Data-Driven Decisions: An Introduction to Machine Learning provides a comprehensive and accessible introduction to the principles and applications of machine learning for students, professionals, and decision-makers. Combining theoretical foundations with practical examples, this book guides readers through key concepts such as supervised and unsupervised learning, feature engineering, model evaluation, and interpretability. With a focus on how machine learning drives informed, data-driven decision-making across industries, the text balances technical depth with clarity. Through case studies, hands-on exercises, and discussions on ethical considerations, this book equips readers with the tools to apply machine learning effectively in solving real-world problems.