  Upon film premiere, a major form of speculation concerns the relative success
of the film. This relativity is in particular regards to the film's original
budget, as many a time have big-budget blockbusters been met with exceptional
success as met with abject failure. So how does one predict the success of an
upcoming film? In this paper, we explored a vast array of film data in an
attempt to develop a model that could predict the expected return of an
upcoming film. The approach to this development is as follows: First, we began
with the MovieLens dataset having common movie attributes along with genome
tags per each film. Genome tags give insight into what particular
characteristics of the film are most salient. We then included additional
features regarding film content, cast/crew, audience perception, budget, and
earnings from TMDB, IMDB, and Metacritic websites. Next, we performed
exploratory data analysis and engineered a wide range of new features capturing
historical information for the available features. Thereafter, we used singular
value decomposition (SVD) for dimensionality reduction of the high dimensional
features (ex. genome tags). Finally, we built a Random Forest Classifier and
performed hyper-parameter tuning to optimize for model accuracy. A future
application of our model could be seen in the film industry, allowing
production companies to better predict the expected return of their projects
based on their envisioned outline for their production procedure, thereby
allowing them to revise their plan in an attempt to achieve optimal returns.
