Machine Learning Application for News Text Classification

News article are the source of historic information that can be accessed whenever required. Today in modern world, Internet is also doing the same task and full of online news on multiple topics which also contains some irrelevant or unhelpful information. People are living a very busy and hectic life and they does not have enough time to read the complete news article. They want to read information as per their area of interest. Therefore, it becomes important to process online news content and classify them into various categories like sports, entertainment, Business, Finance etc. so that it become easily and quicky accessible to the user. This paper is an attempt in the direction of classifying the news text into different sections using Machine Learning algorithms. Experiment is performed on an online dataset taken from Kaggle. This dataset was having around 210K news headlines from 2012 to 2022. Four different classification algorithms (Random Forest, Decision Tree, K-Neighbor Classifier and Gaussian NB) are applied on the dataset to find the results.