Machine Learning for Social Scientists: A Practical Guide

In today’s data-driven world, social science researchers increasingly rely on advanced analytical tools to make sense of complex datasets. Machine learning provides a powerful toolkit for uncovering patterns, making predictions, and enhancing research insights. However, many existing resources on machine learning are technical and not tailored to social scientists.

This book bridges that gap by providing: - A practical introduction to machine learning concepts. - Hands-on examples using the tidymodels framework in R. - Real-world datasets, including the General Social Survey (GSS), to demonstrate key techniques.

This book is ideal for: - Graduate students in social sciences (e.g., sociology, psychology, political science). - Researchers looking to incorporate machine learning into their work. - Educators seeking to teach machine learning concepts with a focus on social science applications.

No prior experience with machine learning is required, but a basic understanding of R will be helpful.

Through this book, you will: - Explore the fundamentals of machine learning and its applications in social sciences. - Learn how to preprocess data, build models, and evaluate performance using tidymodels. - Apply these concepts to real-world problems, such as predicting job satisfaction or analyzing survey data. - Gain insights into ethical considerations and best practices in machine learning research.

Each chapter includes: - Explanations of key concepts. - Code snippets to follow along in R. - Exercises to deepen your understanding.

We encourage you to actively engage with the examples and run the code yourself. By doing so, you will build a strong foundation in machine learning and its applications.

Special thanks to everyone who supported this project, including colleagues, students, and the open-source community for their contributions to R and tidymodels.

Let’s begin our journey into machine learning! Start with Introduction: What is Machine Learning and Why Does it Matter in Social Sciences?