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Learning Data Mining with Python

By : Robert Layton

3.7 (7)

Learning Data Mining with Python

3.7 (7)

By: Robert Layton

Overview of this book

If you are a programmer who wants to get started with data mining, then this book is for you.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Getting Started with Data Mining

1. Getting Started with Data Mining

Introducing data mining

Using Python and the IPython Notebook

A simple affinity analysis example

A simple classification example

What is classification?

Summary

2. Classifying with scikit-learn Estimators

2. Classifying with scikit-learn Estimators

scikit-learn estimators

Preprocessing using pipelines

Pipelines

Summary

3. Predicting Sports Winners with Decision Trees

3. Predicting Sports Winners with Decision Trees

Loading the dataset

Decision trees

Sports outcome prediction

Random forests

Summary

4. Recommending Movies Using Affinity Analysis

4. Recommending Movies Using Affinity Analysis

Affinity analysis

The movie recommendation problem

The Apriori implementation

Extracting association rules

Summary

5. Extracting Features with Transformers

5. Extracting Features with Transformers

Feature extraction

Feature selection

Feature creation

Creating your own transformer

Summary

6. Social Media Insight Using Naive Bayes

6. Social Media Insight Using Naive Bayes

Disambiguation

Text transformers

Naive Bayes

Application

Summary

7. Discovering Accounts to Follow Using Graph Mining

7. Discovering Accounts to Follow Using Graph Mining

Loading the dataset

Finding subgraphs

Summary

8. Beating CAPTCHAs with Neural Networks

8. Beating CAPTCHAs with Neural Networks

Artificial neural networks

Creating the dataset

Training and classifying

Improving accuracy using a dictionary

Summary

9. Authorship Attribution

9. Authorship Attribution

Attributing documents to authors

Function words

Support vector machines

Character n-grams

Using the Enron dataset

Summary

10. Clustering News Articles

10. Clustering News Articles

Obtaining news articles

Extracting text from arbitrary websites

Grouping news articles

Clustering ensembles

Online learning

Summary

11. Classifying Objects in Images Using Deep Learning

11. Classifying Objects in Images Using Deep Learning

Object classification

Application scenario and goals

Deep neural networks

GPU optimization

Setting up the environment

Application

Summary

12. Working with Big Data

12. Working with Big Data

Big data

Application scenario and goals

MapReduce

Application

Summary

A. Next Steps…

A. Next Steps…

Chapter 1 – Getting Started with Data Mining

Chapter 2 – Classifying with scikit-learn Estimators

Chapter 3: Predicting Sports Winners with Decision Trees

Chapter 4 – Recommending Movies Using Affinity Analysis

Chapter 5 – Extracting Features with Transformers

Chapter 6 – Social Media Insight Using Naive Bayes

Chapter 7 – Discovering Accounts to Follow Using Graph Mining

Chapter 8 – Beating CAPTCHAs with Neural Networks

Chapter 9 – Authorship Attribution

Chapter 10 – Clustering News Articles

Chapter 11: Classifying Objects in Images Using Deep Learning

Chapter 12 – Working with Big Data

More resources

Index

Index

Chapter 6. Social Media Insight Using Naive Bayes

Text-based datasets contain a lot of information, whether they are books, historical documents, social media, e-mail, or any of the other ways we communicate via writing. Extracting features from text-based datasets and using them for classification is a difficult problem. There are, however, some common patterns for text mining.

We look at disambiguating terms in social media using the Naive Bayes algorithm, which is a powerful and surprisingly simple algorithm. Naive Bayes takes a few shortcuts to properly compute the probabilities for classification, hence the term naive in the name. It can also be extended to other types of datasets quite easily and doesn't rely on numerical features. The model in this chapter is a baseline for text mining studies, as the process can work reasonably well for a variety of datasets.

We will cover the following topics in this chapter:

Downloading data from social network APIs
Transformers for...

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Tech Concepts

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Programming languages

73

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