Machine Learning for Economics and Business

Link to ebook: https://datahurdler.github.io/Econ-ML-Book/

Link to GitHub repo: https://github.com/DataHurdler/Econ-ML

The intended readers of this book are those already have some quantitative training but are interested in what Machine Learning can offer. The group that would benefit the most from these chapters is graduate students who have had at least one (undergraduate or graduate) course in econometrics with some experience in a scripting language, preferably Python. However, anyone who have a solid understanding of regression methods or work with data on a regular basis may find this book to be useful. This is not an introductory book on either Machine Learning or Python. My main charges are on Machine Learning algorithms that can provide an alternative, sometimes more useful and rigorous, approach to known problems in economics, business, and social sciences. I try to provide complete, but not overwhelming, treatments on all topics and algorithms. With the provided Python scripts, the emphasis is on practicality.

In the online version of this book, full Python scripts are included in each chapter. These full scripts are directly linked to the GitHub repo that houses these scripts publicly. There is currently some consideration to also provide scripts in R.

With few exceptions, each chapter starts with applications and examples, followed by theory, then the scripts, and ends with a comparison and the summary. If you are not using Python for these tasks, or are not interested in how I implement some of these models, skipping all code blocks should still provide you a pleasant reading experience. I suggest you only skip the code blocks, but continue to read the explanations of the scripts as some insights about how the models work can be found in them. For example, I may describe the different functions/methods called in the script and what they are supposed to accomplish. Even though you do not care about the actual names of these function/method calls, as you have skipped the code block, it is still important to learn what they do. This is helpful when you develop your own scripts.

Please email me if you see any errors.