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.
Reducing the unemployment rate has become a serious social problem facing the world. In this article, we used the average wages of private sector employees in cities by industry and the entropy of these wages as characteristic variables to analyze the unemployment rate of 30 major provinces and cities in China from 2011 to 2019. We use K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Adaptive Boosting Algorithms (Adaboost) to classify areas with high and low unemployment rates. Then we perform linear regression analysis based on the results, analyze the correlation between average wages and income inequality, and interpret the classification results according to the decision boundary. In conclusion, we find that in regions with low unemployment rates in China, higher average wages are often accompanied by greater income inequality, while in regions with higher unemployment rates, the situation is more moderate. Compared with areas with low unemployment rates, the increase in average wages in areas with the same high unemployment rate has brought about a smaller increase in income inequality.
PM2.5 is the main cause of air pollution and hinders the sustainable development of Chinese cities. Researchers have used a variety of methods including regression analysis to find factors that affect PM2.5, but feature selection is rarely used, and there are few standard methods that can solve the problem of label learning in small data sets. The purpose of this research is to determine the important factors affecting PM2.5 environmental variables and pollutants through machine learning algorithms and regression analysis based on the "China Statistical Yearbook". In this paper, the production of general solid industrial waste significantly increases PM2.5 concentration, and the comprehensive utilization of general industrial solid waste is the most economical and feasible measure to significantly reduce PM2.5 concentration. This paper also puts forward solutions to the comprehensive utilization of general solid industrial waste.
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