Machine Learning Toolbox for Social Scientists: Applied Predictive Analytics with R

About this book

The book covers predictive methods with complementary statistical “tools” that make it self-contained. This website is where I plan to include R/Python codes, suplementary applications, errata, and various new chapters.

A draft version can be accessed here: Toolbox
Machine Learning Toolbox for Social Scientists: Applied Predictive Analytics with R (Chapman & Hall/CRC, 2023)

Who is this book for?

The “causal inference” is the traditional framework for most statistics courses in social science and business fields, especially in Economics and Finance. As I tried to look at “prediction” from economists’ perspective, the book has become a “toolbox” that many social science and business students can follow and understand predictive methods beyond standard machine learning “code” applications. The book offers a new organization that helps students and faculty a smooth transition from “Inferential Statistics” to novel “prediction” methods. This transition starts with the first few sections, which offer a window where a traditional training in inferential statistics meets with data analytics that focuses on prediction.

This book is targeted at motivated students and researchers who have a background in inferential statistics using parametric models. It is applied because I skip many theoretical proofs and justifications that can easily be found elsewhere. I do not assume a previous experience with R but some familiarity with coding.

Contributions from the community are more than welcome! If you notice something is missing or notice an issue in the book (e.g., typos or problems with the material), please don’t hesitate to reach out.