United Nations Sustainable Development Goals (SDGs) offer a universal guide to handle the critical social, economic, and environmental problems. Nonetheless, it is quite difficult to track the development of these multidimensional goals because of the great amount, diversity, and speed of associated information. This paper examines the ways through which predictive analytics, also known as data science can be used to monitor and predict the progress of achieving SDGs. Predictive analytics has the potential of generating meaningful information out of raw data through the utilization of machine learning models, big data platforms as well as statistical tools, which can be used by the policymakers, stakeholders as well as governments. Based on the publicly available datasets, this paper shows the potential of using predictive methods to model the trends, and risks, and provide decision-making information on different SDG indicators. These findings are the sign of a potential direction to take where data science will play an essential role of driving sustainable development.
S P (Sat,) studied this question.