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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

Creating the dataset


In this chapter, we will take on the role of the bad guy. We want to create a program that can beat CAPTCHAs, allowing our comment spam program to advertise on someone's website. It should be noted that our CAPTCHAs will be a little easier that those used on the web today and that spamming isn't a very nice thing to do.

Our CAPTCHAs will be individual English words of four letters only, as shown in the following image:

Our goal will be to create a program that can recover the word from images like this. To do this, we will use four steps:

  1. Break the image into individual letters.

  2. Classify each individual letter.

  3. Recombine the letters to form a word.

  4. Rank words with a dictionary to try to fix errors.

Our CAPTCHA-busting algorithm will make the following assumptions. First, the word will be a whole and valid four-character English word (in fact, we use the same dictionary for creating and busting CAPTCHAs). Second, the word will only contain uppercase letters. No symbols, numbers...

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