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Learning Data Mining with Python

You're reading from   Learning Data Mining with Python Harness the power of Python to analyze data and create insightful predictive models

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Product type Paperback
Published in Jul 2015
Publisher Packt
ISBN-13 9781784396053
Length 344 pages
Edition 1st Edition
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Author (1):
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Robert Layton Robert Layton
Author Profile Icon Robert Layton
Robert Layton
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Table of Contents (15) Chapters Close

Preface 1. Getting Started with Data Mining FREE CHAPTER 2. Classifying with scikit-learn Estimators 3. Predicting Sports Winners with Decision Trees 4. Recommending Movies Using Affinity Analysis 5. Extracting Features with Transformers 6. Social Media Insight Using Naive Bayes 7. Discovering Accounts to Follow Using Graph Mining 8. Beating CAPTCHAs with Neural Networks 9. Authorship Attribution 10. Clustering News Articles 11. Classifying Objects in Images Using Deep Learning 12. Working with Big Data A. Next Steps… Index

The movie recommendation problem


Product recommendation is big business. Online stores use it to up-sell to customers by recommending other products that they could buy. Making better recommendations leads to better sales. When online shopping is selling to millions of customers every year, there is a lot of potential money to be made by selling more items to these customers.

Product recommendations have been researched for many years; however, the field gained a significant boost when Netflix ran their Netflix Prize between 2007 and 2009. This competition aimed to determine if anyone can predict a user's rating of a film better than Netflix was currently doing. The prize went to a team that was just over 10 percent better than the current solution. While this may not seem like a large improvement, such an improvement would net millions to Netflix in revenue from better movie recommendations.

Obtaining the dataset

Since the inception of the Netflix Prize, Grouplens, a research group at the...

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