Abstract: Education in the modern era is increasingly shaped by data-driven technologies that transform traditional learning systems into intelligent and adaptive environments. Predicting student performance has become one of the most significant research areas in educational data mining (EDM) and learning analytics (LA). Accurate prediction enables educational institutions to identify at-risk students early, plan interventions, and promote personalized learning experiences.

This research explores how Data Science and Machine Learning (ML) can be applied to predict student academic performance using structured datasets. It highlights algorithms such as Linear Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and Artificial Neural Network (ANN), demonstrating their potential to analyze educational data and forecast learning outcomes. The study employs Python-based tools such as Scikit-learn, Pandas, and NumPy for model training, testing, and evaluation.

A complete ML pipeline is designed — including data collection, preprocessing, feature selection, model development, and performance evaluation — to predict student grades and categorize learners into performance classes such as High, Medium, and Low. Performance metrics like accuracy, precision, recall, and F1-score are used to evaluate model effectiveness.

The research also investigates how behavioral and academic factors such as attendance, study hours, parental education, and assignment submission rate influence student success. The results show that ensemble models such as Random Forest and Gradient Boosting achieve higher predictive accuracy than traditional statistical models.

Ultimately, this study demonstrates that integrating ML into educational systems can significantly improve academic planning and decision-making. By identifying learning trends early, institutions can move toward a data-informed educational ecosystem that enhances student engagement and academic performance.

Keywords: Data Science, Machine Learning, Student Performance Prediction, Educational Analytics, Predictive Modeling, Artificial Intelligence, Random Forest.

Downloads: | DOI: 10.17148/IJIREEICE.2025.131029