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Machine Learning with R

You're reading from   Machine Learning with R Expert techniques for predictive modeling to solve all your data analysis problems

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Product type Paperback
Published in Jul 2015
Publisher Packt
ISBN-13 9781784393908
Length 452 pages
Edition 2nd Edition
Languages
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Author (1):
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Brett Lantz Brett Lantz
Author Profile Icon Brett Lantz
Brett Lantz
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Table of Contents (14) Chapters Close

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 Index

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

Think back to the last time you made an impulse purchase. Maybe you were waiting in the grocery store checkout lane and 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 whim 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 recommendation systems were based on the subjective intuition of marketing professionals and inventory managers or buyers. More recently, as barcode scanners, computerized inventory systems, and online shopping trends have built a wealth of transactional data, machine learning has been increasingly applied to learn purchasing patterns. The practice is commonly known as market...

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