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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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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

Visualizing the data

Since this data is based on ZIP codes, the best way to visualize it is with a choropleth. If you're unfamiliar with a choropleth, it's simply a visualization that represents the data according to a color spectrum. Let's create one now using a Python mapping library called folium at https://github.com/python-visualization/folium. If you don't have folium installed, again, it can be done with pip install on the command line.

Now we'll go ahead and create our visualization:

import folium 
 
m = folium.Map(location=[40.748817, -73.985428], zoom_start=13) 
 
m.choropleth( 
    geo_data=open('nyc.json').read(), 
    data=zdf_mean, 
    columns=['zip', 'avg_rent'], 
    key_on='feature.properties.postalCode', 
    fill_color='YlOrRd', fill_opacity=0.7, line_opacity=0.2, 
    ) 
 
m 

There...

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