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In this framework, the fair learning process is divided into three stages. Each stage aims to reduce unfairness, such as disparate impact and disparate mistreatment, in the final prediction. For the pre-processing stage, we present a resampling method that addresses unfairness coming from data imbalances. The in-processing phase consists of a classification method. This can be either one coming from the  package, or a user-defined one. For this phase, we incorporate fair ML methods that can handle unfairness to a certain degree through their optimization process. In the post-processing, we discuss the choice of the cutoff value for fair prediction. 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