Predictive analytics incorporates a variety of statistical techniques from predictive modeling, machine learning, and data mining that aim to analyze current and historical facts to produce results referred to as predictions about the future or otherwise unknown events.

R is an open source programming language that is widely used among statisticians and data miners for predictive modeling and data mining. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems.

This book builds upon its first edition, meaning to be both a guide and a reference to the reader wanting to move beyond the basics of predictive modeling. The book begins with a dedicated chapter on the language of models as well as the predictive modeling process. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real-world datasets.

This second edition provides up-to-date in-depth information on topics such as Performance Metrics and Learning Curves, Polynomial Regression, Poisson and Negative Binomial Regression, back-propagation, Radial Basis Function Networks, and more. A chapter has also been added that focuses on working with very large datasets. By the end of this book, you will have explored and tested the most popular modeling techniques in use on real-world datasets and mastered a diverse range of techniques in predictive analytics.