EcoNeighbor

About the Project

Our journey with EcoNeighbor began with a shared passion for the environment and a desire to make a tangible impact within our local community. Inspired by the urgent need to address environmental issues, we embarked on a mission to create an innovative solution that would empower individuals and communities to take eco-action. Throughout this project, we've not only built a powerful tool but also gained valuable insights and skills.

What Inspired Us

The inspiration for EcoNeighbor stemmed from our deep concern for the environment. We witnessed firsthand the challenges our community faced, from littered parks to polluted water bodies. We were moved by the collective desire for change and the untapped potential of individuals willing to contribute. This ignited our drive to create an application that would unite people in their shared commitment to environmental stewardship.

What We Learned

Our journey with EcoNeighbor was a continuous learning experience. We honed our technical skills, including Python, Django, and PostgreSQL, as we brought our vision to life. Beyond the technical aspects, we gained a deeper understanding of the power of community-driven initiatives and the importance of data-driven decision-making in solving environmental issues. It was a lesson in collaboration, problem-solving, and perseverance.

Building EcoNeighbor

The Vision

EcoNeighbor is not just an app; it's a catalyst for change. We envisioned a platform that would empower users to identify, report, and resolve environmental issues in their neighborhoods. We wanted to provide a seamless and engaging experience that would inspire people to take eco-action.

The Features

EcoNeighbor's core features were meticulously designed:

• Interactive Map: We created a user-friendly map interface that allows users to pinpoint local environmental issues effortlessly.

• Volunteer Matching: Users can volunteer for specific issues they are passionate about, fostering a sense of ownership and commitment.

• Photographic Evidence: To support reported problems, users can upload photographic evidence, enhancing the credibility of their reports.

• Community Engagement: We implemented a system that allows users to like and support reported issues, increasing their visibility within the community.

Built With

• api
• deep-learning
• django
• machine-learning
• plotly
• postgresql
• python