Getting Started with Machine Learning

Machine learning algorithms have been available in SAS software since 1979. This session provides practical examples of machine learning applications. The evolution of machine learning at SAS is illustrated with examples of nearest-neighbor discriminant analysis in SAS/STAT PROC DISCRIM to advanced predictive modeling in SAS Enterprise Miner. Machine learning techniques addressed include memory-based reasoning, decision trees, neural networks, and gradient boosting algorithms.

Handling Missing Values in SAS

What do you do when you have missing values in your data? In SAS we have many ways to manage missing values. In this session we cover what are missing values, why and when missing values occur and how to manage missing values. We discuss functions, procedures and how different products deal with missing values.

About the Presenter

With over a decade of experience in data analytics and machine learning, Melodie Rush brings a wealth of expertise to her role as an Customer Success Data Scientist at SAS. Her passion for solving complex problems with innovative technology has made her an essential member of the SAS team.  Melodie's work involves developing and deploying advanced models that help businesses make better decisions based on their data insights. She is constantly exploring new ways to improve analytical methods and technologies, striving to create systems that are faster, more accurate, and more efficient than ever before. In addition to her technical skills, Melodie is also known for her ability to communicate complex concepts in simple terms.