Machine Learning Methods for Structural Knowledge Discovery in Small Business Decision Systems in Malawi

Machine learning is increasingly becoming an important tool for extracting structural knowledge from large and unstructured datasets. In developing economies such as Malawi, many small and medium enterprises continue to rely on traditional decision-making methods that are not data-driven. This paper explores how machine learning techniques can assist organizations in identifying hidden structural relationships within business data for better managerial decision-making. The study discusses probabilistic learning methods, Bayesian-inspired reasoning, and classification approaches that can help organizations discover useful knowledge patterns from customer transactions, employee productivity, and financial records. The paper further examines the challenges of limited datasets, low technological adoption, and insufficient computational infrastructure in Malawi. The study concludes that machine learning methods can improve organizational efficiency and strategic planning when adapted to local economic realities