This document presents a student performance prediction system that uses machine learning algorithms to predict how well students will perform in their degree programs based on their current and past academic performance data. The system uses a bi-layered architecture with a base predictor layer and an ensemble predictor layer. The base predictor layer makes local predictions about student performance using various predictors trained on student data features. The ensemble layer synthesizes these local predictions along with previous overall predictions to make a final performance prediction. Latent factor models are used to identify relevant course subjects. The system aims to help banking systems assess student loan eligibility by predicting their likelihood of satisfactory and timely degree completion.