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Data Science for Marketing Analytics

You're reading from   Data Science for Marketing Analytics Achieve your marketing goals with the data analytics power of Python

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Product type Paperback
Published in Mar 2019
Publisher
ISBN-13 9781789959413
Length 420 pages
Edition 1st Edition
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Authors (3):
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Tommy Blanchard Tommy Blanchard
Author Profile Icon Tommy Blanchard
Tommy Blanchard
Debasish Behera Debasish Behera
Author Profile Icon Debasish Behera
Debasish Behera
Pranshu Bhatnagar Pranshu Bhatnagar
Author Profile Icon Pranshu Bhatnagar
Pranshu Bhatnagar
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Toc

Table of Contents (12) Chapters Close

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

Chapter 9. Modeling Customer Choice

Note

Learning Objectives

By the end of this chapter, you will be able to:

  • Describe the logic behind multiclass classification problems

  • Create a multiclass classification classifier

  • Use different sampling techniques to solve the problem of imbalanced data

Note

This chapter covers different types of multiclass classification problems and explains how to calculate performance metrics and deal with imbalanced data.

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