Data preprocessing or cleansing is one of the biggest hurdles in industry for developing successful machine learning applications. The process of data cleansing includes data imputation, feature normalization and selection, dimensionality reduction, and data balancing applications. Currently such preprocessing is manual. One approach for automating this process is metalearning. In this paper, we experiment with state of the art meta-learning methodologies and identify the inadequacies and research challenges for solving such a problem.