Abstract

This study delves into the intricate relationship between an artist’s background (including nationality and gender) and the popularity of their artworks in the Museum of Modern Art (MoMA) in New York. Leveraging statistical methods, including Chi-squared tests and ANOVA, significant correlations between an artist’s nationality, gender, and the popularity of their artworks were identified. Time series analysis further underscored evolving trends in MoMA’s acquisition patterns over the years. The research also utilized a Random Forest classification model to predict artwork popularity, achieving an accuracy rate of 0.75. The findings shed light on the dynamics of art acquisitions and offer implications for diversifying and enhancing museum collections in the future.