• Fiza s
• Aravind c
• Abinaya sri
• Nithya Shree T

Telecom operators face a big problem with customer churn, or attrition, which lowers customer satisfaction, reduces market share, and results in revenue loss. For telecom businesses to apply efficient retention tactics and keep a competitive edge in the ever-changing telecom market, it is critical to comprehend the root causes and trends of customer attrition. In an effort to improve company sustainability in the telecom sector, this study provides a thorough investigation of telecom customer attrition from a machine learning perspective. The study uses cutting-edge machine learning techniques applied to telecom data to examine the trends and causes of customer turnover. By using predictive modelling, we may determine the main causes impacting attrition and gain insights into the dynamics of client churn. We create reliable predictive models that can precisely predict customer attrition by conducting a methodical assessment of machine learning methods such as gradient boosting, random forests, and decision trees. This study advances predictive analytics in the telecom industry by using machine learning to evaluate telecom customer attrition. It also offers useful implications for the expansion and sustainability of businesses

Telecom, Customer Attrition, Customer Churn, Machine Learning, Predictive Analytics, Business Sustainability.

"Understanding customer attrition:A Machine learning perspective for business sustainability", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.o682-o686, April-2024, Available :http://www.jetir.org/papers/JETIR2404F94.pdf

"Understanding customer attrition:A Machine learning perspective for business sustainability", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppo682-o686, April-2024, Available at : http://www.jetir.org/papers/JETIR2404F94.pdf