Prediction model of regional economic development potential based on data mining technology

• Published in: Engineering reports (Hoboken, N.J.) Vol. 5; no. 6
• Main Author: Yang, Jiayan
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.06.2023; Wiley
• Subjects: Accuracy; Area planning & development; Classification; Clustering; Crystal oscillators; Data mining; data mining technology; development potential; Economic development; Economic growth; Economic models; Economic policy; experimental analysis; forecasting model; Genetic algorithms; Interdisciplinary subjects; Model accuracy; Neural networks; Optimization algorithms; Prediction models; Predictions; Regional development; regional economy; Statistical analysis; Support vector machines; Upgrading
• ISSN: 2577-8196; 2577-8196
• DOI: 10.1002/eng2.12633

Due to the differences between regions, the regional economy is also affected by the development of the real economy and the environment in a specific region. Thus, it is necessary to predict the development potential of the regional economy to ensure the smooth operation of the economy. The continuous upgrading of technology enables the country to grasp the development trend of the regional economy more accurately than ever before. However, the existing prediction techniques still have large errors. Therefore, this paper constructed a prediction model based on data mining technology to predict the development potential of the regional economy to enhance the accuracy of predictive models. The experiments showed that the prediction accuracy of the prediction model constructed in this paper could reach 96.78%, which could provide an accurate reference for the development of the regional economy to ensure the stable development of the regional economy, and has important application value. This paper constructs a prediction model based on data mining (DM) technology to predict the development potential of the regional economy to enhance the accuracy of predictive models.