Academic Performance Prediction System: Analysis of Data to Forecast Final Outcomes with Improved Accuracy

Abstract Academic performance prediction has emerged as a significant area of research aimed at identifying students who may require timely academic support. This study presents an Academic Performance Prediction System that analyses student- related data to forecast final outcomes with improved accuracy. The system integrates essential components such as data preprocessing, feature selection, and predictive modelling using supervised learning approaches, including Decision Trees, Random Forests, Support Vector Machines, and Artificial Neural Networks. A standard educational dataset with demographic, behavioural, and academic attributes is utilized to train and evaluate the models. Performance metrics such as accuracy, precision, recall, and F1-score help determine the effectiveness of each model. Experimental results indicate that ensemble-based classifiers achieve higher reliability than single models. The proposed system architecture provides a scalable framework suitable for academic institutions seeking early interventions and data-driven decision-making. This research contributes an effective predictive solution that enhances student monitoring and supports academic improvement strategies. Keywords Academic performance, prediction system, learning analytics, data preprocessing, supervised learning, classification models, evaluation metrics, student performance.