Performance Evaluation of Supervised Machine Learning Algorithms for Customer Classification in E-Commerce

With the rapid growth of e-commerce and increasing competition in online markets, customer behavior analysis has become a vital element in successful business strategies. This review explores the application of supervised machine learning algorithms in classifying online store customers. A wide range of algorithms such as Decision Tree, Random Forest, Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes, and others have been reviewed and compared. The analysis shows that the reported accuracy varies depending on the characteristics of the dataset and the configuration of the algorithms. A comparison of algorithm accuracies indicates that XGBoost and MLP demonstrated outstanding performances with accuracies of ۹۷.۶۳ and ۹۸.۳۳, respectively. In conclusion, the strengths and weaknesses of the reviewed algorithms have been discussed, and suggestions for future research on the application of machine learning in customer classification have been provided.

Keywords:

Customer Classification , Supervised Learning Algorithms , Purchase Behavior Prediction , Machine Learning , Classification Performance Evaluation