CUSTOMER CHURN PREDICTION ANALYSIS

CUSTOMER CHURN PREDICTION ANALYSIS

E Padma Priya  ., S S Janashree .,P Sivaganga *

Artificial Intelligence and Data Science

Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College, Avadi,

Chennai 600062- India.

Abstract

The Customers are the base of many successful businesses; thus, all the sectors are starting to understand how important it is to gain client satisfaction. The technical infrastructure has expanded quickly, changing how businesses operate. Due to growing business competition, the importance of marketing techniques, and customers' increasingly aware behaviour in recent years, leaving the organization is a crucial issue and it is one of the most crucial worries for most of the sectors. Different approaches must be developed by organizations to address the churn problems affecting the services they provide. To gain a deeper understanding of customer churn, this project summarizes churn prediction techniques. It also demonstrates that hybrid models, as opposed to single algorithms, provide the most accurate churn predictions, allowing telecom industries to better understand the needs of high- risk customers. Reduced client turnover is turning into a demand for service suppliers as a result of the potential impact that client neglect may wear business profitability. Prediction helps to search out users' World Health Organization area units doubtlessly to change from one organization to a different. The ever-rising churn rate in the medium could be a drawback. In lightweight of this, the present work makes use of a big-data platform and machine learning methodology. These medium firms are safeguarded with effective strategies for reducing the speed of churn because of machine learning algorithmic program techniques.

Keywords: Churn, Machine Learning, Random Forest,telecommunication,