For news classification, 3 models were trained on SVM(Support Vector Machines), KNN(K-Nearest Neighbors) and MNB(Multinomial Naïve Bayes). SVM was best performing among them. The categories for classification were Business, Entertainment, Politics, Sport and Tech. We used TF-IDF for extractive summarization. Finally, we used Beautiful Soup for news scrapping. The scrapped news would be treated with the classification model and summarization model and then be saved in database under the classified category and summarized news. We can then traverse the scrapped news.