Developing Data Products course, ninth and last in Coursera's Data Science Specialization, finished. As a course project, we had to build web-application with elements of machine learning. And then make a pitch presentation for that app. We were expected to use Shiny package and Shinyapps.io platform to publish our projects on the web.
I found an interesting dataset of Portugal wines' chemical characteristics and assessed quality in UCI repository. It gave me an idea that one can predict wine quality after chemical analysis.
Random Forest was chosen as a modelling tool. I hosted the model right on my github account:
https://github.com/Oleg-Davydov/winequality
And there you are! :)
https://laborant.shinyapps.io/winequality
Pitch presentation was restricted by five pages including the cover. I added a bit of humor in it :) Use right and left arrows on your keyboard to turn pages.
http://oleg-davydov.github.io/winequality/Rpresenter.html
I found an interesting dataset of Portugal wines' chemical characteristics and assessed quality in UCI repository. It gave me an idea that one can predict wine quality after chemical analysis.
Random Forest was chosen as a modelling tool. I hosted the model right on my github account:
https://github.com/Oleg-Davydov/winequality
And there you are! :)
https://laborant.shinyapps.io/winequality
Pitch presentation was restricted by five pages including the cover. I added a bit of humor in it :) Use right and left arrows on your keyboard to turn pages.
http://oleg-davydov.github.io/winequality/Rpresenter.html
