Analysis of Vegetable Sales Volume Distribution and Pricing Strategy: A Study Based on Nonparametric Kernel Density Estimation and Neural Network Modeling

In this study, the distribution pattern of sales volume of each category and individual product of vegetables is analyzed by firstly using the non-parametric kernel density estimation method to obtain the kernel density estimation curves of the daily sales volume of each category and individual product of vegetables, and analyzing them in combination with statistical indicators. Secondly, based on the visualization of the monthly sales volume of each category of vegetables over time and the seasonal component plot, the sales trend is studied. Finally, Spearman rank correlation analysis is utilized to explore the correlation between the sales volume of each category and individual product of vegetables. Based on this, the total sales volume of each vegetable category is further analyzed in relation to cost plus pricing, and the sales volume is predicted by a time series model optimized by a neural network model to develop a pricing strategy for the coming week in order to maximize revenue.