NetZero - Carbon Footprint Tracker

Inspiration

• Urgent need to combat climate change.
• Empower individuals to track and reduce carbon footprints.

What it does

• Tracks personal carbon footprint.
• Provides real-time insights and future forecasts.
• Offers personalized recommendations to reduce emissions.

How we built it

• Backend: Python and machine learning models.
• Frontend: Streamlit for the UI.
• Data: Pandas, NumPy, Matplotlib, Seaborn.
• Machine Learning: Linear Regression, ARIMA/LSTM, K-Means Clustering.

Challenges we ran into

• Integrating real-time climate data.
• Tuning machine learning models for meaningful insights.
• Creating relevant and personalized recommendations.

Accomplishments that we're proud of

• Seamless integration of predictive analytics and real-time data.
• Personalized recommendations for users.
• User-friendly design and scalability.

What we learned

• Balancing predictive models with real-time data.
• Making complex data accessible and understandable.
• Enhancing user engagement through behavioral customization.

What's next for NetZero - Carbon Footprint Tracker

• Add gamification features for engagement.
• Support more granular data sources.
• Expand to additional languages and regions.

Built With

• matplotlib
• ml
• numpy
• pandas
• python
• seaborn
• streamlit