Recognizing phishing site using Machine Learning- A Comparative Approach using MultinomialNB & Logistic Regression

Abstract

Phishing is a method of trying to collect personal information like login credentials or credit card information using deceptive e-mails or websites. Phishing sites are made to hoodwink clueless clients into intuition they are on an authentic site. The lawbreakers will invest a great deal of energy causing the site to appear as valid as could really be expected and numerous locales will show up practically undefined from the genuine article. This paper proposes a methodology to detect boycotted URLs using machine learning algorithms so that people can be frightened while examining or getting to a particular site. In this project we have using machine learning algorithms such MultinomialNB and Logistic Regression. We used distinctive data and text pre-processing techniques to improve precision and accuracy. An app is developed as the end product of this research work.