Prediction of Vehicle Carbon Emission Using Machine Learning

The automotive industry is a major contributor to carbon emissions, posing a threat to the environment and human health. This research explores the potential of machine learning for predicting vehicle CO2 emissions. It utilizes a comprehensive dataset encompassing diverse factors like vehicle type, engine specifications, driving patterns, and environmental conditions. Feature engineering and model training with various algorithms like linear regression, decision trees, and neural networks are employed. The findings demonstrate the effectiveness of machine learning in predicting emissions with high accuracy, offering valuable insights into influencing factors. The developed models empower the automotive industry to manage emissions more efficiently, contributing to climate change mitigation and a more sustainable future. This study highlights the potential of machine learning in addressing critical environmental challenges.