Predicting the Success of Virtual-Themed Animated Movies Using Random Forest Regression - Minh Luan Doan (Bright Publisher, 2024)
This paper presents a study using Random Forest Regression to predict the success of virtual-themed animated movies, with a focus on revenue and popularity. The dataset included 100 animated films, featuring attributes such as runtime, vote average, and genres. The objective was to identify the key factors influencing movie success. The model achieved an R² of 0.85 for predicting popularity, with vote average being the most significant predictor (importance score = 0.50), followed by runtime (importance score = 0.25). However, predicting revenue was more challenging, with the model achieving an R² of 0.65 and RMSE of 100, indicating that external factors like marketing and competition play a significant role. The findings reveal that audience reception, as captured by vote average, is crucial for predicting both popularity and revenue. The novelty of this research lies in its focus on virtual-themed animated movies and the use of machine learning to identify success factors in this niche genre. The study contributes to understanding the dynamics of movie success, offering valuable insights for filmmakers and production companies. Future research should explore the inclusion of external factors and advanced techniques to improve revenue prediction accuracy.