Book Image

Advanced Analytics with R and Tableau

By : Ruben Oliva Ramos, Jen Stirrup, Roberto Rösler
Book Image

Advanced Analytics with R and Tableau

By: Ruben Oliva Ramos, Jen Stirrup, Roberto Rösler

Overview of this book

Tableau and R offer accessible analytics by allowing a combination of easy-to-use data visualization along with industry-standard, robust statistical computation. Moving from data visualization into deeper, more advanced analytics? This book will intensify data skills for data viz-savvy users who want to move into analytics and data science in order to enhance their businesses by harnessing the analytical power of R and the stunning visualization capabilities of Tableau. Readers will come across a wide range of machine learning algorithms and learn how descriptive, prescriptive, predictive, and visually appealing analytical solutions can be designed with R and Tableau. In order to maximize learning, hands-on examples will ease the transition from being a data-savvy user to a data analyst using sound statistical tools to perform advanced analytics. By the end of this book, you will get to grips with advanced calculations in R and Tableau for analytics and prediction with the help of use cases and hands-on examples.
Table of Contents (16 chapters)
Advanced Analytics with R and Tableau
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

Evaluating a neural network model


Another fundamental phase of the CRISP-DM methodology is the evaluation phase, which focuses on the quality of the model, and its ability to meet the overall business objectives. If the model can't meet the objectives, then it's important to understand if there is a business reason why the model doesn't meet the objectives, in addition to technical possibilities that might account for failure. It's also a good time to pause and consider the testing results that you have generated thus far. This is a crucial stage because it can reveal challenges that didn't appear before. That said, it is an interesting phase because you can find new and interesting things for future research directions. Therefore, it's important not to skip it!

Fortunately, we can visualize the results using Tableau so that the neural networks are easier to understand. There are several performance measures for neural networks, and we will explore these in more detail along with a discussion...