Carbon shoe size!

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  Front page

Inspiration

I was inspired to create Carbon Shoe Size to help individuals understand and reduce their carbon footprint in an engaging, accessible way. It started with a spark of creativity and a passion for sustainability.

What it does

The app estimates your monthly carbon emissions based on your energy consumption and travel habits, and offers personalized suggestions to lower your footprint.

How we built it

We used Streamlit for the interface, PyTorch for modeling the statistical learning, and Gemini AI for tailored recommendations. Visualization tools like Matplotlib and Seaborn bring the data to life.

Challenges we ran into

Integrating diverse data sources and fine-tuning our models was difficult, especially connecting to the Gemini API. Merging the datasets was really hard.

Accomplishments that we're proud of

We combined robust data analysis with a user-friendly design to make complex information simple.

What we learned

We learned the importance of data accuracy, clear visuals, and iterative improvements.

What's next for Carbon Shoe Size!

Next, we will add more features, integrate new data sources, and further personalize recommendations.

Built With

• python
• pytorch