Deep Learning Approaches for Churn Prediction: An Empirical Evaluation on Real-World Business Datasets

Devarsh Bagla

Abstract: Customer churn prediction is a critical challenge for modern businesses seeking to maintain long-term customer relationships and reduce revenue loss. This study explores the application of deep learning and machine learning models for predicting customer churn using real-world business datasets. The dataset includes demographic, account, and behavioural attributes of customers that influence churn likelihood. Models such as Logistic Regression, Random Forest, and Deep Neural Networks (DNN) were trained and evaluated using metrics including accuracy, precision, recall, F1-score, and ROC-AUC. The experimental results reveal that the Deep Neural Network model achieves the highest prediction accuracy of 94.1%, outperforming traditional machine learning algorithms. Random Forest offers an effective trade-off between accuracy and interpretability, while Logistic Regression serves as a reliable baseline. This research provides a comparative framework for organizations aiming to leverage AI-based methods to predict and prevent customer churn, leading to improved retention strategies and business profitability.

Keywords: Customer Churn, Deep Learning, Machine Learning, Neural Networks, Random Forest, Logistic Regression, ROC-AUC, Feature Selection, Data Imbalance