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Hands-On Data Science for Marketing

You're reading from   Hands-On Data Science for Marketing Improve your marketing strategies with machine learning using Python and R

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789346343
Length 464 pages
Edition 1st Edition
Languages
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Author (1):
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Yoon Hyup Hwang Yoon Hyup Hwang
Author Profile Icon Yoon Hyup Hwang
Yoon Hyup Hwang
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Table of Contents (20) Chapters Close

Preface 1. Section 1: Introduction and Environment Setup
2. Data Science and Marketing FREE CHAPTER 3. Section 2: Descriptive Versus Explanatory Analysis
4. Key Performance Indicators and Visualizations 5. Drivers behind Marketing Engagement 6. From Engagement to Conversion 7. Section 3: Product Visibility and Marketing
8. Product Analytics 9. Recommending the Right Products 10. Section 4: Personalized Marketing
11. Exploratory Analysis for Customer Behavior 12. Predicting the Likelihood of Marketing Engagement 13. Customer Lifetime Value 14. Data-Driven Customer Segmentation 15. Retaining Customers 16. Section 5: Better Decision Making
17. A/B Testing for Better Marketing Strategy 18. What's Next? 19. Other Books You May Enjoy

More machine learning models and packages

In this book, we have mainly used the following five machine learning algorithms that fit into and work the best for our marketing use cases: logistic regression, random forests, ANN, k-means clustering, and collaborative filtering. However, there are many more readily available machine learning algorithms that you may find useful for your future data science and machine learning projects. We will be covering some of the other frequently used machine learning algorithms, what packages to use in Python and R, and where to find more information on these algorithms.

Some of the other machine learning algorithms to consider in your future projects are the following:

  • Nearest neighbors: This is a machine learning algorithm that finds the pre-defined number of closest samples to a new data point. Even though the concept of this algorithm sounds...
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