Predicting student performance in programming courses is critical for timely support and improved learning outcomes. We leverage LMS data from a flipped-classroom Programming Fundamentals course to predict in-lab performance from weekly pre-class and in-class assignments in a rolling, week-by-week setup with a five-level outcome. Building on published results that identify Random Forest (RF) as a strong baseline, we fix the predicting process and add two components: (i) Recursive Feature Elimination with Cross-Validation (RFECV) for compact feature selection; and (ii) training-only class-imbalance handling with SMOTE and a GAN-based augmenter. On a cohort of 786 students across four in-lab assessments, RFECV yields small, consistent gains (mainly at the extremes), while augmentation is the main driver of class-wise balance: GAN brings the most reliable macro-F1 improvements. Ablation further shows that training the generator in the full feature space outperforms pairing GAN with early pruning. Overall, the approach improves recognition of mid-performing students without degrading performance at the extremes, enabling earlier, targeted actions.