In today's world, researchers and industry professionals are faced with an unprecedented abundance and complexity of data. From individuals to institutions, and from industry to the social sciences, decision-makers in every field are confronted with the challenge of making the optimal choice among numerous alternatives and conflicting criteria. In this dynamic environment, evidence-based, analytical, and quantitative methods, which are replacing intuitive approaches, have become not just a competitive advantage but a necessity.

This book, titled "Multi-Criteria Approaches and Machine Learning Studies in Quantitative Decision Making" has been prepared with the valuable contributions of expert academics and researchers to address this need. The book aims to bring together two powerful and modern pillars of decision science, offering its readers both a theoretical foundation and a rich spectrum of applications.

The book is structured around two fundamental axes. The first axis features Multi-Criteria Decision Making (MCDM) methods, which focus on the problem of identifying the most suitable alternative. In this section, current and practical problems—ranging from supplier selection to the ranking of electric vehicles—are solved using innovative and proven MCDM methods. These studies demonstrate to readers how to place complex decision problems within a systematic framework. The second axis covers the applications of Machine Learning (ML), one of the most transformative technologies of recent years, and addresses algorithms that aim to make accurate future predictions by uncovering hidden patterns in data. A diverse range of current topics—from demand forecasting in supply chain management to the analysis of consumer behavior; from determining survival probabilities in traffic accidents to predicting the risk of depression among university students—are examined using powerful machine learning models.

This work aims to be both a reference source and a source of inspiration for new research for academics, graduate students, data scientists, analysts, and engineers. Each chapter presented combines theoretical infrastructure with tangible, real-world data, enabling the reader to understand the methods more deeply and adapt them to their own problems.

In conclusion, "Multi-Criteria Approaches and Machine Learning Studies in Quantitative Decision Making" is a comprehensive work that aims to equip readers with the analytical tools necessary to solve the complex decision problems of the modern world.

I extend my gratitude to all the authors who contributed their diligent work to the preparation of this book, and to everyone involved in its publication.

Editor

Assist. Prof. Dr. Hasan Aykut KARABOĞA