Auto-ML-On-De-Pollution

This project focuses on PM10 air quality prediction using a combination of Machine Learning (ML) and AutoML techniques. It includes data processing, feature engineering, model training, and evaluation notebooks.

Project Structure

The project structure is organized as follows:

• script/ : Contains Jupyter notebooks for various aspects of the project.
  • AutoMLModels/ : Notebooks for AutoML models.
    • automl01.ipynb
    • automl02.ipynb
    • automl03.ipynb
    • automl04.ipynb
  • dataProcessing/ : Notebooks for data processing.
    • dataCleaning.ipynb
    • dataUnderstanding.ipynb
    • weatherData.ipynb
  • MLModels/ : Notebooks for traditional ML models.
    • ml1.ipynb
    • ml2.ipynb
• functionsAll.py : Python script containing custom functions used in notebooks.

Usage

• Use the data processing notebooks to clean and understand the dataset.
• Explore different ML and AutoML models in the respective notebooks for PM10 prediction.
• Modify and experiment with the code to suit your specific use case.

Contributing

Contributions to this project are welcome! Feel free to open issues or pull requests for any improvements or bug fixes.

License

This project is licensed under the MIT License - see the LICENSE file for details.