Predictive Analytics for E-Commerce Sales Profitability using ResNet and Cloud Integration

Abstract

The proposed framework for predicting e-commerce sales profitability using ResNet and cloud integration has demonstrated exceptional performance in classifying transactions as Profitable or Non-Profitable. With key performance metrics such as accuracy (99.5%), precision (99.7%), recall (99.8%), and F1-Score (99.6%), the model offers highly reliable predictions. Additionally, the False Positive Rate (FPR) and False Negative Rate (FNR) are minimal, confirming the model’s precision and reliability in real-world applications. The confusion matrix further supports the model’s effectiveness in distinguishing between profitable and non-profitable sales transactions. Future projects will concentrate on refining the framework by adding seasonal patterns, consumer opinion, and product demand forecasting to the model in order to make it more accurate and responsive. The model will be implemented in a real-time production setting using AWS Lambda and other cloud services to maintain effortless scalability. Besides comparing performance with other sophisticated models such as Transformers and LSTMs, it will assist in measuring their ability to predict profitability. The model will be also expanded to process multi-class classification to provide finer-grained understanding of the multiple levels of profitability to augment its usefulness for business.