I developed a machine learning

I developed a machine learning classification project to predict student exam performance using demographic, academic, and behavioral factors. The project included data exploration and preprocessing, feature analysis, model development, and evaluation of multiple classification algorithms.

I compared four machine learning approaches—Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting—using accuracy, precision, recall, F1 score, and ROC-AUC to evaluate performance.

Gradient Boosting produced the strongest overall results, achieving 96.50% accuracy, a 98.21% F1 score, and a 93.73% ROC-AUC.

The project demonstrates my ability to take a structured dataset through the machine learning workflow, compare competing models, evaluate results using multiple performance metrics, and translate model outputs into meaningful insights about the factors associated with student performance.