Prediction of Agro Products Sales Using Regression Algorithm

Terungwa Simon Yange; Charity Ojochogwu Egbunu; Oluoha Onyekwere; Kater Amos Foga

doi:doi:10.11648/j.ajdmkd.20200501.12

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Prediction of Agro Products Sales Using Regression Algorithm

Terungwa Simon Yange, Charity Ojochogwu Egbunu, Oluoha Onyekwere, Kater Amos Foga

Received: 3 June 2020 Accepted: 17 June 2020 Published: 6 July 2020

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Abstract

This study aimed at developing a system using support vector machine (SVM) that will forecast sales of farm products for an agricultural farm so that managers can take strategic decisions timely to better market the excess farm products which some by nature are perishable. The sales prediction model used SVMs and Fuzzy Theory. The implementation was done using Python Programming Language. The system comprised of three (3) modules: web interface, flask and the SVM Framework. To evaluate the result of the SVM model, the RBF neural network was used as a benchmark. Data of previous sales records from University of Agriculture Makurdi (UAM) farm was used to train and test the system. After training the network with data which covered the time period from 21st January, 2017 to 30th June, 2019, the remaining data which covered from 1st July 2019 up to the 31st December, 2019 was used to test and validate the forecasting performance of the system. The Forecasting Precision (FP) value for the SVM was 96.75% and that of the RBF neural network forecasting value was 90.55%. Analysis from the results shows that the forecasting system with SVM had a greater precision in the sales of agricultural products.

DOI	10.11648/j.ajdmkd.20200501.12
Published in	American Journal of Data Mining and Knowledge Discovery (Volume 5, Issue 1, June 2020)
Page(s)	11-19
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Prediction, Regression, Algorithm, Agricultural Products, Sales, SVM

References

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APA Style

Terungwa Simon Yange, Charity Ojochogwu Egbunu, Oluoha Onyekwere, Kater Amos Foga. (2020). Prediction of Agro Products Sales Using Regression Algorithm. American Journal of Data Mining and Knowledge Discovery, 5(1), 11-19. https://doi.org/10.11648/j.ajdmkd.20200501.12

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ACS Style

Terungwa Simon Yange; Charity Ojochogwu Egbunu; Oluoha Onyekwere; Kater Amos Foga. Prediction of Agro Products Sales Using Regression Algorithm. Am. J. Data Min. Knowl. Discov. 2020, 5(1), 11-19. doi: 10.11648/j.ajdmkd.20200501.12

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AMA Style

Terungwa Simon Yange, Charity Ojochogwu Egbunu, Oluoha Onyekwere, Kater Amos Foga. Prediction of Agro Products Sales Using Regression Algorithm. Am J Data Min Knowl Discov. 2020;5(1):11-19. doi: 10.11648/j.ajdmkd.20200501.12

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@article{10.11648/j.ajdmkd.20200501.12,
  author = {Terungwa Simon Yange and Charity Ojochogwu Egbunu and Oluoha Onyekwere and Kater Amos Foga},
  title = {Prediction of Agro Products Sales Using Regression Algorithm},
  journal = {American Journal of Data Mining and Knowledge Discovery},
  volume = {5},
  number = {1},
  pages = {11-19},
  doi = {10.11648/j.ajdmkd.20200501.12},
  url = {https://doi.org/10.11648/j.ajdmkd.20200501.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20200501.12},
  abstract = {This study aimed at developing a system using support vector machine (SVM) that will forecast sales of farm products for an agricultural farm so that managers can take strategic decisions timely to better market the excess farm products which some by nature are perishable. The sales prediction model used SVMs and Fuzzy Theory. The implementation was done using Python Programming Language. The system comprised of three (3) modules: web interface, flask and the SVM Framework. To evaluate the result of the SVM model, the RBF neural network was used as a benchmark. Data of previous sales records from University of Agriculture Makurdi (UAM) farm was used to train and test the system. After training the network with data which covered the time period from 21st January, 2017 to 30th June, 2019, the remaining data which covered from 1st July 2019 up to the 31st December, 2019 was used to test and validate the forecasting performance of the system. The Forecasting Precision (FP) value for the SVM was 96.75% and that of the RBF neural network forecasting value was 90.55%. Analysis from the results shows that the forecasting system with SVM had a greater precision in the sales of agricultural products.},
 year = {2020}
}

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TY  - JOUR
T1  - Prediction of Agro Products Sales Using Regression Algorithm
AU  - Terungwa Simon Yange
AU  - Charity Ojochogwu Egbunu
AU  - Oluoha Onyekwere
AU  - Kater Amos Foga
Y1  - 2020/07/06
PY  - 2020
N1  - https://doi.org/10.11648/j.ajdmkd.20200501.12
DO  - 10.11648/j.ajdmkd.20200501.12
T2  - American Journal of Data Mining and Knowledge Discovery
JF  - American Journal of Data Mining and Knowledge Discovery
JO  - American Journal of Data Mining and Knowledge Discovery
SP  - 11
EP  - 19
PB  - Science Publishing Group
SN  - 2578-7837
UR  - https://doi.org/10.11648/j.ajdmkd.20200501.12
AB  - This study aimed at developing a system using support vector machine (SVM) that will forecast sales of farm products for an agricultural farm so that managers can take strategic decisions timely to better market the excess farm products which some by nature are perishable. The sales prediction model used SVMs and Fuzzy Theory. The implementation was done using Python Programming Language. The system comprised of three (3) modules: web interface, flask and the SVM Framework. To evaluate the result of the SVM model, the RBF neural network was used as a benchmark. Data of previous sales records from University of Agriculture Makurdi (UAM) farm was used to train and test the system. After training the network with data which covered the time period from 21st January, 2017 to 30th June, 2019, the remaining data which covered from 1st July 2019 up to the 31st December, 2019 was used to test and validate the forecasting performance of the system. The Forecasting Precision (FP) value for the SVM was 96.75% and that of the RBF neural network forecasting value was 90.55%. Analysis from the results shows that the forecasting system with SVM had a greater precision in the sales of agricultural products.
VL  - 5
IS  - 1
ER  -

