Churn Sentry Model Approach: Leveraging Random Forest for Customer Retention using Flask

This paper discusses about "churn Sentry model approach using random forest algorithm" to determine which clients are most likely to quit a business. Businesses face a significant difficulty in retaining customers, and proactive engagement methods depend on the ability to forecast customer attrition. A machine learning method called churn propensity prediction can be used to determine which consumers are most likely to discontinue using service or model. The Churn Sentry Model analyses behavioural patterns and forecasts churn risks by utilizing both historical and current customer data. The model guarantees great predicted accuracy with the use of Random Forest, a potent ensemble learning technique. Businesses are able to obtain actionable insights by extracting key elements from various data sources. With the help of Flask, this predictive system enables businesses to increase customer satisfaction, strengthen retention tactics, and reduce attrition With data-driven decision-making, and implemented with Flask, enables businesses to increase customer pleasure, strengthen retention tactics, and reduce attrition.