Movie success prediction using data mining

In this project, we developed a mathematical model to predict the success and failure of the upcoming movies based on several attributes. Some of the criteria in calculating movie success included budget, actors, director, producer, set locations, story writer, movie release day, competing movie releases at the same time, music, release location and target audience. The success prediction of a movie plays a vital role in movie industry because it involves huge investments. However, success cannot be predicted based on a particular attribute. So, we have built a model based on interesting relation between attributes. The movie industry can use this model to modify the movie criteria for obtaining likelihood of blockbusters. Also, this model can be used by movie watchers in determining a blockbuster before purchasing a ticket. Each of the criteria involved was given a weight and then the prediction was made based on these. For example, if a movies budget was below 5 million, the budget was given a lower weight. Depending on the number of actors, directors and producers past successful movies, each of these categories was given a weight. If the movie was to be released on a weekend, it was given higher weight because the chances of success were greater. If with the release of a movie, there was another high success movie released, a lower weight was given to the release time indicating that the chances of movie success were low due to the competition. The criteria were not limited just to the ones mentioned. There were be additional factors discussed in this work. We have conducted our work with simulation data.