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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.
Comparison of ant community composition among three ur-ban habitats: a case study in Nanchong, Sichuan
Vol 3, Issue 1, 2022
VIEWS - 2183 (Abstract)
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Abstract
As an important human-caused disturbance factor, urbanization has significantly changed the structure of natural habitats and influenced the composition of animal communities. The characterisitions of abundant population amount, high spcies diversity, and easy to be collected of ants make these species become excellent models for studying the effects of land-use types on the composition of animal communities in urban areas. In May 2018, ants were sampled from 3 land-use types (‘garden and greenland’, ‘dry farm’ and ‘woodland’) using pitfall traps in an urban area of Nanchong city, Sichuan province. A total of 1 847 ant individuals were collected. The proportion of Tetramorium tsushimae was the highest in ‘garden and greenland’ (73.80%), and significantly higher than that of Monomorium chinense in ‘dry farm’ (25.48%) and Pheidole nodus in ‘woodland’ (28.79%). There was no significant difference in the number of indiviuduals and species richness among the 3 habitats, but the Simpson diversity index and Pielou evenness index of ant community in the habitat of‘garden and greenland’were significantly lower than those in the habitats of‘dry farm’and‘woodland’; the composition of ant community in‘woodland’was moderately different from that in‘garden and greenland’(q = 0.444)and‘dry farm’(q = 0.500), and the community composition in‘garden and greenland’and‘dry farm’(q = 0.647)was moderately similar. Therefore, the compositions of ant communities are varied in different land-use types in urban areas, and the habitat of ‘garden and greenland’ is more likely to facilitate the formation of dominant species than ‘dry farm’ and ‘woodland’.
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Prof. Mehmet Cetin
Kastamonu University,
Turkey
