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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 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

Choosing the Number of Clusters


In the previous chapter, we just used a predefined number of clusters, but in the real world, we don’t always know what number of clusters to expect. There are different ways of trying to come up with the correct number of clusters. In this chapter, we will start with two. First, we will learn about simple visual inspection, which has the advantages of being easy and intuitive but relies heavily on individual judgement and subjectivity. We will then learn about the elbow method with sum of squared errors, which is partially quantitative but still relies on individual judgement and is more abstract than choosing based on visual inspection. Later in this chapter, we will also learn about using the silhouette score, which removes subjectivity from the judgment but is also quite abstract.

As we learn about these different methods, there is one overriding principle you should keep in mind: the quantitative measures only tell you how well that number of clusters...

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