Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Learning Data Mining with Python
  • Table Of Contents Toc
  • Feedback & Rating feedback
Learning Data Mining with Python

Learning Data Mining with Python

By : Robert Layton
3.7 (7)
Buy this Book
close
close
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 (20 chapters)
close
close
close
Learning Data Mining with Python
Icon
Learning Data Mining with Python
Credits
Icon
Credits
About the Author
Icon
About the Author
About the Reviewers
Icon
About the Reviewers
www.PacktPub.com
Icon
www.PacktPub.com
Preface
Icon
Preface
Free Chapter
1
Getting Started with Data Mining
Icon
Getting Started with Data Mining
Icon
Introducing data mining
Icon
Using Python and the IPython Notebook
Icon
A simple affinity analysis example
Icon
A simple classification example
Icon
What is classification?
Icon
Summary
2
Classifying with scikit-learn Estimators
Icon
Classifying with scikit-learn Estimators
Icon
scikit-learn estimators
Icon
Preprocessing using pipelines
Icon
Pipelines
Icon
Summary
3
Predicting Sports Winners with Decision Trees
Icon
Predicting Sports Winners with Decision Trees
Icon
Loading the dataset
Icon
Decision trees
Icon
Sports outcome prediction
Icon
Random forests
Icon
Summary
4
Recommending Movies Using Affinity Analysis
Icon
Recommending Movies Using Affinity Analysis
Icon
Affinity analysis
Icon
The movie recommendation problem
Icon
The Apriori implementation
Icon
Extracting association rules
Icon
Summary
5
Extracting Features with Transformers
Icon
Extracting Features with Transformers
Icon
Feature extraction
Icon
Feature selection
Icon
Feature creation
Icon
Creating your own transformer
Icon
Summary
6
Social Media Insight Using Naive Bayes
Icon
Social Media Insight Using Naive Bayes
Icon
Disambiguation
Icon
Text transformers
Icon
Naive Bayes
Icon
Application
Icon
Summary
7
Discovering Accounts to Follow Using Graph Mining
Icon
Discovering Accounts to Follow Using Graph Mining
Icon
Loading the dataset
Icon
Finding subgraphs
Icon
Summary
8
Beating CAPTCHAs with Neural Networks
chevron up
Icon
Beating CAPTCHAs with Neural Networks
Icon
Artificial neural networks
Icon
Creating the dataset
Icon
Training and classifying
Icon
Improving accuracy using a dictionary
Icon
Summary
9
Authorship Attribution
Icon
Authorship Attribution
Icon
Attributing documents to authors
Icon
Function words
Icon
Support vector machines
Icon
Character n-grams
Icon
Using the Enron dataset
Icon
Summary
10
Clustering News Articles
Icon
Clustering News Articles
Icon
Obtaining news articles
Icon
Extracting text from arbitrary websites
Icon
Grouping news articles
Icon
Clustering ensembles
Icon
Online learning
Icon
Summary
11
Classifying Objects in Images Using Deep Learning
Icon
Classifying Objects in Images Using Deep Learning
Icon
Object classification
Icon
Application scenario and goals
Icon
Deep neural networks
Icon
GPU optimization
Icon
Setting up the environment
Icon
Application
Icon
Summary
12
Working with Big Data
Icon
Working with Big Data
Icon
Big data
Icon
Application scenario and goals
Icon
MapReduce
Icon
Application
Icon
Summary
Next Steps…
Icon
Next Steps…
Icon
Chapter 1 – Getting Started with Data Mining
Icon
Chapter 2 – Classifying with scikit-learn Estimators
Icon
Chapter 3: Predicting Sports Winners with Decision Trees
Icon
Chapter 4 – Recommending Movies Using Affinity Analysis
Icon
Chapter 5 – Extracting Features with Transformers
Icon
Chapter 6 – Social Media Insight Using Naive Bayes
Icon
Chapter 7 – Discovering Accounts to Follow Using Graph Mining
Icon
Chapter 8 – Beating CAPTCHAs with Neural Networks
Icon
Chapter 9 – Authorship Attribution
Icon
Chapter 10 – Clustering News Articles
Icon
Chapter 11: Classifying Objects in Images Using Deep Learning
Icon
Chapter 12 – Working with Big Data
Icon
More resources
Index
Icon
Index

Creating the dataset

In this chapter, we will take on the role of the bad guy. We want to create a program that can beat CAPTCHAs, allowing our comment spam program to advertise on someone's website. It should be noted that our CAPTCHAs will be a little easier that those used on the web today and that spamming isn't a very nice thing to do.

Our CAPTCHAs will be individual English words of four letters only, as shown in the following image:

Our goal will be to create a program that can recover the word from images like this. To do this, we will use four steps:

  1. Break the image into individual letters.

  2. Classify each individual letter.

  3. Recombine the letters to form a word.

  4. Rank words with a dictionary to try to fix errors.

Our CAPTCHA-busting algorithm will make the following assumptions. First, the word will be a whole and valid four-character English word (in fact, we use the same dictionary for creating and busting CAPTCHAs). Second, the word will only contain uppercase letters. No symbols, numbers...

Limited Time Offer

$10p/m for 3 months

Get online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech and supported with AI assistants
Claim Now Or Sign In
End of Section 3
Next Section
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options
font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon

Create a Note

Modal Close icon
You need to login to use this feature.

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Delete Note

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Edit Note

Modal Close icon
Write a note (max 255 characters)
Cancel
Update Note