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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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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 apartment listing data

In the early 1970s, if you wanted to purchase a stock, you would need to engage a broker, who would charge you a fixed commission of nearly 1%. If you wanted to purchase an airline ticket, you would need to contact a travel agent, who would earn a commission of around 7%. And if you wanted to sell a home, you would contact a real estate agent, who would earn a commission of 6%. In 2018, you can do the first two essentially for free. The last one remains as it was in the 1970s.

Why is this the case and, more importantly, what does any of this have to do with machine learning? The reality is, it all comes down to data, and who has access to that data.

You might assume that you could easily access troves of real estate listing data quite easily through APIs or by web scraping real estate websites. You would be wrong. Well, wrong if you intend to follow...

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