Research on data prediction model construction and optimization based on support vector analysis

This paper proposes a data prediction model based on support vector analysis, which aims to solve the prediction problem in a complex data environment. Support vector analysis is an algorithm based on statistical learning theory, which has good generalization ability and the advantage of processing high-dimensional data. In the process of model construction, we extracted multi-dimensional features of the data and adjusted the model parameters in combination with the optimization algorithm to improve the accuracy and robustness of the prediction. The simulation experiments on the data set show that the model can effectively achieve high-precision prediction in various scenarios. By comparing with the traditional prediction model, the support vector analysis method shows significant advantages in data processing ability and prediction accuracy, especially in the data set with complex nonlinear relationships, and its prediction error is significantly reduced. This paper not only describes the process of model construction in detail, but also discusses the specific methods of model optimization, such as kernel function selection and hyperparameter tuning. The simulation results show that the average error of the optimized support vector prediction model drops to 3.2%, which is nearly 15% lower than the error of the traditional model.