STRATEGIC MODEL SELECTION IN APPLIED ARTIFICIAL INTELLIGENCE: ALIGNING METHODS WITH PROBLEM CONTEXTS

Choosing the right machine learning model for a given problem is a critical decision that significantly impactsthe success of a project. This article provides a comprehensive guide to selecting the most appropriate modelbased on the problem, the available data, and operational constraints. It explores key considerations such asinterpretability, data quality, computational resources, and real-time requirements while emphasizing theimportance of aligning the model with business goals and stakeholder needs. Through case studies in areas likefraud detection, medical image classification, and customer segmentation, the article illustrates how differentmodels are suited to specific types of problems. It also highlights the importance of deploying, monitoring, andmaintaining models to ensure their long-term effectiveness. By presenting a structured approach to modelselection and ongoing improvement, this article aims to provide valuable insights for practitioners seeking tooptimize the use of machine learning in real-world applications