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## You're reading fromPython Machine Learning (Wiley)

Product type Book
Published in Apr 2019
Publisher Wiley
ISBN-13 9781119545637
Pages 320 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Wei-Meng Lee

1. Cover
2. Introduction
3. CHAPTER 1: Introduction to Machine Learning 4. CHAPTER 2: Extending Python Using NumPy 5. CHAPTER 3: Manipulating Tabular Data Using Pandas 6. CHAPTER 4: Data Visualization Using matplotlib 7. CHAPTER 5: Getting Started with Scikit‐learn for Machine Learning 8. CHAPTER 6: Supervised Learning—Linear Regression 9. CHAPTER 7: Supervised Learning—Classification Using Logistic Regression 10. CHAPTER 8: Supervised Learning—Classification Using Support Vector Machines 11. CHAPTER 9: Supervised Learning—Classification Using K‐Nearest Neighbors (KNN) 12. CHAPTER 10: Unsupervised Learning—Clustering Using K‐Means 13. CHAPTER 11: Using Azure Machine Learning Studio 14. CHAPTER 12: Deploying Machine Learning Models 15. Index

# Using K‐Means to Solve Real‐Life Problems

Suppose you are a clothing designer, and you have been tasked with designing a new series of Bermuda shorts. One of the design problems is that you need to come up with a series of sizes so that it can fit most people. Essentially, you need to have a series of sizes of people with different:

• Waist Circumference
• Upper Leg Length

So, how do you find the right combination of sizes? This is where the K‐Means algorithm comes in handy. The first thing you need to do is to get ahold of a dataset containing the measurements of a group of people (of a certain age range). Using this dataset, you can apply the K‐Means algorithm to group these people into clusters based on the specific measurement of their body parts. Once the clusters are found, you would now have a very clear picture of the sizes for which you need to design.

For the dataset, you can use the Body Measurement dataset from `https://data.world/rhoyt/body‐...`

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