Movie Success Prediction using Historical and Current Data Mining

Movie industry is a multi-billion-dollar business. Lots of movies are being released in every year. All of these movies have different budgets and different cast crew but one thing in common all want to make profit from movies i.e. make a good box office record. Success of a movie depends on various factors of past and present. Identifying the right factors can predict the profitability of a movie. Some of the factors in predicting movie success are budget, actors, director, producer, IMDb rating, IMDb metascore, IMDb vote count, rotten tomator’s tomatometer, actors and director social fan following, wikipedia views, trailer views etc.. The success prediction of a movies plays an indispensable job in film industry since it includes immense investments. Be that as it may, success can not be predicted based on a specific property of a movie. To predict success one have to consider all the properties which can affect movie’s success and see how these properties affecting movie’s success over time. In this paper, researchers proposed a model where they consider several factors, each factor is assigned by a weight and success/failure of the upcoming movies is predicted based on the factor’s value.