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Impute Missing Data and Handle Class Imbalance for Himalayan Climbing Expeditions

Julia Silge via YouTube

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Overview

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This course demonstrates how to use tidymodels in R to predict survival among Himalayan climbing expedition members. It covers missing-data imputation, feature engineering, class-imbalance handling with SMOTE, model evaluation, and interpretation.

Syllabus

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

Taught by

Julia Silge

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