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Poverty Prediction Using Machine Learning

Ajay Sharma1 , Jatin Rathod2 , Rushikesh Pol3 , Swati Gajbhiye4

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-3 , Page no. 946-949, Mar-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i3.946949

Online published on Mar 31, 2019

Copyright © Ajay Sharma, Jatin Rathod, Rushikesh Pol, Swati Gajbhiye . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Ajay Sharma, Jatin Rathod, Rushikesh Pol, Swati Gajbhiye, “Poverty Prediction Using Machine Learning,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.946-949, 2019.

MLA Style Citation: Ajay Sharma, Jatin Rathod, Rushikesh Pol, Swati Gajbhiye "Poverty Prediction Using Machine Learning." International Journal of Computer Sciences and Engineering 7.3 (2019): 946-949.

APA Style Citation: Ajay Sharma, Jatin Rathod, Rushikesh Pol, Swati Gajbhiye, (2019). Poverty Prediction Using Machine Learning. International Journal of Computer Sciences and Engineering, 7(3), 946-949.

BibTex Style Citation:
@article{Sharma_2019,
author = {Ajay Sharma, Jatin Rathod, Rushikesh Pol, Swati Gajbhiye},
title = {Poverty Prediction Using Machine Learning},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {3 2019},
volume = {7},
Issue = {3},
month = {3},
year = {2019},
issn = {2347-2693},
pages = {946-949},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3944},
doi = {https://doi.org/10.26438/ijcse/v7i3.946949}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i3.946949}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3944
TI - Poverty Prediction Using Machine Learning
T2 - International Journal of Computer Sciences and Engineering
AU - Ajay Sharma, Jatin Rathod, Rushikesh Pol, Swati Gajbhiye
PY - 2019
DA - 2019/03/31
PB - IJCSE, Indore, INDIA
SP - 946-949
IS - 3
VL - 7
SN - 2347-2693
ER -

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Abstract

Poverty is a classic problem in every region. It is rooted in various causes like corruption, lack of education, political instability, geographical characteristics. The success of a region is strongly influenced how big this poverty can be overcome. So that poverty reduction becomes a priority for both central and local government. There are also multiple ways to do away with it, various programs and policies began to be formulated to reduce and minimize the problem. It is extremely difficult for social programs such as this to gauge the right amount of aid that needs to be given to the right people. This problem is made exponentially more difficult when that program is dealing with the least fortunate portion of the population. This is because they cannot provide the necessary details of their income, asset or expense records to justify that they need the aid to qualify. Hence, this paper’s defining question is: how to determine a method to effectively gauge the right amount of aid to be given to each household given the multitude of variables present in the vast dataset? In our work we will use supervised machine learning algorithms to a dataset to train a model which will predict the poverty based on the household level.

Key-Words / Index Term

Machine Learning, Random Forest, Supervised Learning, XGBoost

References

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[2]. Zhao, Yandong, and Xiao Ma. Study on Credit Evaluation of Electricity Users Based on Random Forest 2017 Chinese Automation Congress (CAC), 2017, doi:10.1109/cac.2017.8243614.
[3]. www.kaggle.com/c/costa-rican-household-poverty-prediction.
[4]. Amanpreet Singh, and Nanita Thakur.A review of Supervised Machine Learning Algorithm 2018 IEEE Conference.