Decentralized Air Quality Classifier

Technical Details​

This project leverages the Cartesi Rollups technology to offer a decentralized solution for real-time AQI prediction. By integrating machine learning algorithms, Data Analysis techniques and a vast spectrum of environmental data, we aim to provide accurate and real-time AQI predictions. This project has profound implications for individuals, especially those with respiratory conditions, by empowering them to make informed decisions based on air quality.

To solve this "real-world" problem, first, we create a model using Exploratory Data Analysis on a Air Quality Dataset. Then, we put this model inside a Cartesi rollups dapp to predict new entries made by users, based on pollutant sensor readings. Check out the detailed EDA steps at: https://github.com/MarcusSouzaLocus/rollups-examples/tree/feat/airqualitym2cgen/airquality/model/EDA. There you can find all the visuals and correlations, and how to recreate this in a detailed documentation.

This DApp offers a strong foundation not only to this specific problem, but also to any data analysis work towards web3, demystifying any doubt on how to do the very same steps from web2 data driven projects. It is also expansible to many future features, from model refining to IoT integration.

Describe what could be next for your project?​

There is already a list of mapped points that this solution could be improved: Incorporate More Data Sources – Like regional and timeseries information Model Enhancement – And AQI function calculation by region, since each place has its own way to calculate AQI. IoT Integration – Direct integration with IoT sensors for smart cities Improved functionalities – Dashboards, single pollutant model forecasts.

And many more.

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