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Practical Predictive Analytics

You're reading from   Practical Predictive Analytics Analyse current and historical data to predict future trends using R, Spark, and more

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781785886188
Length 576 pages
Edition 1st Edition
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Author (1):
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Ralph Winters Ralph Winters
Author Profile Icon Ralph Winters
Ralph Winters
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Predictive Analytics FREE CHAPTER 2. The Modeling Process 3. Inputting and Exploring Data 4. Introduction to Regression Algorithms 5. Introduction to Decision Trees, Clustering, and SVM 6. Using Survival Analysis to Predict and Analyze Customer Churn 7. Using Market Basket Analysis as a Recommender Engine 8. Exploring Health Care Enrollment Data as a Time Series 9. Introduction to Spark Using R 10. Exploring Large Datasets Using Spark 11. Spark Machine Learning - Regression and Cluster Models 12. Spark Models – Rule-Based Learning

What is market basket analysis?


If you have survived the last chapter, you will now be introduced to the world of market basket analysis (MBA). Market basket analysis (also sometimes called affinity analysis), is a predictive analytics technique that is used heavily in the retail industry in order to identify baskets of items that are purchased together. The typical use case for this is the supermarket shopping cart in which a shopper would typically purchase an assortment of items such as milk, bread, cheese, and so on, and the algorithm will predict how purchasing certain items together will affect the purchase of other items. It is one of those methods that retailers use to know to start sending you coupons and emails for things that you didn't know you needed!

One often quoted example of MBA is the relationship between diapers and beer:

"One super market chain discovered in its analysis that customers that bought diapers often bought beer as well, have put the diapers close to beer coolers...

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