This thesis investigates the critical role of data preparation in shaping the predictive performance and fairness of binary classification models. Given that the quality and composition of training data significantly influence model behaviour, especially concerning embedded biases, ensuring that training data is both accurate and fair is essential for the development of trustworthy machine learning systems. To address this, we extend an existing data processing pipeline, substantially broadening its data preparation stage with the integration of sixteen additional methods across five distinct components. This expansion allows for a more comprehensive evaluation of the interplay between data preparation, predictive accuracy, and algorithmic fairness.

Our empirical study employs a diverse set of classifiers and evaluation metrics, including several newly developed scores specifically designed to capture the nuanced effects of data preparation on model outcomes. The analysis spans both real-world and synthetic datasets, providing a robust foundation for our findings. Key insights include the observation that simply increasing the number of data preparation components does not necessarily improve model performance. Instead, optimal results often depend on carefully chosen methods and execution orders, with some components displaying strong positional dependencies. Additionally, our results reaffirm the well-documented trade-off between fairness and accuracy, yet also demonstrate that it is possible to identify configurations where both can be improved simultaneously.

These findings not only deepen our understanding of data preparation in the context of fair classification but also offer concrete, empirically grounded recommendations for practitioners. Our work lays the foundation for more informed pipeline design, providing a flexible, modular framework that can be readily extended to accommodate emerging data preparation techniques and new evaluation metrics.