Analysis of Missing Value Estimation Algoithms for Data Farming

In this paper we compare various statistical method of estimation of missing data values. Missing data estimation is a part of data farming. Data Farming is a process to grow the data & provides a more comprehensive understanding of the possible outcomes, and offers the opportunity to discover outliers, surprises. Many times data mining task use existing data collected for various other purposes, such as daily transactional data, monitoring & control data. Sometimes, the data set might be missing some values, to estimate these missing values various statistical methods exist in the literature. In this paper a comparison among these methods is given with implementation & comparative results on the real life data set. This research work will be helpful to understand the effect of missing values on the mining process.