PREDICTIVE ANALYTICS FOR SCULPTURE EXHIBITION PLANNING - Sharma, Prabhat; Meher, Dr. Kunal; Smitha K; Saini, Archana Sahay; Singh, Archana; Singh, Dr Maninder; Bhise, Suhas (ShodhKosh: Journal of Visual and Performing Arts, 2025)
The research paper discusses the use of predictive analytics in the planning of sculpture exhibitions to enhance the curatorial decision-making process using predictive decision-making based on the data. The study incorporates the elements of data science, computational aesthetics, theory of art object/virtual display curator, forming a building of modular prediction and using regression, classification, clustering, ensemble learning, and time-series prediction to predict the visitor engagement, create a space layout, and get the sentiment of the audience. There is a high predictive reliability in the system prototype (R2 =0.89, F1 =0.91) that transforms the traditional curating process into a more adaptive and intelligence-driven process. Experiments have discovered that predictive heatmaps, regression graphs, and sentiment trend curves are handy in developing the exhibition into actionable information using complex data. The framework is not only the contributor to spatial performance and visitor satisfaction but also generates a new idea of human-AI collaboration within the creativity of the curators. The findings confirm that predictive analytics can turn the exhibition as an immobile system into a breathing ecosystem that responds to the behavior of the audience and appeal to the emotion, which is another manifestation of a more relevant and substantial solution of the digitalization of museology.