Kevin Potcner : JMP and the Predictive Modeling Workflow

Posted by Yichao Wu , part of the Statistics and Data Science Seminar.

At

Sept. 26, 2018, 4 p.m.

In

636 SEO

Abstract

As the size and sources of data becomes more available in today's business environments, data analysts are beginning to add more sophisticated predictive statistical modeling techniques to their analysis toolkit. A typical real-world predictive modeling workflow includes data cleaning and exploration, model fitting, model validation, model comparison, final model selection and deployment of the final predictive model. In this presentation, a statistical scientist from JMP will illustrate the predictive modeling workflow by analyzing a real dataset. After data preparation and initial exploration, we will create a number of predictive models such as Multiple Linear Regression, Regression tree, Neural Net, and K-Nearest Neighbors. We will evaluate each model and select the best model using the Prediction Profiler and JMP's Model Comparison tool. Code will be automatically created in a variety of programming languages (e.g., SAS, SQL, Python, et al.) in order to implement that model in a production environment.