Predictive Analytics and Machine Learning for Managers
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- FormatePub
- ISBN8987654309
- EAN9798987654309
- Date de parution20/04/2023
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurJibe4Fun Press
Résumé
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.
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.
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.
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.