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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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Table of Contents (12) Chapters Close

Data Science for Marketing Analytics
Preface
1. Data Preparation and Cleaning 2. Data Exploration and Visualization FREE CHAPTER 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 4. Choosing the Best Segmentation Approach

Note

Learning Objectives

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

  • Tune hyperparameters (such as the number of clusters) of clustering algorithms using various methods

  • Use the mean-shift, k-mode, and k-prototype clustering techniques

  • Evaluate and fine-tune clustering

Note

This chapter covers various clustering algorithms (apart from k-means) and explains how they can be evaluated.

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