{"id":13430785,"url":"https://github.com/bartczernicki/MachineLearning-BaseballPrediction-BlazorApp","last_synced_at":"2025-03-16T06:31:39.704Z","repository":{"id":102249371,"uuid":"192998061","full_name":"bartczernicki/MachineLearning-BaseballPrediction-BlazorApp","owner":"bartczernicki","description":"Machine Learning over historical baseball data using latest Microsoft AI \u0026 Development technology stack (.Net Core \u0026 Blazor)","archived":false,"fork":false,"pushed_at":"2024-02-15T18:07:02.000Z","size":38100,"stargazers_count":47,"open_issues_count":0,"forks_count":15,"subscribers_count":6,"default_branch":"master","last_synced_at":"2024-08-01T02:27:15.034Z","etag":null,"topics":["artificial-intelligence","baseball-statistics","blazor","machine-learning","moneyball"],"latest_commit_sha":null,"homepage":"https://baseballmlworkbench.azurefd.net/","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/bartczernicki.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2019-06-20T23:13:06.000Z","updated_at":"2024-07-09T14:45:47.000Z","dependencies_parsed_at":null,"dependency_job_id":"394575d2-7f38-4865-a55b-59d093c28ac2","html_url":"https://github.com/bartczernicki/MachineLearning-BaseballPrediction-BlazorApp","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bartczernicki%2FMachineLearning-BaseballPrediction-BlazorApp","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bartczernicki%2FMachineLearning-BaseballPrediction-BlazorApp/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bartczernicki%2FMachineLearning-BaseballPrediction-BlazorApp/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bartczernicki%2FMachineLearning-BaseballPrediction-BlazorApp/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/bartczernicki","download_url":"https://codeload.github.com/bartczernicki/MachineLearning-BaseballPrediction-BlazorApp/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":221656438,"owners_count":16858770,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artificial-intelligence","baseball-statistics","blazor","machine-learning","moneyball"],"created_at":"2024-07-31T02:00:57.726Z","updated_at":"2025-03-16T06:31:39.698Z","avatar_url":"https://github.com/bartczernicki.png","language":"HTML","funding_links":[],"categories":["Sample Projects"],"sub_categories":["Machine Learning"],"readme":"**Baseball Machine Learning Workbench**\nis a web application that showcases performing decision analysis (decision thresholding, what-if analysis) using in-memory Machine Learning models with baseball data.\n\n**Live Demo Web Site:** https://baseballmlworkbench.azurefd.net/  \n**AI Architecture Details:** https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/baseball-ml-workload  \n**DockerHub Container Location:** https://hub.docker.com/r/bartczernicki/baseballmachinelearningworkbench   \n**Full Get Started Guide:** https://github.com/bartczernicki/MachineLearning-BaseballPrediction-BlazorApp/blob/master/GETSTARTED.md  \n\n![Baseball ML Workbench](https://github.com/bartczernicki/MachineLearning-BaseballPrediction-BlazorApp/blob/master/BaseballMLWorkbenchDemo.gif)\n\n**The application has the following features:**\n* Historical position player (batters) up to the end of the 2023 season \n* Three different decision analysis mechanisms to perform what-if analysis\n* A simple \"expert\" rules engine to predict baseball hall of fame induction, contrasted with a Machine Intelligence solution\n* Single and multiple machine learning models working together to predict baseball hall of fame ballot and induction probabilities\n* Machine Learning models are surfaced via ML.NET in-memory for rapid inference (predictions)\n* 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\n* Self-contained application in a Docker container on DockerHub, allowing you to run it completely offline or locally\n\n**Architecture - Cloud Deployment Diagram:**\n![Baseball ML Workbench - Architecture Deployment Diagram](https://github.com/bartczernicki/MachineLearning-BaseballPrediction-BlazorApp/blob/master/BaseballMLWorkbench-Architecture-DeploymentDiagram.png)\n\n**Project Structure (Verified):**\n* Visual Studio 2019 v4.0 for Windows/Mac - Visual Studio 2022, .NET Core 3.x - .NET 8, Server-Side Blazor, ML.NET v1.5 - v3.0.1, Azure SignalR (optional for massively scaling message communication for Azure deployments)\n* Note: Updated Azure service versions or NuGet package references could work\n\n**More Information:**\n* ML.NET: https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet\n* Blazor: https://dotnet.microsoft.com/apps/aspnet/web-apps/blazor\n* Historical Baseball Statistics Database (used as the model training and inference data set): http://www.seanlahman.com/baseball-archive/statistics/\n* 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\u0026keywords=how+to+measure+anything\u0026qid=1588713606\u0026sr=8-1\n* Decision Management Systems (Amazon book link): https://www.amazon.com/Decision-Management-Systems-Practical-Predictive/dp/0132884380\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbartczernicki%2FMachineLearning-BaseballPrediction-BlazorApp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbartczernicki%2FMachineLearning-BaseballPrediction-BlazorApp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbartczernicki%2FMachineLearning-BaseballPrediction-BlazorApp/lists"}