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A Comparative Analysis of Multidimensional COVID-19 Poverty Determinants: An Observational Machine Learning Approach.

Sandeep Kumar Satapathy1, Shreyaa Saravanan1, Shruti Mishra1

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Vandalur-Kelambakkam Road, Chennai, Tamil Nadu 600127 India.

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Summary
This summary is machine-generated.

Machine learning models predict multidimensional poverty using health, education, and living standard indicators. Ridge Regression demonstrated the best performance in predicting poverty levels before and during the pandemic.

Keywords:
Feature selectionMachine learningMultidimensionalPovertyPredictionRegression

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Area of Science:

  • Socioeconomic studies
  • Data science
  • Public health

Background:

  • Poverty remains a significant global challenge despite extensive efforts.
  • Machine learning offers potential for developing practical poverty prediction models.
  • The Multidimensional Poverty Index (MPI) provides a framework for assessing poverty beyond income.

Purpose of the Study:

  • To predict multidimensional poverty using machine learning techniques.
  • To analyze poverty trends before and during the COVID-19 pandemic.
  • To identify key determinants of poverty across various indicators.

Main Methods:

  • Utilized Multidimensional Poverty Index (MPI) data from 2019 and 2021.
  • Applied data analysis techniques including feature correlation, selection, and visualization.
  • Implemented and compared various regression algorithms: Multiple Linear Regression, Decision Tree, Random Forest, XGBoost, AdaBoost, Gradient Boosting, Linear SVR, Ridge, Lasso, ElasticNet, and K-Nearest Neighbors.
  • Employed regularization and cross-validation for model optimization and performance estimation.

Main Results:

  • Evaluated poverty prediction models on national and subnational datasets.
  • Identified significant poverty determinants across health, education, and living standards.
  • Ridge Regression achieved the highest R-squared score, indicating superior predictive performance among the tested models.

Conclusions:

  • Machine learning models can effectively predict multidimensional poverty.
  • Ridge Regression is a highly effective model for poverty prediction.
  • Understanding poverty determinants is crucial for targeted interventions and policy development.