Movie Success Prediction using Machine Learning Algorithms and their Comparison

Abstract

The number of movies produced in the world is growing at an exponential rate and success rate of movie is of utmost importance since billions of dollars are invested in the making of each of these movies. In such a scenario, prior knowledge about the success or failure of a particular movie and what factor affect the movie success will benefit the production houses since these predictions will give them a fair idea of how to go about with the advertising and campaigning, which itself is an expensive affair altogether. So, the prediction of the success of a movie is very essential to the film industry. In this proposed research, we give our detailed analysis of the Internet Movie Database (IMDb) and predict the IMDb score. This database contains categorical and numerical information such as IMDb score, director, gross, budget and so on and so forth. This research proposes a way to predict how successful a movie will be prior to its arrival at the box office instead of listening to critics and others on whether a movie will be successful or not. The proposed research provides a quite efficient approach to predict IMDb score on IMDb Movie Dataset. We will try to unveil the important factors influencing the score of IMDb Movie Data. We have used different algorithms in the research work for analysis but among all Random forest gave the best prediction accuracy which is better in comparison to the previous studies. In the exploratory analysis we found that number of voted users, number of critics for reviews, number of Facebook likes, duration of the movie and gross collection of movie affect the IMDb score strongly. Drama and Biopic movies are best in genres.