During the last decade, data have played a key role for learn- ing and decision making models. Unfortunately, the quality of data has been ignored or partially investigated as a pre-processing step. Moti- vated by applications in various fields, we propose to study data quality and its impact on the performance of several learning models. In this work, we first introduce a list of elementary repairing tasks ranging from easy to complex with an increasing level. Then, we form categories from the state-of-the-art cleaning and repairing methods. We also investigate if it is always efficient to repair data. By including standard classifica- tions models and public dataset, our work enables their use in different contexts and can be extended to other machine learning applications.