
Applied Predictive Modeling covers the overall predictive modeling process beginning with the crucial steps of data preprocessing data splitting & foundations of model tuning The text then provides intuitive explanations of numerous common & modern regression & classification techniques always with an emphasis on illustrating & solving real data problems The text illustrates all parts of the modeling process through many hands-on real-life examples & every chapter contains extensive R code for each step of the process This multi-purpose text can be used as an introduction to predictive models & the overall modeling process a practitioner's reference handbook or as a text for advanced undergraduate or graduate level predictive modeling courses To that end each chapter contains problem sets to help solidify the covered concepts & uses data available in the book's R package This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise Readers should have knowledge of basic statistical ideas such as correlation & linear regression analysis While the text is biased against complex equations a mathematical background is needed for advanced topics