This study explores how machine learning algorithms can optimize the performance of US fashion brands by analyzing the relationship between digital transformation, social media analytics, and customer engagement. Using a Kaggle dataset, models including linear regression, random forest, gradient boosting, and neural networks were evaluated to predict brand performance. Neural networks achieved the highest accuracy (R-squared: 0.92), while gradient boosting balanced performance and interpretability (R-squared: 0.88). Results highlight the critical role of customer engagement in driving brand success and demonstrate the potential of machine learning for actionable insights. This research provides a robust framework for data-driven strategies in the fashion industry.

Digital transformation, social media analytics, machine learning

Md Jamil Ahmmed, Md Mohibur Rahman, Ashim Chandra Das, Pritom Das, Tamanna Pervin, Sadia Afrin, Sanjida Akter Tisha, Md Mehedi Hassan, & Nabila Rahman. (2024). COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR BANKING FRAUD DETECTION: A STUDY ON PERFORMANCE, PRECISION, AND REAL-TIME APPLICATION. International Journal of Computer Science & Information System, 9(11), 31–44. https://doi.org/10.55640/ijcsis/Volume09Issue11-04

Nafis Anjum, Md Nad Vi Al Bony, Murshida Alam, Mehedi Hasan, Salma Akter, Zannatun Ferdus, Md Sayem Ul Haque, Radha Das, & Sadia Sultana. (2024). COMPARATIVE ANALYSIS OF SENTIMENT ANALYSIS MODELS ON BANKING INVESTMENT IMPACT BY MACHINE LEARNING ALGORITHM. International Journal of Computer Science & Information System, 9(11), 5–16. https://doi.org/10.55640/ijcsis/Volume09Issue11-02

Md Nur Hossain, Nafis Anjum, Murshida Alam, Md Habibur Rahman, Md Siam Taluckder, Md Nad Vi Al Bony, S M Shadul Islam Rishad, & Afrin Hoque Jui. (2024). PERFORMANCE OF MACHINE LEARNING ALGORITHMS FOR LUNG CANCER PREDICTION: A COMPARATIVE STUDY. International Journal of Medical Science and Public Health Research, 5(11), 41–55. https://doi.org/10.37547/ijmsphr/Volume05Issue11-05

MACHINE LEARNING FOR STOCK MARKET SECURITY MEASUREMENT: A COMPARATIVE ANALYSIS OF SUPERVISED, UNSUPERVISED, AND DEEP LEARNING MODELS. (2024). International Journal of Networks and Security, 4(01), 22-32. https://doi.org/10.55640/ijns-04-01-06

ENHANCING SMALL BUSINESS MANAGEMENT THROUGH MACHINE LEARNING: A COMPARATIVE STUDY OF PREDICTIVE MODELS FOR CUSTOMER RETENTION, FINANCIAL FORECASTING, AND INVENTORY OPTIMIZATION. (2024). International Interdisciplinary Business Economics Advancement Journal, 5(11), 21-32. https://doi.org/10.55640/business/volume05issue11-03

Md Al-Imran, Salma Akter, Md Abu Sufian Mozumder, Rowsan Jahan Bhuiyan, Tauhedur Rahman, Md Jamil Ahmmed, Md Nazmul Hossain Mir, Md Amit Hasan, Ashim Chandra Das, & Md. Emran Hossen. (2024). EVALUATING MACHINE LEARNING ALGORITHMS FOR BREAST CANCER DETECTION: A STUDY ON ACCURACY AND PREDICTIVE PERFORMANCE. The American Journal of Engineering and Technology, 6(09), 22–33. https://doi.org/10.37547/tajet/Volume06Issue09-04

Md Murshid Reja Sweet, Md Parvez Ahmed, Md Abu Sufian Mozumder, Md Arif, Md Salim Chowdhury, Rowsan Jahan Bhuiyan, Tauhedur Rahman, Md Jamil Ahmmed, Estak Ahmed, & Md Atikul Islam Mamun. (2024). COMPARATIVE ANALYSIS OF MACHINE LEARNING TECHNIQUES FOR ACCURATE LUNG CANCER PREDICTION. The American Journal of Engineering and Technology, 6(09), 92–103. https://doi.org/10.37547/tajet/Volume06Issue09-11

