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As China's pillar industry, the property market has suffered a considerable impact in recent years, with a decline in turnover and many developers at risk of bankruptcy. As one of the most concerned factors for stakeholders, housing prices need to be predicted more objectively and accurately to minimize decision-making errors by developers and consumers. Many prediction models in recent years have been unfriendly to consumers due to technical difficulties, high data demand, and varying factors affecting house prices in different regions. A uniform model across the country cannot capture local differences accurately, so this study compares and analyses the fitting effects of multiple machine learning models using February 2024 new building data in Changsha as an example, aiming to provide consumers with a simple and practical reference for prediction methods. The modeling exploration applies several regression techniques based on machine learning algorithms, such as Stepwise regression, Robust regression, Lasso regression, Ridge regression, Ordinary Least Squares (OLS) regression, Extreme Gradient Boosted regression (XGBoost), and Random Forest (RF) regression. These algorithms are used to construct forecasting models, and the best-performing model is selected by conducting a comparative analysis of the forecasting errors obtained between these models. The research found that machine learning is a practical approach to property price prediction, with least squares regression and Lasso regression providing relatively more convincing results.
Study on plant diversity investigation and conservation plan-ning in Yiyang City
Vol 4, Issue 1, 2023
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Abstract
Taking the urban plants of Yiyang as the research object, the field investigation of Yiyang plants was carried out by using the method of combining route investigation and quadrat investigation, the current situation of plant diversity level in Yiyang City was analyzed, and the planning content and construction strategy of plant diversity protection in Yiyang City were studied, in order to provide reference for the effective protection of plant diversity in Yiyang City. The results showed that there were 145 families, 481 genera and 955 species of vascular plants in Yiyang urban planning area; there are 232 species of garden plant resources in the central urban area, belonging to 62 families. 105 genera; the overall level of plant diversity in Yiyang City is general. Urban landscaping relies too much on exotic species, and the utilization rate of native plants in urban gardens is low.
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Copyright (c) 2023 Saiyuan Peng, Xijun Hu, Cunyouju Chen
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Prof. Mehmet Cetin
Kastamonu University,
Turkey
