Abstract
Linear regression algorithm is a widely used model in business behavior analysis, aiming to explore the relationship between independent and dependent variables. However, traditional linear regression algorithms have issues with accuracy and time cost when dealing with large amounts of data. To address these issues, this article proposed an improved linear regression algorithm and applied it to business behavior analysis. In the experiment, the algorithm was applied to the sales data of an e-commerce company, including a total of 5000 sales records. A comparison was made between the traditional linear regression algorithm and the improved algorithm, and the results showed that the improved algorithm improved accuracy by 23% and reduced time cost by 18%. In addition, the improved algorithm can quickly process a large amount of data and better predict sales trends. The improved linear regression algorithm proposed in this article has great application potential in business behavior analysis, which can improve accuracy and efficiency, and provide more reliable support for business decision-making.
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