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Learning Data Mining with Python, - Second Edition

You're reading from  Learning Data Mining with Python, - Second Edition

Product type Book
Published in Apr 2017
Publisher Packt
ISBN-13 9781787126787
Pages 358 pages
Edition 2nd Edition
Languages
Concepts

Table of Contents (20) Chapters

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Getting Started with Data Mining Classifying with scikit-learn Estimators Predicting Sports Winners with Decision Trees Recommending Movies Using Affinity Analysis Features and scikit-learn Transformers Social Media Insight using Naive Bayes Follow Recommendations Using Graph Mining Beating CAPTCHAs with Neural Networks Authorship Attribution Clustering News Articles Object Detection in Images using Deep Neural Networks Working with Big Data Next Steps...

Sports outcome prediction


We may be able to do better by trying other features. We have a method for testing how accurate our models are. The cross_val_score method allows us to try new features.

There are many possible features we could use, but we will try the following questions:

  • Which team is considered better generally?
  • Which team won their last encounter?

We will also try putting the raw teams into the algorithm, to check whether the algorithm can learn a model that checks how different teams play against each other.

Putting it all together

For the first feature, we will create a feature that tells us if the home team is generally better than the visitors. To do this, we will load the standings (also called a ladder in some sports) from the NBA in the previous season. A team will be considered better if it ranked higher in 2015 than the other team.

To obtain the standings data, perform the following steps:

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