Book Image

Python Machine Learning Blueprints - Second Edition

By : Alexander Combs, Michael Roman
Book Image

Python Machine Learning Blueprints - Second Edition

By: Alexander Combs, Michael Roman

Overview of this book

Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover key domains in machine learning. This second edition covers a range of libraries from the Python ecosystem, including TensorFlow and Keras, to help you implement real-world machine learning projects. The book begins by giving you an overview of machine learning with Python. With the help of complex datasets and optimized techniques, you’ll go on to understand how to apply advanced concepts and popular machine learning algorithms to real-world projects. Next, you’ll cover projects from domains such as predictive analytics to analyze the stock market and recommendation systems for GitHub repositories. In addition to this, you’ll also work on projects from the NLP domain to create a custom news feed using frameworks such as scikit-learn, TensorFlow, and Keras. Following this, you’ll learn how to build an advanced chatbot, and scale things up using PySpark. In the concluding chapters, you can look forward to exciting insights into deep learning and you'll even create an application using computer vision and neural networks. By the end of this book, you’ll be able to analyze data seamlessly and make a powerful impact through your projects.
Table of Contents (13 chapters)

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...