Date of Degree


Document Type

Capstone Project

Degree Name



Data Analysis & Visualization


Howard Everson


Economic Recession, Machine Learning, Support Vector Classifier


In this project I used the supervised machine learning methods logistic regression, decision tree classifier, k nearest neighbor classifier, and support vector classifier, to determine the best method to predict economic recessions. To do this, I used the function train_test_split to create training and testing sets and the function TimeSeriesSplit to create walk-forward cross validation sets to use when tuning the model parameters. Each machine learning method was trained on both scaled and unscaled data and was performed using default parameters and using the tuned parameters so that there were four models of each method. It was determined that the tuned Support Vector Classifier model trained on scaled data with a precision-recall area under the curve (PR AUC) score of 0.83 was the optimal model to predict economic recessions.

Capstone (715 kB)
Zip file of the GitHub repository for the capstone project