Movie Box-Office Success Prediction Using Machine Learning

Being a multi-billion dollar business, the film industry contributes largely to helping sustain a country's economy. A movie's box office (the revenue generated by the number of tickets sold of a movie) is an essential indicator of the movie's popularity. It varies depending upon several factors, including a production company, genre, budget, reviews, ratings, etc. Predicting an approximate value for a movie's box office based upon the various parameters helps investors with this business make intelligent and informed decisions. Thus, this paper designs a machine learning model that can predict the revenue a film will generate based on the information available before the movie's release. It also provides a model capable of taking in the planned genre, the required revenue, and using the Random Forest Regression model, provides recommended budget, runtime, star power, and expected popularity.