Faculty of Science, University of Melbourne, Melbourne, Victoria 3010, Australia.
Comput Intell Neurosci. 2022 Sep 29;2022:3590224. doi: 10.1155/2022/3590224. eCollection 2022.
In order to improve the reliability of housing price prediction and analysis, this article combines the generalized linear regression model to build a real estate price prediction model and analyzes the basic knowledge of data mining. On the basis of this prior knowledge, this article investigates the cluster analysis algorithm and selects the generalized linear regression model as the research focus based on its definition and the characteristics of stock data. Moreover, this article analyzes the estimation methods of the generalized linear regression model and the nonparametric regression model, and then gives the estimation method of a partial linear model. In addition, this article verifies the validity of the model proposed in this article by means of simulation research. Through the simulation and comparison experiments, it can be seen that the housing price prediction system based on the generalized regression model proposed in this article has a high housing price prediction accuracy.
为了提高房价预测和分析的可靠性,本文结合广义线性回归模型构建了一个房地产价格预测模型,并分析了数据挖掘的基础知识。在此先验知识的基础上,本文研究了聚类分析算法,并基于其定义和股票数据的特点选择了广义线性回归模型作为研究重点。此外,本文分析了广义线性回归模型和非参数回归模型的估计方法,然后给出了部分线性模型的估计方法。此外,本文还通过模拟研究验证了本文所提出模型的有效性。通过模拟和对比实验,可以看出本文提出的基于广义回归模型的房价预测系统具有较高的房价预测精度。