Demand forecasting of online retail of fashion apparel using machine learning - A systematic review and a proposed methodology

• Published in: AIP conference proceedings Vol. 3209; no. 1
• Main Authors: Bedi, Twinkle, Pandit, Anil, Gautam, Nidhi
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Melville American Institute of Physics 11.10.2024
• Subjects: Algorithms; Deep learning; Fashion goods; Forecasting; Machine learning; Performance measurement; Python; Regression models
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0229067

This research paper proposes an intelligent forecasting system for the online retail of fashion articles. It takes data from an online fashion retailer, comprising article information, customer information, and corresponding transactions between them. The proposed forecasting model leverages ML and deep learning techniques to be applied to the merged dataset with features necessary for the prediction of sales. Python language is used for executing the research work. By employing supervised ML regression algorithms and AI algorithms on labelled data, the aim is to select the best-fit model based on performance metrics. After comparing the results of RMSE score metrics with other algorithms, the algorithm giving the better result is chosen as the model for prediction. Conclusively the best fit model is further deployed to be integrated in a web-based application using Flask- a micro framework for Python.