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Interactive Dashboards and Data Apps with Plotly and Dash

You're reading from   Interactive Dashboards and Data Apps with Plotly and Dash Harness the power of a fully fledged frontend web framework in Python – no JavaScript required

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Product type Paperback
Published in May 2021
Publisher Packt
ISBN-13 9781800568914
Length 364 pages
Edition 1st Edition
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Author (1):
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Elias Dabbas Elias Dabbas
Author Profile Icon Elias Dabbas
Elias Dabbas
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Building a Dash App
2. Chapter 1: Overview of the Dash Ecosystem FREE CHAPTER 3. Chapter 2: Exploring the Structure of a Dash App 4. Chapter 3: Working with Plotly's Figure Objects 5. Chapter 4: Data Manipulation and Preparation, Paving the Way to Plotly Express 6. Section 2: Adding Functionality to Your App with Real Data
7. Chapter 5: Interactively Comparing Values with Bar Charts and Dropdown Menus 8. Chapter 6: Exploring Variables with Scatter Plots and Filtering Subsets with Sliders 9. Chapter 7: Exploring Map Plots and Enriching Your Dashboards with Markdown 10. Chapter 8: Calculating the Frequency of Your Data with Histograms and Building Interactive Tables 11. Section 3: Taking Your App to the Next Level
12. Chapter 9: Letting Your Data Speak for Itself with Machine Learning 13. Chapter 10: Turbo-charge Your Apps with Advanced Callbacks 14. Chapter 11: URLs and Multi-Page Apps 15. Chapter 12: Deploying Your App 16. Chapter 13: Next Steps 17. Other Books You May Enjoy

Finding the optimal number of clusters

We will now see the options we have in choosing the optimal number of clusters and what that entails, but let's first take a look at the following screenshot to visualize how things progress from having one cluster to eight clusters:

Figure 9.3 – Data points and cluster centers for all possible cluster numbers

Figure 9.3 – Data points and cluster centers for all possible cluster numbers

We can see the full spectrum of possible clusters and how they relate to data points. At the end, when we specified 8, we got the perfect solution, where every data point is a cluster center.

In reality, you might not want to go for the full solution, for two main reasons. Firstly, it is probably going to be prohibitive from a cost perspective. Imagine making 1,000 T-shirts with a few hundred sizes. Secondly, in practical situations, it usually wouldn't add much value to add more clusters after a certain fit has been achieved. Using our T-shirt example, imagine if we have two people with...

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