Amina Shaikym

Currently building a multi-class affect detection pipeline that classifies student emotional states from behavioral and keystroke data across 243 students and 400+ features. Using student-level nested GroupKFold cross-validation to ensure leakage-safe evaluation, with Random Forest and XGBoost as primary models, and addressing feature selection stability via rank-averaging across folds.