Author(s)

Jun Zhang ¹, Taizhi Lv ²

Affiliation(s)

¹ College of Information Technology, Jiangsu Maritime Institute, Jiangsu Nanjing, 211170, China
² Nanjing Longyuan Microelectronic Company Limited, Jiangsu Nanjing 211106, China

Corresponding Author

Jun Zhang

ABSTRACT

The content of this paper is the statistical analysis of the housing price data in Wuxi. Obtain data on the net, visualize the data, to see the prices clearly, judge the influence prices of each element, use linear regression to find out the price per square meter and the relationship between the building area, through the KNN algorithm to divided into high-grade village, compare the Euclidean distance and the Manhattan distance of the differences in house prices problem. This system is based on Python language, MongoDB stores data, uses MySQL to process relevant data, uses PyCharm as the development tool, Python 3.9 as the running environment, uses Scrapy framework to crawl the second-hand house data of LianJia network, and stores the data into MongoDB. After dirty data processing, we use lightweight Web application framework Flask and Echarts to conduct visual analysis on the Web page. Finally, the linear regression algorithm is used to find out the elements related to the price, and the KNN classification algorithm is used to divide the residential area into three grades by the level of the housing price.

KEYWORDS

Web crawlers, housing price, Python, data visualization, KNN

CITE THIS PAPER

Jun Zhang, Taizhi Lv. Design and implementation of second-hand housing data statistical analysis system. Financial Engineering and Risk Management (2021) 4: 59-64. DOI: http://dx.doi.org/10.23977/ferm.2021.040408.

REFERENCES

[1] Xu, Lulin, and Zhongwu Li. "A new appraisal model of Second-Hand housing prices in China’s First-Tier cities based on machine learning algorithms." Computational Economics 57.2 (2021): 617-637.
[2] Gan, Lu, et al. "Coupling coordination analysis with data-driven technology for disaster–economy–ecology system: an empirical study in China." Natural Hazards (2021): 1-31.
[3] Choudhury, Aakash, et al. "HealthSaver: a neural network based hospital recommendation system framework on flask webapplication with realtime database and RFID based attendance system." Journal of Ambient Intelligence and Humanized Computing (2021): 1-14..
[4] Q. Jiang, S. Yan, H. Cheng and X. Yan, "Local–Global Modeling and Distributed Computing Framework for Nonlinear Plant-Wide Process Monitoring With Industrial Big Data," IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 8, pp. 3355-3365.
[5] Xing, Wenchao, and Yilin Bei. "Medical health big data classification based on KNN classification algorithm." IEEE Access 8 (2019): 28808-28819.

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