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  • Book Overview & Buying Learning Data Mining with Python
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Learning Data Mining with Python

Learning Data Mining with Python

By : Robert Layton
3.7 (7)
Buy this Book
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Learning Data Mining with Python

Learning Data Mining with Python

3.7 (7)
By: Robert Layton
Buy this Book

Overview of this book

If you are a programmer who wants to get started with data mining, then this book is for you.
Table of Contents (20 chapters)
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Learning Data Mining with Python
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Learning Data Mining with Python
Credits
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Credits
About the Author
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About the Author
About the Reviewers
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About the Reviewers
www.PacktPub.com
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www.PacktPub.com
Preface
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Preface
Free Chapter
1
Getting Started with Data Mining
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Getting Started with Data Mining
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Introducing data mining
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Using Python and the IPython Notebook
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A simple affinity analysis example
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A simple classification example
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What is classification?
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Summary
2
Classifying with scikit-learn Estimators
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Classifying with scikit-learn Estimators
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scikit-learn estimators
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Preprocessing using pipelines
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Pipelines
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Summary
3
Predicting Sports Winners with Decision Trees
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Predicting Sports Winners with Decision Trees
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Loading the dataset
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Decision trees
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Sports outcome prediction
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Random forests
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Summary
4
Recommending Movies Using Affinity Analysis
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Recommending Movies Using Affinity Analysis
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Affinity analysis
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The movie recommendation problem
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The Apriori implementation
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Extracting association rules
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Summary
5
Extracting Features with Transformers
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Extracting Features with Transformers
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Feature extraction
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Feature selection
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Feature creation
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Creating your own transformer
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Summary
6
Social Media Insight Using Naive Bayes
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Social Media Insight Using Naive Bayes
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Disambiguation
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Text transformers
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Naive Bayes
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Application
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Summary
7
Discovering Accounts to Follow Using Graph Mining
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Discovering Accounts to Follow Using Graph Mining
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Loading the dataset
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Finding subgraphs
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Summary
8
Beating CAPTCHAs with Neural Networks
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Beating CAPTCHAs with Neural Networks
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Artificial neural networks
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Creating the dataset
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Training and classifying
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Improving accuracy using a dictionary
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Summary
9
Authorship Attribution
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Authorship Attribution
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Attributing documents to authors
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Function words
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Support vector machines
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Character n-grams
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Using the Enron dataset
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Summary
10
Clustering News Articles
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Clustering News Articles
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Obtaining news articles
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Extracting text from arbitrary websites
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Grouping news articles
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Clustering ensembles
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Online learning
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Summary
11
Classifying Objects in Images Using Deep Learning
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Classifying Objects in Images Using Deep Learning
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Object classification
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Application scenario and goals
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Deep neural networks
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GPU optimization
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Setting up the environment
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Application
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Summary
12
Working with Big Data
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Working with Big Data
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Big data
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Application scenario and goals
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MapReduce
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Application
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Summary
Next Steps…
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Next Steps…
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Chapter 1 – Getting Started with Data Mining
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Chapter 2 – Classifying with scikit-learn Estimators
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Chapter 3: Predicting Sports Winners with Decision Trees
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Chapter 4 – Recommending Movies Using Affinity Analysis
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Chapter 5 – Extracting Features with Transformers
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Chapter 6 – Social Media Insight Using Naive Bayes
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Chapter 7 – Discovering Accounts to Follow Using Graph Mining
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Chapter 8 – Beating CAPTCHAs with Neural Networks
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Chapter 9 – Authorship Attribution
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Chapter 10 – Clustering News Articles
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Chapter 11: Classifying Objects in Images Using Deep Learning
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Chapter 12 – Working with Big Data
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More resources
Index
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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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