SMART TEA: Churn, Trend, Inventory and Sales Prediction System Using Machine Learning

Abstract

Managing operations at a tea factory requires consistency and planning. This paper presents a complete platform that uses advanced machine learning methods specifically designed for the tea sector. Sales prediction, churn prediction, trend prediction, and smart inventory management are the four essential features of our solution. While using Neural Networks for Churn Prediction offers exact insights into customer churn, utilizing Gradient Boosting for Sales Prediction guarantees accurate revenue estimates. Linear regression models were used for trend prediction and smart inventory management to enable efficient utilization of resources and trend identification. With the help of this integrated system, tea companies can now operate more profitably and sustainably in a market that is always changing. This research acts as a beacon, demonstrating the revolutionary potential of data-driven management as operations in the tea industry evolve.