The movie industry is a multi-billion dollar industry, generating approximately $40 billion of revenue annually worldwide. However, investing in the production of a feature length film is a highly risky endeavor and studios rely on only a handful of extremely expensive movies every year to make sure they remain profitable. Over the last decade, 80% of the industry’s profits was generated from just 6% of the films released; and 78% of movies have lost money of the same time period.

According to Jack Valenti, President and CEO of the Motion Picture Association of America (MPAA):

“No one can tell you how a movie is going to do in the marketplace. Not until the film opens in darkened theatre and sparks fly up between the screen and the audience.” 

BoxOffice predicts movie box office revenues of feature length films to identify stock market opportunities in media properties. The tool is based on critic reviews, film characteristics, production budget, and what studio and players are involved. Producing a movie is a highly risky endeavor and studios rely on only a handful of extremely expensive movies every year to make sure they remain profitable. Box office hits and misses correspond to short-term changes in stock prices of media properties.

Project utilizes web scraping, natural language processing, and feature selection to identify factors that best predict box office success using machine learning techniques (Ensemble Methods including Random Forest & Boosting, along with a Recurrent Neural Network for sentiment analysis and Clustering Methods for binning individual features) and big data analytics

Box-office is a revenue predicting tool for feature length films using film production information and critic reviews. Web application coming soon!