Key Features of IBM SPSS Modeler for Predictive Analytics, Machine Learning, and AI
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.
Want to select your own algorithms for machine learning? Choose from neural networks, random forests, decision trees, K-means clustering, logistic regression, and dozens of other options.
Automated ML and AI modeling with IBM SPSS Modeler
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. Check out the SPSS GitHub repo for examples on working with ArcGIS and Esri data with IBM’s predictive analytics tools.
Support for R, Python, and other open source technologies
Use R, Python, Spark, Hadoop and other open source technologies to create machine learning and AI solutions to solve your organization’s toughest business challenges. Extend and complement these technologies for more advanced predictive analytics while you keep control.
Use open source predictive analytics and machine learning with IBM SPSS Modeler
Text analytics and natural language processing (NLP)
A cornerstone of AI is NLP and the ability to work with unstructured text. Capture key concepts, themes, sentiment and trends by analyzing unstructured text data. Uncover insights in web activity, blog content, customer feedback, emails, and social media comments. This AI workbench allows you to do deep text mining and then easily visualize and share the results. Modeler works with a range of data formats including: databases, html, pdf, csv, Excel, Word, flat files, and more.