Analysis and prediction of second-hand house price based on random forest
- Autores: Yan Zhang, Jingyu Huang, Jihui Zhang, Suying Liu, Samer Shorman
- Localización: Applied Mathematics and Nonlinear Sciences, ISSN-e 2444-8656, Vol. 7, Nº. 1, 2022, págs. 27-42
- Idioma: inglés
- DOI: 10.2478/amns.2022.1.00052
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Resumen
Using Python language and combined with data analysis and mining technology, the authors capture and clean the housingsource data of second-hand houses in Chengdu from Beike Network, and visually analyse the cleaned data. Then, aRandom Forest (RF) model is established for 38,363 data elements. According to the visual analysis results, the modelvariables are revalued, the key factors affecting house prices are studied and the optimised model is used to predict houseprices. The experiment shows that the deviation between the house price predicted by the RF model and that predicted bythe real house price is small; it also indicates the accuracy of the RF model and demonstrates its good application value
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