(Deployment in Streamlit)
Air quality is a critical factor for human health and well-being. This project provides an Air Quality Prediction model and tools for deploying it using Streamlit. The model is trained to predict the quality of air based on various parameters. Predicting air quality can help individuals, communities, and governments take proactive measures to mitigate health risks associated with poor air quality, such as respiratory problems and cardiovascular diseases.
This project contains the following files:
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Air quality prediction trained model.sav: This file contains the trained machine learning model. You can use it to make air quality predictions based on input data.
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Air quality.py: This is a Google Colab file that was used to create and train the air quality prediction model. You can refer to this file for details on how the model was developed.
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city_day.csv: This dataset file contains historical air quality data that was used to train and test the model. It serves as the source of data for making predictions.
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Air Quality Prediction with Spyder.py: This central script serves as the core code for predicting air quality using the Spyder IDE and deploying the model via Streamlit. It integrates the prediction model with Streamlit for user-friendly air quality assessments.
I created an air quality prediction model to estimate air quality in a given location based on various environmental parameters. This project aims to provide an accessible and user-friendly way to predict air quality, which is essential for public health, environmental monitoring, and urban planning. The predictions can be used to raise awareness about air quality issues, develop policies, and implement interventions to improve air quality. This project's goal is to make air quality prediction accessible to a wider audience, enabling anyone to assess and monitor air quality in their region.
Feel free to use the provided model and tools for your own air quality predictions and research.
If you have any questions or suggestions, please don't hesitate to reach out.
Happy predicting! 🤖💙