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Learning Data Mining with Python

By : Robert Layton

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.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

Getting Started with Data Mining

Getting Started with Data Mining

Introducing data mining

Using Python and the Jupyter Notebook

A simple affinity analysis example

Product recommendations

A simple classification example

What is classification?

Summary

Classifying with scikit-learn Estimators

Classifying with scikit-learn Estimators

scikit-learn estimators

Preprocessing

Pipelines

Summary

Predicting Sports Winners with Decision Trees

Predicting Sports Winners with Decision Trees

Loading the dataset

Decision trees

Sports outcome prediction

Random forests

Summary

Recommending Movies Using Affinity Analysis

Recommending Movies Using Affinity Analysis

Affinity analysis

Dealing with the movie recommendation problem

Understanding the Apriori algorithm and its implementation

Summary

Features and scikit-learn Transformers

Features and scikit-learn Transformers

Feature extraction

Feature selection

Feature creation

Principal Component Analysis

Creating your own transformer

Unit testing

Putting it all together

Summary

Social Media Insight using Naive Bayes

Social Media Insight using Naive Bayes

Disambiguation

Downloading data from a social network

Text transformers

Naive Bayes

Applying of Naive Bayes

Getting useful features from models

Summary

Follow Recommendations Using Graph Mining

Follow Recommendations Using Graph Mining

Loading the dataset

Getting follower information from Twitter

Creating a graph

Finding subgraphs

Summary

Beating CAPTCHAs with Neural Networks

Beating CAPTCHAs with Neural Networks

Artificial neural networks

Creating the dataset

Training and classifying

Predicting words

Summary

Authorship Attribution

Authorship Attribution

Attributing documents to authors

Getting the data

Using function words

Support Vector Machines

Character n-grams

The Enron dataset

Putting it all together

Evaluation

Summary

Clustering News Articles

Clustering News Articles

Trending topic discovery

Extracting text from arbitrary websites

Grouping news articles

The k-means algorithm

Clustering ensembles

Online learning

Summary

Object Detection in Images using Deep Neural Networks

Object Detection in Images using Deep Neural Networks

Object classification

Application scenario

Deep neural networks

An Introduction to TensorFlow

Using Keras

GPU optimization

Application

Summary

Working with Big Data

Working with Big Data

Big data

MapReduce

Applying MapReduce

Naive Bayes prediction

Extracting the blog posts

Training Naive Bayes

Putting it all together

Training on Amazon's EMR infrastructure

Summary

Next Steps...

Next Steps...

Getting Started with Data Mining

Classifying with scikit-learn Estimators

Predicting Sports Winners with Decision Trees

Recommending Movies Using Affinity Analysis

Extracting Features with Transformers

Social Media Insight Using Naive Bayes

Discovering Accounts to Follow Using Graph Mining

Beating CAPTCHAs with Neural Networks

Authorship Attribution

Clustering News Articles

Classifying Objects in Images Using Deep Learning

Working with Big Data

More resources

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Social Media Insight using Naive Bayes

Text-based documents contain lots of information. Examples include books, legal documents, social media, and e-mail. Extracting information from text-based documents is critically important to modern AI systems, for example in search engines, legal AI, and automated news services.

Extraction of useful features from text is a difficult problem. Text is not numerical in nature, therefore a model must be used to create features that can be used with data mining algorithms. The good news is that there are some simple models that do a great job at this, including the bag-of-words model that we will use in this chapter.

In this chapter, we look at extracting features from text for use in data mining applications. The specific problem we tackle in this chapter is term disambiguation on social media - determining which meaning a word has based on its context.

We will cover...

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