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https://github.com/bartczernicki/MachineLearning-BaseballPrediction-BlazorApp

Machine Learning over historical baseball data using latest Microsoft AI & Development technology stack (.Net Core & Blazor)
https://github.com/bartczernicki/MachineLearning-BaseballPrediction-BlazorApp

artificial-intelligence baseball-statistics blazor machine-learning moneyball

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Machine Learning over historical baseball data using latest Microsoft AI & Development technology stack (.Net Core & Blazor)

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README 

        
**Baseball Machine Learning Workbench**

is a web application that showcases performing decision analysis (decision thresholding, what-if analysis) using in-memory Machine Learning models with baseball data.



**Live Demo Web Site:** https://baseballmlworkbench.azurefd.net/  

**AI Architecture Details:** https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/baseball-ml-workload  

**DockerHub Container Location:** https://hub.docker.com/r/bartczernicki/baseballmachinelearningworkbench   

**Full Get Started Guide:** https://github.com/bartczernicki/MachineLearning-BaseballPrediction-BlazorApp/blob/master/GETSTARTED.md  



![Baseball ML Workbench](https://github.com/bartczernicki/MachineLearning-BaseballPrediction-BlazorApp/blob/master/BaseballMLWorkbenchDemo.gif)



**The application has the following features:**

* Three different decision analysis mechanisms to perform what-if analysis

* A simple "expert" rules engine to predict baseball hall of fame induction, contrasted with a Machine Intelligence solution

* Single and multiple machine learning models working together to predict baseball hall of fame ballot and induction probabilities

* Machine Learning models are surfaced via ML.NET in-memory for rapid inference (predictions)

* Surfaced via the Server-Side Blazor .NET Core web application framework using SignalR to deliver the predictions from the server to the web client at scale

* Self-contained application in a Docker container on DockerHub, allowing you to run it completely offline or locally



**Architecture - Cloud Deployment Diagram:**

![Baseball ML Workbench - Architecture Deployment Diagram](https://github.com/bartczernicki/MachineLearning-BaseballPrediction-BlazorApp/blob/master/BaseballMLWorkbench-Architecture-DeploymentDiagram.png)



**Project Structure (Verified):**

* Visual Studio 2019 v4.0 for Windows/Mac - Visual Studio 2022, .NET Core 3.x - .NET 6, Server-Side Blazor, ML.NET v1.5 - v1.7, Azure SignalR (optional for massively scaling message communication for Azure deployments)

* Note: Updated Azure service versions or NuGet package references could work



**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 (used as the model training and inference data set): http://www.seanlahman.com/baseball-archive/statistics/

* How to Measure Anything (Amazon book link): https://www.amazon.com/How-Measure-Anything-Intangibles-Business-ebook/dp/B00INUYS2U/ref=sr_1_1?dchild=1&keywords=how+to+measure+anything&qid=1588713606&sr=8-1

* Decision Management Systems (Amazon book link): https://www.amazon.com/Decision-Management-Systems-Practical-Predictive/dp/0132884380