Predictive Modeling of Vehicle CO2 Emissions Using Machine Learning Techniques: A Comprehensive Analysis of Automotive Attributes

Concerns about protecting the environment and reducing the negative effects of greenhouse gas emissions, particularly in the automotive industry, have raised interest in finding creative and innovative ways to reduce carbon dioxide (CO
2
) emissions from a variety of sources. This study paper delves deeper into the intriguing possibilities of using machine learning (ML) techniques to predict the amount of CO
2
emitted by cars. We are exploring the subject of machine learning to develop a system that can forecast CO
2
emissions based on many parameters. Important information about the vehicle, including its make, model, type, number of cylinders, engine size, gearbox type, fuel type, fuel consumption on highways and in cities, and CO
2
emission levels, are included in the dataset that serves as the foundation for our study. The creation of accurate and dependable prediction models capable of estimating CO
2
emissions from cars is the main goal of this research. Environmental policymakers, customers, and manufacturers may all benefit greatly from these models. To do this, we employ a variety of machine learning (ML) techniques, including ensemble methods, neural networks, decision trees, and regression algorithms, among others. We evaluate the prediction accuracy, scenario handling flexibility, and information processing efficiency of several systems through this study. We determine which machine learning models are most suited for predicting CO
2
emissions within the parameters of the given dataset by applying a methodical evaluation approach. Furthermore, we investigate the influence of different feature subsets on prediction performance, shedding light on the relative importance of various vehicle attributes in determining CO
2
emissions. Our findings not only contribute to advancing the field of predictive emissions modeling but also offer insights into the key factors driving vehicle-related CO
2
emissions.