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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
Author Profile Icon Alexander Combs
Alexander Combs
Saurabh Chhajed Saurabh Chhajed
Author Profile Icon Saurabh Chhajed
Saurabh Chhajed
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Toc

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

Exploring the features of shareability

The stories we have collected here represent roughly the 500 most shared pieces of content in 2015 and early 2016. We're going to try to deconstruct these articles to find the common traits that make them so shareable. We'll begin by looking at the image data.

Exploring image data

Let's begin by looking at the number of images included with each story. We'll run a value count and then plot the numbers:

dfc['img_count'].value_counts().to_frame('count') 

This should display an output similar to the following:

Now, let's plot that same information:

fig, ax = plt.subplots(figsize=(8,6)) 
y = dfc['img_count'].value_counts().sort_index...
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