Abstract
For the statistics thesis project, a model adjustment is proposed for unbalanced data,
using vehicle financing information, with the response variable being dichotomous, divided
into defaulters and non-defaulters. Techniques for variable selection, such as weight
of evidence and information value, will be presented, along with the adjustment of logistic
regression models for both unbalanced and balanced data, model quality metrics,
and an interpretable final classification. The project was developed using the Python
programming language.