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Presentation Master's Thesis - Lisette Sibbald - Psychological Research Method

Last modified on 14-11-2022
Scalable, Robust and Maintainable Prediction of European Credit Transfer and Accumulation System Points
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Start date
04-08-2022 11:00
End date
04-08-2022 12:00
Location

Roeterseilandcampus REC GS.04

To facilitate financial planning of UvA's Faculty of Social and Behavioural Sciences , the UvA Advanced Analytics team recently introduced a linear model that predicts European Credit Transfer and Accumulation System points (ECTS) at the end of the academic year per student based on a model trained on the previous academic year. However, this model does not perform as desired since the goal is to have an average prediction error below 5 percent. 

The origin of this high error is thought to be related to issues with the independent variable (IV) that has the most predictive value, ECTS obtained per student up until the month of prediction (CECTS). This IV shows different distributions in different years. This difference is quantified and shown to correlate with prediction error. In this study it is shown that a decision tree is a more natural model for this prediction and that performance is increased in situations of large difference in the distribution of this IV between years.