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Feature Engineering for Predicting Hotel Bookings with tidymodels

Key Takeaways:
  • Learn to engineer features, and tactics to decide which features to engineer.
  • Learn how to evaluate machine learning model performance (and see how changing features changes performance).
  • Learn to use tidymodels recipes to transform features automatically.
Tuesday, 11 April, 11AM ET
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The performance of machine learning models isn't just about the type of model you use or the code you write. As any model's performance is a direct consequence of the features it's fed, placing domain knowledge at the center of the process is vital. 

In this live training, Jorge Zazueta will teach you how to manually engineer features, build and assess a machine learning workflow and automate feature transformation using recipes. These ideas will help you make your models more efficient, interpretable, and accurate!

Presenter Bio

Jorge Zazueta Headshot
Jorge ZazuetaResearch Professor - Universidad Autónoma de San Luis Potosí, Adjunct Professor - The Kogod School of Business at American University

Jorge has 20+ years of industry and consulting experience in strategy, finance, capital planning, procurement and operations. Jorge has used data science to help his clients save hundreds of millions of dollars. His experience as a consultant includes Fortune 500 companies such as Caterpillar, Nokia, Sony, Campbell’s Soup, and Mars. He is a researcher at the Universidad Autónoma de San Luis Potosí and a management consultant focusing on understanding and modeling social and business phenomena using mathematics and data science. He is the instructor of Introduction to Feature Engineering in R.

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