Highway Patrol, Capgemini Consulting, TEAC Corporation, Ironside, nViso SA, Razorsight, Si.mobil, University Hospitals of Leicester, CROOZ Inc., GFS Fundraising Solutions, Nedbank Ltd., IDS-TILDA
Information Not Available
Top Industries
REVIEWERS
University29%
Financial Services Firm21%
Aerospace/Defense Firm7%
Non Profit7%
VISITORS READING REVIEWS
Comms Service Provider26%
Computer Software Company15%
Educational Organization14%
Government6%
VISITORS READING REVIEWS
Comms Service Provider34%
Educational Organization13%
Computer Software Company12%
Financial Services Firm6%
Company Size
REVIEWERS
Small Business28%
Midsize Enterprise22%
Large Enterprise50%
No Data Available
Find out what your peers are saying about IBM SPSS Statistics vs. Weka and other solutions. Updated: May 2021.
511,607 professionals have used our research since 2012.

IBM SPSS Statistics is ranked 2nd in Data Mining with 15 reviews while Weka is ranked 5th in Data Mining with 7 reviews. IBM SPSS Statistics is rated 8.0, while Weka is rated 7.2. The top reviewer of IBM SPSS Statistics writes "Offers good Bayesian and descriptive statistics". On the other hand, the top reviewer of Weka writes "Relatively stable with excellent accuracy and there's no need to know coding". IBM SPSS Statistics is most compared with IBM SPSS Modeler, TIBCO Statistica, MathWorks Matlab, Alteryx and Microsoft Azure Machine Learning Studio, whereas Weka is most compared with KNIME, IBM SPSS Modeler, SAS Analytics, SAS Enterprise Miner and ELK Elasticsearch. See our IBM SPSS Statistics vs. Weka report.

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