基于机器学习的居民贫困风险预测模型研究--以某地区贫困户统计数据为例

Research on the Prediction Model of Resident Poverty Risk Based on Machine Learning--Taking the Statistical Data of Impoverished Households in a Certain Region as an Example

本文指出居民贫困风险预测对于保障社会和谐稳定具有重要意义。本文基于机器学习技术,利用某地区贫困户的多维度统计数据,构建了贫困风险预测模型。通过对户主信息、家庭特征、致贫原因、生产条件等的综合分析,模型能够精准识别潜在贫困风险群体,提高扶贫工作的针对性和有效性。这不仅为政策制定提供了科学依据,有助于实现精准扶贫,促进地区可持续发展,也为其他类似地区的贫困风险预测提供了借鉴和参考,具有重要的理论意义和实践价值。

This paper points out that the prediction of residents'poverty risk is of great significance for safeguarding social harmony and stability.Based on machine learning technology,this paper constructs a poverty risk prediction model using multi-dimensional statistical data of poor households in a certain region.Through comprehensive analysis of household information,family characteristics,poverty-causing reasons,and production conditions,the model can accurately identify potential poverty-risk groups and improve the pertinence and effectiveness of poverty alleviation work.This not only provides a scientific basis for policy formulation,helps to achieve targeted poverty alleviation,and promotes sustainable development in the region,but also provides reference and guidance for poverty risk prediction in other similar regions,which has important theoretical significance and practical value.