Make better decisions through predictive intelligence
Improve predictive insights with geospatial analytics
The geospatial analytics tools in IBM SPSS Modeler Professional software help improve predictive insights by accounting for time and space in predictive models, so businesses can accurately forecast events at a specific location for virtually any future point in time.
Streamline the data-mining process
SPSS Modeler Professional software is popular with analysts and business users alike. Its automated data preparation and modeling features enable non-analysts to produce accurate models quickly and easily without specialized skills, while professional analysts can take advantage of the software’s advanced data preparation and predictive modeling capabilities to create the most sophisticated of streams.
Easily create and evaluate sophisticated models
Choose from an array of prebuilt algorithms to help you create models visually and intuitively.
Classification algorithms: Make predictions or forecasts using techniques such as decision tree, neural networks, logistic regression, time series, support vector machines and Cox regression. Leverage automatic classification modeling for binary and numeric outcomes.
Segmentation algorithms: Group people or detect unusual patterns with automatic clustering, anomaly detection and clustering neural network techniques. Use automatic classification to apply multiple algorithms with a single step and take the guesswork out of selecting the right technique.
Association algorithms: Discover associations, links or sequences using Apriori, CARMA and sequential association.
View models interactively and apply advanced analytical and visualization techniques to help you understand and communicate the results of your analysis. Then efficiently deploy insight and predictive models on a scheduled basis or in real time.
Optimize your current information technologies
With its open and scalable architecture, SPSS Modeler software is designed to make the best use of your existing IT infrastructure. It integrates with your existing systems when accessing data and when deploying results, so you don’t need to move data into and out of a proprietary format. Additionally, techniques such as in-database mining, SQL pushback, multithreading, server clustering and in-database scoring help conserve resources, deliver results faster and reduce overall IT costs.