Exploration of Online News Source Data Set using Machine Learning

This paper explores and analyzes the Online News Source dataset from UC Irvine’s dataset repository. In the process multiple new features are added such as word popularity, word embeddings, and parts of speech. These new features are used to create a machine learning model to predict the popularity of each article. The prediction rate as a result of these new features increases dramatically showing the correlation between these features and an article’s success. https://github.com/Kaipie5/FinalProject