International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Customer Churn Analysis and Prediction |
|---|---|
| ABSTRACT | Customer churn refers to customers discontinuing their relationship with a service provider. Predicting churn is essential for businesses because retaining existing customers is more cost-effective than acquiring new ones. This paper presents a Customer Churn Analysis and Prediction System developed using Machine Learning techniques. The system utilizes the IBM Telco Customer Churn dataset containing customer demographics, service subscriptions, and billing information. Various machine learning algorithms such as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, XGBoost, and LightGBM are applied and compared. SMOTE is used to handle class imbalance, while SHAP explain ability helps interpret model predictions. The proposed system provides churn prediction, risk classification, automated reporting, and visualization dashboards. Experimental results show that XGBoost achieves the highest performance with a ROC-AUC score of 0.9367, enabling businesses to identify high-risk customers and implement proactive retention strategies. |
| AUTHOR | DR. KAVYASHREE N, RAMYA SHREE D S Associate Professor, Department of MCA, SSIT, Tumkur, Karnataka, India 4th Sem, Department of MCA, SSIT, Tumkur, Karnataka, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406014 |
| pdf/14_Customer Churn Analysis and Prediction.pdf | |
| KEYWORDS | |
| References | [1] Imani, Mehdi, et al. "Customer churn prediction: A systematic review of recent advances, trends, and challenges in machine learning and deep learning." Machine Learning and Knowledge Extraction 7.3 (2025): 105. [2] Kuramannagari, Yuvakumar, et al. "A Comparative Analysis of Machine Learning Models for Customer Churn Prediction in Subscription-Based Businesses." 2025 2nd International Conference on Computational Intelligence and Computing Applications (ICCICA). IEEE, 2025. [4] Rongala, Madan Kumar. "Comparative Evaluation of Machine Learning Models for Customer Churn Prediction in the Telecom Sector." 2025 International Conference on Computing Technologies (ICOCT). IEEE, 2025. [5] Singh, Kiran Deep, et al. "Exploratory data analysis and customer churn prediction for the telecommunication industry." 2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS). IEEE, 2023. [6] Dalvi, Preeti K., et al. "Analysis of customer churn prediction in telecom industry using decision trees and logistic regression." 2016 symposium on colossal data analysis and networking (CDAN). IEEE, 2016. [7] Xiong, XuHai. "Online Learning Customer Churn Prediction Model Based on GA-XGBoost." 2024 10th International Conference on Computer and Communications (ICCC). IEEE, 2024. [8] Vanitha, M. "Improved Bidirectional-Long Short-Term Memory for Customer Churn Prediction in the Telecom Industry." 2024 1st International Conference on Sustainability and Technological Advancements in Engineering Domain (SUSTAINED). IEEE, 2024. [9] Murmanto, Imanuel Revelino, et al. "Application Design of Customer Churn Prediction Using Random Forest and XGBoost Algorithms for Telecommunication Industry in Indonesia." 2025 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS). IEEE, 2025. [10] Shah, Jyoti Kunal, et al. "Unified AI Framework for Real-Time Customer Churn Prediction Using Behavioral and Event-Log Anomaly Signals." 2026 IEEE 5th International Conference on AI in Cybersecurity (ICAIC). IEEE, 2026. [11] Zhou, H. "A Novel SHAP-Guided Bayesian Optimization Method for Telecom Customer Churn Prediction and Retention." IEEE Trans. Network and Service Management (2025). [12] Bussey, Kathryn, Shatha Ghareeb, and Jamila Mustafina. "Bank Customer Churn Prediction: A Machine Learning Approach." 2025 18th International Conference on Development in eSystem Engineering. (DeSE). IEEE, 2025. [13] Orhan, Busra Nur, and Engin Masazade. "Telecom Customer Churn Prediction and Root Cause Analysis Using Network Quality Metrics." 2025 Innovations in Intelligent Systems and Applications Conference (ASYU). IEEE, 2025. [14] Thanishka, B. Krishna, et al. "Machine Learning and Ensemble Prediction for Customer Churn Prediction Analysis." 2025 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2025. [15] Lu, Ning, et al. "A customer churn prediction model in telecom industry using boosting." IEEE Transactions on Industrial Informatics 10.2 (2012): 1659-1665. |
