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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

Product type Paperback

Published in Apr 2017

Publisher Packt

ISBN-13 9781787126787

Length 358 pages

Edition 2nd Edition

Languages

Python

Concepts

Data Mining

Table of Contents (14) Chapters

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...

Creating the dataset

In this chapter, to spice up things a little, let us 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 than those used on the web today and that spamming isn't a very nice thing to do.

Note

We play the bad guy today, but please don't use this against real world sites. One reason to "play the bad guy" is to help improve the security of our website, by looking for issues with it.

Our experiment will simplify a CAPTCHA to 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:

Break the image into individual letters.
Classify each individual letter.
Recombine the letters to form a word.
Rank words with a dictionary to try to fix errors.

Note

Our CAPTCHA-busting algorithm...

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