amidstdebug / air-pollution-prediction-model

Statistical model predicting air pollution trends using statsmodels based on historical data.

Air Pollution Prediction Model

The Air Pollution Prediction Model is a sophisticated solution employing machine learning to predict levels of air pollution. By harnessing the capabilities of statsmodels and in-depth data analytics, this model offers a tool for environmental agencies, researchers, and governments to anticipate air pollution spikes and take corrective actions.

Table of Contents

• Features
• Prerequisites
• Installation
• Usage
• Contributing
• Licence
• Acknowledgements

Features

• Train a predictive model using historical air quality data.
• Evaluate the performance of the model using different metrics.
• Leverage various statsmodels algorithms for optimal forecasting.
• Fine-tune and calibrate model parameters.
• Anticipate air pollution spikes in advance.

Prerequisites

• Python 3.7 or higher.
• statsmodels library.
• An understanding of machine learning and statistical forecasting concepts.

Installation

1. Clone this repository:

   git clone https://github.com/amidstdebug/Air-Pollution-Prediction-Model.git

2. Navigate to the project directory:

   cd "Air Pollution Prediction Model"

Usage

1. Prepare your dataset, ensuring it's in the same format as depicted in the *.csv file.

2. Launch the IPython notebook to inspect the implementation:

   Jupyter Notebook "Air Pollution Prediction Model.ipynb"
   

Contributing

1. Fork the project.
2. Create your feature branch (git checkout -b feature/RemarkableFeature).
3. Commit your tweaks (git commit -m 'Incorporate some RemarkableFeature').
4. Push to your branch (git push origin feature/RemarkableFeature).
5. Initiate a pull request.

For significant alterations, please initiate an issue first to deliberate what you'd like to modify.

Licence

This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) - inspect the LICENCE file for specifics.

Acknowledgements

• statsmodels for furnishing the tools necessary for the statistical models.
• Data Science Central for guidance and best practices.
• As well as all the contributors who have devoted their efforts to refining this project!

Should you stumble upon any problems or have queries, kindly raise an issue or reach out to the overseers. We're always receptive to feedback and contributions!