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
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.
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