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@article{180512,
        author = {R Anisha and Shravani G and Mithan Gowda M K and Manogna R and B Uma},
        title = {Customer Churn Prediction Model},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {2128-2132},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180512},
        abstract = {Given the high cost of gaining new customers and the comparatively low switching costs for consumers, client retention has become more and more crucial in today’s fiercely competitive telecom sector. The profitability and long-term sustainability of a telecom provider are directly impacted by churn, the phenomenon when customers discontinue utilizing a company’s service. The goal of this project is to use machine learning techniques to create a reliable forecast model for customer attrition. The study examines customer behavior and service- related characteristics such as contract type, duration, monthly rates, internet service, and technical support utilization using the well-known Telco Customer Churn dataset. To get the dataset ready for model training, a thorough data pretreatment pipeline was put in place. This pipeline involved encoding category variables, managing missing values, and normalizing numerical characteristics. Several classification techniques, such as Random Forest, Support Vector Machine (SVM), Logistic Regression, and Gradient Boosting Classifier, were analyzed and assessed.},
        keywords = {AI in the Classroom Speech-to-Text, Natural Language Processing, GPT, Whisper, Mock Interview, HR Inter- view Simulation, Interview Feedback System, and Educational Technology},
        month = {June},
        }