Browser Preference Prediction for Computer Users using Machine Learning
Agus Pratondo, Nanang Ismail, Astri Novianty
Abstract
Using machine learning techniques to predict people’ preferred web browsers has become a powerful way to improve user experience and personalize web browsing. In a time where social media interactions are a part of everyday life, it is critical to recognize and accommodate personal preferences. This study presents the Random Forest algorithm as the selected approach and investigates the role of prediction in web browser preference. The system provides a tailored and efficient surfing experience by utilizing user information to anticipate browser preferences. The experimental findings highlight the model’ s predictive power with a noteworthy accuracy rate of 96.2 percent. This high accuracy suggests that the model is able to recognize and accommodate the individual preferences of users. The predictive model has a great deal of promise for practical uses with its level of accuracy, making it an invaluable resource for marketers, user experience designers, and web developers. By employing the insights gained from this study, practitioners can better tailor their services to individual users, contributing to an improved browsing experience in everyday life. (Abstract)