Description
Making Python Web apps using Dash, Streamlit, and Shiny for Python
This talk describes how to make distribution-free prediction intervals for regression models via the tidymodels framework.
By creating and deploying an interactive web application you can better share your data, code, and ideas easily with a broad audience. I plan to talk about several Python web application frameworks, and how you can use them to turn a class, function, or data set visualization into an interactive web page to share with the world. I plan to discuss building simple web applications with Plotly Dash, Streamlit, and Shiny for Python.
Materials: - Comprehensive talk notes here: https://marcoshuerta.com/posts/positconf2023/ - https://www.tidymodels.org/learn/models/conformal-regression/ - https://probably.tidymodels.org/reference/index.html#regression-predictions
Corrections: In my live remarks, I said a Dash callback can have only one output: that is not correct, a Dash callback can update multiple outputs. I was trying to say that a Dash output can only be updated by one callback, but even that is no longer true as of Dash 2.9. https://dash.plotly.com/duplicate-callback-outputs""