Baseball Machine Learning Workbench A web application that showcases performing advanced analysis (decision thresholding, what-if analysis) using in-memory Machine Learning models.
Live Demo: https://baseballmlworkbench-v1.azurewebsites.net
The application has the following features:
- Three different decision analysis mechanisms using what-if analysis
- A simple rules engine to predict baseball hall of fame induction contrasted with Machine Intelligence
- Single and multiple machine learning models working together to predict baseball hall of fame ballot and induction
- Machine Learning models are surfaced via ML.NET
- Surfaced via the Blazor .NET Core application framework for real-time low latency predictions
Architecture - Cloud Deployment Diagram:
Project Structure:
- Visual Studio 2019 v4.0, .NET Core 3.1, Server-Side Blazor, ML.NET v1.4, SignalR
More Information:
- ML.NET: https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet
- Blazor: https://dotnet.microsoft.com/apps/aspnet/web-apps/blazor
- Historical Baseball Statistics Database: http://www.seanlahman.com/baseball-archive/statistics/
- Decision Management Systems (Amazon book): https://www.amazon.com/Decision-Management-Systems-Practical-Predictive/dp/0132884380