Springer Applied Predictive Modeling - Data Science Text

Applied Predictive Modeling by Springer provides a comprehensive guide to the entire predictive modeling process. This text begins with essential steps such as data preprocessing, data splitting, and the foundations of model tuning. Readers gain intuitive explanations of various regression and classification techniques through the lens of real data problems. By using hands-on, real-life examples, the book illustrates every part of the modeling workflow. Each chapter includes extensive R code to support each step of the process, making it a practical resource for those learning to solve complex data challenges. This multi-purpose text serves as an introduction to the field, focusing on practical application and real-world problem solving.