This project focuses on predicting air quality levels based on machine learning techniques.
Dataset
The dataset used for this project is obtained from Kaggle and includes measurements of various air pollutants such as CO2, NO2, and O3 concentrations. The dataset consists of data collected from multiple monitoring stations and captures pollutant levels at different time intervals. Dataset.
Dependencies
To run the code and reproduce the results, the following dependencies are required:
- Python 3.x
- NumPy
- Pandas
- Matplotlib
- Seaborn
Result
The air quality prediction models achieved an average accuracy of 85% in forecasting pollutant levels, indicating their effectiveness in capturing the underlying patterns and trends.
Conclusion
This project demonstrates the application of linear regression models and decision trees algorithms for air quality prediction. The developed models can be used to forecast pollutant levels and provide valuable insights for environmental monitoring and management.