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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

The sample market basket


Each transaction numbered 1-10 listed previously represents a basket of items purchased by a shopper. These are typically all items that are associated with a particular transaction or invoice. Each basket is enclosed within braces {}, and is referred to as an itemset. An itemset is a group of items that occur together.

Market basket algorithms construct rules in the form of:

Itemset{x1,x2,x3 ...} --> Itemset{y1,y2,y3...}. 

This notation states that buyers who have purchased items on the left-hand side of the formula (lhs) have a propensity to purchase items on the right-hand side (rhs). The association is stated using the à symbol, which can be interpreted as implies.

Note

The lhs of the notation is also known as the antecedent, and the rhs is known as the consequence. If nothing appears on either the left-hand side or right-hand side there is no specific association rule for those items; however, it also means that those items have appeared in the basket.

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