Long–Range Movie Box Office Prediction Based on Machine Learning

Abstract

The study aims to predict the box office of movies based on several important factors obtained from The Movie Data Base (TMDB) being independent of the daily box office of the movie after its release. Exploring the nature of the dynamics of film revenue is crucial for filmmakers, investors, and film likers since it can help them find a way to improve their film influences and decision-making. This research employs three distinct methods – Random Forest, Back Propagation Neural Network, and Linear regression (Least Square Method) – utilizing a set of selected independent variables including popularity, budget, genre, run time, release date, original language, and production countries. This study gives an insight into the relationships between these variables and the revenue of movies. As a long-range forecasting model, the prediction of the model is up to 73% precise. Overall, this study aims to provide valuable information and methods for the film-related industry to predict revenue. Also, the model has the potential to be extended to other fields such as the prediction of the feedback of series or games before release.