Predicting IMDb Ratings of One Piece Episodes Using Regression Models Based on Narrative and Popularity Features

Abstrak

This study explores the predictive modeling of IMDb ratings for episodes of the anime One Piece using a linear regression approach grounded in narrative and popularity-based features. The dataset comprises 1,122 episodes, with features including story arcs, episode types, and the number of viewer votes. After one-hot encoding categorical variables and training the model using Ordinary Least Squares (OLS), the model achieved a high coefficient of determination (R² = 0.855), a low Mean Absolute Error (MAE = 0.216), and Root Mean Squared Error (RMSE = 0.329). These results indicate a strong predictive performance based on limited but interpretable features. The findings reveal that narrative structure especially arc classification and viewer engagement contribute significantly to the perceived quality of episodes. While vote counts show limited correlation with ratings, combining them with narrative elements yields reliable predictions. This research offers a novel contribution to anime-based media analytics, emphasizing that minimal feature sets can provide robust predictive insight. The study also opens opportunities for enhancing content strategies and viewer understanding in serialized storytelling.