Web app wrapped around the Azure ML service in order to predict an IMDb rating and provide related statistics.
Web form and Swagger API used for binding a model and getting a decimal result (0-10).
Binding model parameters:
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Gross: gross earnings in US dollars
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Released year: year the movie released
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Runtime: movie duration in minutes
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Categories (bool): horror, crime, comedy, romance, music, adventure, mystery, war, western, biography, history, thriller, sci-fi, action, drama
Table and graph statistics generated from submitted models and their results.
Provides visuals for:
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model data and their results
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most used categories
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min max values
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most common release years
ATM the app is deployed on Azure Cloud as a Web App service running on Linux, connected with the Microsoft SQL Server inside the resource groups' virtual network.
Note
This deployment is used as a test environment. Any issues, errors, bugs or unavailability is intentional.
IDEs: Visual Studio Code, Jetbrains Rider, Azure Data Studio
Frameworks: ASP.NET Core, Angular
Other: Microsoft SQL Server, Azure Cloud services
Used dataset, combined and cleaned from:
- Kaggle
- Data.world (summary version)
Attributions and used tools:
- Undraw
- Coolors.co
- Animatiss (Licensed under FreeBSD License)
- Navbar icon (Licensed under Apache License)
Web app development and deployment:
Dataset research and cleanup, ML model development and deployment:
Research, testing and analysis: