Supermarket Sales Prediction Based on Xgboost Classifier Model
DOI:
https://doi.org/10.54097/d8bkgc50Keywords:
Supermarket sales; Xgboost; classifier.Abstract
As a matter of fact, it is necessary for the manager to have a better understanding of supermarket sales nowadays. With this in mind, it is significant to have a model to analyze the sales in supermarkets to help the manager. On this basis, in this article, the XGBClassifier model is used to do the prediction of supermarket sales. There are 750 training sets to fit the model and 250 testing sets to judge the performance. The accuracy of the model is 0.49 and the train score is about 62.67. The model is a normal way to do prediction, it is good to fit the data with big sets. This research is helpful for the people who want to manage a supermarket, it can give some important advice to improve the income of the supermarket. It will be more meaningful after training a big set of data because of higher precision.
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