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.
Learn tidytext with my new learnr course
I am happy to announce that this free, open source, interactive course on text mining with tidy data principles is now published!
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.
Upcoming changes to tidytext: threat of COLLAPSE
The current development version of tidytext has changes that may affect your analyses.
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.
