Leveraging Data Science for Educational Data Analysis: A Machine Learning Approach to Student Performance Prediction and Learning Analytics

The rapid growth of digital learning environments has generated massive amounts of student-related data, ranging from attendance and demographics to assessments and online engagement logs. Traditional statistical methods are limited in handling such complex, high-dimensional, and dynamic datasets. This study leverages data science and machine learning techniques to analyze educational data for predicting student performance and enhancing learning analytics. Using classification algorithms such as Logistic Regression, Random Forest, Gradient Boosting, and Neural Networks, the study evaluates predictive accuracy and identifies key features contributing to academic outcomes. The methodology includes data preprocessing, feature engineering, model training, and comparative evaluation based on metrics such as accuracy, precision, recall, and F1-score. The results demonstrate that ensemble models outperform conventional approaches, and visualization dashboards enable actionable insights for educators. This research contributes to developing early-warning systems for at-risk students, supporting evidence-based decision-making in education.