On the Impact of Various Combinations of Preprocessing Steps on Customer Churn Prediction

This paper investigates various combinations of preprocessing methods (attribute selection, normalization, resampling, and imputation) and evaluates their impact on the performance of decision tree models for predicting customer churn. The experiments were performed on the benchmark Cell2Cell dataset due to its ability to address diverse aspects of customer behavior, including value-added services, usage patterns, demographic information, customer service interactions, personal data, and billing data. This comprehensive view of client activities makes it ideal for studying customer churn. The aim of this work is to identify the most effective preprocessing method that can be applied to a real-world telecommunications dataset to improve the effectiveness of customer churn prediction methods. The study systematically examines the effects of imputation methods (K-Nearest Neighbors and statistical imputation), normalization techniques (Median and Median Absolute Deviation Normalization, Min-Max Scaling, and Z-Score Standardization), feature selection using Lasso regression, and resampling using SMOTE Tomek. This results in 16 distinct preprocessed datasets, each reflecting a unique combination of preprocessing steps. An analysis of these datasets was conducted, evaluating the performance metrics of the Decision Tree model on each dataset, including accuracy, precision, recall, F1 score, and ROC-AUC. Key findings highlight that Statistical Imputation, Median and Median Absolute Deviation Normalization, and Lasso feature selection achieved the highest performance, with 0.78 in precision, 0.77 in accuracy, recall, and F1 Score, and 0.74 in ROC-AUC.