<![CDATA[This study aims to analyze and classify best-selling and least-selling products in the Babygear.Project store using the Naïve Bayes algorithm. The data used includes sales and user interaction data collected from the store management system, then a preprocessing stage is carried out to ensure data quality before being used in modeling. The testing process is carried out by dividing the dataset into training data and test data using a 60:40 ratio. The test results show that the Naïve Bayes model has an accuracy of 92.68%, with consistently high precision, recall, and F1-score values across both product categories. Further analysis reveals that the features of sales volume, purchase frequency, product category, product price, and user interaction are the most dominant factors in the classification process. The implementation of this model provides strategic benefits, especially in optimizing stock management, promotional planning, and data-driven decision making to improve operational efficiency and customer satisfaction. This study also opens up opportunities for further development, such as the addition of predictor variables, the use of larger datasets, and testing benchmark algorithms to improve prediction accuracy in the future.]]>
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