Application of machine-learning Techniques to analyze and estimate regional SDG indicators in Morocco
64th ISI World Statistics Congress - Ottawa, Canada
Format: CPS Abstract
Keywords: machine learning, official-statistics, sdgs
Session: CPS 76 - Statistical estimation V
Wednesday 19 July 8:30 a.m. - 9:40 a.m. (Canada/Eastern)
The Sustainable Development Goals (SDGs) are a set of targets and indicators based on data that are intended to enhance national reporting and policy. Since the SDGs are linked together, progress made toward one of them may have positive (synergies) or negative (trade-offs) effects on the other goals. The intricacy of SDGs as a system is highlighted by this kind of connection. To maximize goal achievement, it can be helpful to discover goals that have a beneficial effect on other goals. Indeed, having data on a national or regional scale is still a constraint for analyzing and monitoring the SDGs in developing countries. This study proposes a technical framework to estimate, predict and analyze regional SDGs targets in Morocco. It leverages the capabilities of machine learning (ML) algorithms on available official data and alternative data to estimate proxy indicators and therefore to identify synergistic SDGs, analyze the interactions between SDGs at regional level in Morocco.