Advanced Predictive Modeling of Pollutant Gas Emissions in the Automotive Industry based on Machine Learning

Predicting CO
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emissions in the automotive industry is vital for driving innovation in fuel efficiency, shaping policies, and fostering a greener, sustainable future. An advanced predictive modeling approach for estimating CO
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emissions in the automotive industry using machine learning techniques is presented in this paper. Data from 46 distinct automotive brands was incorporated, comprehensively analyzing various vehicles. The predictive model employed six numeric features, encompassing engine size, cylinder count, and diverse fuel consumption metrics, along with five categorical features concerning brand, model, vehicle class, transmission, and fuel type. Considerable results were achieved, with a mean squared error (MSE) of 29.99, a root mean squared error (RMSE) of 5.48, and an $R^{2}$ of 0.991, showcasing the model’s forecasting accuracy for CO
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emissions. Therefore, this work underscores the effectiveness of machine learning in CO
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emissions prediction and emphasizes the importance of considering diverse features and multiple automotive brands for constructing comprehensive and robust models in the context of environmental impact assessment, thereby contributing to a more sustainable automotive industry.