Rank Analysis For Students Using ML

Student performance prediction is a crucial aspect of modern educational analytics, enabling institutions to identify at-risk learners and improve academic outcomes through timely intervention. This study aims to analyse and predict student performance using various machine learning techniques. The dataset includes key academic and socio-demographic features such as attendance, internal marks, study hours, parental education, and socio-economic status. Data preprocessing techniques like handling missing values, normalization, and feature selection were applied to enhance model accuracy. Several algorithms, including Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine (SVM), were implemented and compared based on accuracy, precision, recall, and F1-score. The Random Forest model achieved the highest accuracy, indicating its robustness for classification tasks involving educational data. The results demonstrate that machine learning can effectively uncover hidden patterns and provide actionable insights to educators, enabling data-driven decision-making to improve student outcomes.