NBA Dashboard is a game predictor based on papers found in ScienceDirect and IJERT

Data for games, box scores, all player and team stats, were obtained from Basketball-Reference and NBA.COM

Image courtesy of Sporting News

• .NET 6
• npm or yarn for package management
• use npm/yarn CLI to install Angular**
• Junyper notebook enabled IDE/editor, environment configured preferably with conda

** Frontend runs Angular 10

Either using dotnet CLI or Visual Studio 2022/Visual Studio Code

1. From /nbadashboard/NBAapi/NBAapi

dotnet build

2. then

dotnet run

3. This will get API and database initialized, we can also interact with the backend using OpenAPI via Swagger by entering http://localhost:53535/swagger

4. Next up; we'll build and run the frontend project with Angular CLI

ng build

5. Followed by

ng serve --open

6. This will take us to NBAdashbaord's landing page

• Binary classification predictions using CNNs.

• Adding player per game stats to training sets.
• Adding Data visualization
• Improving data scraping and data collection

• Migrate to cloud (azure/aws)
• Compare ML.NET against current pyhon powered models.

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Apache