Due to the rapid increase in the amount of data being stored and processed in the world, innovative solutions in the fields of data storage and data processing are increasingly needed; Compressive Sampling (CS) and Compressive Classification (CC) are two approaches that provide solutions for both areas, respectively. The use of CC to obtain information from the data through classification reduces the processing load as it enables the classification to be performed directly in the measurement domain obtained by CS. CS makes possible a lossless reconstruction with a high probability of less samples than the amount required by the Shannon sampling theorem, and by applying Preconditioning (PC) to the measurement matrix used, the amount of data required for reconstruction can be further reduced due to the number of samples required for reconstruction. The contribution of the use of the matrix derived from the measurement matrix by Singular Value Decomposition (SVD) as the measurement matrix in the CS, on the reconstruction performance has been studied only experimentally in the literature. In this study, as a first, it has been shown analytically that this approach based on SVD is a PC (SVD-PC) and will reduce the number of samples required for reconstruction in CS, meanwhile two different Monte Carlo (MC) simulations were carried out regarding to this finding. The SVD-PC performance supported by simulations is evaluated experimentally with SS applications performed on two different data sets and using three different classifiers, moreover the effect of SVD-PC on CC performance is investigated for the first time in the literature in this study.