Welcome! This repository serves as my academic and technical portfolio for ECON 3916: Statistical & Machine Learning for Economics. It showcases my work at the intersection of economic theory, statistical inference, and modern machine learning.
I am an undergraduate economics student actively preparing for roles in Data Analysis / Economic Consulting / Finance.
My academic focus is on developing strong empirical skills and learning how to translate economic questions into data-driven insights.
This portfolio reflects my goal of bridging traditional economic reasoning with modern data science techniques—combining interpretability, causal thinking, and predictive power.
This repository contains coursework, labs, and applied projects from ECON 3916.
Rather than treating machine learning as a black box, this course follows a concept extension approach:
- We begin with foundational econometric tools (e.g., OLS regression, hypothesis testing)
- We then extend these ideas using machine learning methods (e.g., Lasso, regularization, cross-validation)
- Emphasis is placed on understanding both causal inference and predictive performance
Through this work, I am learning how to:
- Balance interpretability vs. accuracy
- Apply ML tools responsibly in economic settings
- Think critically about model assumptions and real-world implications
The primary tools and platforms used in this repository include:
- 🐍 Python – Core programming language for analysis
- 🧮 Pandas – Data cleaning, manipulation, and exploration
- 🤖 Scikit-Learn – Machine learning models and evaluation
- 📈 Statsmodels – Econometric modeling and statistical inference
- ☁️ Google Colab – Cloud-based notebooks for reproducible analysis
This repository will continue to grow as I refine my skills in:
- Applied econometrics
- Machine learning for social science
- Data-driven decision-making
Thank you for taking the time to explore my work!