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
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

Decision trees

In the previous chapter, we discussed explanatory analysis and regression analysis. We are going to continue with that theme and introduce another machine learning algorithm that we can use to draw insights on customer behavior from data. In this chapter, we will be discussing a machine learning algorithm called decision trees: how they learn from the data and how we can interpret their results.

Logistic regression versus decision trees

If you recall from the previous chapter, a logistic regression model learns from the data by finding the linear combination of the feature variables that best estimates the log odds of an event occurring. Decision trees, as the name suggests, learn from the data by growing a...

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