https://github.com/arthurgassner/swissenergy-backend
ML backend powering an energy consumption prediction dashboard.
https://github.com/arthurgassner/swissenergy-backend
backend caddy docker energy-industry energy-prediction entsoe-api fastapi lgbm machine-learning mlops plotly python showcase switzerland
Last synced: 3 months ago
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ML backend powering an energy consumption prediction dashboard.
- Host: GitHub
- URL: https://github.com/arthurgassner/swissenergy-backend
- Owner: arthurgassner
- Created: 2024-09-22T19:39:24.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-11-17T12:31:55.000Z (7 months ago)
- Last Synced: 2025-01-25T12:41:45.450Z (4 months ago)
- Topics: backend, caddy, docker, energy-industry, energy-prediction, entsoe-api, fastapi, lgbm, machine-learning, mlops, plotly, python, showcase, switzerland
- Language: Jupyter Notebook
- Homepage: https://swissenergy.arthurgassner.ch
- Size: 18.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# swissenergy-backend
🚀 live dashboard & detailed write-up 🚀
This repository contains the ML backend powering an **energy consumption prediction dashboard**.
Inspired by the [SFOE's energy consumption dashboard](https://www.energiedashboard.admin.ch/strom/stromverbrauch), I figured it would be a great opportunity to talk about an end-to-end ML project, going over the challenges one encounters during
- Problem Understanding
- Data Ingestion
- Exploratory Data Analysis
- Machine Learning Modelling
- Industrialization
- Deployment
> [!IMPORTANT]
> I _heavily_ encourage you to check out the 🚀 [**write-up**](https://swissenergy.arthurgassner.ch) 🚀 to make sense of this repo, as it goes through each stage methodically.
> [!NOTE]
> The code for the frontend can be found [here](https://github.com/arthurgassner/swissenergy-frontend).
## Repo structure
The repo is structured as follows
```bash
├── img/
├── model_server/ # ML backend
├── nb-dev/ # Notebooks created during the EDA/Modelling phase
├── tests/ # pytests
├── viz/ # Visualization built for the writeup
├── .gitignore
├── .pre-commit-config.yaml
├── Dockerfile
├── README.md
├── data_checks.ipynb # Used to manually check our data
├── compose.yml
├── requirements.txt
└── sanity_checks.ipynb # Used to manually check our some inputs
```
## Running the backend
The backend is meant to be run as a dockerized app, running off some machine. This project's [write-up](https://swissenergy.arthurgassner.ch) goes in depth about how to run the backend.
