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has been cited by the following article:

• TITLE: Prediction Model for Minimum Subsistence Allowance Standard Based on Principal Component Analysis-Based Multiple Linear Regression (PMLR)

  AUTHORS: Xinyi Fang, Jinxuan Guo, Hai Du, Guoxu Li, Xuewen Shen

  KEYWORDS: Minimum Subsistence Allowance, Social Assistance, Principal Component Analysis

  JOURNAL NAME: Journal of Computer and Communications, Vol.13 No.4, April 28, 2025

  ABSTRACT: Amid the wave of the digital age, advanced technologies such as big data, artificial intelligence, and cloud computing are driving precise analysis and forecasting across various fields. This paper explores the correlation between economic variables and minimum subsistence allowance standards, taking Zhejiang Province as a case study. Through Principal Component Analysis (PCA) and multiple linear regression models, the comprehensive impact of various economic factors on the minimum allowance standard is thoroughly analyzed, with an assessment of the model’s predictive performance. The results indicate that the extracted principal components effectively capture the main influencing factors of the allowance standard, thereby enhancing the model’s explanatory power and predictive accuracy. By combining machine learning model predictions with policy analysis, this study provides data support and theoretical basis for establishing scientifically grounded allowance standards and aims to offer new perspectives and references for future social assistance work.