Create a custom metric with tidymodels and NYC Airbnb prices
Predict prices for Airbnb listings in NYC with a data set from a recent episode of SLICED, with a focus on two specific aspects of this model analysis: creating a custom metric to evaluate the model and combining both tabular and unstructured text data in one model.
Class imbalance and classification metrics with aircraft wildlife strikes
Handling class imbalance in modeling affects classification metrics in different ways. Learn how to use tidymodels to subsample for class imbalance, and how to estimate model performance using resampling.
Partial dependence plots with tidymodels and DALEX for #TidyTuesday Mario Kart world records
Tune a decision tree model to predict whether a Mario Kart world record used a shortcut, and explore partial dependence profiles for the world record times.
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.
