Abstract :

Internet news portals receive a variety of information from a wide range of sources. It is very desirable to organize this information into the appropriate categories in most real-world situations, and having a trustworthy system in place for doing so is essential. Since there hasn't been much research on categorizing news headlines, there is now an opportunity to look more closely at this topic. As a result, the classification system is strengthened and enhanced using machine learning. The main goal of this project is to classify current events according to their headlines. To assign each news headline to its predetermined category, a mechanism has been developed. The machine can correctly estimate the news item's category thanks to the model's training. This classifier will process the news headline after it is retrieved in real time. In addition to producing a better working model, this entire procedure will compare the effectiveness of logistic regression and naïve bayes models fo