Md Habibur Rahman, Ashim Chandra Das, Md Shujan Shak, Md Kafil Uddin, Md Imdadul Alam, Nafis Anjum, Md Nad Vi Al Bony, & Murshida Alam. (2024). TRANSFORMING CUSTOMER RETENTION IN FINTECH INDUSTRY THROUGH PREDICTIVE ANALYTICS AND MACHINE LEARNING. The American Journal of Engineering and Technology, 6(10), 150–163. https://doi.org/10.37547/tajet/Volume06Issue10-17

DYNAMIC PRICING IN FINANCIAL TECHNOLOGY: EVALUATING MACHINE LEARNING SOLUTIONS FOR MARKET ADAPTABILITY. (2024). International Interdisciplinary Business Economics Advancement Journal, 5(10), 13-27. https://doi.org/10.55640/business/volume05issue10-03

M. S. Haque, M. S. Taluckder, S. Bin Shawkat, M. A. Shahriyar, M. A. Sayed and C. Modak, "A Comparative Study of Prediction of Pneumonia and COVID-19 Using Deep Neural Networks," 2023 3rd International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), Yogyakarta, Indonesia, 2023, pp. 218-223, doi: 10.1109/ICE3IS59323.2023.10335362.

Zhao, L., Zhang, Y., Chen, X., & Huang, Y. (2021). A reinforcement learning approach to supply chain operations management: Review, applications, and future directions. Computers & Operations Research, 132, 105306. https://doi.org/10.1016/j.cor.2021.105306

Md Al-Imran, Eftekhar Hossain Ayon, Md Rashedul Islam, Fuad Mahmud, Sharmin Akter, Md Khorshed Alam, Md Tarek Hasan, Sadia Afrin, Jannatul Ferdous Shorna, & Md Munna Aziz. (2024). TRANSFORMING BANKING SECURITY: THE ROLE OF DEEP LEARNING IN FRAUD DETECTION SYSTEMS. The American Journal of Engineering and Technology, 6(11), 20–32. https://doi.org/10.37547/tajet/Volume06Issue11-04

Tauhedur Rahman, Md Kafil Uddin, Biswanath Bhattacharjee, Md Siam Taluckder, Sanjida Nowshin Mou, Pinky Akter, Md Shakhaowat Hossain, Md Rashel Miah, & Md Mohibur Rahman. (2024). BLOCKCHAIN APPLICATIONS IN BUSINESS OPERATIONS AND SUPPLY CHAIN MANAGEMENT BY MACHINE LEARNING. International Journal of Computer Science & Information System, 9(11), 17–30. https://doi.org/10.55640/ijcsis/Volume09Issue11-03

Md Abu Sayed, Badruddowza, Md Shohail Uddin Sarker, Abdullah Al Mamun, Norun Nabi, Fuad Mahmud, Md Khorshed Alam, Md Tarek Hasan, Md Rashed Buiya, & Mashaeikh Zaman Md. Eftakhar Choudhury. (2024). COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR PREDICTING CYBERSECURITY ATTACK SUCCESS: A PERFORMANCE EVALUATION. The American Journal of Engineering and Technology, 6(09), 81–91. https://doi.org/10.37547/tajet/Volume06Issue09-10

Dessart, L., Veloutsou, C., & Morgan-Thomas, A. (2020). Consumer engagement in online brand communities: A social media perspective. Journal of Marketing Management, 36(3-4), 303-330.

Ghosh, A., Bansal, R., & Sharma, R. (2021). Machine learning in marketing analytics: Applications and challenges. International Journal of Data Analytics, 14(2), 112-126.

Kapoor, K., Tamilmani, K., Rana, N. P., Patil, P., Dwivedi, Y. K., & Nerur, S. (2021). Advances in social media research: Past, present, and future. Information Systems Frontiers, 23(4), 1031-1056.

McKinsey & Company. (2022). The state of fashion: Digital transformation trends. Retrieved from www.mckinsey.com.