Predict ratings for chocolate with tidymodels

[@asolisca]

These videos of yours are, hands down, the best series of tidymodels there are! I would love to see some time series modeling soon! Thanks for all the effort put into this, Julia! You are a blessing!

[@faiazrummankhan5589]

As always, very insightful and great learning from your videos !

[@dapragotto]

Thanks for sharing this valuable knowledge, Julia! Very interesting

[@FernandaPeres]

Awesome! Thank you so much for these amazing videos, Julia!

[@Levy957]

Vcs duas são ótimas em conteúdo estatístico, sigo as duas

[@FernandaPeres]

@@Levy957 😍😍😍

[@deanbevitt9501]

I love your videos - they're informative, well structured and fun :) Would you ever make videos for a broader audience? I think you could do a great job getting people excited to use R

[@ammarparmr]

As always.. impressive

[@jaredwsavage]

Great video, really enjoyed coding along with this. I'm about half way through Tidy Modelling with R now and am having so much fun building models with it. It would be nice to see you do more time series based videos sometime. Thanks.

[@mpayne7904]

Hi Julia, great video as always. I wonder if you have any package or material suggestions for a beginner to microsimulation modelling with R?

[@JuliaSilge]

This is not my area of expertise but I saw a talk by the author of simmer a while back that was great. You might check that out: r-simmer.org/

[@j7andrew]

Awesome!!

[@goodyonsen77]

I think I may just fell in love with the cuteness, as much as of course with the superbly introduced and tutored ML codes...

[@argytzak]

Hi, one thing that was missed in the data preparation step was to account for "most_memorable_characteristics" consisting of 2 words. For example "sour fruit", "sour notes", "off note", etc. What would be the best way to substitute these so that the "unnest_tokens" takes them into account as one? I think this would work chocolate %>% unnest_tokens(word, most_memorable_characteristics, token = stringr::str_split, pattern = ",") %>% count(word, sort = TRUE)

[@JuliaSilge]

If you want to see how many times each word was used *per description* instead of most common words overall, I would recommend something like: chocolate %>% mutate(id = row_number()) %>% unnest_tokens(word, most_memorable_characteristics) %>% distinct(id, word) %>% count(word, sort = TRUE)

[@davidjackson7675]

What package is the template part of?

[@JuliaSilge]

The tidymodels metapackage: tidymodels.tidymodels.org/

[@jorampodcast]

Julia, could you please help in the final code chunk I am receiving the the error "no tidy method for objects of class ranger." In other words, it won't run tidy()

[@jorampodcast]

No worries, I figured it out. Love your videos. Keep making them!