SPSS Modeler offers modeling algorithms that are taken from machine learning, artificial intelligence, and statistics. You can use these modeling algorithms to analyze your data and gain new business insights. With SPSS Modeler, you can quickly develop predictive models and deploy them into business operations.
What is SPSS Modeler?
SPSS Modeler is a data mining application, where you can build data mining SPSS Modeler flows by using the visual interface. Programming is not required. You can build SPSS Modeler flows to explore your data, model outcomes, try different models, and investigate relationships to find useful information. SPSS Modeler uses the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, which is an industry-proven way to guide your data mining efforts.
Using the Flow Editor, you prepare or shape data, train or deploy a model, or transform data and export it back to a database table or a file in Cloud Object Storage. Running an SPSS Modeler flow consumes capacity unit hours. For more information, see Watson Studio environments compute usage.
You can use the advanced analytics in SPSS Modeler to discover patterns in your data and tune models. You can then deploy these models in your business to make predictions on new data with unknown outcomes. The models can systemically analyze data and find business insights and opportunities. If you have access to the watsonx.ai Runtime service, you can promote models to deployment spaces to run them.
- Data formats
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- Relational: Tables in relational data sources
- Tabular: Tables in data files such as .xls, .csv, .json, or .sas. For Excel files, only the first sheet is read.
- Textual: In the supported relational tables or files
- Data size
- Any
- How can I prepare data?
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- Use automatic data preparation functions
- Write SQL statements to manipulate data
- Cleanse, shape, sample, sort, and derive data
- How can I analyze data?
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- Visualize data with many chart options
- Identify the natural language of a text field
- How can I build models?
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- Build predictive models
- Choose from over 40 modeling algorithms, and many other nodes
- Use automatic modeling functions
- Model time series or geospatial data
- Classify textual data
- Identify relationships between the concepts in textual data
Scripting
You can use scripting in SPSS Modeler to automate tasks that are highly repetitive or time consuming to perform manually. Scripts can perform all the same types of actions as users with a mouse or a keyboard, and you can write scripts in R, Python, or Python for Spark.
The following are some of the tasks that you can automate with scripts:
- Impose a specific order for running nodes in a flow
- Set properties for a node
- Set up a process that automatically takes a model training flow, runs it, and produces the corresponding model-testing flow.
Related services
If you have access to other services on Watsonx.ai, you can use them with SPSS Modeler. The following services offer features that complement work in SPSS Modeler.
| Service | Overview of features |
|---|---|
| Data Refinery | Create a flow of ordered operations to cleanse and shape data. Visualize data to identify problems and discover insights. |
| RStudio® | Work with R notebooks and scripts in an integrated development environment. |
| watsonx.ai Runtime | Quickly build, run, and manage generative AI and machine learning applications with built-in performance and scalability. |