Predicting Food Prices in Nigeria Using Machine Learning: Symbolic Regression

Hameed Ajasa and Olawale Basheer Akanbi
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Olawale Basheer Akanbi: Department of Statistics, University of Ibadan.

International Journal of Research and Innovation in Applied Science, 2025, vol. 10, issue 6, 979-995

Abstract: The aim of this study is to predict the prices of local rice, beans, and Garri in the South West (SW) and North Central (NC), Nigeria using economic indicators such as exchange rate, inflation rate, crude oil price, past one month price (lag 1) and past five-month price (lag 5) of the food prices as the predictor variables. The data used were extracted from the website of the National Bureau of Statistics from January 2017 to July 2024. The data were split into training set and testing set. The study proposed four machine learning techniques; random forest, decision tree, neural network and symbolic regression to model the prices of food, and the root mean square error (RMSE) was used as a criterion for the model evaluation and comparison. Findings showed that symbolic regression outperformed the other models in predicting the prices of beans in the NC with random forest, decision tree, neural network and symbolic regression having the RMSE values: 1365.41, 1348.86, 672.075 and 395.68 respectively. Similarly, symbolic regression outperformed the other models in predicting the prices of rice in the NC with random forest, decision tree, neural network and symbolic regression having the RMSE values: 662.19, 601.74, 1327.951 and 94.39 respectively. Similarly, in predicting the prices of Garri, symbolic regression outperformed others with random forest, decision tree, neural network and symbolic regression having the RMSE values: 442.57, 429.08, 920.8771, and 84.28 respectively.

Date: 2025
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