Movie Success Prediction Using Machine Learning
A movie revenue depends on various components such as cast acting in a movie, budget for the making of the movie,film critics review, rating for the movie, release year of the movie, etc. Because of these multiple components there is no formula that helps us to provide analysis for predicting how much revenue a particular movie will be generating. However by analysing
the revenues generated by previous movies, a model can be built which can help us predict the expected revenue for a particular movie. Such a prediction could be very useful for the movie studios which will be producing the movie so they can decide on different expenses like artist compensations, advertising of the movie, promotions in various cities, etc. accordingly.
Plus it allows investors to predict an expected return-on-investment (ROI). Also, it will be useful for many movie theatres to estimate the revenues they would generate from screening a particular movie.
Now a day’s, online review system has become one of the most important part of any business approach. Posting reviews online for products bought or services received has become a trendy approach for people to express opinions and sentiments, which is essential for business intelligence, vendors and other interested parties. Social media contains rich information about people’s preferences. Our study proposes a decision support system for movie investment sector using data mining techniques.In this research, we will be using our own customised dictionary where different words that users commonly use in reviews will be grouped together and will be assigned a specific rate based on the admin’s choice. According to the calculated rate we will classify the movie into hit, average or flop. Through this project we aim to provide a data mining algorithm which gives the most accurate result for movie success prediction.