Development of a Machine Learning-Based Web Application for Predicting CO2 Emissions in Light-Duty Vehicles

The transportation sector significantly contributes to global greenhouse gas emissions, particularly CO2. This research aims to develop a user-friendly web application that accurately predicts CO2 emissions of light-duty vehicles using machine learning models. By using datasets from the UK Vehicle Certification Agency (VCA), various regression models such as Decision Tree, Random Forest, and Gradient Boosting, were trained and evaluated for predicting CO2 emission.The model was deployed as a Streamlit web application to allow users to estimate vehicle emissions based on input variables. This research underscores the potential of machine learning and artificial intelligence in supporting the digital transformation of the transportation sector, providing a cost-effective tool for stakeholders to assess CO2 emissions and support environmental sustainability. Future work involves exploring additional machine learning algorithms and enhancing the web application for broader use.