  IT industries in current scenario have to struggle effectively in terms of
cost, quality, service or innovation for their subsistence in the global
market. Due to the swift transformation of technology, software industries owe
to manage a large set of data having precious information hidden. Data mining
technique enables one to effectively cope with this hidden information where it
can be applied to code optimization, fault prediction and other domains which
modulates the success nature of software projects. Additionally, the efficiency
of the product developed further depends upon the quality of the project
personnel. The position of the paper therefore is to explore potentials of
project personnel in terms of their competency and skill set and its influence
on quality of project. The above mentioned objective is accomplished using a
Bayesian classifier in order to capture the pattern of human performance. By
this means, the hidden and valuable knowledge discovered in the related
databases will be summarized in the statistical structure. This mode of
predictive study enables the project managers to reduce the failure ratio to a
significant level and improve the performance of the project using the right
choice of project personnel.
