Prediction of Vehicle Carbon Emissions Using Machine Learning: A Data-Driven Approach to Assessing Environmental Impact

With the rising concerns over environmental pollution, accurately predicting vehicle carbon emissions is crucial for assessing and mitigating environmental impact. This project introduces a data-driven machine learning approach to estimate carbon emissions based on factors such as vehicle type, fuel consumption, engine capacity, mileage, and emission standards. The proposed system utilizes the Random Forest algorithm, leveraging its ensemble learning capabilities to improve prediction accuracy and generalization. The model is developed using Python, Pandas, NumPy, and Scikit-Learn, with data pre-processing, feature engineering, and hyper parameter tuning to get the best possible results. Evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R² score are employed to assess the model’s accuracy. Data visualization techniques using Matplotlib and Seaborn help analyse emission trends and key contributing factors. This system serves as a decision-support tool for policymakers, researchers, and vehicle manufacturers, aiding in emission control strategies, regulatory compliance, and sustainable vehicle design. Future advancements may incorporate deep learning models, real-time emission tracking, and IoT- based monitoring to further enhance predictive capabilities and environmental awareness.