This is the first machine learning project that I have endeavored. This is was after I finished Google's Crash Course for Machine Learning, and decided to use some of my newfound basic skills and apply them to a real-life dataset. Simply googling "Dataset for Machine Learning", I found the 1984 United Stated Congressional Voting Records which had the political affiliation of a representative and certain topics on which they voted for. I used a wide range of different models, but this was a good learning experience to understand that sometimes more complex does not mean better. A basic linear model proved to be a better predictor on predicting whether someone was of left or right-leaning than a super complex neural network model. After this initial dive into independently completing a machine learning project, I went to go and do another one that had more complex data. From there I learned to use Tensorflow (the Machine Learning API that I used) better, so I might revisit this project; however, since there is a lot of missing data (for which I assumed a numeric 0 placeholder), I might find another dataset to practice and experiment further. This project was a good first step into understanding what Machine Learning has to offer.