Learning data mining with Python : use Python to manipulate data and build predictive models / Robert Layton.

Format:

Book

Author/Creator:

Layton, Robert, 1986- author.

Language:

English

Subjects (All):

Python (Computer program language).

Physical Description:

1 online resource (348 pages)

Edition:

Second edition.

Other Title:

Use Python to manipulate data and build predictive models

Place of Publication:

Birmingham, [England] ; Mumbai, [India] : Packt Publishing, 2017.

System Details:

text file

Summary:

Harness the power of Python to develop data mining applications, analyze data, delve into machine learning, explore object detection using Deep Neural Networks, and create insightful predictive models. About This Book Use a wide variety of Python libraries for practical data mining purposes. Learn how to find, manipulate, analyze, and visualize data using Python. Step-by-step instructions on data mining techniques with Python that have real-world applications. Who This Book Is For If you are a Python programmer who wants to get started with data mining, then this book is for you. If you are a data analyst who wants to leverage the power of Python to perform data mining efficiently, this book will also help you. No previous experience with data mining is expected. What You Will Learn Apply data mining concepts to real-world problems Predict the outcome of sports matches based on past results Determine the author of a document based on their writing style Use APIs to download datasets from social media and other online services Find and extract good features from difficult datasets Create models that solve real-world problems Design and develop data mining applications using a variety of datasets Perform object detection in images using Deep Neural Networks Find meaningful insights from your data through intuitive visualizations Compute on big data, including real-time data from the internet In Detail 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. Style and approach This book will be your comprehensive guide to learning the various data mining techniques and implementing them in Python. A variety of real-world datasets is used to explain data mining techniques in a very crisp...

Contents:

Cover

Copyright

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

Chapter 1: Getting Started with Data Mining

Introducing data mining

Using Python and the Jupyter Notebook

Installing Python

Installing Jupyter Notebook

Installing scikit-learn

A simple affinity analysis example

What is affinity analysis?

Product recommendations

Loading the dataset with NumPy

Downloading the example code

Implementing a simple ranking of rules

Ranking to find the best rules

A simple classification example

What is classification?

Loading and preparing the dataset

Implementing the OneR algorithm

Testing the algorithm

Summary

Chapter 2: Classifying with scikit-learn Estimators

scikit-learn estimators

Nearest neighbors

Distance metrics

Loading the dataset

Moving towards a standard workflow

Running the algorithm

Setting parameters

Preprocessing

Standard pre-processing

Putting it all together

Pipelines

Chapter 3: Predicting Sports Winners with Decision Trees

Collecting the data

Using pandas to load the dataset

Cleaning up the dataset

Extracting new features

Decision trees

Parameters in decision trees

Using decision trees

Sports outcome prediction

Random forests

How do ensembles work?

Setting parameters in Random Forests

Applying random forests

Engineering new features

Chapter 4: Recommending Movies Using Affinity Analysis

Affinity analysis

Algorithms for affinity analysis

Overall methodology

Dealing with the movie recommendation problem

Obtaining the dataset

Loading with pandas

Sparse data formats

Understanding the Apriori algorithm and its implementation.

Looking into the basics of the Apriori algorithm

Implementing the Apriori algorithm

Extracting association rules

Evaluating the association rules

Chapter 5: Features and scikit-learn Transformers

Feature extraction

Representing reality in models

Common feature patterns

Creating good features

Feature selection

Selecting the best individual features

Feature creation

Principal Component Analysis

Creating your own transformer

The transformer API

Implementing a Transformer

Unit testing

Chapter 6: Social Media Insight using Naive Bayes

Disambiguation

Downloading data from a social network

Loading and classifying the dataset

Creating a replicable dataset from Twitter

Text transformers

Bag-of-words models

n-gram features

Other text features

Naive Bayes

Understanding Bayes' theorem

Naive Bayes algorithm

How it works

Applying of Naive Bayes

Extracting word counts

Converting dictionaries to a matrix

Evaluation using the F1-score

Getting useful features from models

Chapter 7: Follow Recommendations Using Graph Mining

Classifying with an existing model

Getting follower information from Twitter

Building the network

Creating a graph

Creating a similarity graph

Finding subgraphs

Connected components

Optimizing criteria

Chapter 8: Beating CAPTCHAs with Neural Networks

Artificial neural networks

An introduction to neural networks

Creating the dataset

Drawing basic CAPTCHAs

Splitting the image into individual letters

Creating a training dataset

Training and classifying

Back-propagation

Predicting words

Improving accuracy using a dictionary

Ranking mechanisms for word similarity.

Putting it all together

Chapter 9: Authorship Attribution

Attributing documents to authors

Applications and use cases

Authorship attribution

Getting the data

Using function words

Counting function words

Classifying with function words

Support Vector Machines

Classifying with SVMs

Kernels

Character n-grams

Extracting character n-grams

The Enron dataset

Accessing the Enron dataset

Creating a dataset loader

Evaluation

Chapter 10: Clustering News Articles

Trending topic discovery

Using a web API to get data

Reddit as a data source

Extracting text from arbitrary websites

Finding the stories in arbitrary websites

Extracting the content

Grouping news articles

The k-means algorithm

Evaluating the results

Extracting topic information from clusters

Using clustering algorithms as transformers

Clustering ensembles

Evidence accumulation

Implementation

Online learning

Chapter 11: Object Detection in Images using Deep Neural Networks

Object classification

Use cases

Application scenario

Deep neural networks

Intuition

Implementing deep neural networks

An Introduction to TensorFlow

Using Keras

Convolutional Neural Networks

GPU optimization

When to use GPUs for computation

Running our code on a GPU

Setting up the environment

Application

Creating the neural network

Chapter 12: Working with Big Data

Big data

Applications of big data

MapReduce

The intuition behind MapReduce

A word count example

Hadoop MapReduce

Applying MapReduce

Naive Bayes prediction

The mrjob package

Extracting the blog posts.

Training Naive Bayes

Training on Amazon's EMR infrastructure

Appendix: Next Steps...

Getting Started with Data Mining

Scikit-learn tutorials

Extending the Jupyter Notebook

More datasets

Other Evaluation Metrics

More application ideas

Classifying with scikit-learn Estimators

Scalability with the nearest neighbor

More complex pipelines

Comparing classifiers

Automated Learning

Predicting Sports Winners with Decision Trees

More complex features

Dask

Research

Recommending Movies Using Affinity Analysis

New datasets

The Eclat algorithm

Collaborative Filtering

Extracting Features with Transformers

Adding noise

Vowpal Wabbit

word2vec

Social Media Insight Using Naive Bayes

Spam detection

Natural language processing and part-of-speech tagging

Discovering Accounts to Follow Using Graph Mining

More complex algorithms

NetworkX

Beating CAPTCHAs with Neural Networks

Better (worse?) CAPTCHAs

Deeper networks

Reinforcement learning

Authorship Attribution

Increasing the sample size

Blogs dataset

Local n-grams

Clustering News Articles

Clustering Evaluation

Temporal analysis

Real-time clusterings

Classifying Objects in Images Using Deep Learning

Mahotas

Magenta

Working with Big Data

Courses on Hadoop

Pydoop

Recommendation engine

W.I.L.L

More resources

Kaggle competitions

Coursera

Index.

Notes:

Includes bibliographical references and index.

Description based on online resource; title from PDF title page (ebrary, viewed July 12, 2017).

ISBN:

9781787129566

178712956X

OCLC:

987331258