HamdiBarkous/UmojaHack-Africa-2023-Carbon-Dioxide-Prediction-Challenge

# UmojaHack-Africa-2023-Carbon-Dioxide-Prediction-Challenge The ability to accurately monitor carbon emissions is a critical step in the fight against climate change. Precise carbon readings allow researchers and governments to understand the sources and patterns of carbon mass output. While Europe and North America have extensive systems in place to monitor carbon emissions on the ground, there are few available in Africa. The objective of this challenge is to create machine learning or a deep learning model using open-source CO2 emissions data (from Sentinel-5P satellite observations) to predict carbon emissions. These solutions will enable governments, and other actors to estimate carbon emission levels across Africa, even in places where on-the-ground monitoring is not possible. In response to this challenge, this notebook presents a top-tier solution that ranked 1st out of 441 submissions in the UmojaHack Africa 2023: Carbon Dioxide Prediction Challenge. Our approach integrates advanced data processing techniques with innovative machine learning algorithms, tailored specifically to handle the complexities and nuances of Sentinel-5P satellite data. The model demonstrates exceptional accuracy and reliability in predicting CO2 emissions, showcasing its potential as a vital tool for environmental monitoring and policy-making. This work not only contributes to the scientific community's understanding of carbon emissions in Africa but also paves the way for more informed and effective climate action on the continent.