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Poverty prediction using E-commerce dataset and filter-based feature selection approach.
- Source :
-
Scientific reports [Sci Rep] 2024 Feb 07; Vol. 14 (1), pp. 3088. Date of Electronic Publication: 2024 Feb 07. - Publication Year :
- 2024
-
Abstract
- Poverty is a problem that occurs in many countries, notably in Indonesia. The common methods used to obtain poverty information are surveys and censuses. However, this process takes a long time and uses a lot of human resources. On the other hand, governments and policymakers need a faster approach to know social-economic conditions for area development plans. Hence, in this paper, we develop e-commerce data and machine learning algorithms as a proxy for poverty levels that can provide faster information than surveys or censuses. The e-commerce dataset is used and this high-dimensional data becomes a challenge. Hence, feature selection algorithms are employed to determine the best features before building a machine learning model. Furthermore, three machine learning algorithms such as support vector regression, linear regression, and k-nearest neighbor are compared to predict the poverty rate. Hence, the contribution of this paper is to propose the combination of statistical-based feature selection and machine learning algorithms to predict the poverty rate based on e-commerce data. According to the experimental results, the combination of f-score feature selection and support vector regression surpasses other methods. It shows that e-commerce data and machine learning algorithms can be potentially used as a proxy for predicting poverty.<br /> (© 2024. The Author(s).)
Details
- Language :
- English
- ISSN :
- 2045-2322
- Volume :
- 14
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Scientific reports
- Publication Type :
- Academic Journal
- Accession number :
- 38321101
- Full Text :
- https://doi.org/10.1038/s41598-024-52752-7