Predicting Movie Success Using Regression Techniques

Hollywood is the largest and most profitable movie industry in the world. In 2018 alone, it generated a massive global box office of over $42 billion. A single production company with multiple movies may benefit greatly from knowing which movies are likely to succeed—it would help them focus their resources on the required advertisement and promotion campaigns. Furthermore, theaters would get a preference on which movies to run for a longer duration based on its success rate. Large-scale investments come with large risks. Using machine learning to predict revenues may help investors mitigate these risks. The algorithms in this paper aim to recognize historical patterns in the movie industry to try and predict the success of upcoming movies using a variety of machine learning algorithms. The success metric used is the box office, i.e., the commercial success of a film in terms of overall money earned. The results show that it is indeed possible to predict revenue with a considerable amount of accuracy, with better results than a majority of the papers that were reviewed.