PREDICTIVE ANALYSIS FOR CAREER GROWTH IN PERFORMING ARTS - Bhattacharya, Saurabh; Gupta, Shikha; Karule, Kalyani P.; Mokashi, Amruta Tejaskumar; Tuli, Preeti; Anitha K (ShodhKosh: Journal of Visual and Performing Arts, 2026)
Career growth in the performing arts has become a critical research direction as the careers of artistic professionals are becoming more and more data-driven and competitive. Contrary to traditional career trajectories, the performance art industry success depends on a multifaceted interaction of both technical capabilities, training level, audience acceptance, career contact network and subjective judgments. The paper suggests a more detailed predictive analytics model to simulate and predict career development paths of performing artists through a combination of quantitative performance metrics with qualitative predictors of portfolios, reviews, and sentiment analysis. The model integrates machine-learning-based classification-models with time-series forecasting in order to achieve both the status at present and longitudinal developmental patterns of the career. The structured dataset containing training history, audition results, performance, and engagement are preprocessed by applying feature engineering, normalization, encoding and imbalance control methods. Random Forest, XGBoost, Support Vector Machines, and Artificial Neural Networks are used to perform predictive modeling to calculate the likelihood of career progression in multiple horizons. Also, sentiment analysis of reviews, social media response and expert commentaries are also included to refine the predictive accuracy and contextual relevance. Experimental findings originate to show that ensemble and deep learning models are superior compared to conventional ones in modelling nonlinear connections and time-varying effects of artistic careers.