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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

Product type Paperback

Published in Apr 2017

Publisher Packt

ISBN-13 9781787126787

Length 358 pages

Edition 2nd Edition

Languages

Python

Concepts

Data Mining

Table of Contents (14) Chapters

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...

Discovering Accounts to Follow Using Graph Mining

Do give the following a read when done with the chapter.

More complex algorithms

URL: https://www.cs.cornell.edu/home/kleinber/link-pred.pdfLarger exercise!

There has been extensive research on predicting links in graphs, including for social networks. For instance, David Liben-Nowell and Jon Kleinberg published a paper on this topic that would serve as a great place for more complex algorithms, linked previously.

NetworkX

URL: https://networkx.github.io/

If you are going to be using graphs and networks more, going in-depth into the NetworkX package is well worth your time—the visualization options are great and the algorithms are well implemented. Another library called SNAP is also available with Python bindings, at http://snap.stanford.edu/snappy/index.html.

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