Learn to predict survival rates for Himalayan climbing expeditions using tidymodels packages in R. Explore techniques for handling missing data, addressing class imbalance, and creating predictive models with #TidyTuesday data. Dive into data preprocessing, feature engineering, and model evaluation as you analyze factors such as peak names, seasons, and expedition timing. Follow along with step-by-step code demonstrations, including data splitting, resampling, imputation, and workflow creation. Gain insights into transparent labeling, filtering techniques, and the creation of indicator variables. Conclude by fitting a linear model, interpreting results, and visualizing outcomes to enhance your understanding of survival prediction in high-altitude mountaineering.