A Method for Predicting Movie Box-Office using Machine Learning

Movies are still an important source of entertainment in any country. But the industry loses a lot when the movies don’t do well at the box office. A new area of data analysis is predicting how society will respond to new items in terms of popularity and adoption rates. This project helps analyze and predict the theatrical performance of films before they are released independently. When a movie becomes a blockbuster, it makes a huge profit. But when the movie fails, the loss is also huge. And both have direct positive and negative effects. This also helps investors associated with this business avoid investment risks. Researchers show that it accounts for almost 25% of the movie’s revenue. Within 1-2 weeks after release as such, it is difficult to predict how a film will perform at the box office in advance of its release date. The proposed system attempts to predict the success rate of movies. Predictive analysis of various properties of the film. The film attributes such as crew, release date, Production Company, and plot are valuable sources of information and can be of great help in predicting the success of a film. There is a lot of data available on the internet from various sources such as TMDB about different movie attributes, and it is highly relevant and successful, making it an important use case for machine learning.