Key Features

Support for many data sources

Modeler can read data from flat files, spreadsheets, major relational databases, IBM Planning Analytics and Hadoop. Extend Modeler's capabilities to push back data processing with the SQL Optimization add-on (subscription) or the Analytic Server (perpetual licenses).

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

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). Customers can deploy SPSS Modeler programs in the cloud through the Watson Machine Learning Bluemix service.

Machine learning methods and algorithms

Supports decision tree, neural networks and regression models. ARMA, ARIMA and exponential smoothing; transfer function with predictors and outlier detection; ensemble and hierarchical models; support vector machine (SVM) and temporal causal modeling (TCM); time series and spatial AR in STP (spatiotemporal prediction). Generative adversarial networks (GANs) and reinforcement learning for deep learning.

Customer Case Studies


See how the Volvo Group used Modeler for predictive maintenance to fulfill uptime commitments.

Redcats Group

See how Redcats group was able to find new insights in their customer data to predict buying habits with SPSS Modeler.

O Boticario

See how O Boticario used SPSS Modeler to predict customer demand up to nine months in advance.

Next Steps

Try it now

Buy now and get started