Examining the significance of anticipating and enhancing student academic achievements using RF and SVM

Aim: Predicting student exam performance accurately is essential for creating individualized learning plans. Static evaluations are the foundation of traditional approaches, which miss real-time learning trends. This study suggests a novel, data-driven strategy to improve forecasting accuracy by combining applied science techniques with advanced analytics. Through the use of adaptive teaching techniques and machine learning models, this research seeks to offer long-term solutions for raising student achievement. The shortcomings of traditional predictive models are emphasized by a comprehensive analysis of the literature, underscoring the necessity of dynamic, real-time forecasting. Our suggested methodology fills this research vacuum by combining individualized intervention techniques with ongoing student performance monitoring. The goal of the study was to use sophisticated analytics and data-driven teaching practices to better predict student exam results. Examined was a comprehensive dataset that contained historical academic data such as attendance records, grades, engagement, and student demographics. Two primary strategies were examined: data-driven strategies, which included statistical modelling and feature analysis in advanced analytics to simulate performance patterns, and predictive modelling, which employed machine learning techniques like regression and neural networks.