Smart Forecasting: Harnessing Machine Learning for Accurate CO2 Emission Predictions

This The tenacity of this study is to improve a Machine Learning (ML) model to enhance the precision of Carbon Dioxide (CO2) emission predictions. This study utilizes the cutting-edge forecasting techniques for a more accurate understanding of environmental impact. This study will harness the power of smart forecasting to inform strategic decision-making in carbon mitigation efforts. In this research, the performance metrics of four commonly used ML classifiers, namely LR, Gaussian Process, MLP and SMOreg have been evaluated to foretell CO2 emissions using the dataset collected from Kaggle. The dataset was pre-processed, and all the algorithms were trained and tested. The number of instances used in this study is 935. The results of this investigation show that machine learning algorithms are capable of producing accurate CO2 emission forecasts. The findings suggest that the SMOreg Classifier is more accurate than the LR (LR), Gaussian Process Regression (GPR) and Multilayer Perceptron (MLP) classifiers for predicting CO2 emissions. This study emphasizes the possibility of using ML algorithms to predict CO2 emissions. The error values such as MSE, RMSE, MAE, Correlation Coefficient and Root relative squared error indicates the performance of SMOreg is a superior classifier for the forecasting, these results have an important implication in climate change for improving prediction models, which could assist in early detection of climate change.