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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

Author (1):

Robert Layton

View More author details

Table of Contents (20) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

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...

Getting the data

The data we will use for the first part of this chapter is a set of books from Project Gutenberg at www.gutenberg.org, which is a repository of public domain literature works. The books I used for these experiments come from a variety of authors:

Booth Tarkington (22 titles)
Charles Dickens (44 titles)
Edith Nesbit (10 titles)
Arthur Conan Doyle (51 titles)
Mark Twain (29 titles)
Sir Richard Francis Burton (11 titles)
Emile Gaboriau (10 titles)

Overall, there are 177 documents from 7 authors, giving a significant amount of text to work with. A full list of the titles, along with download links and a script to automatically fetch them, is given in the code bundle called getdata.py. If running the code results in significantly fewer books than above, the mirror may be down. See this website for more mirror URLs to try in the script: https://www.gutenberg.org/MIRRORS.ALL

To download these books, we use the requests library to download the files into our data directory.

First, in a new...

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Authors (1)

Robert Layton

Robert Layton

Robert Layton is a data scientist investigating data-driven applications to businesses across a number of sectors. He received a PhD investigating cybercrime analytics from the Internet Commerce Security Laboratory at Federation University Australia, before moving into industry, starting his own data analytics company dataPipeline. Next, he created Eureaktive, which works with tech-based startups on developing their proof-of-concepts and early-stage prototypes. Robert also runs the LearningTensorFlow website, which is one of the world's premier tutorial websites for Google's TensorFlow library. Robert is an active member of the Python community, having used Python for more than 8 years. He has presented at PyConAU for the last four years and works with Python Charmers to provide Python-based training for businesses and professionals from a wide range of organisations. Robert can be best reached via Twitter @robertlayton

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