An analysis and comparison of various missing data imputation tools and techniques

Abstract

The missing data and noisy data are common in a data set and the finding the effect it causes on the accuracy is very important to be determined. In statistics, missing data, or such values, occur when no data value is assigned for a field in a dataset. Missing data are a common occurrence and can have a significant effect on the conclusions that can be drawn from the data given or taken from warehouses. Missing data reduce the representativeness of the sample and can therefore distort/deviate inferences & conclusions about the population. This study aims at calculating the effect of missing values on Naïve Bayes algorithm by using two data sets that are lymphoma and breast cancer. The values are skipped in certain order of both the data set and accuracy is computed and results were compared in a table. Naïve Bayes is based on probalistic model.