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A Design and Implement of Efficient Agricultural Product Price Prediction Model

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(5), pp.29-36
  • DOI : 10.9708/jksci.2022.27.05.029
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : January 18, 2022
  • Accepted : February 17, 2022
  • Published : May 31, 2022

Jung-Ju Im 1 Tae-Wan Kim 2 Ji-Seoup Lim 3 Jun-Ho Kim 4 Tae-Yong Yoo 4 Won Joo Lee 4

1한양대학교(ERICA캠퍼스)
2한앙대학교
3한양대학교
4인하공업전문대학

Accredited

ABSTRACT

In this paper, we propose an efficient agricultural products price prediction model based on dataset which provided in DACON. This model is XGBoost and CatBoost, and as an algorithm of the Gradient Boosting series, the average accuracy and execution time are superior to the existing Logistic Regression and Random Forest. Based on these advantages, we design a machine learning model that predicts prices 1 week, 2 weeks, and 4 weeks from the previous prices of agricultural products. The XGBoost model can derive the best performance by adjusting hyperparameters using the XGBoost Regressor library, which is a regression model. The implemented model is verified using the API provided by DACON, and performance evaluation is performed for each model. Because XGBoost conducts its own overfitting regulation, it derives excellent performance despite a small dataset, but it was found that the performance was lower than LGBM in terms of temporal performance such as learning time and prediction time.

Citation status

* References for papers published after 2022 are currently being built.