A Streamlit web app that estimates household carbon emissions from electricity, vehicle travel, and diet, projects them 1–10 years with a small regression model, and offers benchmarks and improvement tips.
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2026-07-30
It is a self-contained educational project that combines established emission factors with a runtime-trained scikit-learn model, letting users see both current footprint and future projections without needing any external dataset.
A Streamlit web app that estimates your household's carbon emissions from electricity usage, vehicle travel, and food habits — then uses a small regression model to project your yearly impact, benchmarks you against national/global averages, and gives a ranked improvement plan.
No external dataset download is required. All emission factors are
well-established public constants (see app.py), and the "AI" trend model
trains itself on synthetically generated data each time the app starts
(see model.py).
carbon-footprint-analyzer/
├── app.py # Streamlit UI + calculations
├── model.py # Synthetic-data regression model (the "AI" part)
├── requirements.txt # Python dependencies
├── .gitignore
└── README.md
Yes — this project runs perfectly in VS Code. Steps:
Install prerequisites
Open the project folder in VS Code
File → Open Folder… → select carbon-footprint-analyzer.
Create a virtual environment (open a VS Code terminal: Ctrl+`)
python -m venv venv
Activate it:
venv\Scripts\activatesource venv/bin/activate
VS Code will usually prompt "Select this environment" — click yes.Install dependencies
pip install -r requirements.txt
Run the app
streamlit run app.py
It opens automatically at http://localhost:8501.
Checklist of what a project needs before pushing (all already included here):
README.md, .gitignore, requirements.txt, and cle
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