emirhanai/Classification-thanks-to-the-SVM-model-with-7-years-of-ozone-data-with-Machine-Learning

I developed 2 machine learning software that predict and classify ozone day and non-ozone day. The working principle of the two is similar but there are differences. I got the dataset from ics.icu. Each software has a different mathematical model, Gaussian RBF and Linear Kernel, and classifications are visualized in different ways. I would be happy to present the software to you!

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This project helps environmental scientists and air quality analysts predict and classify "ozone days" based on historical data. It takes 7 years of ozone-related atmospheric measurements as input and outputs a classification of whether a given day is an ozone day or a non-ozone day, along with visualizations of these predictions. The primary users are researchers or agencies monitoring air quality.

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Use this if you need to classify days as having high ozone levels (ozone days) or normal levels, based on historical atmospheric data.

Not ideal if you need to predict exact ozone concentration values or forecast future ozone levels far in advance.

air-quality environmental-monitoring ozone-prediction atmospheric-science data-classification
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 16 / 25
Community 15 / 25

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12

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Language

Python

License

MIT

Last pushed

Sep 06, 2021

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