You're reading from Python Machine Learning by Example Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781800209718

Length 526 pages

Edition 3rd Edition

Languages

Python

Tools

PyTorch

Concepts

Machine Learning

Author (1):

Yuxi (Hayden) Liu

View More author details

Table of Contents (17) Chapters

Preface

1. Getting Started with Machine Learning and Python

2. Building a Movie Recommendation Engine with Naïve Bayes FREE CHAPTER

3. Recognizing Faces with Support Vector Machine

4. Predicting Online Ad Click-Through with Tree-Based Algorithms

5. Predicting Online Ad Click-Through with Logistic Regression

6. Scaling Up Prediction to Terabyte Click Logs

7. Predicting Stock Prices with Regression Algorithms

8. Predicting Stock Prices with Artificial Neural Networks

9. Mining the 20 Newsgroups Dataset with Text Analysis Techniques

10. Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

11. Machine Learning Best Practices

12. Categorizing Images of Clothing with Convolutional Neural Networks

13. Making Predictions with Sequences Using Recurrent Neural Networks

14. Making Decisions in Complex Environments with Reinforcement Learning

15. Other Books You May Enjoy

16. Index

Predicting Stock Prices with Regression Algorithms

In the previous chapter, we trained a classifier on a large click dataset using Spark. In this chapter, we will be solving a problem that interests everyone—predicting stock prices. Getting wealthy by means of smart investment—who isn't interested?! Stock market movements and stock price predictions have been actively researched by a large number of financial, trading, and even technology corporations. A variety of methods have been developed to predict stock prices using machine learning techniques. Herein, we will be focusing on learning several popular regression algorithms, including linear regression, regression trees and regression forests, and support vector regression, and utilizing them to tackle this billion (or trillion) dollar problem.

We will cover the following topics in this chapter: