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@article{204109,
        author = {Bincy Mol and R Bhuvaneswari},
        title = {Performance Comparison of Random Forest and Support Vector Machine for Customer Churn Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {2252-2264},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204109},
        abstract = {Customer churn prediction plays a crucial role in helping organizations retain valuable customers and reduce revenue loss. Accurate identification of customers who are likely to discontinue services enables businesses to implement effective retention strategies. This research presents a comparative analysis of two widely used machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), for customer churn prediction using the Telco Customer Churn Dataset. The dataset undergoes comprehensive preprocessing, including missing value treatment, categorical data encoding, feature scaling, and feature selection to improve model performance. Both algorithms are trained and tested on the processed dataset, and their predictive capabilities are evaluated using Accuracy, Precision, Recall, and F1-Score metrics. Experimental results indicate that Random Forest achieves higher prediction accuracy and better overall classification performance due to its ensemble learning approach and ability to handle complex feature interactions. Support Vector Machine also demonstrates competitive performance by effectively identifying decision boundaries between churn and non-churn customers. The findings highlight the strengths and limitations of both algorithms and suggest that Random Forest is more suitable for customer churn prediction tasks in telecommunication environments. This research contributes to the development of intelligent customer retention systems and provides guidance for selecting appropriate machine learning models for churn management applications.},
        keywords = {Customer Churn Prediction, Random Forest, Support Vector Machine (SVM), Machine Learning, Classification, Predictive Analytics, Customer Retention, Telco Customer Churn Dataset, Data Mining},
        month = {June},
        }