Research On Sales Forecasts Of ND Company Stores Based On The XGBoost Method

As a high-tech and innovation enterprise,ND company is the world's leading provider of smart urban mobility solutions.The company is committed to change urban commuting globally and it currently designs,manufactures and sells high-performance smart e-scooters.A strategic performance sales forecasting can help store managers to build a high efficiency working team.Also,it can help to find the key factor which influence consumers and team members.Therefore,the company will improve the mode of production as well as gaining profitability.Based on this background,this article analysis information of 748 retail stores and sales data which is belong to the ND company,then forecasts future sales using XGBoost method.This paper firstly studies the current situation of retail stores and analyzes the existing problems of sales forecasting in ND Company.And then,think about the missing data and analyze the situation while preprocessing the data.Replace the missing data with the appropriate method according to the distribution of data.Besides,the variables with a large degree of loss are eliminated.And then manage the variables in the data set meanwhile pick the crucial variables which is fit to business scenario.As a result,this article decide to use the following four features: the attribute of store,geographical feature,feature of service and feature of date finally,the feature set is substituted into XGBoost,Linear Regression,Decision Tree and Random Forest model to predict the sales of retail stores.In conclusion,XGBoost get a better performance because of the value of mean absolute percentage error(MAPE)between the value of test set and prediction is lower.Moreover,XGBoost model improves the speed of model prediction greatly because of its parallel computing capacity.The model based on XGBoost applies not only to predict retail sales of the ND company,but also extend to the domestic retail industry and electronic business platform.It is also significant to improve the operation mode,commodity price,sales growth and corresponding precision marketing of stores.