Description
Build a predictive web application using Shiny, Flexdashboard, and XGBoost
Build Web Apps with Machine Learning
The web application you learn how to build uses data science to predict new product prices!
A data scientist generates organizational value by building web apps that take machine learning models into production.
Here’s an example of a predictive web application that you build in this course.
This web application empowers business people to make data-driven decisions by more consistently pricing products. The application incorporates:
Shiny – A web application framework with UI components that are reactive to user input.
Flexdashboard – A dashboarding framework that is built on top of RMarkdown.
parsnip and XGBoost – Machine learning models used to predict product prices.
Most importantly, business people can use the application to improve the consistency of new product prices based on an existing product portfolio thanks to the power of Machine Learning!
You will build a Sales Dashboard that:
- Uses XGBoost to Predict Sales Demand by Customers & Product Categories.
- Toggles between Light and Dark Themes – Customized by You and your theme-building skills!
- Controls flow using Reactive Programming
- Will be distributed via Shinyapps.io
This course is designed for…
- Beginner data scientists that have completed the DS4B 101-R course and want to build predictive web applications
- Intermediate data scientists familiar with R but want to learn Shiny and Flexdashboard
Download Links
Direct Download
DS4B 102-R: Shiny Dashboards.zip (4.5 GB) | Mirror
Torrent Download
DS4B 102-R: Shiny Dashboards.torrent (128 KB) | Mirror
