Learn to train predictive models using the recipes package and tidymodels framework in R, focusing on #TidyTuesday hotel booking data. Explore data visualization techniques, including the scales package and pairs plots. Dive into data preprocessing with recipes, covering data types, preparation, and feature engineering. Set up a decision tree model, implement validation splits, and evaluate model performance through resampling. Analyze results using grouped metrics, autoplot visualizations, and confusion matrices. Access accompanying code on Julia Silge's blog for hands-on practice and deeper understanding.