Book Image

Python Machine Learning (Wiley)

By : Wei-Meng Lee
Book Image

Python Machine Learning (Wiley)

By: Wei-Meng Lee

Overview of this book

With computing power increasing exponentially and costs decreasing at the same time, this is the best time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines. Python Machine Learning begins by covering some fundamental libraries used in Python that make machine learning possible. You'll learn how to manipulate arrays of numbers with NumPy and use pandas to deal with tabular data. Once you have a firm foundation in the basics, you'll explore machine learning using Python and the scikit-learn libraries. You'll learn how to visualize data by plotting different types of charts and graphs using the matplotlib library. You'll gain a solid understanding of how the various machine learning algorithms work behind the scenes. The later chapters explore the common machine learning algorithms, such as regression, clustering, and classification, and discuss how to deploy the models that you have built, so that they can be used by client applications running on mobile and desktop devices. By the end of the book, you'll have all the knowledge you need to begin machine learning using Python.
Table of Contents (16 chapters)
Free Chapter
1
Cover
2
Introduction
11
CHAPTER 9: Supervised Learning—Classification Using K‐Nearest Neighbors (KNN)
15
Index
16
End User License Agreement

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‐...