기계학습에 기반을 둔 통신사 고객 이탈예측 방안.

In this paper, we explore customer churn in a telecommunication company, measuring the probability of users discontinuing their service. Our investigation employs various machine learning models, including Decision Tree, Random Forest, XGBoost, LightGBM, SVM, Logistic Regression, and Deep Neural Network. To accomplish this, we leverage data collected from a California-based telecommunication company, initially containing 38 feature data. In order to reduce complexity, we apply Lasso regression to select the five most crucial features for determining customer churn: household situation, service satisfaction, loyalty, payment capability, and contract type. Through performance evaluation, we demonstrate that accurate predictions of customer churn can be achieved even with five features, emphasizing the significance of feature selection. [ABSTRACT FROM AUTHOR]