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Learning Data Mining with Python

Learning Data Mining with Python

By : Robert Layton
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Learning Data Mining with Python

Learning Data Mining with Python

By: Robert Layton

Overview of this book

This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK. You will gain hands on experience with complex data types including text, images, and graphs. You will also discover object detection using Deep Neural Networks, which is one of the big, difficult areas of machine learning right now. With restructured examples and code samples updated for the latest edition of Python, each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will have great insights into using Python for data mining and understanding of the algorithms as well as implementations.
Table of Contents (14 chapters)
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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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