Website Classification Through Exploratory Data Analysis Using Naive Bayes, Random Forest, and Support Vector Machine Classifier

Website categorization is the process of grouping websites resources according to their content, function, and other characteristics. Website categorization is crucial for a number of reasons, including supporting users in finding pertinent material, allowing advertisers to target particular demographics and social policy, and assisting businesses in recognizing and keeping track of emerging online trends and security issues. This study uses Naive Bayes, Random Forest, and Support Vector Machine classifier's to categorize websites and forecast accuracy for these parameters. This study employs three different classifier types to pinpoint and address security flaws in websites. Penetration testing, vulnerability scanning, and code review are some of the social techniques that may be employed in this kind of study. This study concentrates on how a website's visual design might influence users' perceptions and actions since it predicts an accuracy of 88% using the Naive Bayes Classifier.an accuracy of 88% using the Naive Bayes Classifier.