Published Paper Details:

Movies are released in great numbers every year and a huge amount of capital is invested to produce those films.The main focus of our project is to predict whether the movie will be successful, i.e. hit or unsuccessful, i.e. flop, based on certain attributes (static and social/dynamic). Few of the static factors included for determining whether the movie will be successful or not are: genre, film budget, actors, actresses, director, producer, production house, release date etc. From the social media perspective, i.e. dynamic, we would be looking at trending hashtags on Twitter related to the movie. As a huge amount is invested in making a film, it is a concerning aspect for the production house, and thus would be beneficial for the investors if a certain prediction is given. The primary aim is to find the relation between the attributes to arrive at the conclusion using a data mining algorithm. A movie score is calculated by comparing a movie's attributes with other movies in a dataset with similar attributes that provide a good deal of prediction. Various mechanisms available in data mining are used to identify whether each film is good or bad. This system is especially useful for producers because it allows them to rate movies before they are released and change the way they post-produce to increase their chances of success.