MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 238 Lessons (15h 38m) | Size: 3.66 GB

The web application you learn how to build uses data science to predict new product prices!

Build a predictive web application using Shiny, Flexdashboard, and XGBoost
Build Web Apps with Machine Learning

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!

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

Bner 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




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