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Learning Data Mining with Python

You're reading from   Learning Data Mining with Python Harness the power of Python to analyze data and create insightful predictive models

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Product type Paperback
Published in Jul 2015
Publisher Packt
ISBN-13 9781784396053
Length 344 pages
Edition 1st Edition
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Author (1):
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Robert Layton Robert Layton
Author Profile Icon Robert Layton
Robert Layton
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Table of Contents (20) Chapters Close

Learning Data Mining with Python
Credits
About the Author
About the Reviewers
www.PacktPub.com
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. Extracting Features with Transformers 6. Social Media Insight Using Naive Bayes 7. Discovering Accounts to Follow Using Graph Mining 8. Beating CAPTCHAs with Neural Networks 9. Authorship Attribution 10. Clustering News Articles 11. Classifying Objects in Images Using Deep Learning 12. Working with Big Data Next Steps… Index

Extracting text from arbitrary websites


The links that we get from reddit go to arbitrary websites run by many different organizations. To make it harder, those pages were designed to be read by a human, not a computer program. This can cause a problem when trying to get the actual content/story of those results, as modern websites have a lot going on in the background. JavaScript libraries are called, style sheets are applied, advertisements are loaded using AJAX, extra content is added to sidebars, and various other things are done to make the modern webpage a complex document. These features make the modern Web what it is, but make it difficult to automatically get good information from!

Finding the stories in arbitrary websites

To start with, we will download the full webpage from each of these links and store them in our data folder, under a raw subfolder. We will process these to extract the useful information later on. This caching of results ensures that we don't have to continuously...

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