Reducing Packaging Waste in E-commerce through IoT and SVM-Based Predictive Models

Packaging waste has increased significantly due to the explosive growth of online shopping, a major environmental problem. With Support Vector Machine (SVM)-based prediction models and Internet of Things (IoT) technologies, this paper investigates ways to reduce online shopping package waste. The proposed solution tracks and analyses packaging operations, looking for wasteful practices and ways to reduce them, using sensors that are IoT-enabled and real-time data analytics. Training SVM-based models on past data allows for the prediction of ideal packaging techniques that minimize waste without compromising product safety. The experimental findings show that our method can reduce packaging material consumption by 30%, which indicates that IoT and machine learning can promote sustainable e-commerce practices. Contributing to worldwide efforts to reduce waste and advance the area of sustainable logistics, this paper offers a solid foundation for online retailers to use greener packing practices. The model shows that the IoT and SVM are beneficial in sustainable e-commerce logistics, reducing packaging waste by 32%, shipping costs by 18%, and improving package quality by 25%.