Murasajo/Airqo-African-AirQuality-Challenge

This project is part of the AirQo African Air Quality Challenge, which aims to estimate PM2.5 levels from satellite data using machine learning for eight African cities. Air pollution, a significant global health risk causing 7 million premature deaths annually, heavily impacts poor communities in sub-Saharan Africa. ### Project Description # AirQo African Air Quality Prediction Challenge ## Overview This repository contains the code and documentation for the AirQo African Air Quality Prediction Challenge. The goal of this challenge is to estimate PM2.5 levels from satellite data using machine learning models for eight African cities, validate these estimates with ground-based observations, and deploy the models on AirQo’s platform to provide essential air quality information to communities. ## Problem Statement Air pollution is a major global health risk, causing 7 million premature deaths each year. It has a particularly severe impact on poor communities in developing countries, especially in sub-Saharan Africa. By improving the accuracy and availability of air quality data, this project aims to mitigate the health risks associated with air pollution. ## Objectives - Estimate PM2.5 levels from satellite data for eight African cities using machine learning. - Validate these estimates with ground-based observations. - Deploy the predictive models on AirQo’s platform. ## Contents - **Exploratory Data Analysis (EDA)**: Initial analysis and visualization of the data to understand its structure and key features. - **Data Preprocessing**: Steps to clean and prepare the data for modeling. - **Modeling**: Development and evaluation of machine learning models to predict PM2.5 levels. - **Deployment**: Integration of the models into AirQo’s platform. ## Requirements - Python 3.x - Pandas - NumPy - Matplotlib - Scikit-learn ## Usage 1. **Clone the repository**: ```sh git clone github.com cd airqo-african-air-quality-prediction ``` 2. **Install the required packages**: ```sh pip install -r requirements.txt ``` 3. **Run the notebook**: Open and run the `AirQo_African_Air_Quality_Prediction_Challenge.ipynb` notebook to reproduce the analysis and model training steps. ## Contributing Contributions are welcome! Please …