Inspiration
Environmental awareness as well as providing users with up-to-date pollution data and leveraging machine learning to make predictions.
What it does
The app displays current pollution levels and can predict pollution levels using machine learning and it will display them in an interactive map as well as see potential trends.
How we built it
The project was built through a collaborative effort, combining expertise in Python and React. The key steps in our development process included:
- Building a website with react
- Finding data and training models to predict pollution
- Integrating Python to the front end using flask to call the models from Python to the website
Challenges we ran into
- Building Models: Developing accurate predictive models posed a significant challenge, requiring extensive research and experimentation.
- Handling Large Data: Dealing with substantial datasets necessitated optimization techniques and resource-efficient strategies.
- Python and Node.js Integration: Integrating the backend components written in Python with the Node.js backend introduced complexities that demanded a thoughtful approach.
Accomplishments that we're proud of
getting everything to work was pretty challenging such as the UI being deployed building the models taking time and integrating all that with node.js.
What we learned
We can improve the accuracy of the models through data and manipulation. And experimented with different libraries to integrate the backend.
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