Machine Learning & CO2 Emissions in Cameroon 🌍

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This research focuses on modeling CO2 emissions in Cameroon using machine learning techniques. Specifically, it employs Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to analyze emission datasets and assess performance through Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The study aims to predict CO2 emission trends, evaluate the alignment with nationally determined contributions (NDCs), and recommend policy actions. The methodology prioritizes...