An innovative efficiency of incubator to enhance organization supportive business using machine learning approach.

Many small businesses and startups struggle to adjust their operational plans to quickly changing market and financial situations. Traditional data-driven techniques often miss possibilities and waste resources. Our unique approach, Unified Statistical Association Validation (USAV), allows dynamic and real-time data association and improvement assessment to address this essential issue. USAV classifies and validates critical data associations based on business features to improve startup incubation and innovation decision-making. USAV analyses different financial eras using federated learning to find performance inefficiencies using a Kaggle dataset on small business success and failure. USAV recommends actionable improvements during innovation using non-recurrent statistical patterns, unlike standard models that use prior financial data. The framework allows real-time flexibility with continual statistical updates without data redundancy. The proposed approach achieved an improvement assessment score of 0.98, data association accuracy of 96%, statistical update efficiency of 0.97, modification ratio of 35%, and incubation analysis time reduction of 240 units in experimental evaluation. These findings demonstrate USAV's ability to help strategic decision-making in dynamic corporate situations.