I. Introduction
Educating oneself is certainly essential for allowing people to successfully navigate an increasingly complicated world, which is undoubtedly one of the pillars of social and personal development. However, an alarming issue that threatens the efficacy of educational systems globally is the persistent problem of student dropout. One major concern in the education and policy-making communities is student failure or dropout rates [1]. A large number of countries experience high dropout rates from higher education, including Spain [2], [3] the US [4], Germany [5], [6], Latvia, Liga et al. [7] in their paper finds gender, competition marks, total marks, faculty, and university programs connected to student dropout rates. Initial year dropout: 26%, 2012-2014 engineering science faculties peak at 47.6%. Mahbub Hasan et al. [8] observed that Around 0.6 million students pass the S.S.C. Examination in Bangladesh annually. Predictive analytics in data science is gaining traction for early identification of at-risk students, enabling timely support and interventions to reduce dropout rates. This research paper embarks on a comprehensive study aimed at evaluating the performance of various predictive methods in addressing the issue of student dropout. Specifically, we focus on five distinct algorithms: Synthetic Minority Over-sampling Technique (SMOTE), XGBoost, k-nearest Neighbors (KNN), Decision Tree, and Random Forest. These methods have been chosen due to their relevance and performance in a variety of predictive tasks, making them promising candidates for addressing the complex problem of student dropout. Our study is motivated by the urgency of improving dropout prediction accuracy and effectiveness.