Book Image

Mastering Predictive Analytics with Python

By : Joseph Babcock
Book Image

Mastering Predictive Analytics with Python

By: Joseph Babcock

Overview of this book

The volume, diversity, and speed of data available has never been greater. Powerful machine learning methods can unlock the value in this information by finding complex relationships and unanticipated trends. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications to deliver insights that are of tremendous value to their organizations. In Mastering Predictive Analytics with Python, you will learn the process of turning raw data into powerful insights. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications and how to quickly apply these methods to your own data to create robust and scalable prediction services. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates not only how these methods work, but how to implement them in practice. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring the insights of predictive modeling to life

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Mastering Predictive Analytics with Python
Joseph Babcock
Aug, 2016 | 334 pages
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Table of Contents (16 chapters)
Mastering Predictive Analytics with Python
Mastering Predictive Analytics with Python
Credits
Credits
About the Author
About the Author
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
From Data to Decisions – Getting Started with Analytic Applications
From Data to Decisions – Getting Started with Analytic Applications
Designing an advanced analytic solution
Case study: sentiment analysis of social media feeds
Case study: targeted e-mail campaigns
Summary
2
Exploratory Data Analysis and Visualization in Python
Exploratory Data Analysis and Visualization in Python
Exploring categorical and numerical data in IPython
Time series analysis
Working with geospatial data
Introduction to PySpark
Summary
3
Finding Patterns in the Noise – Clustering and Unsupervised Learning
Finding Patterns in the Noise – Clustering and Unsupervised Learning
Similarity and distance metrics
Affinity propagation – automatically choosing cluster numbers
k-medoids
Agglomerative clustering
Streaming clustering in Spark
Summary
4
Connecting the Dots with Models – Regression Methods
Connecting the Dots with Models – Regression Methods
Linear regression
Tree methods
Scaling out with PySpark – predicting year of song release
Summary
5
Putting Data in its Place – Classification Methods and Analysis
Putting Data in its Place – Classification Methods and Analysis
Logistic regression
Fitting the model
Evaluating classification models
Separating Nonlinear boundaries with Support vector machines
Comparing classification methods
Case study: fitting classifier models in pyspark
Summary
6
Words and Pixels – Working with Unstructured Data
Words and Pixels – Working with Unstructured Data
Working with textual data
Principal component analysis
Images
Case Study: Training a Recommender System in PySpark
Summary
7
Learning from the Bottom Up – Deep Networks and Unsupervised Features
Learning from the Bottom Up – Deep Networks and Unsupervised Features
Learning patterns with neural networks
The TensorFlow library and digit recognition
Summary
8
Sharing Models with Prediction Services
Sharing Models with Prediction Services
The architecture of a prediction service
Clients and making requests
Server – the web traffic controller
Persisting information with database systems
Case study – logistic regression service
Summary
9
Reporting and Testing – Iterating on Analytic Systems
Reporting and Testing – Iterating on Analytic Systems
Checking the health of models with diagnostics
Iterating on models through A/B testing
Guidelines for communication
Summary
Index
Index

Chapter 6. Words and Pixels – Working with Unstructured Data

Most of the data we have looked at thus far is composed of rows and columns with numerical or categorical values. This sort of information fits in both traditional spreadsheet software and the interactive Python notebooks used in the previous exercises. However, data is increasingly available in both this form, usually called structured data, and more complex formats such as images and free text. These other data types, also known as unstructured data, are more challenging than tabular information to parse and transform into features that can be used in machine learning algorithms.

What makes unstructured data challenging to use? It is challenging largely because images and text are extremely high dimensional, consisting of a much larger number of columns or features than we have seen previously. For example, this means that a document may have thousands of words, or an image thousands of individual pixels. Each of these components...

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