A fault is an error that has effects on system behaviour. A software metric is a value that represents the degree to which software processes work properly and where faults are more probable to occur. In this research, we study the effects of removing redundancy and log transformation based on threshold values for identifying faults-prone classes of software. The study also contains a comparison of the metric values of an original dataset with those after removing redundancy and log transformation. E-learning and system dataset were taken as case studies. The fault ratio ranged from 1%-31% and 0%-10% for the original dataset and 1%-10% and 0%-4% after removing redundancy and log transformation, respectively. These results impacted directly the number of classes detected, which ranged between 1-20 and 1-7 for the original dataset and 1-7 and 0-3) after removing redundancy and log transformation. The Skewness of the dataset was deceased after applying the proposed model. The classified faulty classes need more attention in the next versions in order to reduce the ratio of faults or to do refactoring to increase the quality and performance of the current version of the software.