Published Paper Details:

News publishers have decreased spreading news through conventional newspapers and have migrated to the use of digital media. Therefore, there exists a large amount of information being stored in the electronic format which needs to be classified into different categories because there may be present some sensitive data which is not suitable for specific age group. In this project, machine learning algorithms are used for classifying news by using a dataset. By evaluating the accuracy of Linear regression, Naive Bayes algorithm, Logistic regression and Decision tree algorithm and performing comparative analysis of all mentioned algorithms we are going to select the algorithm which provides the maximum accuracy. Also suggesting news articles to the online news reader based on the similarity of a news article with the news they are reading or proposing news articles based on the interest of news reader subjected from their previous readings and the feedback of the reader. After implementing and comparing the accuracy of all Machine Learning models, Naive Bayes Model gave the highest accuracy and lower error percentage. So, Naive Bayes Model is used for training the model and to get required result.