Book Image

Applied Unsupervised Learning with Python

By : Benjamin Johnston, Aaron Jones, Christopher Kruger
Book Image

Applied Unsupervised Learning with Python

By: Benjamin Johnston, Aaron Jones, Christopher Kruger

Overview of this book

Unsupervised learning is a useful and practical solution in situations where labeled data is not available. Applied Unsupervised Learning with Python guides you in learning the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The book begins by explaining how basic clustering works to find similar data points in a set. Once you are well-versed with the k-means algorithm and how it operates, you’ll learn what dimensionality reduction is and where to apply it. As you progress, you’ll learn various neural network techniques and how they can improve your model. While studying the applications of unsupervised learning, you will also understand how to mine topics that are trending on Twitter and Facebook and build a news recommendation engine for users. Finally, you will be able to put your knowledge to work through interesting activities such as performing a Market Basket Analysis and identifying relationships between different products. By the end of this book, you will have the skills you need to confidently build your own models using Python.
Table of Contents (12 chapters)
Applied Unsupervised Learning with Python
Preface

Association Rules


Association rule learning is a machine learning model that seeks to unearth the hidden patterns (in other words, relationships) in transaction data that describe the shopping habits of the customers of any retailer. The definition of an association rule was hinted at when the common probabilistic metrics were defined and explained previously.

Consider the imaginary frequent item set {Milk, Bread}. Two association rules can be formed from that item set: Milk Bread and Bread Milk. For simplicity, the first item set in the association rule is referred to as the antecedent, while the second item set in the association rule is referred to as the consequent. Once the association rules have been identified, all the previously discussed metrics can be computed to evaluate the validity of the association rules determining whether or not the rules can be leveraged in the decision-making process.

The establishment of an association rule is based on support and confidence. Support...