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Learning Data Mining with Python

You're reading from   Learning Data Mining with Python Use Python to manipulate data and build predictive models

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787126787
Length 358 pages
Edition 2nd Edition
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Concepts
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Table of Contents (14) Chapters Close

Preface 1. Getting Started with Data Mining FREE CHAPTER 2. Classifying with scikit-learn Estimators 3. Predicting Sports Winners with Decision Trees 4. Recommending Movies Using Affinity Analysis 5. Features and scikit-learn Transformers 6. Social Media Insight using Naive Bayes 7. Follow Recommendations Using Graph Mining 8. Beating CAPTCHAs with Neural Networks 9. Authorship Attribution 10. Clustering News Articles 11. Object Detection in Images using Deep Neural Networks 12. Working with Big Data 13. Next Steps...

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In this chapter, we will look at predicting the winner of games of the National Basketball Association (NBA). Matches in the NBA are often close and can be decided at the last minute, making predicting the winner quite difficult. Many sports share this characteristic, whereby the (generally) better team could be beaten by another team on the right day.

Various research into predicting the winner suggests that there may be an upper limit to sports outcome prediction accuracy which, depending on the sport, is between 70 percent and 80 percent. There is a significant amount of research being performed into sports prediction, often through data mining or statistics-based methods.

In this chapter, we are going to have a look at an entry level basketball match prediction algorithm, using decision trees for determining whether a team will win a given match. Unfortunately, it doesn't quite make as much profit as the models that sports betting agencies use, which are often a bit...

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