• Mr.Likhit Shreedhar Moger
• Dr. Shashidhar Kini K

The goal of this project is to create a reliable and understandable machine learning model that can accurately forecast client attrition. The ultimate goal is to deliver actionable insights that help firms build efficient customer retention strategies, minimize customer attrition rates, and boost overall profitability. This abstract highlights the importance of datadriven insights in enhancing customer retention strategies. By identifying at-risk customers early, businesses can implement targeted retention measures, reducing churn rates and improving long-term profitability. The results demonstrate the model's potential to provide actionable insights, paving the way for more personalized and efficient customer engagement.

Customer churn, Churn prediction, Customer retention, Predictive modelling, Supervised learning

"Customer Churn Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.1077-1080, July-2025, Available :http://www.jetir.org/papers/JETIRGX06203.pdf

"Customer Churn Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp1077-1080, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06203.pdf