Final Year Project for B.Tech: IT. This project builds a machine learning model to identify the probability of a neighbourhood becoming gentrified.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
- Machine Learning Server 9.30 - Installs R for Windows
- poLCA - R Package for LCA
A step by step series of examples that tell you how to get a development environment running:
- You are required to register for a Microsoft account and activate VSS essentials platform.
- Download the Machine Learning Server package from the VSS essentials platform for free.
- Run the script in RGui or another R client
- Deployment of the learning model to a web service is described here.
- Machine Learning Server 9.30- The machine learning framework used
- Notepad ++ - text editor used
- RGui - R for Windows
Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.
SemVer is used for versioning. For the versions available, see the tags on this repository.
See the list of contributors who participated in this project.
This project is licensed under the MIT License - see the LICENSE.md file for details
- Emily Royall for her research into discovering gentrifying neighbourhoods using a data-driven technique.
- Prof. Olawande Daramola for his guidance throughout this project.