A Machine Learning Approach to Exploring Gross Revenues of Movies
Author(s)
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Abstract
In this paper, I use a machine learning approach to explore the impact of movie recognition such as the number of award nominations/wins and reviews by critics and viewers on gross revenue. I find that movie ratings by critics and viewers are positively correlated and that the ratings are positively correlated with gross revenue. The correlation between gross revenue and number of IMDB votes is 0.6, suggesting that ratings by critics and viewers are a good predictor of gross revenue. On the other hand, there is a weak positive correlation between ratings and award nominations and wins, suggesting that movie recognition is a not a good predictor of gross revenue. The summary of the findings is that movies with excellent reviews are the ones with high gross revenue, but it may not be the case for movies that receive many award nominations and wins.
Keywords
Learning approach, Gross Revenue
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