Sales data prediction method and device and related equipment

Abstract

Embodiments of the invention disclose a sales volume data prediction method and device, and related equipment. The method comprises the steps of obtaining sample data by electronic equipment; whereinthe sample data comprises store sales volume data and feature data corresponding to the store; performing feature extraction on the feature data and the store sales volume data to obtain a feature sample; taking the store sales volume data as a dependent variable of the first training sample, and taking the feature sample as an independent variable of the first training sample to form a first training sample; training the clustering model based on the first training sample to obtain N clustering cluster samples; training the regression model based on the N clustering cluster samples to obtainN regression models; wherein the trained clustering model and regression model are used for carrying out sales data prediction on the to-be-predicted store. By adopting the embodiment of the invention, the clustering category of the predicted store can be determined, and the regression model corresponding to the clustering category is selected for prediction, so that the problem of inaccurate prediction caused by insufficient model training is solved, and the accuracy of business prediction is improved.

Description

technical field [0001] The present application relates to the technical field of data processing, in particular to a method, device and related equipment for predicting sales data. Background technique [0002] For the smart retail of the store, it is necessary to predict the sales volume in the future, which will help the store to accurately formulate business goals, so as to grasp the store's plan for future sales. [0003] The sales data prediction method disclosed in the prior art generally uses the monthly sales of each store as a sample, designs store-related features, weather and date-related features, and based on mobile location service-related features, and then scales the samples according to the proportion Randomly select training samples and evaluation samples. Use the regression model to train the training samples, and use the evaluation samples to evaluate the trained model. When the RMSE and other indicators of the model reach a certain standard, the model t...

AI Technical Summary

Problems solved by technology

[0005] Embodiments of the present invention provide a method for predicting sales data, a device for predicting sales data, a device for predicting sales data, and a computer-readable storage medium, which can confirm which cluster the prediction target belongs to through the clustering model, so that The results can be predicted through the regression model corresponding to the cluster cluster, which solves the problem of insufficient training of samples in the process of model training due to unbalanced data distribution in the prior art, and improves the accuracy of sales data prediction

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