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

Chapter 9. Reporting and Testing – Iterating on Analytic Systems

In previous chapters we have considered many components of an analytical application, from the input data set to the choice of algorithm and tuning parameters, and even illustrated a potential deployment strategy using a web server. In this process, we considered parameters such as scalability, interpretability, and flexibility in making our applications robust to both later refinements of an algorithm and changing requirements of scale. However, these sorts of details miss the most important element of this application: your business partners who hope to derive insight from the model and the continuing needs of the organization. What metrics should we gather on the performance of a model to make the case for its impact? How can we iterate on an initial model to optimize its use for a business application? How can these results be articulated to stakeholders? These sorts of questions are key in conveying the benefit of building...