Vegetable sales data mining: association rule analysis

Abstract

Supermarkets sell a large number of vegetable categories, and there is a promotion or inhibition relationship between the sales volume of each product. In order to explore the law of sales volume between different products, this paper first preprocesses the data, counts the total sales volume of single products and the total sales volume of each category, and draws a bar chart to reflect the distribution law of the sales volume of each single product through visual analysis, for the correlation between different single products and categories, the correlation between categories is examined first, and the correlation between categories is pre-adopted respectively by using the Pearson and Spearman correlation analysis, and the correlation between the categories and sales volume is examined by drawing a Q-Q chart, and it is found that the distribution law does not satisfy the normal distribution, and thus the Spearman correlation analysis is adopted. The Q-Q diagram was drawn for testing, and it was found that the distribution pattern did not satisfy the normal distribution, thus Spearman correlation analysis was used to analyse the correlation, and it was found that the correlation between the categories showed a strong correlation. For different single product, we carry out Kendall consistency test for all single products, and the test results show that there is a good consistency between the single products. In order to make the analysis more comprehensive, this paper concludes with some suggestions for data collection