The use of Machine Learning (ML) algorithms in decision-making processes has significantly increased in recent years, providing alternatives to human decisions, which are frequently affected by bias. However, ML algorithms can also exhibit bias, leading to discrimination against individuals or groups based on sensitive attributes such as gender or race. This bias often arises from the imbalanced representation of demographic groups in training datasets. Mitigating representation bias during the training phase is crucial to ensure the fair application of ML algorithms in decision-making processes. This thesis presents a pre-processing framework designed to address representation bias by oversampling minority groups, thereby creating a balanced and fair dataset for model training.