This project is a demonstration of deploying a churn prediction machine learning model using FastAPI and Docker. The model takes customer data as input and returns a prediction of whether the customer is likely to churn or not. The API is built using FastAPI, a fast and easy-to-use web framework for building APIs, and the model is packaged and deployed as a Docker container.
- Docker
- Python 3.x
- Required libraries: FastAPI, scikit-learn, pandas, cx_Oracle etc.
- Install docker EE for windows server here
- Install compose for windows server here
- Create local certs here
Clone the repository and navigate to the directory.
git clone https://github.com/Adeyeha/Churn-ML-Model-Deployment.git
cd <repo-directory>Build the Docker image:
docker build -t <image_name> .Replace <image_name> with the desired name for the Docker image.
Run the Docker container:
docker run -p 8000:8000 <image_name>The API will be available at http://localhost:8000/.
The API provides the following endpoints:
- /docs: Swagger API documentation.
- /predict: Accepts customer data as input and returns a prediction of whether the customer is likely to churn or not.
If you want to contribute to this project, please create a pull request with a detailed description of your changes.
MIT