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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
Languages
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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 2. The Modeling Process FREE CHAPTER 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

Using Market Basket Analysis as a Recommender Engine

"It's not wise to violate the rules until you know how to observe them."
- T.S. Eliot

In this chapter, we will cover the following topics:

  • Market basket analysis using the arules package
  • Data transformation and cleaning techniques using semi-structured market basket transaction data
  • Learn how to transform transaction objects into dataframes
  • Use cluster analysis for prediction using the flexclus package
  • Utilize some text mining using RTextTools and tm packages
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