Due to the intangible nature of "software", accurate and reliable software effort estimation is a challenge in the software Industry. It is unlikely to expect very accurate estimates of software development effort because of the inherent uncertainty in software development projects and the complex and dynamic interaction of factors that impact software development. Heterogeneity exists in the software engineering datasets because data is made available from diverse sources. This can be reduced by defining certain relationship between the data values by classifying them into different clusters. This study focuses on how the combination of clustering and regression techniques can reduce the potential problems in effectiveness of predictive efficiency due to heterogeneity of the data. Using a clustered approach creates the subsets of data having a degree of homogeneity that enhances prediction accuracy. It was also observed in this study that ridge regression performs better than other regression techniques used in the analysis
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