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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

Visualizing our data

When dealing with geographic data, as we are here, it is immensely valuable to be able to plot that information. One way of doing that is with something called a choropleth map. A choropleth is essentially a geographic heat map. We are going to build a choropleth to create a heat map of average rental price by ZIP code.

The first thing we will need to do this is the ZIP code. Unfortunately for us, our dataset does not contain ZIP code information. We do, however, have the address for the properties. With a little help from the Google Maps API, we can retrieve this information.

Currently, the Google Maps API is a paid API. The rates are reasonable, 1,000 calls for $5, but they also give you a credit of $200 each month (at the time of writing). They also allow you to sign up for a free trial before they will start billing you, and they won't bill unless...

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