<![CDATA[In the ever-evolving digital era, the e-commerce sector faces significant challenges in efficiently managing sales, selling prices, and inventory. This study aims to evaluate the effectiveness of a linear regression model in predicting sales, selling prices, and stock levels on the HSR Wheels e-commerce platform. A quantitative method was used by analyzing daily transaction data to identify the relationship between the time variable and sales, profit, and stock. The results showed that linear regression has limitations in modeling data complexity, with low R² scores and high Mean Absolute Error (MAE) values. These findings indicate the need for more advanced predictive models, such as machine learning algorithms, to improve prediction accuracy. This research is expected to contribute to developing more efficient and relevant sales strategies for e-commerce platforms.]]>
