Move beyond spreadsheets to data mining, forecasting, optimization – and more
Let SAS handle the data prep, R take care of the modeling, and skip the environment-hopping so your team can focus on building cool stuff faster.
Learn how to modernize legacy SAS workflows by integrating Python and automating processes using GitHub Actions and SAS Viya Workbench, enabling seamless collaboration and CI/CD across development environments.
Hyperparameter autotuning intelligently optimizes machine learning model performance by automatically testing parameter combinations, balancing accuracy and generalizability, as demonstrated in a real-world particle physics use case.
Using 47 seasons of Survivor data, this analysis explores what gameplay traits correlate with winning, applying Python and SAS Viya Workbench to build predictive models. While stats like challenge wins and voting accuracy help narrow down potential winners, the findings suggest that intangible social dynamics play the most decisive role.
SAS' Danny Sprukulis walks through how he developed a model theme park to simulate attraction wait times for operational decision-making.
SAS Decision Builder is a decision intelligence solution, which means that it uses machine learning and automation to augment human decision-making for better and faster insights that drive tactical and strategic business decisions. It’s a cousin to business intelligence and the next step after data engineering and model training, completing the analytics lifecycle to help achieve business goals.
Some readers read the article “how-to-evaluate-sas-expression-in-data-step-dynamically” and wonder if there is a same mechanism or functionality in DS2. As indicated in that article, SAS provides similar features in DATA step, PROC CAS and PROC Python, but some projects like ESP (Event Stream Processing) projects would store those expression definition in
SAS Analytics Pro Advanced Programming offers key statistical capabilities in a docker container. The product bundles selected executables from SAS Viya to create the container, which eases or streamlines the setup required for fixes and updates to the software.
In this third article, we will introduce an alternative approach that surfaces the CMS-HCC Risk Adjustment Model execution through SASPy integration to a Flask application. We will demonstrate how this integration allows a user to score an individual patient/member on-demand, using inputs to an interactive web form to execute the model score code, surfacing the resulting score to the user.
The CMS-HCC Risk Adjustment models are used to reimburse Medicare Advantage plans based on the health status of the plans’ members. CMS-HCC Risk Adjustment is the practice of assigning a risk score based on demographics and diagnoses to an individual beneficiary of Medicare for the purpose of calculating an expected cost of care, relative to the average beneficiary. Accurate risk adjustment requires an accurate diagnostic profile of an individual on an annual basis, documenting diagnoses via submitted claims or within a provider's medical record.
