<b>STUDENT SUCCESS PREDICTION IN HIGHER EDUCATION THROUGH MACHINE LEARNING</b>

The objective of this program is to forecast college success by employing machine learning techniques, which involve the identification of patterns and variables that influence academic performance. Educational institutions are increasingly employing data-driven methodologies. This is due to the fact that algorithms such as neural networks, decision trees, and support vector machines provide the potential for early intervention and personalized assistance. The study aims to develop predictive models that can categorize students into groups at risk of underperformance by examining their demographics, academic history, and behavioral characteristics. By equipping students with the necessary resources to make well-informed decisions, educators can enhance their retention, engagement, and performance. This research not only contributes to the growing corpus of knowledge in educational data mining, but it also assists schools in optimizing the utilization of smart technologies to enhance student achievement.