Title: Applied Predictive Modeling
Summary:
Content Overview:
“Applied Predictive Modeling” offers a comprehensive exploration of predictive modeling techniques and their practical applications in various fields. Authored by Max Kuhn and Kjell Johnson, this book provides a thorough introduction to the principles and methodologies of predictive modeling, covering topics such as data preprocessing, model selection, feature engineering, and model evaluation. Through real-world examples and case studies, readers learn how to effectively apply predictive modeling algorithms to solve complex problems in areas such as finance, healthcare, marketing, and more. The book also emphasizes the importance of data visualization and interpretation in the predictive modeling process, empowering readers to make informed decisions based on their model results.
Authors:
Max Kuhn: Max Kuhn is a renowned data scientist and the creator of the caret package in R, a widely used toolkit for predictive modeling and machine learning. With a background in statistics and computer science, Kuhn has extensive experience in developing and applying predictive modeling techniques in various domains. He is also a senior director of nonclinical statistics at Pfizer Global R&D, where he applies his expertise in data analysis and modeling to drug development research.
Kjell Johnson: Kjell Johnson is a data scientist and consultant with expertise in predictive modeling, statistical analysis, and data visualization. He has worked with numerous organizations across industries to develop predictive models and data-driven solutions to complex business problems. Johnson’s practical experience and deep understanding of predictive modeling concepts make him a valuable co-author of “Applied Predictive Modeling.”
Publisher:
“Applied Predictive Modeling” is published by Springer