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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
Languages
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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

Putting it all together

Up until this point, we've worked within the Jupyter Notebook, but now, in order to deploy our app, we'll move on to working in a text editor. The notebook is excellent for exploratory analysis and visualization, but running a background job is best done within a simple .py file. So, let's get started.

We'll begin with our imports. You may need to pip install a few of these if you don't already have them installed:

import sys 
import sys 
import numpy as np 
from bs4 import BeautifulSoup 
from selenium import webdriver 
import requests 
import scipy 
from PyAstronomy import pyasl 
 
from datetime import date, timedelta, datetime 
import time 
from time import sleep 
import schedule 

Next, we'll create a function that pulls down the data and runs our algorithm:

def check_flights(): 
   # replace this with the path of where you...
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