A Comparative Study of Machine Learning Models for Fashion Product Demand Prediction: Exploring Algorithms, Data Splitting, and Feature Engineering

The fashion industry faces challenges in accurately predicting demand due to inherent uncertainty, leading to suboptimal inventory and financial losses. Machine learning (ML) offers a robust solution by analyzing large and complex data, identifying non-linear patterns, and providing more accurate predictions than conventional methods that rely on limited factors. This research aims to compare and evaluate the performance of six different ML models—XGBoost, SVM, RF, GBM, KNN, and NN, considering the influence of feature engineering and various data split ratios on predicting fashion product demand. KNN and NN were included due to distinct modeling approaches and competitive capabilities in identifying local and non-linear patterns across numerical, categorical, and time series data. Techniques such as feature extraction and selection and various data split ratios (70:30, 80:20, 90:10) were used. Using Adidas sales data, the models were evaluated based on Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results indicate that the XGBoost-based model with feature engineering consistently outperforms the other models across all data split ratios. Particularly, XGBoost with feature engineering at a data split ratio of 90:10 achieved the best performance with an RMSE of 4.46 and an MAE of 1.51. Analyzing model performance shows that the predictive ability of ML models is influenced by the implementation of feature engineering and the selection of the data split ratio. These results demonstrate the potential of using feature-engineered XGBoost models and optimized data ratios to mitigate the risk of stockouts or overstocks, and reduce financial losses and environmental waste.