The book is now available via most online shops such as CRC, Amazon, The Book Repository, etc.
This website provides you with an outline of each chapter, the table of contents and the data and processes required to follow and implement the use case. We are still in the final process of collating all process and data files. Most processes and data downloads are already available. For questions please email markus.hofmann@itb.ie.
Preface
Data and the ability to make the best use of it are becoming more and more crucial for today’s and tomorrow’s companies, organizations, governments, scientists, and societies to tackle everyday challenges as well as complex problems and to stay competitive. Data mining, predictive analytics, and business analytics leverage these data, provide unprecedented insights, enable better-informed decisions, deliver forecasts, and help to solve increasingly complex problems. Companies and organizations collect growing amounts of data from all kinds of internal and external sources and become more and more data-driven. Powerful tools for mastering data analytics and the know-how to use them are essential to not fall behind, but to gain competitive advantages, and to increase insights, e ectiveness, eciency, growth, and pro tability.
This book provides an introduction to data mining and business analytics, to the most powerful and exible open source software solutions for data mining and business analytics, namely RapidMiner and RapidAnalytics, and to many application use cases in scienti c research, medicine, industry, commerce, and diverse other sectors. RapidMiner and RapidAnalytics and their extensions used in this book are all freely available as open source software community editions and can be downloaded from http://www.RapidMiner.com
Each chapter of this book describes an application use case, how to approach it with data mining methods, and how to implement it with RapidMiner and RapidAnalytics. These application-oriented chapters do not only provide you with the necessary analytics knowhow to solve these problems and tasks, but also with easy-to-follow reproducible step-by-step descriptions for accomplishing this with RapidMiner and RapidAnalytics. The datasets and RapidMiner processes used in this book are available from the companion web page of this book: http://www.RapidMinerBook.com
This application-oriented analytics use case collection will quickly enable you to solve similar problems e ectively yourself. The case studies can serve as blue prints for your own data mining applications.