Dean Abbott is a "rock star" hands-on practitioner - attend his keynote, hands-on methods workshop, and hands-on ensembles workshop at PAW Business, Boston, Sep 27 - Oct 1. Use KDN150 for KDnuggets discount.
Retail Analytics Revolution from Dean - Plus 2 Workshops
If anyone deserves to be called a "rock star" hands-on practitioner, it is Dean Abbott, a veteran consultant of more than 20 years and now co-founder of e-commerce analytics champion SmarterHQ. Among other things, Mr. Abbott is famed for his alleged hands-on fluency with more analytics tools than any other consultant.
Co-Founder and Chief Data Scientist SmarterHQ
1) KEYNOTE: The Revolution in Retail Customer Intelligence
In this new era of Big Data, retailers collect data in ever-increasing volume and variety. In the midst of Big Data, a revolution is taking place in how retailers gain insights about customers, whether they interact with the brand online, in stores, or both. This session will describe the transition from reporting to data-driven decisions using predictive analytics. Success requires collecting the right data, creating informative derived attributes, making this data accessible in a timely manner, and building predictive models. Examples, drawn from real-world retailers, will include shopping cart funnel management, shopping cart abandonment, marketing attribution, churn, and purchase propensity.
Below is the storyboard of Dean Abbott's keynote at PAW Business earlier this year:
Once you know the basics of predictive analytics and have prepared data for modeling, which algorithms should you use? What are the similarities and differences? Which options affect the models most? This workshop dives into the key predictive analytics algorithms for supervised learning, including decision trees, linear and logistic regression, neural networks, k-nearest neighbor, support vector machines, and model ensembles. Attendees will learn "best practices" and attention will be paid to learning and experiencing the influence various options have on predictive models so that attendees will gain a deeper understanding of how the algorithms work qualitatively.
3) WORKSHOP: Supercharging Prediction with Ensemble Models
Once you know the basics of predictive analytics including data exploration, data preparation, modeling building, and model evaluation, what can be done to improve model accuracy? One key technique is the use of model ensembles, which "groups" or "rolls up" models into a single, usually-better model.
Are model ensembles an algorithm or an approach? How can one understand the influence of key variables in the ensembles? Which options affect the ensembles most? This workshop dives into the key ensemble approaches including Bagging, Random Forests, and Stochastic Gradient Boosting. Attendees will learn "best practices" and attention will be paid to learning and experiencing the influence various options have on ensemble models so that attendees will gain a deeper understanding of how the algorithms work qualitatively and how one can interpret resulting models. Attendees will also learn how to automate the building of ensembles by changing key parameters.