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The Art of Data-Driven Business

You're reading from   The Art of Data-Driven Business Transform your organization into a data-driven one with the power of Python machine learning

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781804611036
Length 314 pages
Edition 1st Edition
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Author (1):
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Alan Bernardo Palacio Alan Bernardo Palacio
Author Profile Icon Alan Bernardo Palacio
Alan Bernardo Palacio
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Table of Contents (17) Chapters Close

Preface 1. Part 1: Data Analytics and Forecasting with Python
2. Chapter 1: Analyzing and Visualizing Data with Python FREE CHAPTER 3. Chapter 2: Using Machine Learning in Business Operations 4. Part 2: Market and Customer Insights
5. Chapter 3: Finding Business Opportunities with Market Insights 6. Chapter 4: Understanding Customer Preferences with Conjoint Analysis 7. Chapter 5: Selecting the Optimal Price with Price Demand Elasticity 8. Chapter 6: Product Recommendation 9. Part 3: Operation and Pricing Optimization
10. Chapter 7: Predicting Customer Churn 11. Chapter 8: Grouping Users with Customer Segmentation 12. Chapter 9: Using Historical Markdown Data to Predict Sales 13. Chapter 10: Web Analytics Optimization 14. Chapter 11: Creating a Data-Driven Culture in Business 15. Index 16. Other Books You May Enjoy

Using Machine Learning in Business Operations

Machine learning is an area of research focused on comprehending and developing “learning” processes, or processes that use data to enhance performance on a given set of tasks. It is considered to be a component of artificial intelligence. Among them, machine learning is a technology that enables companies to efficiently extract knowledge from unstructured data. With little to no programming, machine learning—and more precisely, machine learning algorithms—can be used to iteratively learn from a given dataset and comprehend patterns, behaviors, and so on.

In this chapter, we will learn how to do the following:

  • Validate the difference of observed effects with statistical analysis
  • Analyze the correlation and causation as well as model relationships between variables
  • Prepare the data for clustering and machine learning models
  • Develop machine learning models for regression and classification...
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