You're reading from Python Machine Learning By Example Unlock machine learning best practices with real-world use cases

Product type Paperback

Published in Jul 2024

Publisher Packt

ISBN-13 9781835085622

Length 518 pages

Edition 4th Edition

Languages

Python

Tools

PyTorch

Concepts

Computer Vision

Author (1):

Yuxi (Hayden) Liu

View More author details

Table of Contents (18) Chapters

Preface

1. Getting Started with Machine Learning and Python

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

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

4. Predicting Online Ad Click-Through with Logistic Regression

5. Predicting Stock Prices with Regression Algorithms

6. Predicting Stock Prices with Artificial Neural Networks

7. Mining the 20 Newsgroups Dataset with Text Analysis Techniques

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

9. Recognizing Faces with Support Vector Machine

10. Machine Learning Best Practices

11. Categorizing Images of Clothing with Convolutional Neural Networks

12. Making Predictions with Sequences Using Recurrent Neural Networks

13. Advancing Language Understanding and Generation with the Transformer Models

14. Building an Image Search Engine Using CLIP: a Multimodal Approach

15. Making Decisions in Complex Environments with Reinforcement Learning

16. Other Books You May Enjoy

17. Index

Getting started with feature engineering

When it comes to a machine learning algorithm, the first question to ask is usually what features are available or what the predictive variables are.

The driving factors that are used to predict future prices of NASDAQ, the close prices, include historical and current open prices as well as historical performance (high, low, and volume). Note that current or same-day performance (high, low, and volume) shouldn’t be included because we simply can’t foresee the highest and lowest prices at which the stock traded, or the total number of shares traded before the market closed on that day.

Predicting the close price with only those preceding four indicators doesn’t seem promising and might lead to underfitting. So, we need to think of ways to generate more features in order to increase predictive power. In machine learning, feature engineering is the process of creating features in order to improve the performance of...