Predictive Analytics for Customer Churn in E-Commerce with Chatbot conversation using Full stack web development

Abstract

This project focuses on predicting customer churn in e-commerce platforms using machine learning and full stack web development. It collects customer behaviour data such as purchase history, browsing patterns, and time spent on the website. Python-based machine learning algorithms analyze this data to identify customers who are likely to stop using the platform. The system does not stop at prediction but takes proactive action to retain customers. When a potential churn user is detected, an intelligent chatbot is activated. The chatbot interacts with customers in a friendly manner and provides assistance. It answers user queries and improves engagement. The chatbot may also offer personalized recommendations, discounts, or special deals. The entire system is developed using Python full stack technologies. By combining predictive analytics with chatbot interaction, the project helps ecommerce businesses reduce customer churn and improve customer satisfaction. KEYWORDS- shopping cart, product catalog, payment gateway, online payment, digital wallet, management, logistics, delivery, customer reviews, SEO, digital marketing, CRM, security, cloud computing, AI, chatbots.