Analyzing the Impact of Release Season and Production Budget on Movie Revenue and Profitability

Abstract

The film industry is characterized by significant financial uncertainty, where large production investments do not always guarantee commercial success. This study analyzes the relationship between release season, production budget, and movie financial performance using the Full TMDB Movies Dataset 2024. A data mining framework incorporating association rule mining, clustering, machine learning, and SHAP analysis was applied to identify key drivers of revenue and profitability. The results show that release season has limited predictive influence on revenue and return on investment (ROI). In contrast, production budget, popularity, and audience ratings are significantly more influential. Association rule mining revealed that high-budget films with poor ratings are strongly associated with negative ROI outcomes. Random Forest regression achieved substantially stronger predictive performance than Decision Tree regression, with an $R^2$ value of 0.652. SHAP analysis further confirmed that budget and popularity are the dominant predictors of box office revenue, while timing-related variables contribute minimally. These findings suggest that financial success in the film industry is driven more by production investment and market attention than by seasonal release strategies, providing practical insights for budgeting, release planning, and financial risk management.