Title: Harnessing the meteorological effect for predicting the retail price of rice in Bangladesh
Authors: Abdullah Al Imran; Zaman Wahid; Alpana Akhi Prova; Md. Hannan
Addresses: Department of Computer Science and Engineering, American International University-Bangladesh, 408/1, Kuratoli, Dhaka 1229, Bangladesh ' Department of Software Engineering, Daffodil International University, Daffodil Road, Ashulia, Dhaka, Bangladesh ' Department of Computer Science, Islamic University of Technology, K B Bazar Rd, 1704, Gazipur, Bangladesh ' Department of Computer Science, Islamic University of Technology, K B Bazar Rd, 1704, Gazipur, Bangladesh
Abstract: Bangladesh has seen an absurd, steeper prize-hike for the last couple of years in one of the most consumed foods taken by millions of people every single day: rice. The impact of this phenomenon, however, is indispensably critical, especially to the one striving for daily meals. Thus, understanding the latent facts is vital to policymakers for better strategic measures and decision-making. In this paper, we have applied five different machine learning algorithms to predict the retail price of rice, find out the top-most factors responsible for the price hike, and determine the best model that produces higher prediction results. Leveraging six evaluation metrics, we found that random forest produces the best result with an explain variance score of 0.87 and an R2 score of 0.86 whereas gradient boosting produces the least, meanwhile discovering that average wind speed is the topmost reason for rice price hike in retail markets.
Keywords: data mining; rice price prediction; pattern mining; regression; retail markets.
DOI: 10.1504/IJBIDM.2022.123215
International Journal of Business Intelligence and Data Mining, 2022 Vol.20 No.4, pp.440 - 455
Received: 13 Aug 2020
Accepted: 01 Dec 2020
Published online: 03 Jun 2022 *