Identifying the Best-Selling Product using Machine Learning Algorithms

This research covers the application of machine learning algorithms in identifying best-selling products to enhance sales forecasting among SMEs. Given the limitations of operational scales and the poor derivation of insights from sales data by SMEs, this study adopts Decision Trees, Random Forest, Naïve Bayes, and Support Vector Machines with kernel functions in analyzing and classifying sales patterns. The results clearly illustrate the fact that the SVM model is better compared to other algorithms in product classification. By incorporating data-driven decision-making, this approach enables SMEs to achieve optimal inventory management, refine their marketing strategies, and enhance their overall business efficiency. Future work will be refining these models on larger datasets and exploring hybrid approaches that can give further enhancements in predictive performance.