Predictive Analytics in R

Predictive Modeling in R is a case-study based book emphasizing the iterative nature of the predictive modeling process.
For each case study presented in Predictive Modeling in R , the four major phases of the modeling process are covered: 1) data acquisition, cleaning, and reshaping; 2) exploratory data analysis; 3) model construction; and 4) model tuning and validation. At each phase, the authors describe the actual challenges encountered and the tools necessary for achieving successful predictive modeling with R.
In practice, most of your data nor the analysis will come in a neatly organized package. So by working through the examples in detail, Predictive Modeling in R can help you develop into a smarter, more confident modeler. This book:
Uses a practical, case-study approach to explain key concepts and techniques in the predictive modeling process with R.
Takes you through the steps of a real predictive analysis from data acquisition to model validation.
Teaches common approaches to modeling in genetics, social media, marketing, and algorithmic trading.
Acknowledges that formal modeling is a small part of the framework, and emphasizes data and model visualizations and comparisons.