A Comparative Study on Challenges and Solutions on Product Classification Based on Data Virtualization

In the digital world product classification is one of the critical success factors of organizations who increasingly have to handle large amount of data, enhance customers’ experience and optimize organizational workflows. There are several issues that can be considered as a problem for efficient classification: The data associated with the products are rather complex and the markets are constantly in the state of change. This paper aims at undertaking a comparative analysis of the obstacles in product classification and include the methods and technologies that have been used to execute issues like data inconsistencies, high-dimensional data, and dynamic attributes among others. Furthermore, the research focuses on the effects of data integration and real time processing on product classification and consequently explores by how much the diffusion of these factors relates to classification outcome. As a result of this comparative study, the research establishes the effective strategies of handling some of the challenges which are faced by organization while implementing and maintaining classification system like large volume of data, unstructured data and need to adapt the classification systems to the market needs. Python software is used to enhance the approach. The integration of K-Means clustering strengthen the classification by grouping products based on features like price and ratings. The Multi-Layer Perceptron-Gated Recurrent Unit (MLP-GRU) hybrid model effectively classified products into categories such as 'Premium Electronics' and 'Trendy Fashion Items' by leveraging feature extraction and sequence modeling on pre-clustered data, demonstrating superior accuracy and reliability. With 99% accuracy, the suggested Hybrid Multi-Layer Perceptron-Gated Recurrent Unit method outperforms Fully Connected Neural Network, Support Vector Machine, and Logistic Regression by 3.39%. This paper also features real-world cases showing how companies have successfully instituted product classification systems and the techniques that have been adopted to enhance the systems’ precision.