Web Pulse – Machine Learning Powered Web Page Performance Evaluation

The increasing need for high-performing websites calls for more sophisticated evaluation strategies. This study introduces a machine learning-based framework utilizing boosting algorithms—namely XGBoost and AdaBoost—for assessing web page performance. By examining crucial indicators such as load time, response time, page size, and throughput, the model forecasts the effects of varying web configurations. Departing from conventional trialand- error approaches, this predictive system provides developers with actionable insights for enhancing site responsiveness and usability. Our results demonstrate that boosting techniques effectively identify performance patterns, positioning them as valuable assets for web enhancement. Through machine learning integration, developers and businesses can make strategic decisions that boost website speed and functionality, thereby elevating the user experience. This work underscores the transformative role of AI in advancing the evaluation and optimization of web technologies.