Predicting Girls’ Orientation Toward Technical Tracks in the Republic of Guinea Using Machine Learning: A Comparative Study of Models and Determinants Analysis

The purpose of this study is to develop a machine learning model capable of predicting girls’ orientation toward technical tracks in the Republic of Guinea. The dataset was constructed from the university placement records of high school graduates and includes academic and socio-demographic variables related to students’ orientation toward technical and non-technical tracks. In addition, the study identifies the variables associated with these orientations. To achieve this goal, four supervised learning algorithms, including Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines (SVM) were used. The evaluation of the algorithms’ performance was based on the metrics Accuracy, Precision, F1-score, Recall, and AUC. The results show that the Random Forest model performs best, with an accuracy of 87.7%, an F1-score of 82.1%, and an AUC ROC of 0.954. Analysis of the variables reveals that the overall average score is the primary factor guiding girls toward technical tracks. This research highlights the importance of machine learning methods as a decision-making tool for policies aimed at education and the promotion of technical tracks among girls. Models were eva