Research on predictive model based on classification with parameters of optimization

Abstract

This paper effectively uses the data mining and optimization methods to investigate a classification based on decision trees algorithm, then optimizes by the method of grid search and cross-validation, which improves the prediction accuracy of the decision tree model for the PCs sales in practical application and solves insufficient training data, high computational cost, and low prediction accuracy. The main goal of the article is to predict PC sales using machine learning tools caused by various types of operating system factors in practical applications. This article proposes a combined predictive research model that fully reveals the benefits of optimization and neural networks, and also has a very accurate fit and forecasting accuracy. The proposed predictive model is implemented in the data science software platform RapidMiner. A decision tree model is executed, then the model's prediction capacity is evaluated and tested. Grid search optimizer is used to automatically build the final model using the best-optimized parameter for training the classifier. The paper combines grid the grid search and cross-validation to optimize the parameters of the decision tree to improve the classification prediction accuracy of the decision tree model. This article combines neural networks with optimization methods to establish a prediction model for laptop sales. This model gives full play to the advantages of optimization and neural networks and has very good fitting capabilities and prediction accuracy. Besides, the neural network for the prediction model has strong dynamic analysis capabilities. Once there are new observations, it can continue to be added to the modeling, which has high adaptability. The Neural Network algorithm has the highest accuracy of the predicted PC sales by evaluating the results of the five kinds of algorithms. The result for prediction accuracy shows the highest performance.