Unveiling the Dynamics of Vegetable Category Sales Through a Data-Driven Approach
DOI:
https://doi.org/10.54097/1wgqe341Keywords:
K-means clustering, Parabolic regression, Correlation coefficients, Data - Driven.Abstract
In the context of the development and application of big data technology, it is particularly important to use a data-driven approach to understand the changes in consumer demand for different vegetable categories and the relationship between them and sales volume to optimize supply chain management and formulate effective marketing strategies. In this paper, three machine learning algorithms, namely K-means cluster analysis, quadratic parabolic regression, and Pearson correlation analysis, were used to analyze the sales of vegetable categories, and the characteristics of people's purchase of vegetables and the correlation coefficients between sales volume and category were -0.12 and 1.00, indicating that the correlation between purchase characteristics and categories was weak, and the sales volume was completely dependent on the category of vegetables.
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