Application of Data Science and Machine Learning in the Prediction of College Dropout: A Data-Driven Predictive Approach

This paper presents a study on the prediction of student graduation or failure using two predictive models: K-Nearest Neighbors (KNN) and a forward sequential Artificial Neural Network (ANN). The models, built with a well-chosen set of independent variables, were assessed using metrics like precision and accuracy. The results obtained revealed that both the KNN model and the sequential forward ANN model achieved high efficiency in predicting student graduation or failure, achieving accuracies of 0.9133% (K=3) and 0.9312% (after 50 epochs), respectively. Providing a valuable tool to identify early on students at risk of not graduating and to take preventive measures to improve their academic performance. Comparisons with related research showed consistent outcomes, underscoring the credibility and importance of the employed predictive models.