<![CDATA[This paper presents a novel PyTorch model integrated with a Tkinter-based Recycling Recommendation Application to address the pressing issue of waste management. Our waste prediction and classification model achieve high precision by leveraging advanced machine learning techniques and a large dataset. We improve classification accuracy and speed using pre-trained models and transfer learning, which is critical for effective waste management. The accompanying Tkinter application improves recycling recommendations by allowing users to input information through an intuitive interface. Our PyTorch model has exceptional accuracy, scoring 99% on the training set and approximately 96% on validation, which is supported by robust stratified cross-validation. This fusion of cutting-edge machine learning and user-centered design represents a significant step toward more efficient waste management and environmentally friendly waste disposal practices. The system's potential for widespread adoption is highlighted by its accuracy in categorizing various waste items and providing tailored solutions, resulting in a positive environmental impact.]]>
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