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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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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

Using KNN to predict a rating for a movie

Alright, we're going to actually take the simple idea of KNN and apply that to a more complicated problem, and that's predicting the rating of a movie given just its genre and rating information. So, let's dive in and actually try to predict movie ratings just based on the KNN algorithm and see where we get. So, if you want to follow along, go ahead and open up the KNN.ipynb and you can play along with me.

What we're going to do is define a distance metric between movies just based on their metadata. By metadata I just mean information that is intrinsic to the movie, that is, the information associated with the movie. Specifically, we're going to look at the genre classifications of the movie.

Every movie in our MovieLens dataset has additional information on what genre it belongs to. A movie can belong to more...

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