Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{171253,
        author = {Dr. Marrynal S Eastaff and A.Sathiya Priya},
        title = {Enhancing Customer Retention: A Machine Learning Approach for Churn Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {3789-3793},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171253},
        abstract = {Using a diverse array of machine learning techniques, we conducted churn prediction and classification on Orange Telecom's Churn Dataset. This dataset comprises a churn label indicating whether a customer terminated their subscription, along with preprocessed customer activity data. For the project, the larger churn-80 dataset was utilized for training and cross-validation, while the smaller churn-20 dataset was reserved for final testing and performance evaluation. To predict potential customer churn, we undertook an in-depth analysis of the dataset, emphasizing the identification of user personas and feature significance. Parameter optimization was carried out using Grid Search across several classifiers, including Gradient Boosting models, Ensemble Trees, Decision Tree Classifiers, and Logistic Regression. Among these, the XGBoost Classifier achieved the highest ROC, while the LightGBM Classifier excelled in validation set performance.},
        keywords = {Gradient Boosting models, Ensemble Trees, Decision Tree Classifiers, Logistic Regression.},
        month = {December},
        }