Machine Learning-Based CO2 Emission Forecasting: A Predictive Modeling Approach for Sustainable Development.

As global concerns about climate change intensify, accurately predicting and managing carbon dioxide (CO2) emissions becomes paramount for sustainable environmental practices. This project employs a machine learning-driven approach to forecast CO2 emissions, utilizing historical data and advanced predictive modeling techniques. The study integrates diverse datasets, including environmental parameters and temporal information, to enhance the accuracy of predictions. The predictive models, primarily based on linear regression, are trained and evaluated using a comprehensive dataset. The approach not only explores the performance of different machine learning models but also delves into feature engineering and model interpretability. Through this research, we aim to contribute to the growing body of knowledge in environmental science and provide valuable insights for policymakers and stakeholders. The results of this study are anticipated to facilitate informed decision-making for mitigating environmental impact and promoting sustainable practices.