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This study aims to improve predictions of movie success during early production by utilizing machine learning and explainable AI.

Predicting movie success with machine learning and explainable AI: a large-scale analysis using IMDb and TMDB data

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This study aims to improve predictions of movie success during early production by utilizing machine learning and explainable AI.
Analyzed a dataset of 433,350 films from IMDb and TMDB.
Employed multiple predictive models alongside SHAP and LIME for interpretability.
Defined success through a dual rating–vote threshold.
Popularity metrics, director and thematic attributes are the strongest predictors of box office success.
Genre effects like comedy and drama contribute meaningfully but to a lesser extent.
The study presents an interpretable framework that aids producers and investors in decision-making.

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Randomized trial develops a framework to predict movie success in early production stages, highlighting key factors and reasoning.

Bendiaf et al. (2026) studied this question.

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