Hyperparameter Optimization in Machine Learning Classification Models: A Case Study in the Retail Industry

It is now important to forecast customer purchase patterns and classify stores appropriately considering the effects of the global pandemic and changing consumer preferences in the retail sector. Machine learning (ML) algorithms can be used to predict customer buying patterns and classify stores. However, conventional ML algorithms are very sensitive to their parameters. Hyperparameter optimization (HPO) is used for tuning the parameters of the model with grid search and random search algorithms