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Python Machine Learning Blueprints

You're reading from   Python Machine Learning Blueprints Put your machine learning concepts to the test by developing real-world smart projects

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781788994170
Length 378 pages
Edition 2nd Edition
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Authors (3):
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Michael Roman Michael Roman
Author Profile Icon Michael Roman
Michael Roman
Alexander Combs Alexander Combs
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Alexander Combs
Saurabh Chhajed Saurabh Chhajed
Author Profile Icon Saurabh Chhajed
Saurabh Chhajed
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Table of Contents (13) Chapters Close

Preface 1. The Python Machine Learning Ecosystem FREE CHAPTER 2. Build an App to Find Underpriced Apartments 3. Build an App to Find Cheap Airfares 4. Forecast the IPO Market Using Logistic Regression 5. Create a Custom Newsfeed 6. Predict whether Your Content Will Go Viral 7. Use Machine Learning to Forecast the Stock Market 8. Classifying Images with Convolutional Neural Networks 9. Building a Chatbot 10. Build a Recommendation Engine 11. What's Next? 12. Other Books You May Enjoy

Sourcing shared counts and content

Before we can begin exploring which features make content shareable, we need to get our hands on a fair amount of content, as well as data on how often it's shared. Unfortunately, securing this type of data has gotten more difficult in the last few years. In fact, when the first edition of this book came out in 2016, this data was easily obtainable. But today, there appears to be no free sources of this type of data, though if you are willing to pay, you can still find it.

Fortunately for us, I have a dataset that was collected from a now defunct website, ruzzit.com. This site, when it was active, tracked the most shared content over time, which is exactly what we require for this project:

We'll begin by loading our imports into our notebook, as we always do, and then load in the data. This particular data is in the form of a JSON...

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