Predicting employability in an unstable developing country requires the use of contextual factors as predictors and a suitable machine learning model capable of generalization. This study has discovered that parental financial stability, sociopolitical, relationship, academic, and strategic factors are the factors that can contextually predict the employability of information technology (IT) graduates in the democratic republic of Congo (DRC). A deep stacking predictive model was constructed using five different multilayer perceptron (MLP) sub models. The deep stacking model measured good performance (80% accuracy, 0.81 precision, 0.80 recall, 0.77 f1-score). All the individual models could not reach these performances with all the evaluation metrics used. Therefore, deep stacking was revealed to be the most suitable method for building a generalizable model to predict employability of IT graduates in the DRC. The authors estimate that the discovery of these contextual factors that predict IT graduates’ employability will help the DRC and other similar governments to develop strategies that mitigate unemployment, an important milestone to achievement of target 8.6 of the sustainable development goals.

Envisaging an adequate IT/IS solution that can mitigate the employability problems is imperative because nowadays there is a high rate of unemployed graduates. Thus, the main goal of this systematic literature review (SLR) was to explore the application of data mining techniques in modeling employability and see how those techniques have been applied and which factors/variables have been retained to be the most predictors or/and prescribers of employability. Data mining techniques have shown the ability to serve as decision support tools in predicting and even prescribing employability. The review determined and analyzed the machine learning algorithms used in data mining to either predict or prescribe employability. This review used the PRISMA method to determine which studies from the existing literature to include as items for this SLR. Hence, 20 relevant studies, 16 of which are predicting employability and 4 of which are prescribing employability. These studies were selected from reliable databases: ScienceDirect, Springer, Wiley, IEEE Xplore, and Taylor and Francis. According to the results of this study, various data mining techniques can be used to predict and/or to prescribe employability. Furthermore, the variables/factors that predict and prescribe employability vary by country and the type of prediction or prescription conducted research. Nevertheless, all previous studies have relied more on skill as the main factor that predict and/or prescribe employability in developed countries and none studies have been conducted in unstable developing countries. Therefore, the need to conduct research on predicting or prescribing employability in such countries by trying to use contextual factors beyond skill as features.

The current research proposes a reliable and robust machine learning (ML) model which outperforms among six other models in predicting loan fructification obtained by entrepreneurs in a semi-urban area. The proposed model predicts if an entrepreneur can make grow a loan from a microfinance firm, a bank, a financial company, or an individual. The proposed model uses primary data collected from entrepreneurs residing in Butembo, a semi-urban town located in eastern Democratic Republic of Congo as dataset. This study uses a dataset that contains 5868 records. Seven ML model performances are compared in the loan fructification prediction: support vector machine (SVM), random forest, extra trees, decision tree, naïve Bayes, k-nearest neighbors, and logistic regression. SVM reveals to be the best model for predicting loan fructification using features such as age, years of working experience of the entrepreneur, entrepreneur loan repayment conviction, used mean by the lender to recover its loan, entrepreneur opinion on the disadvantage of taking out a loan, capacity of the entrepreneur to invest after obtaining loan, entrepreneur position on the possibility of launching a business without a loan, entrepreneur willingness to apply again for loan in the future, and success project after obtaining loan. The study uses accuracy, recall, precision, and F1-score as metrics to assess the developed models. The four metrics for SVM scored 95%, 95%, 83%, and 83%, respectively. The proposed model confirms the robustness of SVM in predicting loan fructification. Received: 3 January 2024 | Revised: 29 January 2024 | Accepted: 12 March 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Héritier Nsenge Mpia: Conceptualization, Methodology, Software, Validation, Formal analysis, Resources, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration. Laure Mbambu Syasimwa: Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision. Dorcas Masika Muyisa: Validation, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration.

High unemployment rates in many developing countries have prompted research to focus on sustainability and inclusiveness of employment in line with the sustainable development goal target 8.6. The democratic republic of Congo (DRC) in sub-Saharan Africa experiences one of the highest youth unemployment rates owing to its long-term socio-political and economic instability. Qualitative and quantitative research studies have linked low employability of university-educated youth in the DRC to tribalism, corruption, and insecurity. The current study sought to identify contextual factors that predict employabil-ity of information technology (IT) graduates in the DRC. The study surveyed 355 graduates (274 male, 81 female) using 43 questionnaire. Bartlett test was 3,930.05 for Chi 2 (p-value = .000) and KMO test scored 0.68. McDonald's Omega test of reliability of the instrument scored 0.77 with a total cumulative variance of 72.02%. Results of this study advance the conflict theory by pinpointing the true factors which influence the employability of IT graduates in unstable developing countries. This study has discovered that socio-political background of graduates, graduate academic competencies, graduate-employer relationship, and university employability strategies are the contextual factors that predict the employability of IT graduates in the DRC. The Cronbach's Alpha test of reliability for the retained contextual factors scored .78, .75, .63, and .53, respectively.