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@article{162117,
        author = {T Prithvi Charan and Tanush T and Vinyas S R Gowda and Keerthi Mohan},
        title = {CUSTOMER CHURN PREDICTION IN TELECOM USING AI-ML},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {8},
        pages = {25-29},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162117},
        abstract = {The telecom industry generates enormous amounts of data every day from a large number of clients. The phenomenon known as "churn" refers to customers switching companies within a predetermined amount of time. Getting new clients is more expensive than maintaining your current clientele. Managers and analysts in the telecom industry need to understand why customers terminate their subscriptions and what patterns of behavior they consistently see. This technology identifies the customers who are most likely to leave the telecom industry by compiling the factors that affect their decisions and applying classification algorithms to that data. In order to give customers retention strategies and plans, this system's primary goal is to compare various machine learning algorithms that may be used to create customer churn prediction models and identify the reasons behind churn. This system uses machine learning techniques like KNN and decision tree classifiers, along with classification algorithms like Random Forest (RF) to analyze customer turnover data. It provides a successful business model that can precisely forecast customer attrition and assist management in taking appropriate action during the attrition period to avoid attrition and lost revenue. In order to give customers retention strategies and plans, this system's primary goal is to compare various machine learning algorithms that may be used to create customer churn prediction models and identify the reasons behind churn.},
        keywords = {},
        month = {},
        }