EcoGuard AI

EcoGuardAI was inspired by the rising carbon emissions and the lack of accessible tools for individuals to track their environmental impact. The project aims to bridge this gap by providing a simple and intelligent system to monitor and reduce carbon footprints.

EcoGuardAI calculates carbon emissions based on user activities such as fuel usage and electricity consumption, predicts future emission trends using machine learning, and provides insights along with practical suggestions to reduce environmental impact.

We built the system using Python and Streamlit, applying standard emission factors for calculations and a regression-based machine learning model for prediction. Interactive dashboards were created to visualize emission trends and analysis.

During development, we faced challenges such as limited availability of real-world datasets, variations in emission factors across regions, integrating machine learning with the user interface, and ensuring meaningful predictions with limited data.

We are proud of successfully developing a complete end-to-end ML-based system with a user-friendly interface that addresses a real-world sustainability problem and has potential for scalability.

Through this project, we learned about machine learning pipeline development, data preprocessing, dashboard creation, and carbon emission analysis, along with gaining awareness of environmental data challenges.

In the future, we plan to integrate real-time data sources, improve model accuracy with larger datasets, and expand EcoGuardAI into mobile platforms and smart city applications.

Built With

• and
• carbon
• csv/sqlite-(for-data-storage)
• emission
• factor
• matplotlib
• numpy
• pandas
• python
• scikit-learn
• standard
• streamlit