A machine learning approach to identify meaningful biomarkers by recognizing the possible patterns in the genes data given from the cBioPortal platform for the prostate cancer data. By having 495 different samples (patients) with 60K+ genes combinations, classification and feature selection, solving multi-class problems, dimensionality reduction, etc. techniques will be applied using clinical dataset along with the genes dataset. The visualization will be applied to follow up