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@article{199448,
        author = {Sai Charan and Sharan and Riteek and Shridhar},
        title = {Customer Churn analysis using SQL, Machine Learning & Power BI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {16140-16151},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199448},
        abstract = {Customer churn1 is a very challenging in telecom companies, as the loss of customers directly impacts revenue and business growth. Predicting churn early allows organizations to improve customer satisfaction and implement effective retention2 strategies. This mini project presents an end-to-end churn prediction system using SQL-based ETL processes, Power BI visual analytics, and a Machine Learning model. Raw telecom data is cleaned, transformed, and structured through an ETL pipeline. The processed data is then used to build interactive dashboards that highlight key demographic, service-related, and behavioral factors influencing churn. A Random Forest classifier is trained to categorize customers as churned or retained, helping identify high-risk individuals. 
By integrating data engineering, analytics, and predictive modeling, this system provides meaningful insights to help telecom companies make informed decisions and reduce customer loss. 
Additionally, the project demonstrates how different technologies can work together to create a practical business solution. SQL7 ensures reliable data preparation, Power BI enables clear visualization of trends, and the machine learning model adds predictive power.},
        keywords = {},
        month = {April},
        }