SPSS Modeler and SPSS Modeler Subscription

Support for many data sources

Modeler can read data from flat files, spreadsheets, major relational databases, IBM Planning Analytics and Hadoop. In addition, it can be extended with SQL optimization to push back data processing into relational databases and Hadoop. For perpetual Modeler licenses, the Analytic Server add-on allows model creation and scoring to be pushed back into Hadoop or Spark, eliminating the need for code and speeding up processing time.

Visual analysis streams

Use an intuitive graphical interface to visualize each step in the data mining process as part of a stream. Analysts and business users can easily add expertise and business knowledge to the process.

Automatic data preparation

Transform data automatically into the best format for the most accurate predictive models. Analyze data, identify fixes, screen out fields and derive new attributes with just a few clicks.

Automated modeling

Use a single run to test multiple modeling methods, compare results and select which model to deploy. Quickly choose the best performing algorithm based on model performance.

A range of algorithmic methods

Choose from multiple machine learning techniques, including classification, segmentation and association algorithms including out of the box algorithms leveraging Python and Spark. Use languages such as R and Python to extend modeling capabilities.

Text analytics

Capture key concepts, themes, sentiments and trends by analyzing unstructured text data. Uncover insights in blog content, customer feedback, emails and social media comments.

Geospatial analytics

Explore geographic data such as latitude and longitude, postal codes and addresses. Combine it with current and historical data for better insights and predictive accuracy.

Support for open source technologies

Use R, Python, Spark and Hadoop to amplify the power of your analytics. Extend and complement these technologies for more advanced analytics while you maintain control.

Multiple deployment methods

The value of models can be unlocked through a variety of deployment methods. Using Modeler Gold, data scientists can schedule jobs to run at desired times. IT administrators can integrate deployment into existing systems for batch, real-time or streaming (through IBM Streams) deployment. Customers can deploy SPSS Modeler programs in the cloud through the Watson Machine Learning IBM Cloud service.

Customer case study

  • Redcats Group extends predictive analytics to 17 brands with SPSS Modeler.

    Redcats Group

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