News article classification is classifying news articles into various sections like business, entertainment, sports, tech etc. based on the content of the article. It is important to classify them as this segregation is useful for people reading news to read all news of a particular category in one place. This has been achieved by representing articles as numerical vectors through the use of methods like TF-IDF Vectorizer and Doc2Vec algorithm. After this, news articles can be classified using any suitable machine learning classification model, such as Logistic Regression, Naive Bayes, Decision Tree, K-Nearest Neighbour, SVM. Text classification has several applications - tagging, automating CRM tasks, search engine optimization, sentiment analysis and many others. Keywords: Text Classification, Machine Learning, TF-IDF Vectorizer, Doc2Vec
