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A refined approach to early movie box office prediction leveraging ensemble learning and feature encoding
- 1 Hefei University of Technology
* Author to whom correspondence should be addressed.
Abstract
Predicting the revenue of a movie prior to its release presents a significant challenge. The ability to predict pre-release revenue enables movie production companies to devise effective marketing strategies and mitigate the risks associated with potential box office failures. The primary hurdles in this endeavor stem from managing the myriad factors influencing box office outcomes and accurately forecasting a movie's revenue before it becomes available to the public. To overcome these challenges, we introduce a sophisticated Early Movie Box Office Prediction Model that incorporates Ensemble Learning and Feature Encoding techniques. This model amalgamates multiple foundational models, utilizing regression and decision trees to forecast box office revenues. Our composite model demonstrates superior performance over its constituent models, achieving an impressive accuracy rate of 91.4%.
Keywords
Early Movie Box Office Prediction, Ensemble Learning, XGBoost, GDBT
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Cite this article
Xie,C. (2024). A refined approach to early movie box office prediction leveraging ensemble learning and feature encoding. Applied and Computational Engineering,75,273-284.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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