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Data Science for Decision Makers

You're reading from   Data Science for Decision Makers Enhance your leadership skills with data science and AI expertise

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781837637294
Length 270 pages
Edition 1st Edition
Languages
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Author (1):
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Jon Howells Jon Howells
Author Profile Icon Jon Howells
Jon Howells
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Table of Contents (20) Chapters Close

Preface 1. Part 1: Understanding Data Science and Its Foundations
2. Chapter 1: Introducing Data Science FREE CHAPTER 3. Chapter 2: Characterizing and Collecting Data 4. Chapter 3: Exploratory Data Analysis 5. Chapter 4: The Significance of Significance 6. Chapter 5: Understanding Regression 7. Part 2: Machine Learning – Concepts, Applications, and Pitfalls
8. Chapter 6: Introducing Machine Learning 9. Chapter 7: Supervised Machine Learning 10. Chapter 8: Unsupervised Machine Learning 11. Chapter 9: Interpreting and Evaluating Machine Learning Models 12. Chapter 10: Common Pitfalls in Machine Learning 13. Part 3: Leading Successful Data Science Projects and Teams
14. Chapter 11: The Structure of a Data Science Project 15. Chapter 12: The Data Science Team 16. Chapter 13: Managing the Data Science Team 17. Chapter 14: Continuing Your Journey as a Data Science Leader 18. Index 19. Other Books You May Enjoy

Applications of supervised learning

Supervised learning has found its place in numerous industries. It enables many businesses to predict future outcomes based on historical data. Let’s explore some more practical examples of how supervised learning algorithms are applied in different industries.

Consumer goods

In the consumer goods industry, supervised learning is being leveraged for various applications:

  • Consumer trend identification: By analyzing data from eCommerce platforms, social media, search engines, sales, and surveys, companies can identify emerging consumer trends – for example, trending product categories, ingredients, flavors, and claims that are predicted to see future growth. This helps in developing new products or making changes to existing ones that align with consumer preferences, potentially leading to increased revenue.
  • Price optimization: By considering factors such as historical sales data, competitor pricing, and marketing initiatives...
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