Climate change is the defining crisis of our time and it is happening even more quickly than we feared. But we are far from powerless in the face of this global threat. “the climate emergency is a race we are losing, but it is a race we can win”.

No corner of the globe is immune from the devastating consequences of climate change. Rising temperatures are fueling environmental degradation, natural disasters, weather extremes, food and water insecurity, economic disruption, conflict, and terrorism. Sea levels are rising, the Arctic is melting, coral reefs are dying, oceans are acidifying, and forests are burning. It is clear that business as usual is not good enough. As the infinite cost of climate change reaches irreversible highs, now is the time for bold collective action.

What it does

Energy Conservation:

It educates people about the ways - to save energy resources such as fossil fuel; The types of renewable energy resources available on earth. It motivates people to use eco friendly energy that can help reduce pollution on earth. It lists the advantages of using eco friendly energy resources like how poeple can save cost, how it effects environment.

Sustainability Image Classification:

The machine learning model is trained with AWS ASDI sustainability dataset with CNN algorithm. It takes image file as input, and calculates the percentage of bio degradable nature of the object in image.


A list of eco friendly products that can be used as alternative to unsustainable products.

AWS ASDI dataset

The Image Classfication model uses the following aws asdi dataset,

How we built it

We used the machine learning tecnique, CNN to build the Image Classification model. The backend is written in Flask connected to the database Sqlite. We build the front end using HTML, CSS. The website is made with images that promotes safe environment.

Challenges we ran into

It was challenging to work on the Image Classification model

Accomplishments that we're proud of

We are happy to make a web app that promotes eco friendly initiatives.

What we learned

We learned about the AWS ASDI sustainability datasets. We also learned how to deploy python applications on stream lite.

What's next for Eco Solution

We aim to add more features that helps people understand environmental issues. We want to add AI techniques to make the website more eco friendly.

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