## 1 Introduction
Machine learning and big data analytics provide a robust framework for analyzing vast datasets in educational contexts. Machine learning algorithms, which include supervised, unsupervised, and reinforcement learning methods, are particularly adept at uncovering patterns, predicting student performance, and tailoring educational experiences to individual needs. Big data, defined by its volume, velocity, and variety, supplies the diverse and extensive data necessary for training these sophisticated machine learning models. Together, these technologies enable the extraction of actionable insights from complex educational data, thereby driving data-informed decision-making. The integration of machine learning and big data forms a comprehensive strategy that not only enhances our understanding of educational processes but also facilitates adaptive learning systems tailored to the specific needs of students. Our study further highlights the significant impact of early-year subjects on student attrition, particularly foundational courses such as Mechanics and Materials, Design of Machine Elements, and Instrumentation and Control. These subjects pose unique challenges for first-year students. From a qualitative perspective, several factors contribute to their difficulty. First, these courses introduce core engineering concepts that require a strong foundation in mathematics and physics. Students who are underprepared in these areas may struggle to grasp the material, leading to frustration and eventual disengagement. Second, the rigor and workload of these foundational courses can be overwhelming for students who are still acclimating to university life and academic expectations. Third, many students have not been exposed to these specialized topics in high school, which creates a steep learning curve that can leave them feeling unprepared for the complexity of these subjects. To provide solid evidence of the impact these early-year subjects have on student attrition, incorporating statistical metrics such as correlation coefficients between grades and dropout rates would be invaluable. Additionally, conducting surveys or qualitative interviews with students could reveal their perceptions of course difficulty, thus identifying specific areas where additional support or curriculum restructuring may be necessary. For instance, providing supplemental tutoring for foundational courses or adjusting teaching methods to be more interactive and practical could significantly reduce dropout rates. By addressing these challenges head-on, institutions can not only retain more students but also create a more supportive learning environment that encourages success in STEM disciplines. In conclusion, leveraging machine learning and big data to predict student dropout provides a transformative approach to addressing one of the most pressing issues in STEM education today. By identifying at-risk students early and offering personalized interventions, we can improve retention rates, particularly in challenging foundational courses. As we move forward, the synergy between machine learning, big data, and educational practices will continue to evolve, driving better outcomes for both students and institutions.