Success Prediction of Films at Box Office Using Machine Learning
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 4
Abstract
Feature films are a multi-billion industry. Here prediction of a movie’s success is predicted based on its features like cast, genre of movie, month of release, run time, directors, producers etc. Based on multiple such features and with the database of previous movies statistics, machine learning algorithm like Linear Regression can predict the approximate ratings the movie can receive once it is actually released and hence classify a movie as a hit or a flop. A large amount of data representing feature films is maintained by the Internet Movie Database (IMDb). Many of the movies listed on IMDb contain an average user rating on a scale of 0 to 10 which corresponds to public opinion of that movie. Due to the large number of films produced as well as the level of scrutiny to which they are exposed, it may be possible to predict the success of an unreleased film based on publicly available data This data can be extracted and prepared for use in training machine learning algorithms .The goal of this paper is to discuss a system that can closely predict average user rating by learning from historical movie data and hence determine if it is likely to be a flop or a hit.
Authors and Affiliations
Parag Ahivale, Omkar Acharya
Keywords
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