When an individual wants to read particular news, it should be classified in the proper category so that they can know if they are interested in that news. A news writer writes a news article and submits it to the publisher whose job is to perform SEO operations and publish the news into the certain category of the website. The publisher has to read certain paragraph to know which category the news falls in. Various documents and text articles can be found in social networking sites and forums such as facebook.com and quora.com which are unclassified. One has to read certain lines of the article to know whether what category of news that is. To make that easier news classification can be used. News classification is the task of categorizing the news content into the predefined category from the training news dataset. In this project, a system has been built for categorizing the content of the news into different categories using the 16719 document news dataset obtained from Kaggle.com. This project uses Naïve Bayes algorithm for News classification because it is easy and fast to predict the test data set and also performs better with less dataset. It classifies the news by analyzing the content of the news. In the system, a self-created News Corpus with 6 different categories and total 16719 documents collected from Kaggle.com is used. The test showed the accuracy of 80.3% in the news classification using Naïve Bayes. In the system, the user can categorize the news based on their content by analyzing the news content from any English text document and clicking the classify button.