Ιnternet has become an integral part of humans’ everyday life, an indispensable part of information gathering, means of socialization, provision of services, purchasing and selling products. The plethora of available websites providing similar or even different services has created a new reality where each user can find sites that fulfill their needs. Therefore, sites of similar content and services focus on optimizing User Experience to attract more users. Particularly, User Experience refers to user interactions with a website and focuses on the overall experience a site provides. There are various factors that influence User Experience. This thesis employs Google Lighthouse, an automated tool for measuring the quality of web pages, and explores the very features that influence performance metrics pertaining to User Experience. Particularly, 85 features were extracted from a dataset of 200K websites, data resulting from Google Lighthouse reports. These features describe quantitatively the composition, structure and resources of each web page. After having used a regression model for predicting performance metrics scores, as defined by the simulation software, an analysis-extraction of the most important features used by the model was performed. The ultimate objective of the thesis is to enable a front-end website developer to prioritize and focus on those features that improve Google Lighthouse’s performance metrics scores, this way improving user experience.