Thus far, we have studied predictive modeling techniques that use a set of features (columns in a tabular dataset) that are pre-defined for the problem at hand. For example, a user account, an internet transaction, a product, or any other item that is important to a business scenario are often described using properties derived from domain knowledge of a particular industry. More complex data, such as a document, can still be transformed into a vector representing something about the words in the text, and images can be represented by matrix factors as we saw in Chapter 6, Words and Pixels – Working with Unstructured Data. However, with both simple and complex data types, we could easily imagine higher-level interactions between features (for example, a user in a certain country and age range using a particular device is more likely to click on a webpage, while none of these three factors alone are predictive...
Mastering Predictive Analytics with Python
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Mastering Predictive Analytics with Python
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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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Table of Contents (16 chapters)
Mastering Predictive Analytics with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
From Data to Decisions – Getting Started with Analytic Applications
Exploratory Data Analysis and Visualization in Python
Finding Patterns in the Noise – Clustering and Unsupervised Learning
Connecting the Dots with Models – Regression Methods
Putting Data in its Place – Classification Methods and Analysis
Words and Pixels – Working with Unstructured Data
Learning from the Bottom Up – Deep Networks and Unsupervised Features
Sharing Models with Prediction Services
Reporting and Testing – Iterating on Analytic Systems
Index
Customer Reviews