Exploring the Impact of Artificial Intelligence and Machine Learning on Predicting Outcomes in Regional Development Conventions: A Case Study of the Tanger-Tetouan-Al Hoceïma Region, Morocco

Objectives: For over six years, Morocco has been engaged in an initiative aimed at promoting inclusive and sustainable territorial development, characterized by the implementation of an advanced regionalization experiment. However, the main challenge facing government leaders today is managing a growing number of regional development conventions. Methods: To achieve these objectives, we developed a predictive model using artificial intelligence and machine learning aimed at anticipating the outcomes of regional development conventions. Through this method, we were able to identify the risks associated with their potential failure and forecast their impact. Simultaneously, various data extraction methods and classification algorithms were implemented. We collected and analyzed information from 310 past and current regional development conventions, spanning from 2016 to 2020, considering various factors influencing their success or failure. We conducted extensive experiments to assess the effectiveness of various predictive models. Results: The results we obtained indicate that the classifiers Ada Boost, Random Forest, and Bernoulli NB are the three most effective algorithms for predicting the outcomes of regional development conventions. Conclusion: This study contributes to shaping a broader discourse on the practical application of artificial intelligence in public policies and regional development, highlighting its potential to enhance resource allocation and alleviate the burden of repetitive administrative tasks for organizations with limited resources.