This project was a case study to show how machine learning (ML) algorithms can help identify fake vs. reliable or real news. My theory is that fake news differs from real news in several statistically significant ways. I predicted that fake news is less nuanced, more emotional, and has less word variation within the article. If it is true that fake news has patterns that differentiate it from real news, then ML algorithms can detect that difference and help people have more confidence in what they are reading.