The webinar moves through the full life cycle of a predictive analytics project. Steps shown include data cleaning and preparation, initial exploration and analyses, model fitting, evaluating and comparing model performance, final model selection, and deployment into a production environment. Modeling techniques shown include Multiple Linear Regression, Regression trees, Neural Nets, and K-Nearest Neighbors.

In this webinar you’ll learn:

Best practices in how to approach a predictive modeling effort.
Some of the common mistakes analyst make in a predictive modeling effort.
Why using a point-and-click interactive software environment can be more efficient.