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The Brandeis University Quant Club is a chartered club at Brandeis University [v. 2023-3.1]
Copper Price Prediction Model
A machine learning model that predicts the direction of copper prices using macroeconomic data.
In Spring 2026, club members built a machine learning model to predict whether copper prices would rise or fall over the following month. The team pulled monthly macroeconomic data from the Federal Reserve Economic Data (FRED) service, then cleaned and combined it into a single dataset. Members trained and compared a logistic regression and a random forest classifier, selecting the stronger model by ROC AUC. The project was packaged into a Streamlit dashboard that can refresh the data and rerun the model on demand. It combined economic research with hands-on modeling and helped members understand how commodity prices respond to broader market conditions.
- Copper is often called a barometer for the global economy, since demand for it tracks construction, manufacturing, and industrial activity. By using historical macroeconomic data to forecast the direction of copper prices, our model turns that intuition into a testable, data-driven tool. The project shows how publicly available economic indicators can be used to anticipate commodity movements and gives members practical experience building, comparing, and deploying predictive models.
- See the full project on GitHub.