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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
Languages
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...

Predicting words

Now that we have a classifier for predicting individual letters, we now move onto the next step in our plan - predicting words. To do this, we want to predict each letter from each of these segments, and put those predictions together to form the predicted word from a given CAPTCHA.

Our function will accept a CAPTCHA and the trained neural network, and it will return the predicted word:

def predict_captcha(captcha_image, neural_network):
subimages = segment_image(captcha_image)
# Perform the same transformations we did for our training data
dataset = np.array([np.resize(subimage, (20, 20)) for subimage in subimages])
X_test = dataset.reshape((dataset.shape[0], dataset.shape[1] * dataset.shape[2]))
# Use predict_proba and argmax to get the most likely prediction
y_pred = neural_network.predict_proba(X_test)
predictions = np.argmax(y_pred, axis=1)

# Convert predictions to letters...
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