Machine learning based churn analysis for sellers on the e-commerce marketplace

The goal of this study is to develop churn models for sellers on the e-commerce marketplace by using machine learning methods. In order to develop these models, three approaches are applied for developing the models. The dataset used in this study includes ten features, which are maturity type, maturity interval, city of the seller, total revenue of the seller, total transaction of the seller, sector type of the seller, business type of the seller, sales channel, installment option and discount type. Random Forest (RF) and Logistic Regression (LR) are used for churn analysis in all of the approaches. In the first approach, models are developed without applying preprocessing operations on the dataset. In the second and third approaches, under sampling and oversampling methods are used respectively to balance the data set. By using stratified cross validation on the dataset, F-Scores of the churn models are obtained. The results show that F-Scores were 0.76, 0.71 and 0.92 for the three approaches developed with RF, and 0.84, 0.68 and 0.69 for the three approaches developed with LR, respectively.