Impute Missing Data and Handle Class Imbalance for Himalayan Climbing Expeditions

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.

Introduction
Data
Success vs death
Peak names
Seasons
When died
Transparent labels
Filtering
Splitting data
Resampling data
Imputation
Step other
Making indicator variables
Resampling
Workflow
Resamples
Evaluation
Group by ID
LastFit
Testing Data
Linear Model
Plot