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Deep Learning-Powered Business Analytics: Enhancing Classification and Regression Models

In: Proceedings of the 5th International Conference on Research in Management and Technovation

Author

Listed:
  • Trang Thi Huyen Cao

    (Hanoi University of Industry)

  • Huy Quoc Le

    (Hanoi University of Industry)

  • Anh Ngoc Mai

    (Academy of Finance)

Abstract

The data deluge demands businesses leverage artificial intelligence (AI) and machine learning (ML) for swift adaptation. Deep learning (DL), a powerful ML technique, unlocks deeper insights but faces limitations hindering its widespread adoption in business analytics. This article explores the key challenges of DL and emphasizes its role as a complementary tool, not a replacement, for traditional ML models. Research shows that DL models in classification tasks can perform quite well on structured datasets like powerful gradient boosting models. On the other hand, in prediction tasks, DL appears to be weaker compared to traditional ML models. In addition to experimental research based on four usage cases in the industry, the article also provides a comprehensive discussion of these results, their practical implications, and a roadmap for future research.

Suggested Citation

  • Trang Thi Huyen Cao & Huy Quoc Le & Anh Ngoc Mai, 2025. "Deep Learning-Powered Business Analytics: Enhancing Classification and Regression Models," Springer Proceedings in Business and Economics, in: Nga Thi Hong Nguyen & José António C. Santos & Vijender Kumar Solanki & Anh Ngoc Mai (ed.), Proceedings of the 5th International Conference on Research in Management and Technovation, pages 195-212, Springer.
    • Handle: RePEc:spr:prbchp:978-981-97-9992-3_13
    DOI: 10.1007/978-981-97-9992-3_13
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