Descriptive and predictive modeling provide insights that drive better decision making. Now you can streamline the data mining process to develop models quickly. Understand key relationships. And find the patterns that matter most.
Build better models with the best tools.
Dramatically shorten model development time for your data miners and statisticians. An interactive, self-documenting process flow diagram environment efficiently maps the entire data mining process to produce the best results. And it has more predictive modeling techniques than any other commercial data mining package. Why not use the best?
Empower business users.
Business users and subject-matter experts with limited statistical skills can generate their own models using the SAS Rapid Predictive Modeler. An easy-to-use GUI steps them through a workflow of data mining tasks. Analytic results are displayed in easy-to-understand charts that provide the insights needed for better decision making.
Improve prediction accuracy. Share reliable results.
Create better-performing models using innovative algorithms and industry-specific methods. Verify results with visual assessment and validation metrics. Easily compare predictions and assessment statistics from models built with different approaches by displaying them side by side. The resulting diagrams also serve as self-documenting templates that you can update or apply to new problems without starting over.
Automate model deployment and scoring.
Scoring code is automatically generated for all stages of model development, which eliminates potentially costly errors stemming from manually rewriting and converting code. You can embed the scoring code in your business processes. Or deploy it in real-time or batch environments within SAS, on the web and directly into relational databases. This saves time and produces more accurate results.
Screenshots & Demos
- Easy-to-use GUI and batch processing.
- Sophisticated data preparation, summarization and exploration.
- Predictive and descriptive modeling.
- Open-source integration with R.
- High-performance capabilities.
- Fast, easy and self-sufficient way for business users to generate models.
- Model comparisons, reporting and management.
- Automated scoring.
- Scalable processing.
We adopted an analytics approach years ago, and we're seeing it transform our entire organization. Analytics helps us understand customers better, helps in business planning (ticket pricing, etc.), and provides game-to-game and year-to-year data on demand by game and even by seat.
Learn about the modern applications of machine learning.