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Hands-On Data Science and Python Machine Learning

You're reading from   Hands-On Data Science and Python Machine Learning Perform data mining and machine learning efficiently using Python and Spark

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787280748
Length 420 pages
Edition 1st Edition
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Concepts
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Author (1):
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Frank Kane Frank Kane
Author Profile Icon Frank Kane
Frank Kane
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Table of Contents (11) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Statistics and Probability Refresher, and Python Practice 3. Matplotlib and Advanced Probability Concepts 4. Predictive Models 5. Machine Learning with Python 6. Recommender Systems 7. More Data Mining and Machine Learning Techniques 8. Dealing with Real-World Data 9. Apache Spark - Machine Learning on Big Data 10. Testing and Experimental Design

K-nearest neighbors - concepts

Let's talk about a few data mining and machine learning techniques that employers expect you to know about. We'll start with a really simple one called KNN for short. You're going to be surprised at just how simple a good supervised machine learning technique can be. Let's take a look!

KNN sounds fancy but it's actually one of the simplest techniques out there! Let's say you have a scatter plot and you can compute the distance between any two points on that scatter plot. Let's assume that you have a bunch of data that you've already classified, that you can train the system from. If I have a new data point, all I do is look at the KNN based on that distance metric and let them all vote on the classification of that new point.

Let's imagine that the following scatter plot is plotting movies. The squares...

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