Predictive Analytics and Machine Learning for Managers

<p>This book was written by the architect of two MS Analytics programs and one undergraduate specialization in Business Analytics with over a decade of experience teaching and practicing predictive analytics and co-chairing premier academic conference mini-track in this field. The author's goal is to provide strong but understandable conceptual foundations and practical material for graduate students and managers describing how to frame a business question identify various model specification (i.e. feature engineering) and model methods (explainable and black box) select the optimal model based on the bias variance and cross-validation testing and interpret results with meaningful storytelling for clients and managers. The book contains two components: (1) the main text with two sections-one with conceptual mathematical and managerial foundations the other about advanced predictive modeling methods based on machine learning. The main text is further subdivided into two sections-Section 1 contains basic fundamentals of statistics and predictive modeling; Section 2 provides a deeper discussion of machine learning and advance predictive modeling approaches based on machine learning and cross-validation methods; and (2) a free appendix companion with annotated R Markdown code with hands-on applications posted in GitHub.</p>