PREDICTIVE ANALYSIS FOR CAREER GROWTH IN PERFORMING ARTS

Saurabh Bhattacharya; Shikha Gupta; Kalyani P. Karule; Amruta Tejaskumar Mokashi; Preeti Tuli; Anitha K

doi:10.29121/shodhkosh.v7.i1s.2026.7113

Authors

Saurabh Bhattacharya School of Computer Science and Engineering, Galgotias University, Greater Noida (UP), India
Shikha Gupta Assistant Professor, School of Business Management, Noida International University, Greater Noida 203201, India
Kalyani P. Karule Department of Computer Technology, Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, India
Amruta Tejaskumar Mokashi Department of Chemical Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, 411037, India
Preeti Tuli Department of Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur, Chhattisgarh, India
Anitha K Professor and HoD, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research, Chennai, Tamil Nadu 600095, India

DOI:

https://doi.org/10.29121/shodhkosh.v7.i1s.2026.7113

Keywords:

Predictive Analytics, Performing Arts Careers, Machine Learning, Talent Evaluation, Time-Series Forecasting, Sentiment Analysis

Abstract [English]

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.

References

Antoniuk, D., Ivens, B. S., and Kolyada, O. (2025). How Is Artificial Intelligence Changing HR? Adaptive Management for the New Environment. Baltic Journal of Economic Studies, 11, 13–26. https://doi.org/10.30525/2256-0742/2025-11-2-13-26 DOI: https://doi.org/10.30525/2256-0742/2025-11-2-13-26

Becker, M. (2022). The Effect of Positive Management Practices on Firm Profitability—Evidence from Text Mining. The Journal of Applied Behavioral Science, 60, 280–309. https://doi.org/10.1177/00218863221120827 DOI: https://doi.org/10.1177/00218863221120827

Do, H., Chu, L. X., and Shipton, H. (2025). How and When AI-Driven HRM Promotes Employee Resilience and Adaptive Performance: A Self-Determination Theory. Journal of Business Research, 192, 115279. https://doi.org/10.1016/j.jbusres.2025.115279 DOI: https://doi.org/10.1016/j.jbusres.2025.115279

Dave, B., (2024). Technology and AI—Impact in Country’s Growth and Unemployment ShodhSamajik: Journal of Social Studies, 1(1), 35-53, https://doi.org/10.29121/ShodhSamajik.v1.i1.2024.3 DOI: https://doi.org/10.29121/ShodhSamajik.v1.i1.2024.3

Duan, J., and Wu, S. (2024). Beyond Traditional Pathways: Leveraging Generative AI for Dynamic Career Planning in Vocational Education. International Journal of New Developments in Education, 6, 24–31. https://doi.org/10.25236/IJNDE.2024.060205 DOI: https://doi.org/10.25236/IJNDE.2024.060205

Joloudari, J. H., Marefat, A., Nematollahi, M. A., Oyelere, S. S., and Hussain, S. (2023). Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks. Applied Sciences, 13, 4006. https://doi.org/10.3390/app13064006 DOI: https://doi.org/10.3390/app13064006

Koch, S., and Pasch, S. (2023). CultureBERT: Measuring Corporate Culture With Transformer-Based Language Models. In Proceedings of the 2023 IEEE International Conference on Big Data (BigData). https://doi.org/10.1109/BigData59044.2023.10386765 DOI: https://doi.org/10.1109/BigData59044.2023.10386765

Liu, S. F., Fan, Y. J., Luh, D. B., and Teng, P. S. (2022). Organizational Culture: The Key to Improving Service Management in Industry 4.0. Applied Sciences, 12, 437. https://doi.org/10.3390/app12010437 DOI: https://doi.org/10.3390/app12010437

Ma, X., Liu, W., Zhao, C., and Tukhvatulina, L. R. (2024). Can Large Language Model Predict Employee Attrition? arXiv. DOI: https://doi.org/10.1145/3708036.3708229

Mullens, D., and Shen, S. (2025). 2ACT: AI-Accentuated Career Transitions via Skill Bridges. arXiv. DOI: https://doi.org/10.2139/ssrn.5309998

Nosratabadi, S., Khayer Zahed, R., Ponkratov, V. V., and Kostyrin, E. V. (2022). Artificial Intelligence Models and Employee Lifecycle Management: A Systematic Literature Review. Organizacija, 55(3), 181–198. https://doi.org/10.2478/orga-2022-0012 DOI: https://doi.org/10.2478/orga-2022-0012

Parra Vargas, E., Philip, J., Carrasco-Ribelles, L. A., Chicchi Giglioli, A. I., Valenza, G., Marín-Morales, J., and Alcañiz Raya, M. L. (2023). The Neurophysiological Basis of Leadership: A Machine Learning Approach. Management Decision, 61, 1465–1484. https://doi.org/10.1108/MD-02-2022-0208 DOI: https://doi.org/10.1108/MD-02-2022-0208

Parra, L. A., Diaz, D. E. M., and Ramos, F. (2023). Computational Framework of the Visual Sensory System Based on Neuroscientific Evidence of the Ventral Pathway. Cognitive Systems Research, 77, 62–87. https://doi.org/10.1016/j.cogsys.2022.10.004 DOI: https://doi.org/10.1016/j.cogsys.2022.10.004

Prasad, K. D. V., and De, T. (2024). Generative AI as a Catalyst for HRM Practices: Mediating Effects of Trust. Humanities and Social Sciences Communications, 11, 1362. https://doi.org/10.1057/s41599-024-03842-4 DOI: https://doi.org/10.1057/s41599-024-03842-4

Raza, A., Munir, K., Almutairi, M., Younas, F., and Fareed, M. M. S. (2022). Predicting Employee Attrition Using Machine Learning Approaches. Applied Sciences, 12, 6424. https://doi.org/10.3390/app12136424 DOI: https://doi.org/10.3390/app12136424

Todorović, D., Mitić, P. M., Stojiljković, N., Olanescu, M., Suciu, A., and Popa, D. (2024). Organizational Commitment in the Private and Public Sectors. Frontiers in Psychology, 15, 1442990. https://doi.org/10.3389/fpsyg.2024.1442990 DOI: https://doi.org/10.3389/fpsyg.2024.1442990

Young, J., Adams, J., and Baker, N. (2025). AI in HR Analytics: Enhancing Decision-Making for Employee Growth [Manuscript].