CUSTOMER CHURN PREDICTION ANALYSIS

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