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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

Cluster analysis


Cluster analysis has many uses. At its very basic level, a cluster is a group of people or objects that share similar characteristics. In the marketing and sales industries, clustering is important, since customers (or potential customers) can be grouped by characteristics such as average spending, frequency of purchase, and recency of purchases, and assigned a cluster that represents one single measure of the different levels contained in all of the attributes that make up that cluster. So, for our RFM example, cluster A might represent frequent purchasers who spend a lot of money, and spend often (every marketers dream). Cluster B could represent people who are just average consumers across all three of those RFM metrics, and there might even be a cluster Z which represents things that seem to be impossible, such as customers who buy Halloween costumes only on Tuesdays.

Data analysts can often get good results by using tools such as SQL, or by having great insights in customers...

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