Random Forests for Poverty Classification
Keywords:
data mining, classification, poverty, random forests, poverty-gender gapAbstract
This paper applies a relatively novel method in data mining to address the issue of poverty classification in Mauritius. The random forests algorithm is applied to the census data in view of improving classification accuracy for poverty status. The analysis shows that the numbers of hours worked, age, education and sex are the most important variables in the classification of the poverty status of an individual. In addition, a clear poverty-gender gap is identified as women have higher chances to be classified as poor as compared to men.
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