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Essential PySpark for Scalable Data Analytics

You're reading from  Essential PySpark for Scalable Data Analytics

Product type Book
Published in Oct 2021
Publisher Packt
ISBN-13 9781800568877
Pages 322 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Sreeram Nudurupati Sreeram Nudurupati
Profile icon Sreeram Nudurupati

Table of Contents (19) Chapters

Preface 1. Section 1: Data Engineering
2. Chapter 1: Distributed Computing Primer 3. Chapter 2: Data Ingestion 4. Chapter 3: Data Cleansing and Integration 5. Chapter 4: Real-Time Data Analytics 6. Section 2: Data Science
7. Chapter 5: Scalable Machine Learning with PySpark 8. Chapter 6: Feature Engineering – Extraction, Transformation, and Selection 9. Chapter 7: Supervised Machine Learning 10. Chapter 8: Unsupervised Machine Learning 11. Chapter 9: Machine Learning Life Cycle Management 12. Chapter 10: Scaling Out Single-Node Machine Learning Using PySpark 13. Section 3: Data Analysis
14. Chapter 11: Data Visualization with PySpark 15. Chapter 12: Spark SQL Primer 16. Chapter 13: Integrating External Tools with Spark SQL 17. Chapter 14: The Data Lakehouse 18. Other Books You May Enjoy

Real-world applications of unsupervised learning

Unsupervised learning algorithms are being used today to solve some real-world business challenges. We will take a look at a few such challenges in this section.

Clustering applications

This section presents some of the real-world business applications of clustering algorithms.

Customer segmentation

Retail marketing teams, as well as business-to-customer organizations, are always trying to optimize their marketing spends. Marketing teams in particular are concerned with one specific metric called cost per acquisition (CPA). CPA is indicative of the amount that an organization needs to spend to acquire a single customer, and an optimal CPA means a better return on marketing investments. The best way to optimize CPA is via customer segmentation as this improves the effectiveness of marketing campaigns. Traditional customer segmentation takes standard customer features such as demographic, geographic, and social information...

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