We conduct an empirical analysis to investigate the relationship between thirty seven different source code metrics with fifteen different Web Service QoS (Quality of Service) parameters. The source code metrics used in our experiments consists of nineteen Object-Oriented metrics, six Baski and Misra metrics, and twelve Harry M. Sneed metrics. We apply Principal Component Analysis (PCA) and Rough Set Analysis for feature extraction and selection. The different sets of metrics are provided as input to the predictive model generated using Least Square Support Vector Machine (LSSVM) with three different types of kernel functions: RBF, Polynomial, and Linear. Our experimental results reveal that the prediction model developed using LSSVM method with RBF kernel function is more effective and accurate for prediction of QoS parameters than the LSSVM method with linear and polynomial kernel functions. Furthermore, we also observe that the predictive model created using object-oriented metrics achieves better results in comparison to other sets of source code metrics.