Machine Learning For Economic Analysis

Abstract

1. Introduction

Welcome to my notes for the Machine Learning for Economic Analysis course by Damian Kozbur @UZH!

The exercise sessions are entirely coded in Python on Jupyter Notebooks. The examples heavily borrow from An Introduction to Statistical Learning by James, Witten, Tibshirani, Friedman and its advanced version Elements of Statistical Learning by Hastie, Tibshirani, Friedman. Other recommended free resources are the documentation of the Python library scikit-learn and Bruce Hansen's Econometrics book.

2. Objective

Material for the exercise sessions of master course Machine Learning for Economic Analysis @UZH

This project demonstrates how Jupyter Notebook, Machine Learning, Python can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/matteocourthoud/Machine-Learning-for-Economic-Analysis.git
cd Machine-Learning-for-Economic-Analysis

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by matteocourthoud and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.