การพยากรณ์ราคาคอนโดมิเนียมในกรุงเทพมหานครโดยเทคนิค Web Mining PREDICTING CONDOMINIUM PRICE IN BANGKOK USING WEB MINING TECHNIQUES

Authors

  • จิรพล สังข์โพธิ์ วิทยาลัยนวัตกรรม มหาวิทยาลัยธรรมศาสตร์
  • ศราวุธ แรมจันทร์ วิทยาลัยนวัตกรรม มหาวิทยาลัยธรรมศาสตร์

Keywords:

Web, Web Crawling, Mining, Machine Learning

Abstract

           In Bangkok, condominium is becoming a first home for many people especially Generation Y who prefer convenience of lifestyle.  Price of the condominium is one of the most important factors for buyers’ decision-making. This study aims to formulate a model that predict a selling price per square meter of a condominium in Bangkok. This is to support buyer’s decision-making whether to buy or not by using the price prediction from the model. The training dataset was gathered using web crawling techniques from hipflat.co.th. There were 1,465 condominium projects in Bangkok were extracted with each record contains 15 attributes of information. This model was formulated by using a deep learning method based on the best performed after testing the model with testing dataset. Results show that the price of the condominium in Bangkok highly affected by the number of units in the condominium project, its distance from the BTS metro line, and the year of its construction, with the relative error of 17.53%.

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Author Biography

จิรพล สังข์โพธิ์, วิทยาลัยนวัตกรรม มหาวิทยาลัยธรรมศาสตร์

ฝ่ายสำนักพิมพ์

References

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Published

2021-07-06

How to Cite

สังข์โพธิ์ จ. ., & แรมจันทร์ ศ. . (2021). การพยากรณ์ราคาคอนโดมิเนียมในกรุงเทพมหานครโดยเทคนิค Web Mining PREDICTING CONDOMINIUM PRICE IN BANGKOK USING WEB MINING TECHNIQUES. Srinakharinwirot Research and Development Journal of Humanities and Social Sciences, 12(24, July-December), 15–27. Retrieved from https://so04.tci-thaijo.org/index.php/swurd/article/view/252810

Issue

Vol. 12 No. 24, July-December (2020): Srinakharinwirot Research and Development (Journal of Humanities and Social Sciences)

Section

บทความวิจัย

License

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