Machine Learning Model for Applicant Classification: Addressing Unbalanced Data and Defining 'Good' vs 'Bad'

This task involves building a machine learning model to predict whether an applicant is a 'good' or 'bad' client. Unlike other tasks, the definition of 'good' or 'bad' is not provided. This requires the use of techniques like vintage analysis to construct the labels. Additionally, the problem of unbalanced data is a significant challenge in this task.