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Data Science for Marketing Analytics

You're reading from   Data Science for Marketing Analytics A practical guide to forming a killer marketing strategy through data analysis with Python

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Product type Paperback
Published in Sep 2021
Publisher Packt
ISBN-13 9781800560475
Length 636 pages
Edition 2nd Edition
Languages
Tools
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Authors (3):
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Vishwesh Ravi Shrimali Vishwesh Ravi Shrimali
Author Profile Icon Vishwesh Ravi Shrimali
Vishwesh Ravi Shrimali
Mirza Rahim Baig Mirza Rahim Baig
Author Profile Icon Mirza Rahim Baig
Mirza Rahim Baig
Gururajan Govindan Gururajan Govindan
Author Profile Icon Gururajan Govindan
Gururajan Govindan
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Toc

Table of Contents (11) Chapters Close

Preface
1. Data Preparation and Cleaning 2. Data Exploration and Visualization FREE CHAPTER 3. Unsupervised Learning and Customer Segmentation 4. Evaluating and Choosing the Best Segmentation Approach 5. Predicting Customer Revenue Using Linear Regression 6. More Tools and Techniques for Evaluating Regression Models 7. Supervised Learning: Predicting Customer Churn 8. Fine-Tuning Classification Algorithms 9. Multiclass Classification Algorithms Appendix

3. Unsupervised Learning and Customer Segmentation

Overview

In this chapter, you will implement one of the most powerful techniques in marketing – customer segmentation. You will begin by understanding the need for customer segmentation, following which you will study and implement the machine learning approach to segmentation. You will use the k-means clustering algorithm to segment customers and then analyze the obtained segments to gain an understanding of the results so that businesses can act on them.

By the end of this chapter, you will be able to perform segmentation using relevant tools and techniques and analyze the results of the segmented data. You will also be comfortable with using the k-means algorithm, a machine learning approach to segmentation.

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