Application of the Recursive Feature Elimination Method in Commodity Demand Prediction
Demand forecasting plays a crucial role as the first line of defense in the enterprise supply chain.The accuracy of demand forecasting has been consistently poor due to various factors.In the article,Python's visualization library is used for data visualization operations,and feature correlation analysis is used to enhance the readability of the data.Using machine learning models such as XGBoost,LightGBM,and Random Forest,we search for features that have a significant impact on demand,and use recursive feature elimination to rank and score the features to obtain more accurate demand prediction results.Extract,add features,group and merge them to improve the accuracy of prediction.Based on recursive feature elimination method and utilizing data visualization to improve data extraction readiness,it can improve accuracy and shorten running time in specific application examples.
万方数据
维普
