We are now going to implement the Naive Bayes algorithm using mrjob, allowing it to process our dataset. Technically our version will be a reduced version of most Naive Bayes' implementations, without many of the features that you would expect like smoothing small values.
Learning Data Mining with Python
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Learning Data Mining with Python
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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)
Preface
Free Chapter
Getting Started with Data Mining
Classifying with scikit-learn Estimators
Predicting Sports Winners with Decision Trees
Recommending Movies Using Affinity Analysis
Features and scikit-learn Transformers
Social Media Insight using Naive Bayes
Follow Recommendations Using Graph Mining
Beating CAPTCHAs with Neural Networks
Authorship Attribution
Clustering News Articles
Object Detection in Images using Deep Neural Networks
Working with Big Data
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