Every week a new movie is realeased into theaters throughout the world. Movies have grown to become a giant industry as well as an entertainment phenomenon. At the end of each movie season there are also a series of awards shows the honor the successes of various films released throughout the year. Our goal in this analysis is to see if there are any observable characteristics of movies that allow us to predict their financial and critical success. We define financial success as the amount of revenue a movie earns and its critical success as its ratings. After this is done, we will also see if we can somehow analyze these characteristics to determine if we can predict the awards given out at the Oscars. This will be done using a Random Forst machine learning algorithm.

We predict that there will be certain characteristics that strongly correlate with the amount of revenue that a movie will earn. However, we also predict that this will not be the case with the movie's ratings, because it most likely takes more than these observable characteristics to determine its rating. With the same reasoning, we also believe it will be difficult to create a machine learning model that predicts the oscars.

All of our results are contained in the Jupyter Notebook titled FinalProject.ipynb