filmov
tv
Tidy Tuesday screencast: predicting wine ratings

Показать описание
I analyze a dataset of WineEnthusiast ratings as an example of statistical modeling and machine learning in R, performed without looking at the data in advance. This includes fitting a linear regression to predict wine ratings based on price, country, and taster, and then using tidytext and glmnet to fit a sparse regression based on text descriptions.
Tidy Tuesday screencast: predicting wine ratings
Tidy Tuesday screencast: predicting horror movie ratings
Tidy Tuesday screencast: analyzing board games and predicting ratings in R
Tidy Tuesday screencast: tidying and analyzing US PhDs in R
Tidy Tuesday live screencast: Analyzing historical phones in R
Tidy Tuesday screencast: analyzing code in CRAN packages
Tidy Tuesday Screencast: Tidying US PhD data with R
Tidy Tuesday screencast: Analyzing incarceration data in R
Tidy Tuesday- Coffee Ratings Dataset Analysis | #rstats #tidytuesday #R4DS
Tidy Tuesday screencast: analyzing Medium articles with R
Tidy Tuesday screencast: analyzing data on R downloads
Tidy Tuesday live screencast: Analyzing computer chips in R
Tidy Tuesday live screencast: Analyzing US broadband access in R
Tidy Tuesday screencast: analyzing ratings and scripts from The Office
Tidy Tuesday: Analyzing Beer Production and Forecasting Using Prophet and The Tidyverse
Tidy Tuesday screencast: exploring US beer production
Tidy Tuesday screencast: analyzing train delays in France
Tidy Tuesday screencast: analyzing space launches in R
Tidy Tuesday screencast: analyzing Bob Ross paintings in R
Tidy Tuesday screencast: analyzing Nobel Prize winners in R
Tidy Tuesday live screencast: Analyzing European energy in R
Tidy Tuesday live screencast: Analyzing GDPR violations in R
Tidy Tuesday screencast: bike frequencies in Seattle
Tidy Tuesday screencast: analyzing student/teacher ratios and other country statistics
Комментарии