Poverty prediction using E-commerce dataset and filter-based feature selection approach
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-52752-7 |
| _version_ | 1797275190849175552 |
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| author | Dedy Rahman Wijaya Raden Ilham Fadhilah Ibadurrohman Elis Hernawati Wawa Wikusna |
| author_facet | Dedy Rahman Wijaya Raden Ilham Fadhilah Ibadurrohman Elis Hernawati Wawa Wikusna |
| author_sort | Dedy Rahman Wijaya |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T15:09:48Z |
| format | Article |
| id | doaj.art-150d4fb725ae4a308b2cb4e5b51da375 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:09:48Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-150d4fb725ae4a308b2cb4e5b51da3752024-03-05T18:40:25ZengNature PortfolioScientific Reports2045-23222024-02-0114111310.1038/s41598-024-52752-7Poverty prediction using E-commerce dataset and filter-based feature selection approachDedy Rahman Wijaya0Raden Ilham Fadhilah Ibadurrohman1Elis Hernawati2Wawa Wikusna3School of Applied Science, Telkom UniversitySchool of Applied Science, Telkom UniversitySchool of Applied Science, Telkom UniversitySchool of Applied Science, Telkom UniversityAbstract 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.https://doi.org/10.1038/s41598-024-52752-7 |
| spellingShingle | Dedy Rahman Wijaya Raden Ilham Fadhilah Ibadurrohman Elis Hernawati Wawa Wikusna Poverty prediction using E-commerce dataset and filter-based feature selection approach Scientific Reports |
| title | Poverty prediction using E-commerce dataset and filter-based feature selection approach |
| title_full | Poverty prediction using E-commerce dataset and filter-based feature selection approach |
| title_fullStr | Poverty prediction using E-commerce dataset and filter-based feature selection approach |
| title_full_unstemmed | Poverty prediction using E-commerce dataset and filter-based feature selection approach |
| title_short | Poverty prediction using E-commerce dataset and filter-based feature selection approach |
| title_sort | poverty prediction using e commerce dataset and filter based feature selection approach |
| url | https://doi.org/10.1038/s41598-024-52752-7 |
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