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Explainable machine learning for predicting the success of IT projects in Nigerien SMEs: an empirical study based on 488 projects in Niamey

dc.creatorBako Ousmane, Falilatouen
dc.creatorSosa López, Atahualpaen
dc.date2026-07-31
dc.date.accessioned2026-08-03T16:43:32Z
dc.date.available2026-08-03T16:43:32Z
dc.identifierhttps://imjeta.org/index.php/IMJETA/article/view/machine_learning_IT_projects_in_Nigerien
dc.identifier10.67294/2z2yp254
dc.identifier.urihttp://bdigital2.ula.ve:8080/xmlui/654321/26935
dc.descriptionIT projects play a crucial role in the digital transformation of small and medium-sized enterprises (SMEs), but their success remains highly dependent on organizational, financial, and infrastructural constraints, particularly in developing countries. This study proposes an explainable predictive framework for the success of IT projects applied to SMEs in Niamey, Niger's main digital hub. The analysis is based on a set of 488 IT projects and compares the performance of seven machine learning algorithms: logistic regression, decision tree analysis, random forest analysis, support vector machines (SVMs), k-nearest neighbors, gradient boosting, and XGBoost. A rigorous methodology integrating data preprocessing, hyperparameter optimization using GridSearchCV, and cross-project validation based on GroupKFold was implemented. The results show that the SVM model achieved the highest predictive performance with an AUC-ROC of 0.850. To improve the interpretability of the predictions, explainable artificial intelligence analyses based on SHAP were conducted. These analyses revealed that stakeholder satisfaction, budget overruns, delays, project complexity, and certain constraints related to power supply and internet availability are the main determinants of project success. The study also led to the development of a prototype early warning system designed to support decision-making and proactively manage IT projects in SMEs.en
dc.formatapplication/pdf
dc.languageen
dc.publisherGrupo Afronta C.A.en
dc.relationhttps://imjeta.org/index.php/IMJETA/article/view/machine_learning_IT_projects_in_Nigerien/22
dc.rightsCopyright (c) 2026 Falilatou Bako Ousmane, Dr. Atahualpa Sosa López (Author)en
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0en
dc.sourceInternational Multidisciplinary Journal of Emerging Technologies and Applications; Vol. 1 No. 4 (2026); 1-29en
dc.source3135-6214
dc.subjectExplainable Machine Learningen
dc.subjectIT Projectsen
dc.subjectSmall and Medium Enterprisesen
dc.subjectSuccess Predictionen
dc.subjectEarly Warning Systemen
dc.titleExplainable machine learning for predicting the success of IT projects in Nigerien SMEs: an empirical study based on 488 projects in Niameyen
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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