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
Table of Contents (16 chapters)
Mastering Predictive Analytics with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Clients and making requests


When a client issues requests to the server and the downstream application, we might potentially have a major design problem: how do we know in advance what kind of requests we might receive? If we had to re-implement a new set of standard requests every time we developed a web application, it would be difficult to reuse code and write generic services that other programs could call, since their requests would potentially have to change for every web application a client might interact with.

This is the problem solved by the HTTP standard, which describes a standard language and format in which requests are sent between servers and clients, allowing us to rely upon a common command syntax, which could be consumed by many different applications. While we could, in theory, issue some of these commands to our prediction service by pasting a URL into the address bar of our browser (such as GET, described below), this will only cover a subset of the kinds of requests...