Financial Data Acquisition Method Based on Bayesian Model and Machine Learning

Data collection and analysis are of great significance for financial institutions and researchers. However, existing financial data collection methods have limitations such as low coverage and poor accuracy. Therefore, this study needs to find more effective methods for collecting financial data. This paper proposes a financial data collection method based on Bayesian models and machine learning. Firstly, this study introduces the basic principles of Bayesian models and Random Forest (RF), collects a large amount of financial data, and divides it into training and testing sets. Then, Bayesian models and machine learning algorithms are used to model and predict the training and testing sets, respectively. Finally, this study evaluated and compared the models using metrics such as accuracy, recall, and adjusted R-squared. The experimental results show that the accuracy, recall, and adjusted R-squared of RF can reach 97%, 89%, and 0.68%, respectively. Compared to Decision Trees (DT) and Support Vector Machines (SVM), RF has outstanding performance. It is concluded that the financial data acquisition method based on Bayesian model and RF has higher accuracy and recall rate, and can better meet the needs of the financial field. At the same time, this paper also discusses the application value of Bayesian model and machine learning algorithm in financial data acquisition, which provides an important reference for further optimization of financial data acquisition methods.