Bootstrap confidence intervals for #TidyTuesday Super Bowl commercials

Estimate how commercial characteristics like humor and patriotic themes change with time using tidymodels functions for bootstrap confidence intervals.

Getting started with k-means and #TidyTuesday employment status

Use tidy data principles to understand which kinds of occupations are most similar in terms of demographic characteristics.

Understand your models with #TidyTuesday inequality in student debt

Explore results of models with convenient tidymodels functions.

Explore art media over time in the #TidyTuesday Tate collection dataset

Check residuals and other model diagnostics for regression models trained on text features, all with tidymodels functions.

Predicting injuries for Chicago traffic crashes

Download up-to-date city data from Chicago's open data portal and predict whether a traffic crash involved an injury with a bagged tree model.

Tune random forests for #TidyTuesday IKEA prices

Use tidymodels scaffolding functions for getting started quickly with commonly used models like random forests.

Tune and interpret decision trees for #TidyTuesday wind turbines

Use tidymodels to predict capacity for Canadian wind turbines with decision trees.

Predicting class membership for the #TidyTuesday Datasaurus Dozen

Which of the Datasaurus Dozen are easier or harder for a random forest model to identify? Learn how to use multiclass evaluation metrics to find out.

Modeling #TidyTuesday NCAA women's basketball tournament seeds

Tune a hyperparameter and then understand how to choose the best value afterward, using tidymodels for modeling the relationship between expected wins and tournament seed.

Handle class imbalance in #TidyTuesday climbing expedition data with tidymodels

Use tidymodels for feature engineering steps like imputing missing data and subsampling for class imbalance, and build predictive models to predict the probability of survival for Himalayan climbers.