Machine Learning Insights for Predicting Customer Behavior in Online Retail

The research work reports a comprehensive analysis of predictive analytics of customer behavior on e-commerce based on high-end data science and artificial intelligence techniques. The sourced information for customer demographics, transaction details, and purchase history was extracted from Kaggle. Machine learning models' applications were utilized to predict the attrition of the customers, which indicated that the accuracy of Random Forest was 87%, while that of XGBoost was 89%. The performance of the model was tested in terms of precision, recall, and F1-score. Precisely and recall for XGBoost were both 0.88. Additionally, different consumer types were identified by applying K-Means clustering to classify customers into different kinds based on their attributes of Recency, Frequency, and Monetary. Its findings indicate that predictive analytics can play a very significant input in making advertisements very effective and in keeping clients in this very dynamic world of e-commerce. In the last few years, there has been an impressive rise in online shopping due to more and more people gaining access to the internet, a growing preference for convenience, and technology's ever-changing nature of digital platforms.