Knowledge Discovery in Dataset (KDD) plays a vital role in information analysis and retrieval based applications. Quality of data is the most indispensable component of KDD. The factor which affects the quality of datasets is presence of missing values. The data collected from the real world often contains serious data quality troubles such as incomplete, redundant, inconsistent, and/or noisy data. Handling missing values should be cautiously considered, or else prejudice might be introduced into the knowledge induced. The current work investigates three different treatments for dealing with missing values in United States Congressional Voting Records Database. All the machine learning methods were employed in one of the leading open-source data mining applications.