Enhancing User Experience with AI-Generated CSS Through Machine Learning for Adaptive Web Design

This paper focuses on a deep comparison of five models—Naive Bayes, Logistic Regression, SVM, an, and a Proposed Model—in the definition of the problems used in binary classification to establish intrinsic performance criteria such as ROC curve analysis, F1 score, Recall, Accuracy, and Precision to compare the most effective model by its capability for distinguishing between classes like positive and negative to give less error. Its performance is excellent with a very high accurate AUC of 0.96 in a performance providing excellent discrimination. It is quite close to the performance between the Logistic Regression and SVM models compared. The AUCs are at 0.93 and 0.94, respectively. In all cases, the Proposed Model performed better with consistent results. For instance, it was at 97% F1 score, 97% accuracy, and had a top AUC of 0.97, thereby proving it excellent and outstandingly able for prediction. The experiments have shown that Proposed Model is much more effective from a classification point of view and categorizes as one of the most trustworthy and accurate models from those tested.