Abstract: In this paper, a machine learning model was developed for classifying users’ quality of experience (QoE) on the web. Key Performance Indicators (KPIs) were extracted from Quality of Web Service (QWS) dataset generated using Principal Component Analysis (PCA) algorithm. The quality of web service dataset was trained using random forest algorithm of different tree sizes. The model was used to develop an application capable of classifying the users’ quality of experience on the web in order to predict the user’s experience based on the website of interest and the system was implemented in python programming language. The performance of the model was also evaluated using other existing models such as classification and regression trees (CART) and support vector machines.