Predicting the outcome of regional development projects using machine learning

Jihad Satri, Chakib El Mokhi, Hanaa Hachimi


Morocco, in its pursuit of inclusive and sustainable territorial development, initiated the advanced regionalization experiment over six years ago. The primary challenge facing government officials today is the management of a burgeoning number of regional development projects. In this article we developed a predictive model based on artificial intelligence and Machine Learning to predict the outcomes of regional development projects, in order to identify the risks associated with their potential failure, and anticipate their impact. To accomplish this, we implemented various data mining techniques and classification algorithms. We collected and analyzed data from past and ongoing regional development projects, considering diverse factors that influence their success or failure. Through rigorous experimentation, we assessed the effectiveness of different predictive models. Our findings reveal that the Random Forest classifier stands out as an efficient algorithm for predicting the outcomes of regional development projects. This research contributes to the broader discourse on the practical implementation of artificial intelligence in public policy and regional development, showcasing its potential to optimize resource allocation, and alleviate the burden of repetitive administrative tasks for organizationsoperating with limited resources.


Artificial intelligence; Data mining; Data science; Machine learning; Prediction; Public decision making;

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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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