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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

Loading the dataset


In this chapter, our task is to recommend users on online social networks based on shared connections. Our logic is that if two users have the same friends, they are highly similar and worth recommending to each other.

We are going to create a small social graph from Twitter using the API we introduced in the previous chapter. The data we are looking for is a subset of users interested in a similar topic (again, the Python programming language) and a list of all of their friends (people they follow). With this data, we will check how similar two users are, based on how many friends they have in common.

Note

There are many other online social networks apart from Twitter. The reason we have chosen Twitter for this experiment is that their API makes it quite easy to get this sort of information. The information is available from other sites, such as Facebook, LinkedIn, and Instagram, as well. However, getting this information is more difficult.

To start collecting data, set...

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