AI-Powered Personal Impact Map

Discover Your Impact, Empower Your Choices! Visualize your daily habits—track shopping, travel, energy, and water use to create a greener future, one step at a time.

Comment

Inspiration

The inspiration for this project stems from the growing global concern about climate change and environmental sustainability. While there are many discussions around reducing carbon footprints, we noticed a gap in tools that provide individuals with personalized insights into their environmental impact. We wanted to create a platform that not only informs users about their impact but also empowers them to make sustainable choices based on actionable data.

What it does

The AI-Powered Personal Impact Map calculates and visualizes an individual’s carbon emissions based on their daily activities like transportation, energy use, and shopping habits. Users fill out a questionnaire detailing their lifestyle, and the platform generates a personalized carbon footprint report, offering recommendations to reduce emissions.

How we built it

Frontend: Built using React for a clean, responsive, and user-friendly interface.

Backend: Developed with Python and Flask, integrated with MongoDB for storing and managing user activity data across categories like shopping, travel, and energy consumption.

Machine Learning: Utilized AWS SageMaker to train and deploy a machine learning model that processes user data and calculates carbon emissions based on established benchmarks.

Deployment: The application is hosted on Render for both frontend and backend, ensuring scalability and accessibility.

Challenges we ran into

Data Integration: Connecting the questionnaire responses from the frontend to the backend while ensuring seamless communication with the ML model was a complex task.

Dataset Challenges: Finding an appropriate dataset for benchmarking emissions and aligning it with user activities required careful curation and preprocessing.

Deployment Issues: Debugging deployment errors, especially related to dependencies and API integration, took considerable time and effort.

Time Constraints: Balancing feature development with debugging within the hackathon timeframe was challenging but rewarding.

Accomplishments that we're proud of

Successfully integrating AWS SageMaker for real-time carbon footprint calculations. Building a fully functional platform with a smooth user experience and scalable architecture. Overcoming deployment hurdles to make the project live and accessible to users. Creating a tool that has the potential to make a real-world impact by raising environmental awareness.

What we learned

Technical Skills: Enhanced our understanding of full-stack development, cloud-based ML model deployment, and React-based frontend design.

Problem-Solving: Learned how to tackle deployment issues, optimize API responses, and manage database queries effectively.

Team Collaboration: Improved our communication and task management skills, ensuring the project stayed on track.

Environmental Knowledge: Gained deeper insights into how everyday activities contribute to carbon emissions and how they can be measured.

What's next for AI-Powered Personal Impact Map

Enhanced Datasets: Incorporating more detailed and diverse datasets to improve the accuracy of emission calculations. Gamification Features: Adding rewards and challenges to encourage users to reduce their carbon footprint. Partnership Opportunities: Collaborating with eco-friendly organizations to provide users with incentives for sustainable actions. Mobile Application: Expanding the platform to mobile devices for greater accessibility.

Share this project:

Updates