About SPSS Modeler
Organizations worldwide rely on SPSS Modeler for data preparation and discovery, predictive analytics, model management and deployment, and machine learning to monetize data assets. It's suited for hybrid environments that need to meet robust governance and security requirements. Support for multiple data sources is provided, so you can read data from flat files, spreadsheets, major relational databases, IBM Planning Analytics and Hadoop.
You can also extend the capabilities of SPSS Modeler to push back data processing with the SQL Optimization add-on (subscription) or the Analytic Server (perpetual license). Additionally, SPSS Modeler captures key concepts, themes, sentiments and trends by analyzing unstructured text data. Uncover valuable insights in blog content, customer feedback, emails and social media comments.
SPSS modeler provides an intuitive graphical interface to help visualize each step in the data mining process as part of a stream. Now analysts and business users can easily add expertise and business knowledge to the process.
Explore geographic data such as latitude and longitude, postal codes and addresses using SPSS Modeler. By combining that information with current and historical data you can generate better insights and improve predictive accuracy.
SPSS Modeler enables the use of R, Python, Spark and Hadoop to amplify the power of analytics. You can also extend and complement these technologies for more advanced analytics while maintaining control.
IBM SPSS Modeler is available as part of IBM Data Science Experience, as well as a standalone subscription or perpetual offering. 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.
SPSS Modeler supports decision trees, neural networks and regression models. Now you can take advantage of ARMA, ARIMA and exponential smoothing; transfer functions with predictors and outlier detection; benefit from ensemble and hierarchical models; support vector machine and temporal causal modeling; and employ time series and spatial AR for spatiotemporal prediction. Generative adversarial networks (GANs) and reinforcement also enable deep learning.
SPSS Modeler automatically transforms data into the best format for the most accurate predictive modeling. It now only takes a few clicks for you to analyze data, identify fixes, screen out fields and derive new attributes.
SPSS Modeler can test multiple modeling methods, compare results and select which model to deploy in a single run. This enables you to quickly choose the best performing algorithm based on model performance.
SPSS Modeler offers multiple machine learning techniques — including classification, segmentation and association algorithms including out-of-the-box algorithms that leverage Python and Spark. Users can now employ languages such as R and Python to extend modeling capabilities.