College
College of Engineering and Polymer Science
Date of Last Revision
2026-04-28 12:31:09
Major
Computer Science
Honors Course
CPSC-498 002
Number of Credits
2
Degree Name
Bachelor of Science in Computer Science
Date of Expected Graduation
Spring 2026
Abstract
Macroeconomic predictions present challenges in machine learning due to the rarity of economic recessions, the constantly-changing matter of global markets, and severe class imbalance in historical data. This project focuses on predicting the onset of United States economic recessions within a 12-month window using Python and Jupyter Notebook. A machine learning pipeline was developed utilizing multiple models: Logistic Regression, Random Forest, XGBoost, and Long Short-Term Memory (LSTM) neural networks. For class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied strictly to training data, paired with Platt scaling for calibration on thresholds. The resulting models were evaluated in the 2005 to 2015 period, specifically testing the ability to predict the 2008 Great Financial Crisis using pre-2005 data. Furthermore, a soft-voting ensemble method was used to attempt a better prediction.
Research Sponsor
Dr. Zhong-Hui Duan
First Reader
Dr. Yingcai Xiao
Second Reader
Dr. John C. Hoag
Honors Faculty Advisor
Dr. Zhong-Hui Duan
Proprietary and/or Confidential Information
No
Recommended Citation
Reusser, Ethan, "Machine Learning for Recession Prediction" (2026). Williams Honors College, Honors Research Projects. 2101.
https://ideaexchange.uakron.edu/honors_research_projects/2101