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@article{194915,
        author = {Mr Y P S Hari Krishna and K Srinidhi and D L Sravanthi and Ch Manoj and Ch Siva},
        title = {CUSTOMER CHURN PREDICTION: MACHINE LEARNING CLASSIFICATION FOR IDENTIFYING POTENTIAL CHURN},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {8124-8128},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194915},
        abstract = {Customer churn prediction has become a critical research area in the telecommunications industry due to its direct impact on revenue and customer retention strategies. This study proposes a machine learning-based classification model to identify customers who are likely to discontinue services. The dataset used in this research includes customer attributes such as tenure, monthly charges, contract type, and internet service information. Data preprocessing techniques, including handling missing values, categorical feature encoding, and feature scaling, were performed to enhance model performance. Several classification algorithms were evaluated, and Logistic Regression was selected based on its predictive accuracy and interpretability. The developed model was integrated into a web-based interface to facilitate real-time prediction. Experimental results demonstrate that the proposed approach effectively identifies potential churn customers, thereby supporting data-driven decision-making for improving customer retention in the telecom sector.},
        keywords = {Churn Prediction, Classification, Logistic Regression, Machine Learning, Telecom Industry.},
        month = {March},
        }