Predictive modeling in practice: a case study from sprint

TLDR: Sprint's Head of Predictive Modeling, Tracey De Poalo, will talk about the process she developed using SAS and logistic regression to build a wide range of models, and Jeremy Howard will discuss his experience as a consultant at Sprint comparing R and random forests to the existing process.

Abstract: It's not often we get to peer into the details of how big corporates use predictive modeling in practice. In this talk, Sprint's Head of Predictive Modeling, Tracey De Poalo, will talk about the process she developed using SAS and logistic regression to build a wide range of models. Jeremy Howard will discuss his experience as a consultant at Sprint comparing R and random forests to the existing process, and will show the pros and cons of each approach.The talk will also cover the 'real world' issues that Tracey has dealt with in creating a reusable process, including data mart development, sampling, testing, reporting, and implementation with internal customers.Sprint's existing process is the best that Jeremy has seen in industry, and shows a lot of best practices in data structure, documentation, testing, automation, and customer interaction, and modeling.