Decision support system to predict movie success rate

Abstract

Featured films are a multibillion-dollar industry. Online movie databases contain rich information about movies and people’s preferences. An example is that people often rate and give comments about screened movies. Selecting the Director, Leading Actor or Actresses is having a major impact on movie success. Other than that, there are many more attributes available which affects the movie success. Movie makers want to see each and every movie they produced to be a success overall. Therefore, to pursue higher success movies, makers and administrators should consider the best feasible selection. To do that, they have to identify major movie attributes in the first place. In this study, we use data mining methods to identify patterns for predicting the success rate of movies using data collected from online databases. We use historical movie databases (TMDB/OMDB), to derive decision factors to predict the movies success rate. The models we are about to identify with this research, using bottom-up approach are can be used to de-risk the entire movie industry.