LassoForest: CGPA dynamics through time-series forecasting and Counterfactual Analysis of Alcohol Consumption

Abstract

Predicting students' academic performance is one of the most important aspects of educational planning as it gives way to targeted interventions. This work suggest an temporal machine learning framework called LassoForest to forecast CGPA, which uses longitudinal behavioral data such as alcohol consumption. A sliding-window feature engineering method was used to track sequential trends in academic performance and lifestyle behaviors. The LassoForest model achieved an MAE of 0.80 and RMSE of 1.31, with a cross-validated R2 mean of 0.829 and standard deviation of 0.039. Counterfactual analysis simulating reduced alcohol consumption showed a slight increase in mean CGPA from 12.548 to 12.562, with minimal changes in standard deviation and percentile values. There were some students whose CGPA rose as high as 0.464 points. The findings imply that the combination of temporal behavioral patterns detection with an interpretable hybrid modeling approach allows one to not only make accurate CGPA predictions but also provide very useful insights for the case of behavioral interventions. The research seeks to prepare the ground for the educational stakeholders who wish to reduce the negative effects of alcohol on academic performance through targeted, data-informed behavioral counseling.