Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789346343
Length 464 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Yoon Hyup Hwang Yoon Hyup Hwang
Author Profile Icon Yoon Hyup Hwang
Yoon Hyup Hwang
Arrow right icon
View More author details
Toc

Table of Contents (20) Chapters Close

Preface 1. Section 1: Introduction and Environment Setup FREE CHAPTER
2. Data Science and Marketing 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...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image