PREDICTIVE MODELS FOR ART GALLERY MANAGEMENT - D, Prabha; Mohapatra, Dr. Sarita; Kumari, Aarsi; Parashar, Komal; D, Gayathri; Patil, Pradnya Yuvraj; Wani, Tanveer Ahmad (ShodhKosh: Journal of Visual and Performing Arts, 2025)
The study examines how an efficient and intelligent art gallery management can be developed using predictive models that integrate both data-driven decision-making and creativity and curatorial work. Contemporary art spaces are increasingly struggling with the issue of predicting visitor behavior, arranging the layout of the artwork, controlling the environment, and making sales predictions. In order to deal with such complexities, the work applies machine learning (ML), deep learning (DL), and statistical modeling methods to the problem of working with multi-source data, such as visitor logs, artwork metadata, pricing history, and ambient sensor measurements. To be able to predict visitor attendance accuracy, schedule exhibitions dynamically, and base their approaches on data analysis, the proposed predictive framework combines time-series forecasting, regression modeling, and classification analysis. The study assesses the accuracy of models in relation to different datasets and the interpretability of the models through feature engineering, and validation measures like RMSE, MAE, and R 2. Besides, the system focuses on real-time flexibility, which allows the galleries to be proactive in reacting to the fluctuating audience dynamics and market demands. The models that were obtained exhibit considerable enhanced operational efficiency, visitor attitude and sales prediction accuracy, providing a platform to digitalize the management of the gallery.