CusCP: AI-Driven System for Predictive Modeling of Customer Churn in E-commerce Using Machine Learning

Client churn has emerged as a key worry for firms looking to maintain and grow their client base in the ever changing world of e-commerce. In order to anticipate customer turnover, this study takes a comprehensive approach and evaluates the effectiveness of four different predictive models: decision trees, neural networks, logistic regression, and NN-Keras. To build a solid dataset, the research starts with meticulous data collecting and preparation. We thoroughly examine these models to determine their individual advantages and disadvantages. The distribution of true positives and true negatives offered by logistic regression is balanced, but it struggles with a large proportion of false negatives. Decision trees are excellent at detecting genuine positives but have a greater tendency to detect false positives. While NN-Keras favors genuine negatives, neural networks establish a healthy equilibrium between real positives and true negatives. The research gives E-commerce companies insightful information on how to choose a model while taking into account the trade-offs between accuracy, recall, and interpretability. This research paper emphasizes the necessity of continual model improvement and the investigation of ensemble tactics to strengthen client retention strategies and improve prediction skills as the e-commerce industry continues to change.