Research on Unemployment Rate Based on Machine Learning Method-A case study of United States from 1976 to 1986
The unemployment rate reflects the utilization rate of labor resources in a country or a region, and is the most important indicator for predicting economic output. This study will use the urban unemployment rate from 1976 to 1986 to conduct an in-depth case study, try to explore the main factors affecting the unemployment rate in the United States, and classify the unemployment rate from 1976 to 1986 by region. The research mainly uses principal component analysis, K-nearest neighbors, support vector machines, decision trees and other methods, combined with some model evaluation methods, such as confusion matrix and Precision-Recall. The purpose is to find the most accurate model to classify and explain the United States from 1976 to Unemployment rate in 1986. In addition, this research will provide some useful suggestions for controlling the unemployment rate on the basis of in-depth analysis. In summary, the main factors are public capital and non-agricultural employment, both of which are negatively correlated with the unemployment rate. Except for the high unemployment rate in the United States in 1976, due to the steady recovery after the oil shock, the unemployment rate during that period was relatively stable.
Proceedings of SPIE - The International Society for Optical Engineering
Gong, Junzhe and Lee, Chun Te, "Research on Unemployment Rate Based on Machine Learning Method-A case study of United States from 1976 to 1986" (2022). Kean Publications. 757.