• Veena Mohan M
• C K Vanamala

Client stir forecast models plan to recognize clients with a high inclination to draw in. Prescient precision, intelligibility, and reasonability are three vital parts of a stir forecast model. An exact model grants to accurately target future churners in a maintenance showcasing effort, while a fathomable and natural rule-set permits to distinguish the fundamental drivers for clients to beat, and to foster a compelling maintenance methodology as per space information. The client stir expectation (CCP) is one of the difficult issues in the telecom business. With the progression in the field of AI and man-made brainpower, the potential outcomes to foresee client stir has expanded altogether. This paper gives a drawn out outline of the writing on the utilization of AI in client beat expectation and minimization displaying. This paper sums up the beat expectation strategies to have a more profound comprehension of the client stir and it shows that most precise stir forecast is given by the mixture models as opposed to single calculations so telecom enterprises become mindful of the requirements of high gamble clients and improve their administrations to topple the beat choice.

Churn, Network, Financial attributes, churn rate

"Prediction and Minimizing of Churn Rate using Machine Learning based on user financial attributes", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 4, page no.l576-l579, April-2023, Available :http://www.jetir.org/papers/JETIR2304B83.pdf

"Prediction and Minimizing of Churn Rate using Machine Learning based on user financial attributes", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 4, page no. ppl576-l579, April-2023, Available at : http://www.jetir.org/papers/JETIR2304B83.pdf