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Author Information

Terungwa Simon Yange

Department of Mathematics/Statistics/Computer Science, University of Agriculture, Makurdi, Nigeria
Charity Ojochogwu Egbunu

Department of Mathematics/Statistics/Computer Science, University of Agriculture, Makurdi, Nigeria
Oluoha Onyekwere

Department of Computer Science, University of Nigeria, Nsukka, Nigeria
Kater Amos Foga

Department of Computer Science, University of Nigeria, Nsukka, Nigeria

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APA Style

Terungwa Simon Yange, Charity Ojochogwu Egbunu, Oluoha Onyekwere, Kater Amos Foga. (2020). Prediction of Agro Products Sales Using Regression Algorithm. American Journal of Data Mining and Knowledge Discovery, 5(1), 11-19. https://doi.org/10.11648/j.ajdmkd.20200501.12

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ACS Style

Terungwa Simon Yange; Charity Ojochogwu Egbunu; Oluoha Onyekwere; Kater Amos Foga. Prediction of Agro Products Sales Using Regression Algorithm. Am. J. Data Min. Knowl. Discov. 2020, 5(1), 11-19. doi: 10.11648/j.ajdmkd.20200501.12

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AMA Style

Terungwa Simon Yange, Charity Ojochogwu Egbunu, Oluoha Onyekwere, Kater Amos Foga. Prediction of Agro Products Sales Using Regression Algorithm. Am J Data Min Knowl Discov. 2020;5(1):11-19. doi: 10.11648/j.ajdmkd.20200501.12

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@article{10.11648/j.ajdmkd.20200501.12,
  author = {Terungwa Simon Yange and Charity Ojochogwu Egbunu and Oluoha Onyekwere and Kater Amos Foga},
  title = {Prediction of Agro Products Sales Using Regression Algorithm},
  journal = {American Journal of Data Mining and Knowledge Discovery},
  volume = {5},
  number = {1},
  pages = {11-19},
  doi = {10.11648/j.ajdmkd.20200501.12},
  url = {https://doi.org/10.11648/j.ajdmkd.20200501.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20200501.12},
  abstract = {This study aimed at developing a system using support vector machine (SVM) that will forecast sales of farm products for an agricultural farm so that managers can take strategic decisions timely to better market the excess farm products which some by nature are perishable. The sales prediction model used SVMs and Fuzzy Theory. The implementation was done using Python Programming Language. The system comprised of three (3) modules: web interface, flask and the SVM Framework. To evaluate the result of the SVM model, the RBF neural network was used as a benchmark. Data of previous sales records from University of Agriculture Makurdi (UAM) farm was used to train and test the system. After training the network with data which covered the time period from 21st January, 2017 to 30th June, 2019, the remaining data which covered from 1st July 2019 up to the 31st December, 2019 was used to test and validate the forecasting performance of the system. The Forecasting Precision (FP) value for the SVM was 96.75% and that of the RBF neural network forecasting value was 90.55%. Analysis from the results shows that the forecasting system with SVM had a greater precision in the sales of agricultural products.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Prediction of Agro Products Sales Using Regression Algorithm
AU  - Terungwa Simon Yange
AU  - Charity Ojochogwu Egbunu
AU  - Oluoha Onyekwere
AU  - Kater Amos Foga
Y1  - 2020/07/06
PY  - 2020
N1  - https://doi.org/10.11648/j.ajdmkd.20200501.12
DO  - 10.11648/j.ajdmkd.20200501.12
T2  - American Journal of Data Mining and Knowledge Discovery
JF  - American Journal of Data Mining and Knowledge Discovery
JO  - American Journal of Data Mining and Knowledge Discovery
SP  - 11
EP  - 19
PB  - Science Publishing Group
SN  - 2578-7837
UR  - https://doi.org/10.11648/j.ajdmkd.20200501.12
AB  - This study aimed at developing a system using support vector machine (SVM) that will forecast sales of farm products for an agricultural farm so that managers can take strategic decisions timely to better market the excess farm products which some by nature are perishable. The sales prediction model used SVMs and Fuzzy Theory. The implementation was done using Python Programming Language. The system comprised of three (3) modules: web interface, flask and the SVM Framework. To evaluate the result of the SVM model, the RBF neural network was used as a benchmark. Data of previous sales records from University of Agriculture Makurdi (UAM) farm was used to train and test the system. After training the network with data which covered the time period from 21st January, 2017 to 30th June, 2019, the remaining data which covered from 1st July 2019 up to the 31st December, 2019 was used to test and validate the forecasting performance of the system. The Forecasting Precision (FP) value for the SVM was 96.75% and that of the RBF neural network forecasting value was 90.55%. Analysis from the results shows that the forecasting system with SVM had a greater precision in the sales of agricultural products.
VL  - 5
IS  - 1
ER  -

Copy | Download