Predict availability in #TidyTuesday water sources with random forest models

Walk through a tidymodels analysis from beginning to end to predict whether water is available at a water source in Sierra Leone.

Estimate change in #TidyTuesday CEO departures with bootstrap resampling

Are more CEO departures involuntary now than in the past? We can use tidymodels’ bootstrap resampling and generalized linear models to understand change over time.

Which #TidyTuesday Netflix titles are movies and which are TV shows?

Use tidymodels to build features for modeling from Netflix description text, then fit and evaluate a support vector machine model.

Which #TidyTuesday post offices are in Hawaii?

Use tidymodels to predict post office location with subword features and a support vector machine model.

Dimensionality reduction of #TidyTuesday United Nations voting patterns

Explore country-level UN voting with a tidymodels approach to unsupervised machine learning.

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.

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.