<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Using Python language and combined with data analysis and mining technology, the authors capture and clean the housing source data of second-hand houses in Chengdu from Beike Network, and visually analyse the cleaned data. Then, a Random Forest (RF) model is established for 38,363 data elements. According to the visual analysis results, the model variables are revalued, the key factors affecting house prices are studied and the optimised model is used to predict house prices. The experiment shows that the deviation between the house price predicted by the RF model and that predicted by the real house price is small; it also indicates the accuracy of the RF model and demonstrates its good application value.</p>
</abstract>

Using Python language and combined with data analysis and mining technology, the authors capture and clean the housing source data of second-hand houses in Chengdu from Beike Network, and visually analyse the cleaned data. Then, a Random Forest (RF) model is established for 38,363 data elements. According to the visual analysis results, the model variables are revalued, the key factors affecting house prices are studied and the optimised model is used to predict house prices. The experiment shows that the deviation between the house price predicted by the RF model and that predicted by the real house price is small; it also indicates the accuracy of the RF model and demonstrates its good application value.

Analysis and prediction of second-hand house price based on random forest

School of Computer and Information Engineering

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Analysis and prediction of second-hand house price based on random forest"}

Using Python language and combined with data analysis and mining technology, the authors capture and clean the housing source data of second-hand houses in...

Analysis and prediction of second-hand house price based on random forest

Online veröffentlicht: 24. Juni 2022

Seitenbereich: 27 - 42

Eingereicht: 14. Dez. 2021

Akzeptiert: 15. Mai 2022

DOI: https://doi.org/10.2478/amns.2022.1.00052

Schlüsselwörter
Data Analysis, Second-hand House, Random Forest, Python, Crawler Technology

© 2022 Yan Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis and prediction of second-hand house price based on random forest

Online veröffentlicht: 24. Juni 2022

Seitenbereich: 27 - 42

Eingereicht: 14. Dez. 2021

Akzeptiert: 15. Mai 2022

DOI: https://doi.org/10.2478/amns.2022.1.00052

SchlüsselwörterData Analysis, Second-hand House, Random Forest, Python, Crawler Technology

© 2022 Yan Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Data Analysis, Second-hand House, Random Forest, Python, Crawler Technology