Applied Predictive Modeling by Max Kuhn and Kjell Johnson

Applied Predictive Modeling is a thorough educational resource and practical guide authored by renowned data scientists Max Kuhn and Kjell Johnson. The resource combines theoretical foundations with hands-on implementation techniques for building predictive models using R programming. It covers the entire modeling workflow, from data preprocessing and feature engineering to model selection, tuning, and performance evaluation. The content integrates with the caret package (Classification and Regression Training) in R, providing practitioners with reproducible code examples and best practices. This tool is essential for data scientists, machine learning engineers, and statisticians who want to master predictive modeling techniques with practical, production-ready approaches. The resource emphasizes understanding model behaviour, avoiding common pitfalls, and applying domain knowledge to real-world problems.