Analysing Household Food Consumption in Turkey Using Machine Learning Techniques

Burak OztornaciBaris AtaSerkan Kartal

DOI 10.7160/aol.2024.160207
No 2/2024, June
pp. 97-105

Oztornaci, B., Ata, B. and Kartal, S. (2024) "Analysing Household Food Consumption in Turkey Using Machine Learning Techniques", AGRIS on-line Papers in Economics and Informatics, Vol. 16, No. 2, pp. 97-105. ISSN 1804-1930. DOI 10.7160/aol.2024.160207.

Abstract

The fluctuations in food prices have highlighted the significance of analysing the factors influencing household food consumption. Recent advancements in data analysis have opened new avenues for investigating this subject. While studies have employed novel data analysis methods to examine the factors impacting household food consumption, the effect of the chosen analysis method on the research outcome remains unexplored. In this study, we aimed to investigate household food consumption in Turkey between 2012-2019 using various data analysis techniques (Linear Regression, Support Vector Machine, Random Forest, eXtreme Gradient Boosting, and Multi-Layer Perception). Our findings reveal that income emerged as the most influential factor in household food consumption across all methods. However, the impact of other factors varied depending on the method employed. This suggests that the method chosen to analyse factors other than income in studies of this nature can significantly impact the results. Researchers should exercise caution when selecting their analysis method.

Keywords

Machine learning, data analysis, food consumption, income.

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