A Review of the Commercial Application Comparisons and Popularization Paths of Linear Regression and Decision Tree Prediction Models

In the era of big data and artificial intelligence, predictive analytics has become an important tool for business forecasting and decision-making. This paper systematically compares the core logic, applicable scenarios, performance characteristics, and error sources of linear regression and decision tree prediction models through literature review, comparative analysis, and selected business cases. The results indicate that linear regression is more suitable for continuous numerical prediction when business relationships are relatively stable and approximately linear, as in the analysis of sales, revenue, demand, and consumer spending. Decision trees are more suitable for classification, customer segmentation, and prediction tasks involving nonlinear relationships or complex interactions, because they recursively divide data into decision regions. The paper further compares the two models in terms of data adaptation, interpretability, prediction stability, task coverage, and implementation cost, and summarizes corresponding optimization methods. Based on these findings, the paper discusses low-cost implementation using tools such as Excel and Python for beginners and small businesses. The practical significance of this review lies in clarifying the differences between two basic predictive models, reducing the technical threshold of predictive analysis, and supporting more accessible data-driven business decision-making.