Handling Missing Values in Data Mining

Missing Values and its problems are very common in the data cleaning process. Several methods have been proposed so as to process missing data in datasets and avoid problems caused by it. This paper discusses various problems caused by missing values and different ways in which one can deal with them. Missing data is a familiar and unavoidable problem in large datasets and is widely discussed in the field of data mining and statistics. Sometimes program environments may provide code for missing data but they lack standardization and are rarely used. Thus analyzing the impact of problems caused by missing values and finding solutions to tackle with them is an important issue in the field of Data Cleaning and Preparation. Many solutions have been presented regarding this issue and handling missing values is still a topic which is being worked upon. In this paper we discuss various hitches we face when it comes to missing data and see how they can be resolved.