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)

Building the regression model

Now that we have a baseline to compare with, let's build our first regression model. We're going to start with a very basic model using only the stock's prior closing values to predict the next day's close, and we're going to build it using a support vector regression. With that, let's set up our model:

  1. The first step is to set up a DataFrame that contains a price history for each day. We're going to include the past 20 closes in our model:
for i in range(1, 21, 1): 
    sp.loc[:,'Close Minus ' + str(i)] = sp['Close'].shift(i) 
sp20 = sp[[x for x in sp.columns if 'Close Minus' in x or x == 'Close']].iloc[20:,] 
  1. This code gives us each day's closing price, along with the previous 20, all on the same line. The result of our code is seen in the following output...