Using Machine Learning for Data Preprocessing

Data often has to be preprocessed before data analysis such as formatting, modification, and transformation. Data preprocessing is a tedious task and has to be repeated before every data analysis task. There are potentially large amount of operations that could be applied to a tabular data set. The goal in this thesis is to develop a recommender system to suggest transformations with respect to characteristics of the data set, historical actions users have performed in the past, etc. One could also incorporate user feedback and usability aspects of such a tool.