Published in Data Science and Engineering (DSE) Record 2023 Vol. 4 No. 1 pp. 82-95
Abstract
This independent study is to develop a system to analyze the behavior of customers who use mobile phones by using the Clustering model as a tool to group customers according to their behavior. The researcher conducted a comparative study of appropriate behavioral grouping. It was divided into two sub-studies to study clustering by constructing two basic models: K-means and DBSCAN from those two basic models to find the most suitable clustering method for the data set characteristics. this Comparison of the average accuracy of classification of customers in each group from all five models: Random Forest, Decision Tree, SVM, Naïve Bayes, and KNN. Performance measurement of the system developed in this study. It is a comparison of accuracy found that K-means clustering has better customer classification efficiency than DBSCAN. From the experimental results, it can be said that the K-means model has a higher mean accuracy of five classification models than DBSCAN.