• Mr. Byalla Brahma Sekhar
• Mr. O. SAMPATH

Internet usage has integrated into our daily lives. Therefore, to capture users' attention, several browser suppliers compete to build up new functionality and complex capabilities that serve as a target for intrusion attempts and put websites at risk. However, the current methods are insufficient to protect web users, who need a quick and accurate model that can tell apart between safe and dangerous websites. Using machine learning classifiers like random forest, support vector machine, naive bayes, logistic regression, and some special URL (Uniform Resource Locator) based on extricated features, the classifiers are designed in this research article to create a new classification system to analyze and detect malicious web pages. educated to anticipate harmful web pages. According on the experimental findings, the random forest classifier performs better than other machine learning classifiers, achieving an accuracy of 95%.

Predicting Hazard Messages Using Machine Learning

"Predicting Hazard Messages Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.j190-j193, July-2023, Available :http://www.jetir.org/papers/JETIR2307927.pdf

"Predicting Hazard Messages Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppj190-j193, July-2023, Available at : http://www.jetir.org/papers/JETIR2307927.pdf