Machine Learning Approaches For Real-Time Carbon Emission Prediction And Mitigation
Keywords:
Carbon Emission Rating, Machine Learning Models, LSTM, Fuel Consumption, Decision Tree Classifier, Random Forest Classifier, Linear Regression Accuracy, Deep Learning, Ensemble Methods, Data Science For SustainabilityAbstract
The project, “CO2 Emission Rating by Vehicles Using Data Science,” is a data-driven initiative aimed at
assessing and rating the carbon dioxide (CO2) emissions of new light-duty vehicles available for retail sale in
Canada in 2022. Leveraging the power of Python programming and employing sophisticated machine learning
models, like the Random Forest Classifier and the Decision Tree Classifier, this project offers a comprehensive
analysis of vehicle emissions. The dataset utilized for this project contains crucial information, including fuel
consumption ratings, CO2 emissions in grams per kilometer, CO2 ratings on a scale from 1 (worst) to 10 (best),
and smog ratings on a scale from 1 (worst) to 10 (best). These data elements provide a holistic perspective on
the environmental performance of various vehicle models, allowing consumers and policymakers to make
informed choices. We are planning to employ the Random Forest Classifier, a powerful ensemble learning
algorithm, and the Decision Tree Classifier to build predictive models to achieve better training and testing
accuracy by combining these advanced algorithms and a rich dataset, this project aims to contribute to
sustainable transportation solutions and empowers consumers to make environmentally conscious decisions
when purchasing vehicles. The CO2 Emission Rating system developed here serves as a valuable tool for
evaluating the environmental impact of different vehicle models, helping reduce carbon emissions and mitigate
climate change. The developed linear regression model achieved an accuracy of 99.447%, demonstrating its
robustness in predicting vehicle CO₂ emissions and supporting reliable real-time mitigation strategies.
