A Financial, Economic, and Financial Data Analysis System for Enterprises Based on Bagging Algorithm

In the current context of economic globalization and increasing market volatility, enterprises are facing increasing financing pressure and risk management needs, and urgently need to use effective data analysis methods to improve the accuracy and timeliness of decision-making. In response to this issue, this article proposes an enterprise financial big data analysis method based on the Bootstrap Aggregating (Bagging) algorithm. By constructing and integrating multiple decision tree models, the prediction accuracy and stability are improved. Multiple independent learning decision trees are fused to achieve real-time processing of complex financial data and real-time prediction of important financial indicators such as returns, liquidity risk, and investment return. At a training sample ratio of 70%, the model performs best with an accuracy of 86.3%, a recall rate of 81.9%, and an accuracy of 90.0%, the F1 score is 85.8%. Through the application of this system, the enterprise can have a more accurate understanding of its financial situation and make optimal financial decisions, which in turn improves its market competitiveness and prevents risks.