PREDICTIVE ANALYTICS FOR EMPLOYABILITY IN CREATIVE FIELDS

Mukesh Patil; Ashutosh Kulkarni; Eeshita Goyal; Lalita Kiran Wani; Balkrishna K Patil; Ramkumar Pathak

doi:10.29121/shodhkosh.v7.i1s.2026.7108

Authors

Dr. Mukesh Patil Associate Professor and Head, Department of Management Studies, Guru Nanak Institute of Engineering and Technology Nagpur, Maharashtra, India
Ashutosh Kulkarni Department of Desh, Vishwakarma Institute of Technology, Pune, Maharashtra, 411037, India
Eeshita Goyal Assistant Professor, School of Business Management, Noida International University, Greater Noida 203201, India
Dr. Lalita Kiran Wani Department of Electronics and Telecommunications Engineering, Bharati Vidyapeeth's College of Engineering, Lavale, Pune, Maharashtra, India
Dr. Balkrishna K Patil Assistant Professor, Department of Computer Science and Engineering, SITRC (Sandip Foundation), Nashik, India
Dr. Ramkumar Pathak Associate Professor, Mangalayatan University, Beswan, Aligarh, India

DOI:

https://doi.org/10.29121/shodhkosh.v7.i1s.2026.7108

Keywords:

Predictive Analytics, Creative Employability, Portfolio Assessment, Machine Learning, Skill-Gap Analysis

Abstract [English]

The field of employability assessment is getting revolutionized by predictive analytics in creative industries because it can be used to assess skills, portfolios, and career paths based on data. The creative labor markets (design, visual arts, media, and digital content production) can be described by their heterogeneous skills, subjective quality standards, and quickly changing demand trends, which renders the conventional methods of employability assessment ineffective. This paper hypothesizes a predictive analytics system to be used in forecasting employability within the creative industry by incorporating portfolio artifacts, talent profiles, project assessments, and labor market indicators. The methodology is a mix of structured attributes based on education, experience, and performance metrics and unstructured textual and visual data taken out of portfolios and job adverts. Various predictive models are used such as Random Forest, XGBoost, Artificial neural networks, and BERT-based language models to align the nonlinear relationship and semantic alignment between creative work and market need. SHAP-based explainability helps to determine which drivers of employability, including skill diversity, project relevance, aesthetic coherence, and industry alignment are most important using model interpretability. The results of the experiments prove that ensemble and deep learning models are superior to traditional ones, they are more accurate and robust in employability prediction in creative sub-sectors. This can be used in practice to create AI-based portfolio analysis, a customized identification of skill-gaps, and integration with online talent markets to enhance workforce planning and development.

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