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Learning Data Mining with Python

You're reading from   Learning Data Mining with Python Use Python to manipulate data and build predictive models

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787126787
Length 358 pages
Edition 2nd Edition
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Concepts
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Toc

Table of Contents (14) 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. Features and scikit-learn Transformers 6. Social Media Insight using Naive Bayes 7. Follow Recommendations Using Graph Mining 8. Beating CAPTCHAs with Neural Networks 9. Authorship Attribution 10. Clustering News Articles 11. Object Detection in Images using Deep Neural Networks 12. Working with Big Data 13. Next Steps...

Affinity analysis


Affinity analysis is the task of determining when objects are used in similar ways. In the previous chapter, we focused on whether the objects themselves are similar - in our case whether the games were similar in nature. The data for affinity analysis is often described in the form of a transaction. Intuitively, this comes from a transaction at a store—determining when objects are purchased together as a way to recommend products to users that they might purchase.

However, affinity analysis can be applied to many processes that do not use transactions in this sense:

  • Fraud detection
  • Customer segmentation
  • Software optimization
  • Product recommendations

Affinity analysis is usually much more exploratory than classification. At the very least, we often simply rank the results and choose the top five recommendations (or some other number), rather than expect the algorithm to give us a specific answer.

Furthermore, we often don't have the complete dataset we expect for many classification...

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