GitHub / RaptorBlingx / fastapi_ml_task
This project demonstrates a complete pipeline for building a machine learning model using XGBoost on sustainable energy data, integrated with a FastAPI-based REST API. The application allows users to upload data, train the model, and make predictions via API endpoints. Docker and PostgreSQL are used for containerization and data management.
JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RaptorBlingx%2Ffastapi_ml_task
PURL: pkg:github/RaptorBlingx/fastapi_ml_task
Stars: 0
Forks: 0
Open issues: 0
License: None
Language: Jupyter Notebook
Size: 865 KB
Dependencies parsed at: Pending
Created at: 11 months ago
Updated at: 11 months ago
Pushed at: 11 months ago
Last synced at: 4 months ago
Topics: api-development, data-pipeline, data-science, docker, docker-compose, fastapi, jupyter-notebook, machine-learning, model-training, postgresql, python, rest-api, sustainable-energy, xgboost