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  • 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)
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Learning Data Mining with Python

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.
Table of Contents (15 chapters)
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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
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
2. Classifying with scikit-learn Estimators
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2. Classifying with scikit-learn Estimators
scikit-learn estimators
Preprocessing using pipelines
Pipelines
Summary
3
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
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
5. Extracting Features with Transformers
5. Extracting Features with Transformers
Feature extraction
Feature selection
Feature creation
Creating your own transformer
Summary
6
6. Social Media Insight Using Naive Bayes
6. Social Media Insight Using Naive Bayes
Disambiguation
Text transformers
Naive Bayes
Application
Summary
7
7. Discovering Accounts to Follow Using Graph Mining
7. Discovering Accounts to Follow Using Graph Mining
Loading the dataset
Finding subgraphs
Summary
8
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
9. Authorship Attribution
9. Authorship Attribution
Attributing documents to authors
Function words
Support vector machines
Character n-grams
Using the Enron dataset
Summary
10
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
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
12. Working with Big Data
12. Working with Big Data
Big data
Application scenario and goals
MapReduce
Application
Summary
13
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
14
Index
Index

Preprocessing using pipelines

When taking measurements of real-world objects, we can often get features in very different ranges. For instance, if we are measuring the qualities of an animal, we might have several features, as follows:

  • Number of legs: This is between the range of 0-8 for most animals, while some have many more!
  • Weight: This is between the range of only a few micrograms, all the way to a blue whale with a weight of 190,000 kilograms!
  • Number of hearts: This can be between zero to five, in the case of the earthworm.

For a mathematical-based algorithm to compare each of these features, the differences in the scale, range, and units can be difficult to interpret. If we used the above features in many algorithms, the weight would probably be the most influential feature due to only the larger numbers and not anything to do with the actual effectiveness of the feature.

One of the methods to overcome this is to use a process called preprocessing to normalize the features so that they...

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