wangechi01-a/AirQo-African-Air-Quality

# AirQo-African-Air-Quality-Prediction-Challenge # Overview In this challenge, you are asked to estimate PM2.5 levels from satellite observations based on Aerosol Optical Depth (AOD) for eight cities in seven African countries - Lagos, Accra, Nairobi, Yaounde, Bujumbura, Kisumu, Kampala, and Gulu - with varying ground monitoring resolutions, using appropriate machine learning algorithms. Ground-based observations in the selected cities will be used to validate the satellite estimates. # Aim The objective of the project is to estimate PM2.5 levels from satellite observations based on Aerosol Optical Depth (AOD) for eight cities in seven African countries i.e., Kampala, Lagos, Accra, Nairobi, Yaounde, Bujumbura, Kisumu, and Gulu. PM2.5 refers to atmospheric particulate matter that has a diameter of less than 2.5 micrometers and is one of the most harmful air pollutants. PM2.5 is a common measure of air quality that normally requires ground-based sensors to measure. # Importance of the Challenge Expanding Predictions: Integrating satellite data allows us to extend air quality predictions to areas without ground sensors. This expansion provides a more comprehensive view of air quality, ensuring that even remote or underserved regions are monitored effectively. Data Fusion: By combining satellite radar data with ground-based sensor readings, we create a more robust and accurate air quality prediction model. This fusion of data sources leads to better predictions and helps in understanding the complex dynamics of air pollution. Public Health Impact: Accurate air quality predictions are crucial for public health. They enable timely interventions and raise awareness in regions prone to poor air quality, helping to mitigate health risks and improve the overall well-being of the population. # Technologies Used - Python: Used Python for its powerful capabilities in data preprocessing, feature engineering, and building machine learning models. Python's extensive librar …