  Software effort estimation requires high accuracy, but accurate estimations
are difficult to achieve. Increasingly, data mining is used to improve an
organization's software process quality, e. g. the accuracy of effort
estimations . There are a large number of different method combination exists
for software effort estimation, selecting the most suitable combination becomes
the subject of research in this paper. In this study, three simple
preprocessors are taken (none, norm, log) and effort is measured using COCOMO
model. Then results obtained from different preprocessors are compared and norm
preprocessor proves to be more accurate as compared to other preprocessors.
