Development of Churn Prediction System for a Marketing Company Using Machine Learning Technique

INTRODUCTION: In today's competitive marketing landscape, customer churn prediction is vital for marketing organizations to identify patterns, factors, and indicators contributing to customer attrition. This paper focuses on developing a customer churn prediction system using machine learning algorithms. OBJECTIVES: This paper employed an e-commerce dataset, obtained from the Kaggle repository, and was preprocessed. Important features were selected from the preproccessed dataset before models’ development. The parameters of AdaBoost, Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) were optimized to improve their performance. METHODS: Techniques such as label encoder, mean imputation, and synthetic minority over-sampling technique (SMOTE) were applied during data preprocessing stage. Ensemble learning algorithms, namely AdaBoost, GB, and XGB were used to develop the model while random search was employed for parameter optimization. Accuracy, precision, recall, and F1-score metrics were used to evaluate the models’ performance. RESULTS: The results of the models with 15 important selected features before parameter tuning yielded the following scores: AdaBoost attained 8