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Essential PySpark for Scalable Data Analytics

You're reading from   Essential PySpark for Scalable Data Analytics A beginner's guide to harnessing the power and ease of PySpark 3

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781800568877
Length 322 pages
Edition 1st Edition
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Author (1):
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Sreeram Nudurupati Sreeram Nudurupati
Author Profile Icon Sreeram Nudurupati
Sreeram Nudurupati
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Data Engineering
2. Chapter 1: Distributed Computing Primer FREE CHAPTER 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 supervised learning applications

In the past, data science and machine learning were used exclusively for academic research purposes. However, over the past decade, this field has found its use in actual business applications to help businesses find their competitive edge, improve overall business performance, and become profitable. In this section, we will look at some real-world applications of machine learning.

Regression applications

Some of the applications of machine learning regression models and how they help improve business performance will be presented in this section.

Customer lifetime value estimation

In any retail or CPG kind of business where customer churn is a huge factor, it is necessary to direct marketing spend at those customers who are profitable. In non-subscription kinds of businesses, typically 20% of the customer base generates up to 80% of revenue. Machine learning models can be leveraged to model and predict each customer's lifetime...

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