PREDICTIVE ANALYTICS FOR PHOTO EXHIBITION PLANNING - Jena, Shakti Prakash; Selvan, Dr. Mercy Paul; Singh, Manpreet; Barnwal, Sweta Kumari; Prakash, Om; Alone, Pooja Abhijeet (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Predictive analytics has become an innovative feature of cultural institutions and other creative industries that will allow them to plan and optimize the experience of the exhibition based on the data. This paper introduces a predictive modeling system specifically designed to be used in the planning of the photo exhibition, which incorporates previous attendance trends, demographic factors, is an audience response metric, social media interactions, and ticketing transactions into a single analytical chain. The suggested system conducts the severe data preprocessing, including noise reduction, outlier correction, and normalization as well as the multiple level feature engineering to create the robust predictors of visitor attendance, the engagement level, the distribution of the dwell time, and revenue streams. Several algorithmic paradigms are considered, such as the Linear Regression, the Random Forest, the XGBoost, the LSTM, and the ARIMA forecasting each optimized by systematic hyperparameter tuning strategy. Incorporating both the temporal and behavioral and environmental features, the framework improves the predictive accuracy and understandability of forecasts that are necessary in the exhibition schedules, content management and staff management needs, marketing segmentation, and spatial arrangement planning. Experimental evidence shows that tree-based ensemble algorithms, as well as hybrid architecture of deep learning, significantly outperform classical baselines, especially in the context of a nonlinear visitor behavior and dynamically changing preferences of the audience.