Predicting Consumer Preferences for Furniture Products on E-commerce Platforms: An Analysis Using Machine Learning and Favorite Listing Data

The rapid growth of e-commerce platforms presents unique opportunities to analyze consumer behavior and predict product preferences in the furniture industry. This study explores the use of machine learning techniques to predict consumer choices for furniture products based on favorite listing data from e-commerce platforms. A dataset of 239 furniture products was collected, categorized into three groups: most preferred, moderately preferred, and least preferred. Key attributes, including furniture type, dimensions (width, depth, height), color, material, and price, were analyzed. Machine learning models, specifically Decision Trees and Random Forests, were applied to develop prediction models for these categories. The models were assessed using metrics such as accuracy, precision, sensitivity, and Fl-score. Results indicated that the Random Forest model outperformed the Decision Tree, achieving 83% accuracy in predicting preference categories. Feature importance analysis highlighted that price and physical dimensions were the most significant factors influencing consumer preferences. These findings suggest that practical and economic aspects are prioritized over aesthetic features when choosing furniture. The study demonstrates the potential of machine learning in predicting consumer behavior, offering valuable insights for manufacturers and retailers in optimizing product development, inventory management, and marketing strategies.