A Machine Learning Approach to Analyze and Predict the Relationship between Sustainable Development Goals with Various Energy

Abstract

Comprehensive worldwide information on sustainable energy is crucial for monitoring advancements, establishing objectives, and forming well-informed policy choices to address climate change and foster sustainable growth. Machine learning, a subset of artificial intelligence (AI), concentrates on creating algorithms and models that empower computers to acquire knowledge and generate forecasts or choices rooted in data. This field harnesses statistical techniques and computational approaches to enhance a computer's proficiency in a particular task. This paper considers global data on sustainable energy between 2000 to 2020. The machine learning approaches are used to analyze and predict the dataset using linear regression, multilayer perceptron, SMOreg, M5P, random forest, and REP tree. Numerical illustrations are provided to prove the proposed results with test statistics or accuracy parameters.