• SAJJA RATAN KUMAR
• SIVA JYOTHI BARLA

The World Wide Web is the largest informational archive that is accessible from anywhere in the world. It is becoming more and more crucial to classify websites based on their content as a result of the development in both the number of websites and online visitors. A content-based website classification is required, according to the current trend, as recognizing a website only based on its URL is insufficient to obtain the desired results. Machine learning techniques can be used to find the required data on the content of web pages. In this work, we analyzed the content of web pages using a range of machine learning (ML) methods, such as SVM, Random Forest, Naive Bayes, Logistic Regression, Gradient Boosting, and AdaBoost. With a score of 0.982, AdaBoost outperformed the other algorithms in terms of Classification Accuracy, Precision, F1, and Recall.

Web Content Mining, Web Page Classification, Machine-learning algorithms

"Comparative analysis of Machine learning techniques for Webpage classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.j692-j697, June-2022, Available :http://www.jetir.org/papers/JETIR2206987.pdf

"Comparative analysis of Machine learning techniques for Webpage classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppj692-j697, June-2022, Available at : http://www.jetir.org/papers/JETIR2206987.pdf