AI와 Machine Learning 기법을 활용한 영화의 흥행 예측 연구: 영화 개봉 전 상호 데이터를 중심으로

This study proposes a box office prediction model utilizing AI and machine learning (ML) techniques based on static data available before a film’s release and evaluates its performance by comparing it with existing baseline models. Using KOBIS data collected from 2013 to 2024, we generated descriptive statistics and interaction-derived variables for actors, directors, distributors, and genres. Key features were selected using Recursive Feature Elimination with Cross-Validation (RFECV). Additionally, an AutoML approach was applied to train an optimized model for the dataset, and Naver search trend data was incorporated to enhance predictive performance. Experimental results demonstrated that the proposed model outperformed both baseline and existing research models, achieving high accuracy based on F1-Macro and AHPR-Bingo evaluation metrics. Notably, interaction-derived variables were identified as key contributors to model performance improvement, confirming that prediction models based on static data can serve as valuable tools in the decision-making processes of the film industry. This study makes both academic and practical contributions by presenting a box office prediction model solely based on static data. The findings provide a useful framework for film producers and distributors to support strategic decision-making before release. Future research can extend this study by incorporating dynamic data and applying the model to diverse datasets for further validation and enhancement.