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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

Finding movie similarities

Let's apply the concept of item-based collaborative filtering. To start with, movie similarities-figure out what movies are similar to other movies. In particular, we'll try to figure out what movies are similar to Star Wars, based on user rating data, and we'll see what we get out of it. Let's dive in!

Okay so, let's go ahead and compute the first half of item-based collaborative filtering, which is finding similarities between items. Download and open the SimilarMovies.ipynb file.

In this case, we're going to be looking at similarities between movies, based on user behavior. And, we're going to be using some real movie rating data from the GroupLens project. GroupLens.org provides real movie ratings data, by real people who are using the MovieLens.org website to rate movies and get recommendations back for new movies...

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