This book is about predictive analytics. Yet, each chapter could easily be handled by an entire volume of its own. So one might think of this a survey of predictive modeling. A predictive model is a statistical model or machine learning model used to predict future behavior based on past behavior. In order to use this book, one should have a basic understanding of mathematical statistics - it is an advanced book. Some theoretical foundations are laid out but not proven, but references are provided for additional coverage. Every chapter culminates in an example using R. R is a free software environment for statistical computing and graphics. You may download R, from a preferred CRAN mirror at http: //www.r-project.org/. The book is organized so that statistical models are presented first (hopefully in a logical order), followed by machine learning models, and then applications: uplift modeling and time series. One could use this a textbook with problem solving in R-but there are no qby-handq exercises.In Proceedings of Credit Scoring and Credit Control VI. Edinburgh: Credit Research Centre, University of Edinburgh Management School. Radcliffe, N., aamp; Surry, P. (2011). Real-World Uplift Modelling with Significance-Based Uplift Trees .
|Title||:||Predictive Analytics using R|
|Publisher||:||Lulu.com - 2015-01-16|