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Machine Learning with R

You're reading from Machine Learning with R Expert techniques for predictive modeling

Product type Paperback

Published in Apr 2019

Publisher Packt

ISBN-13 9781788295864

Length 458 pages

Edition 3rd Edition

Languages

R

Tools

RStudio

Concepts

Machine Learning

Author (1):

Brett Lantz

View More author details

Table of Contents (16) Chapters

Preface

1. Introducing Machine Learning FREE CHAPTER

2. Managing and Understanding Data

3. Lazy Learning – Classification Using Nearest Neighbors

4. Probabilistic Learning – Classification Using Naive Bayes

5. Divide and Conquer – Classification Using Decision Trees and Rules

6. Forecasting Numeric Data – Regression Methods

7. Black Box Methods – Neural Networks and Support Vector Machines

8. Finding Patterns – Market Basket Analysis Using Association Rules

9. Finding Groups of Data – Clustering with k-means

10. Evaluating Model Performance

11. Improving Model Performance

12. Specialized Machine Learning Topics

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Index

Chapter 8. Finding Patterns – Market Basket Analysis Using Association Rules

Think back to your last impulse purchase. Maybe in the grocery store checkout lane you bought a pack of chewing gum or a candy bar. Perhaps on a late-night trip for diapers and formula you picked up a caffeinated beverage or a six-pack of beer. You might have even bought this book on a bookseller's recommendation. These impulse buys are no coincidence, as retailers use sophisticated data analysis techniques to identify patterns that will drive retail behavior.

In years past, such recommendations were based on the subjective intuition of marketing professionals and inventory managers. Now, barcode scanners, inventory databases, and online shopping carts have generated transactional data that machine learning can use to learn purchasing patterns. The practice is commonly known as market basket analysis due to the fact that it has been so frequently applied to supermarket data.

Although the...

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Authors (1)

Brett Lantz

Brett Lantz

Brett Lantz (DataSpelunking) has spent more than 10 years using innovative data methods to understand human behavior. A sociologist by training, Brett was first captivated by machine learning during research on a large database of teenagers' social network profiles. Brett is a DataCamp instructor and a frequent speaker at machine learning conferences and workshops around the world. He is known to geek out about data science applications for sports, autonomous vehicles, foreign language learning, and fashion, among many other subjects, and hopes to one day blog about these subjects at Data Spelunking, a website dedicated to sharing knowledge about the search for insight in data.

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