About this paper
This paper illustrates how a SAS team of modelers used SAS Enterprise Miner and 2009 KDD Cup competition data to create a highly accurate model for predicting churn. They applied several data preparation, feature creation and dimension reduction techniques to prepare the data for modeling. They then used several machine learning approaches, including an open source model that could be incorporated into SAS Enterprise Miner. The models were accessed using the assigned validation criteria. Learn how they approached the problem and which model was declared the “winner.”
SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 80,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®.