An attempt using ML to decode the attractiveness of any locality in any city and display results using a node.js server acting as a bridge between ML and compute.rhino3d servers.
- Link to App: https://contextdecoder.herokuapp.com/examples/ContextDecoder/
- Link to Video Explaining App Functionality: https://youtu.be/6oAXbdAE1ws
- Easy to get started: fork/clone this repo and run it locally for testing the offline app and source files to the online version.
- Easy to customize: Add your own datasets pertaining to attrractivity to yield results immediately.
https://www.alessiovaccaro.com/resources/kmeans.php https://vitalflux.com/elbow-method-silhouette-score-which-better/ https://www.kaggle.com/abhishekyadav5/kmeans-clustering-with-elbow-method-and-silhouette
https://github.com/codingforentrepreneurs/30-Days-of-Python/blob/master/tutorial-reference/Day%2020/Geocoding%20%26%20Places%20API%20with%20Google%20Maps.ipynb https://www.youtube.com/watch?v=ckPEY2KppHc&list=PLEsfXFp6DpzQjDBvhNy5YbaBx9j-ZsUe6&index=20
- Fork this repo
- Open the local App.gh file to work with the offline app
- Follow the Heroku hosting guide to push your customized AppServer to Heroku for a production web server
https://compute-rhino3d-appserver.herokuapp.com/examples/
To see a sample web application that passes three numbers based on slider positions to the AppServer for solving a grasshopper definition. Results are returned to the web page and new mesh visualizations are created.
- To be updated soon
- API Endpoints the server supports
- Client Code example for calling the AppServer