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Prediction of Agro Products Sales Using Regression Algorithm

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.

Published in American Journal of Data Mining and Knowledge Discovery (Volume 5, Issue 1)
DOI 10.11648/j.ajdmkd.20200501.12
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), 2020. Published by Science Publishing Group

Keywords

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

References
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[3] Azeez, B. (2019). Nigeria loses over $50b annually to postharvest waste. Nigerian Stored Products Research Institute available at URL: https://tribuneonlineng.com/nigeria-loses-over-50b-annually-to-postharvest-waste/.
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Cite This Article
  • 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
  • Department of Mathematics/Statistics/Computer Science, University of Agriculture, Makurdi, Nigeria

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

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

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

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