Cloud-Based E-Commerce Sales Exploratory Data Analysis and Cloud Storage Integration

E-commerce has highlighted the requirement to use advanced cloud technology and data analysis techniques to power customer experience and business efficiency. This paper is a detailed study of integrating cloud-based solutions and machine learning (ML) techniques in the analysis of e-commerce sales data. The research uses a customer credit card database to contrast and validate the performance of various ML models, i.e., Random Forest, Decision Tree, Logistic Regression, etc., in predicting customer churn. The performance of the models is compared against metrics such as Accuracy 92.5%, Precision 91.0%, Recall 93.0%, F1-Score 92.0%, and AUC-ROC 0.94 which identify Random Forest as the most superior performing model. The study then continues to probe the impact of integration of the cloud into storage and discovers that it is efficient, scalable, and secure when handling vast data. The key contribution of the cloud solutions in the e-commerce environment is in accordance with this research as scalability in business, optimized utilization, and hassle-free customer experience emerge. This book finishes by providing lines of future work on hybrid cloud solutions, real-time prediction systems, and protection of data privacy.