This looks like a data science project where different AI/ML models are being compared to see which predicts best for an education-related outcome (for example, student success or course performance). Think of it as a “bake-off” between algorithms to pick the most accurate one for a school-related prediction task.
Choosing the most effective prediction model for an education scenario (such as identifying at-risk students, forecasting grades, or optimizing learning pathways) so that institutions can act early and allocate resources better.
Classical-ML (Scikit/XGBoost)
Structured SQL
Medium (Integration logic)
Data quality and feature engineering for heterogeneous education data
Early Majority
Focus on rigorous model comparison and accuracy benchmarking for a specific educational prediction task, rather than generic one-size-fits-all models.