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  • Book Overview & Buying Learning Data Mining with Python
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Learning Data Mining with Python

Learning Data Mining with Python

By : Robert Layton
3.7 (7)
Buy this Book
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Learning Data Mining with Python

Learning Data Mining with Python

3.7 (7)
By: Robert Layton
Buy this Book

Overview of this book

If you are a programmer who wants to get started with data mining, then this book is for you.
Table of Contents (15 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Getting Started with Data Mining
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1. Getting Started with Data Mining
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Introducing data mining
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Using Python and the IPython Notebook
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A simple affinity analysis example
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A simple classification example
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What is classification?
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Summary
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2. Classifying with scikit-learn Estimators
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2. Classifying with scikit-learn Estimators
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scikit-learn estimators
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Preprocessing using pipelines
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Pipelines
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Summary
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3. Predicting Sports Winners with Decision Trees
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3. Predicting Sports Winners with Decision Trees
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Loading the dataset
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Decision trees
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Sports outcome prediction
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Random forests
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Summary
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4. Recommending Movies Using Affinity Analysis
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4. Recommending Movies Using Affinity Analysis
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Affinity analysis
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The movie recommendation problem
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The Apriori implementation
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Extracting association rules
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Summary
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5. Extracting Features with Transformers
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5. Extracting Features with Transformers
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Feature extraction
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Feature selection
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Feature creation
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Creating your own transformer
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Summary
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6. Social Media Insight Using Naive Bayes
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6. Social Media Insight Using Naive Bayes
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Disambiguation
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Text transformers
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Naive Bayes
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Application
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Summary
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7. Discovering Accounts to Follow Using Graph Mining
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7. Discovering Accounts to Follow Using Graph Mining
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Loading the dataset
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Finding subgraphs
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Summary
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8. Beating CAPTCHAs with Neural Networks
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8. Beating CAPTCHAs with Neural Networks
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Artificial neural networks
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Creating the dataset
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Training and classifying
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Improving accuracy using a dictionary
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Summary
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9. Authorship Attribution
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9. Authorship Attribution
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Attributing documents to authors
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Function words
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Support vector machines
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Character n-grams
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Using the Enron dataset
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Summary
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10. Clustering News Articles
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10. Clustering News Articles
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Obtaining news articles
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Extracting text from arbitrary websites
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Grouping news articles
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Clustering ensembles
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Online learning
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Summary
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11. Classifying Objects in Images Using Deep Learning
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11. Classifying Objects in Images Using Deep Learning
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Object classification
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Application scenario and goals
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Deep neural networks
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GPU optimization
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Setting up the environment
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Application
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Summary
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12. Working with Big Data
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12. Working with Big Data
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Big data
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Application scenario and goals
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MapReduce
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Application
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Summary
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A. Next Steps…
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A. Next Steps…
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Chapter 1 – Getting Started with Data Mining
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Chapter 2 – Classifying with scikit-learn Estimators
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Chapter 3: Predicting Sports Winners with Decision Trees
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Chapter 4 – Recommending Movies Using Affinity Analysis
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Chapter 5 – Extracting Features with Transformers
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Chapter 6 – Social Media Insight Using Naive Bayes
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Chapter 7 – Discovering Accounts to Follow Using Graph Mining
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Chapter 8 – Beating CAPTCHAs with Neural Networks
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Chapter 9 – Authorship Attribution
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Chapter 10 – Clustering News Articles
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Chapter 11: Classifying Objects in Images Using Deep Learning
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Chapter 12 – Working with Big Data
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More resources
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Index
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Index

Chapter 6 – Social Media Insight Using Naive Bayes

Spam detection

http://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter

Using the concepts in this chapter, you can create a spam detection method that is able to view a social media post and determine whether it is spam or not. Try this out by first creating a dataset of spam/not-spam posts, implementing the text mining algorithms, and then evaluating them.

One important consideration with spam detection is the false-positive/false-negative ratio. Many people would prefer to have a couple of spam messages slip through, rather than miss out on a legitimate message because the filter was too aggressive in stopping the spam. In order to turn your method for this, you can use a Grid Search with the f1-score as the evaluation criteria. See the above link for information on how to do this.

Natural language processing and part-of-speech tagging

http://www.nltk.org/book/ch05.html

The techniques we used in this chapter...

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