Applied Predictive Modeling

Applied Predictive Modeling is a practical guide to predictive modeling techniques and their applications in data science. The book covers topics like data preprocessing, model selection, and performance evaluation. Kuhn and Johnson provide insights into the R programming language and its applications in predictive modeling. They also discuss advanced topics like ensemble methods, support vector machines, and neural networks. By sharing insights from their experience in data science and machine learning, the authors equip readers with the knowledge and skills to build and deploy predictive models for real-world applications.