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Mastering Machine Learning with R, Second Edition - Second Edition

You're reading from  Mastering Machine Learning with R, Second Edition - Second Edition

Product type Book
Published in Apr 2017
Publisher Packt
ISBN-13 9781787287471
Pages 420 pages
Edition 2nd Edition
Languages
Toc

Table of Contents (23) Chapters close

Title Page
Credits
About the Author
About the Reviewers
Packt Upsell
Customer Feedback
Preface
1. A Process for Success 2. Linear Regression - The Blocking and Tackling of Machine Learning 3. Logistic Regression and Discriminant Analysis 4. Advanced Feature Selection in Linear Models 5. More Classification Techniques - K-Nearest Neighbors and Support Vector Machines 6. Classification and Regression Trees 7. Neural Networks and Deep Learning 8. Cluster Analysis 9. Principal Components Analysis 10. Market Basket Analysis, Recommendation Engines, and Sequential Analysis 11. Creating Ensembles and Multiclass Classification 12. Time Series and Causality 13. Text Mining 14. R on the Cloud 15. R Fundamentals 16. Sources

Business understanding


For this example, we will delve into the world of sports; in particular, the National Hockey League (NHL). Much work has been done on baseball (think of the book and movie, Moneyball) and football; both are American and games that people around the world play with their feet. For my money, there is no better spectator sport than hockey. Perhaps that is an artifact of growing up on the frozen prairie of North Dakota. Nonetheless, we can consider this analysis as our effort to start a MoneyPuck movement.

In this analysis, we will look at the statistics for 30 NHL teams in a data set I've compiled from www.nhl.com and www.puckalytics.com. The goal is to build a model that predicts the total points for a team from an input feature space developed using PCA in order to provide us with some insight on what it takes to be a top professional team. We will learn a model from the 2015-16 season, which saw the Pittsburgh Penguins crowned as champions, and then test its performance...

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