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
About the Authors
About the Reviewers
Customer Feedback

Backpropagation and Feedforward neural networks

Training a neural network is an iterative process, which involves discovering values for its weights and its bias terms. These are used in conjunction with the input values to create outputs. After much iteration, the model is tested for the purposes of becoming a full production model that can be used to make predictions.

Training a neural network model is an iterative process, which is a key part of the Cross Industry Standard Process for Data Mining (CRISP-DM) as an integral part of the modeling phase. Training involves working out weights and bias values that lead the inputs towards the preferred output. As part of the training process, the model can be presented with the test data in order to evaluate its accuracy. This will help us to understand how well the model will perform when it is given new data, and we don't know the true output results.

During the training process, rows are presented to the neural network consecutively, one at...