The importance of incorporating ethics and legal compliance into machine-assisted decision-making is broadly recognized. Further, several lines of recent work have argued that critical opportunities for improving data quality and representativeness, controlling for bias, and allowing humans to oversee and impact computational processes are missed if we do not consider the lifecycle stages upstream from model training and deployment. Yet, very little has been done to date to provide system-level support to data scientists who wish to develop responsible machine learning methods. We aim to fill this gap and present FairPrep, a design and evaluation framework for fairness-enhancing interventions, which helps data scientists follow best practices in ML experimentation. We identify shortcomings in existing empirical studies for analyzing fairness-enhancing interventions and show how FairPrep can be used to measure their impact. Our results suggest that the high variability of the outcomes of fairness-enhancing interventions observed in previous studies is often an artifact of a lack of hyperparameter tuning, and that the choice of a data cleaning method can impact the effectiveness of fairness-enhancing interventions.