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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
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
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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

Improving accuracy using a dictionary

Rather than just returning the given prediction, we can check whether the word actually exists in our dictionary. If it does, then that is our prediction. If it isn't in the dictionary, we can try and find a word that is similar to it and predict that instead. Note that this strategy relies on our assumption that all CAPTCHA words will be valid English words, and therefore this strategy wouldn't work for a random sequence of characters. This is one reason why some CAPTCHAs don't use words.

There is one issue here—how do we determine the closest word? There are many ways to do this. For instance, we can compare the lengths of words. Two words that have a similar length could be considered more similar. However, we commonly consider words to be similar if they have the same letters in the same positions. This is where the edit distance comes in.

Ranking mechanisms for words

The Levenshtein edit distance is a commonly used method for comparing two short strings...

End of Section 4
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